Assessment of static flood modeling techniques application tocontrasting marshes flooded during Xynthia (western France)
J F Breilh1 E Chaumillon1 X Bertin1 and M Gravelle1
1UMR 7266 LIENSs CNRS-Universite de La Rochelle La Rochelle France Invited contribution by J F Breilh winner of the EGU Outstanding Student Poster (OSP) Awards 2012
Correspondence toJ F Breilh (jbreil01univ-lrfr)
Received 20 July 2012 ndash Published in Nat Hazards Earth Syst Sci Discuss ndashRevised 20 March 2013 ndash Accepted 8 May 2013 ndash Published 20 June 2013
Abstract This study aims to assess the performance ofraster-based flood modeling methods on a wide diversity ofcoastal marshes These methods are applied to the flood-ing associated with the storm Xynthia which severely hitthe western coast of France in February 2010 Static andsemi-dynamic methods are assessed using a combination ofLiDAR data post-storm delineation of flooded areas andsea levels originating from both tide gauge measurementsand storm surge modeling Static methods are applied to27 marshes showing a wide geomorphological diversity Itappears that these methods are suitable for marshes witha small distance between the coastline and the landwardboundary of the marsh which causes these marshes to floodrapidly On the contrary these methods overpredict floodedareas for large marshes where the distance between the coast-line and the landward boundary of the marsh is large be-cause the flooding cannot be considered as instantaneous Inthis case semi-dynamic methods based on surge overflowingvolume calculations can improve the flooding prediction sig-nificantly This study suggests that static and semi-dynamicflood modeling methods can be attractive and quickly de-ployed to rapidly produce predictive flood maps of vulnera-ble areas under certain conditions particularly for small dis-tances between the coastline and the landward boundary ofthe low-lying coastal area
1 Introduction
Flooding is one of the major natural disasters and affectsmany regions of the world Besides causing considerablematerial damage this natural hazard leads to the loss of
hundreds (sometimes thousands) of human lives every year(Cook and Merwade 2009) Because of the current sea-levelrise and the potential increase in storminess (Schmith et al1998) resulting from climate change extreme coastal flood-ing events are likely to be more frequent in the future (Brownet al 2010) while the population in coastal zones is expectedto grow (IPCC 2007) Moreover anthropogenic effects suchas land reclamation and coastal defense impact the naturalbehavior of the coastal zones and the risk of flooding andstorm damage (Bates et al 2005) In this context it appearsfundamental to accurately forecast storm surges and associ-ated coastal floods
Flooding of coastal lowlands by ocean waters is mainlydue to tsunamis (Wachter et al 2012) and storm-related pro-cesses (Benavente et al 2006) that generate a sea level abovethe ordinary tide level Among these processes the wind ef-fect is often responsible for a large part of the storm surgeparticularly in coastal zones bordered by extensive conti-nental shelves and shallow shoreface (Kennedy et al 2012Rego and Li 2009) Thus low-lying coasts (delta in river-dominated coastal areas estuaries in tide-dominated coastalareas and lagoons in mixed energy and wave-dominatedcoastal areas) bordered by large shelves and located on thetrack of hurricanes and extra-tropical storms are particularlyvulnerable The Bay of Bengal which includes extensivedeltaic environments is the region in the world where thedeadliest coastal floods resulting from hurricanes have beenreported In 1970 the tropical cyclone Bhola killed morethan 300 000 people (Das 1972) and in 2008 the tropicalcyclone Nargis killed over 130 000 people (Wolf 2008) An-other very vulnerable low-lying coast is the Gulf of Mex-ico which includes deltas and lagoons This vulnerability
Published by Copernicus Publications on behalf of the European Geosciences Union
1596 J F Breilh et al Assessment of static flood modeling techniques
was illustrated by hurricane Katrina in 2005 which was thesixth-strongest Atlantic hurricane ever reported with the as-sociated flood cost 1500 lives and 84 billion dollars in dam-ages (Blake 2007) The coastal morphology of northwest-ern Europe is dominated by estuarine environments (Perillo1995) while this region is located on the track of extra-tropical storms regularly inducing storm surges above onemeter (Bertin et al 2012a Brown et al 2010 Nicolle etal 2009 Wolf 2008) Low-lying zones of northwestern Eu-rope are thus also vulnerable to coastal flooding Over thepast century the most serious case took place in the southernNorth Sea in February 1953 A severe storm induced a three-meters-high surge (Wolf and Flather 2005) combined witha high spring tide which caused the flooding of a large partof the Netherlands (Gerritsen 2005) and to a slighter degreein the UK and Germany This catastrophe was responsiblefor 1836 deaths (Gerritsen 2005 Wolf and Flather 2005) Inthe last fifteen years in France the storms Martin and Xyn-thia (Bertin et al 2012a) hit the western coast and causedthe flooding of large coastal areas Xynthia (February 2010)was responsible for 47 deaths and at least 12 billion eurosin damages in France (Lumbroso and Vinet 2011) Of these47 deaths 41 occurred in the study area
Beside loss of human lives and material damages thechanges of environmental conditions in coastal habitats asso-ciated with marine floods cause a cascade of direct and indi-rect ecological responses that range from immediate to long-term For example inundation of fresh marshes by storm-driven seawater tends to damage or kill all the vegetationsometimes for several years (Morton and Barras 2011)
This study is focused on Xynthia and the associated surgebecause for the first time the flooded areas were accuratelymapped in this region of France Following this storm a re-gional storm surge modeling system was developed (Bertin etal 2012a) and accurate LiDAR (Light Detection and Rang-ing) data were obtained in order to identify vulnerable coastalareas Previous topographic data could not be used for suchapplication because they were not accurate enough to rep-resent coastal defenses and sedimentary barriers Indeed Li-DAR is able to measure ground elevation with a horizontalresolution (sim 1 m) and a vertical accuracy (sim 10ndash15 cm) thatare adequate for many flood mapping applications (Gallienet al 2011) The airborne LiDAR-derived Digital ElevationModels (DEMs) are commonly used to evaluate vulnerabil-ity to sea-level rise (Chust et al 2009 2010 Webster 2010Webster et al 2006) coastal flood risks (Bernatchez et al2011 Webster et al 2006) and also fluvial flood risks (Cookand Merwade 2009 Haile and Rientjes 2005)
This study aims to evaluate the benefits and limita-tions of a raster-based static method and a semi-dynamicflood modeling method based on high accuracy LiDAR-derived DEMs Such methods are commonly used to de-lineate areas vulnerable to flooding like the Coastal FloodRisk Prevention Plans (PPR-SMhttpwwwrisquesgouvfrrisques-naturelsinondation) in France the Flood Insurance
Rate Map (FIRM) from the Federal Emergency Manage-ment Agency (FEMAhttpwwwfemagov) in the USA orflood maps from the UK environment agency (httpwwwenvironment-agencygovuk)
The originality of this study stems from the analysis of awide diversity of flooded areas for which the extension of theflooding was accurately delineated More than 40 separatedareas were flooded and mapped allowing linking the skill ofstatic modeling methods with geomorphological character-istics of flooded areas The performance of static modelingmethods is evaluated against generic morphological parame-ters from which this study concludes on the applicability ofsuch methods for other vulnerable coastal environments
2 Study area
21 Geomorphologic setting
The study area is located along the Atlantic Coast of Francenorthward of the Gironde Estuary (Fig 1) The coastlineis irregular and characterized by large embayments (locallynamed ldquoPertuis Charentaisrdquo) corresponding to three drownincised-valley (IV) segments (from north to south the Lay-Sevre IV the Charente IV and the Seudre IV (Chaumillon etal 2008) bounded by the Arvert Peninsula Re and OleronIslands and the south Vendee coastline (Fig 1) The max-imum water depth is 43 m below the 0 NGF (French ver-tical datum (Nivellement General de la France) resultingfrom mean sea level observations at the Marseille tide gaugebetween the 2 February 1885 and the 31 December 1896)within the Charente IV and 61 m within the Lay-Sevre IVNevertheless because 65 of the Pertuis Charentais seafloor area is less than 10 m deep the marine part of the studyarea can be considered as shallow
The landward part of those embayments displays exten-sive intertidal mudflats that can reach 5 km width In thepast (from millenaries to centuries) the seaward parts of thoseonshore incised-valley segments were flooded by the sea (Al-lard et al 2008 Billeaud et al 2004 Chaumillon et al2008 Pawlowski 1998) The rapid siltation and sediment-fill of those IV segments led to the development of extensivecoastal marshes one of them corresponding to the largestcoastal marsh of France (the Poitevin Marsh no 27 Fig 1)The natural infilling of those marshes was enhanced by an-thropogenic activities mainly deforestation (Poirier et al2011) and land reclamation (Allard et al 2008 Bertin et al2005 Chaumillon et al 2004)
Those marshes are bounded by rocky outcrops corre-sponding to the interfluves of the IVs The elevation of alarge part of these marshes is commonly below the high-est sea levels reached during spring tides Considering acoastal area spanning from 10 km inland to the coastlinebetween 45 and 50 is below the highest astronomi-cal tides (Table 1) To prevent marine flooding extensive
J F Breilh et al Assessment of static flood modeling techniques 1597
Fig 1 LiDAR derived Digital Terrain Model (DTM) of the study area Elevation is shown in meter NGF and the horizontal projection is inmeters of Lambert 93 projected coordinate system Circled numbers 1 to 27 are the studied marshes The red dotted line shows the extensionof the observed flooded areas caused by the Xynthia storm
dykes levees (approximately 240 km) and locks have beenbuilt over the last centuries (6 m) Due to the construction ofall these flood management measurements wetlands are dis-connected from the sea During high tides locks are closedpreventing saltwater incursion and during low tides locksare opened allowing the drainage of marshes
22 Hydrodynamic setting
The study area is a mixed tide- and wave-dominated sys-tem Tides are semi-diurnal with amplitude ranging fromless than 2 m (neap tides) to more than 6 m (spring tides)Mean annual offshore (about 120 km offshore Oleron Island
1598 J F Breilh et al Assessment of static flood modeling techniques
Table 1Percentages of the 10 km band land area (from the present-day coastline to 10 km inshore) below (1) the sea level of the mean highwater neaps (MHWN) (2) the sea level of the mean high water springs (MHWS) and (3) the sea level of highest astronomical tide (HAT)
Corresponding tide MHWN MHWS HAT HAT+ Storm surge
Water elevation (m NGF) 1 15 2 25 3 35 4 45
of below sea level surface area of the10 km wide coastal band
1 3 11 32 45 50 54 56
Fig 1) wave conditions are characterized by significantwave heights of 2 m and peak periods ranging from 8 to12 s coming predominantly from the W to NW althoughwinter storms can episodically produce waves higher than9 m (Bertin et al 2008)
Four small coastal rivers contribute to moderate fresh-water input the Lay and Sevre Niortaise rivers that flowinto the Pertuis Breton and Aiguillon Cove and the Char-ente and Seudre rivers that flow into the Marennes-OleronBay (Fig 1) The analysis of available fluvial dischargedata (Banque Hydro 2012) reveals that fluvial dischargesduring Xynthia were close to yearly-mean conditions (Ta-ble 2) which are too weak to induce any freshwater flood
23 The Xynthia storm and the associated damages
Xynthia was a windstorm that hit the coasts of France dur-ing the night of the 27th to the 28th of February 2010The sea-level pressure reached its minimum at 969 mbarSouthern to southwestern winds ranging from 25 to 35 msminus1
(hourly mean at 10 m elevation) blew over the southern partof the Bay of Biscay (Fig 1) and maximum gusts reach-ing 45 msminus1 were recorded on Re Island (Fig 1) (Bertin etal 2012a) Xynthia generated a storm surge that reached itsmaximum in the central part of the Bay of Biscay (Bertinet al 2012a) Storm surges during Xynthia were estimatedby comparing the predicted astronomical tide to the mea-sured sea level and this comparison showed that the stormsurge in La Pallice harbor exceeded 150 m (Fig 2) Thisstorm surge was in phase with a high spring tide caus-ing an extreme water level of 45 m NGF Considering thework of Simon (2008) on extreme water levels this valuewould be associated with a return period larger than 100 yrMany natural barriers and sea-walls were submerged andorbreached causing the flooding of very large areas (approxi-mately 400 km2 in the study area)
Xynthia was one of the costliest and deadliest storms toever strike France in modern history Tourism farming andaquaculture are three major economic activities of this partof France Saltwater flooding of farmlands was disastrous tothe farming industry firstly because the saltwater-inundatedlands can remain contaminated by salt for several years ren-dering them impossible to crop Secondly hundreds of cattlewere drowned Many aquaculture infrastructures located inmarshes as well as tourism infrastructures were destroyed
In term of loss of life some were due to urbanized heavily-inhabited areas also being flooded Thus the total number offatalities directly related to Xynthia exceeded 40 on the west-ern coast of France and the material damages were estimatedto be more than 12 billion euros
24 Classification based on geomorphology and exten-sion of flooded areas
For this study only the inundated marshes with surface areaslarger than 005 km2 are considered The 27 correspondingmarshes (Fig 1) display a huge variety in terms of shapesand surface areas In order to quantify the surface area ofthose marshes the 5 m NGF isoline is considered the arbi-trary landward boundary of the marshes and the coastlinedefined as the maximum landward inundation during high-est astronomical tides is considered as the seaward bound-ary of the marshes The marshes are arbitrarily classified ac-cording to their size (Fig 1 and Table 3) These parametersallow 3 classes of marshes small marshes (lt 30km2) largemarshes (gt 30km2 andlt 500km2) and very large marshes(gt 500km2)
3 Data and methods
31 Sea level during Xynthia
Sea level measurements during the Xynthia storm at La Pal-lice tide gauge (Fig 1) were collected from the REFMAR(wwwrefmarshomfr) database The maximum sea levelreached at this tide gauge during the storm was about 45 mNGF (Fig 2) In order to investigate the spatial variationsof the maximum sea level during the Xynthia storm a newmodeling system was developed and implemented over thenortheast Atlantic Ocean This modeling system realizes thecoupling in two horizontal dimensions between the circula-tion model SELFE (Zhang and Baptista 2008) and the spec-tral wave model WaveWatch III (Tolman 2009) SELFE usesa combination of finite volume and finite element methods tosolve the shallow water equations and employs a Lagrangianmethod to treat the advective terms which guaranties goodstability even when using large time steps WWIII uses finitedifferences on regular grids to solve the spectral wave actionequation A detailed description of this modeling system andits application can be found in Bertin et al (2012a) These
J F Breilh et al Assessment of static flood modeling techniques 1599
Table 2Mean maximum and daily for the day of Xynthia discharges of the four main rivers of the study area
River Lay Sevre Niortaise Charente Seudre
Period of measure 2003ndash2012 1969ndash2012 1998ndash2012 1998ndash2012Mean discharge for all period ( m3sminus1) 12 116 69 15Maximum daily discharge ( m3sminus1) 214 255 1037 19Daily discharge 28022010 ( m3sminus1) 31 62 120 15
Fig 2 Predicted tide (blue line) observed water level at the La Pallice tide gauge (black circles) and modeled water level from Bertin etal (2012a) storm surge modeling system (red line) in meter NGF during the Xynthia storm
authors showed that the storm surge associated with Xyn-thia could only be predicted accurately if the wind stress wascomputed using a wave-dependent parameterization Thisbehavior was explained by a particular sea state during Xyn-thia characterized by young and steep wind waves whichenhanced the ocean roughness and thereby the wind stress
From the model results it appeared that the maximum sealevel reached during Xynthia showed significant spatial vari-ations Maximum sea level varied from 4 m NGF at the en-trances of the Pertuis de Maumusson and Pertuis drsquoAntiocheto almost 5 m NGF within the Aiguillon Cove (Fig 3)
32 Topographic and bathymetric datasets
The high resolution topographic datasets originate from bothLiDAR and RTKndashGPS (Real-Time Kinematic ndash Global Po-sitioning System) measurements LiDAR is a mapping tech-nology that is increasingly used for coastal geomorphologicstudies This technology is appropriate for such analysessince it provides spatially dense and accurate topographicdata (Chust et al 2008 Goff et al 2009 Haile and Rientjes2005 Mazzanti et al 2009 Poulter and Halpin 2008 Web-ster 2010 Young et al 2011) The LiDAR is a laser altime-ter that measures the range from a platform with a positionand altitude determined from GPS and an inertial measure-ment unit (IMU) Basically it uses a scanning device thatdetermines the distance from the sensor to a set of groundpoints roughly perpendicular to the direction of flight (Chust
et al 2008) In 2010 the French National Geographic In-stitute (IGN) carried out the topographic mapping of the en-tire coastal area of the Pertuis Charentais four months af-ter Xynthia using the LiDAR technology The aerial flightswere carried out between low- and mid-tide A terrestrialDEM was generated from the LiDAR data with a resolutionof 1 m and a vertical accuracy of 015 m (root mean squareerror hereafter RMSE) in low vegetated and gently slopingareas The accuracy was assessed by IGN in test zones us-ing GPS receivers with RTK system In this study a ground(bare-earth ie excluding objects such as buildings treesand shrubs) DTM obtained from the DEM was used In or-der to better represent some key topographic features such asdykes levees and seawalls additional measurements basedon RTKndashGPS were included The theoretical vertical accu-racy of our devices (Topcon hyperpro) is 002 m but the op-erational accuracy which includes uncertainties related tothe measurement would rather be of the order of 005 mThis data could locally improve the reliability of the LiDARDTM as shown by Gallien et al (2011)
The bathymetric datasets shown in Fig 1 is a combinationfrom different sources The bathymetry of intertidal areaswas determined using the LiDAR technology between low-and mid-tide For subtidal areas the bathymetry originatesfrom the SHOM (French Navy Hydrographic and Oceano-graphic Service) and was measured with echo sounders Inareas where there was a lack of data between the intertidalLiDAR data and the subtidal bathymetry complementary
1600 J F Breilh et al Assessment of static flood modeling techniques
Table 3 The 27 marshes of the study area classified according to size Surface area of the flooded area during Xynthia and maximum sealevel during this storm computed by the Bertin et al (2012a) storm surge modeling system
Marsh no Marsh name Marsh area(km2)
Observed flooded area(km2)
Modeled maximum sealevel at the seawardboundary of the marshduring Xynthia (m)
1 La Flotte 006 004 4562 Port des Minimes 013 010 4443 CG17 butte de tir 023 030 4444 Rivedoux-Goguette 027 008 4305 Golf de la Pree 031 029 4706 Fouras 042 029 4457 Port des Barques Ouest 042 017 4468 Coup de Vague 048 044 4759 Port Neuf 050 034 44310 Pampin 051 037 46011 Aix 052 046 44312 Ile Madame 054 047 44513 Parc La Rochelle 130 012 44614 Loix Est 175 152 44315 Port du Plomb 190 140 46116 Saint-Trojan 269 038 41017 La Rochelle Centre 586 056 44618 Aytre-Angoulins 815 338 44419 Loix Ouest Couarde 1380 913 44120 Chateau drsquoOleron 1403 788 44021 Re Nord 2115 1070 45322 Boyardville 6450 1680 44423 Charente 8300 4825 44624 Brouage 12000 2875 44325 Seudre Estuary 12500 8831 41726 Chatelaillon-Yves 16000 1400 44527 Poitevin Marsh 99700 15821 475
bathymetric measurements were carried out by our team us-ing a single beam echo sounder mounted with the sameRTKndashGPS as used for topographic surveys
33 Observed flooded areas related to the Xynthia storm
The area flooded by Xynthia in the northern part of the studyarea ie marsh no 27 northward of the Sevre Niortaise Es-tuary was determined using flood inundation maps from theSERTIT (regional service of image processing and remotesensing) combining images from SPOT 4 (10 m resolutiontaken two days after the storm) ENVISAT ASAR (125 mresolution taken two days after the storm) and RADARSAT2 (6 m resolution taken 4 days after the storm) satellites Forall other flooded areas field observations were carried out bySOGREAH a French consulting agency (DDTM-17 2011)In situ limits of storm deposits physical marks or markersand damages to vegetation were observed to determine hori-zontal and vertical water limits By compiling all these datain a GIS the polygons of the inundated areas (Fig 1) werethen obtained Considering the delay between the storm and
the satellite images it is not possible to assess the polygonextension accuracy for the northern part of the marsh no 27Nevertheless SERTIT inundation maps were compared withSOGREAH field observations for areas where both datasetswere available These comparisons showed a good agreementbetween the two datasets Considering this difficulty to accu-rately assess the horizontal accuracy of maximum water lim-its we arbitrarily set it to 10 m for urbanized flooded areasand 100 m for marshes without any structures These poly-gons were considered as the observed flooded areas for Xyn-thia and were used to evaluate the simulated flooded areas
34 Flooding methods
The following methods are presented according to three lev-els of increasing complexity (1) method SM1 is a static floodmodeling method that uses the maximum sea level recordedduring the storm at La Pallice tide gauge (Fig 2) (2) methodSM2 is a static flood modeling method considering the space-varying maximum sea levels extracted from the modelingsystem of Bertin et al (2012a) (Fig 3) and (3) method SO
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
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Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1596 J F Breilh et al Assessment of static flood modeling techniques
was illustrated by hurricane Katrina in 2005 which was thesixth-strongest Atlantic hurricane ever reported with the as-sociated flood cost 1500 lives and 84 billion dollars in dam-ages (Blake 2007) The coastal morphology of northwest-ern Europe is dominated by estuarine environments (Perillo1995) while this region is located on the track of extra-tropical storms regularly inducing storm surges above onemeter (Bertin et al 2012a Brown et al 2010 Nicolle etal 2009 Wolf 2008) Low-lying zones of northwestern Eu-rope are thus also vulnerable to coastal flooding Over thepast century the most serious case took place in the southernNorth Sea in February 1953 A severe storm induced a three-meters-high surge (Wolf and Flather 2005) combined witha high spring tide which caused the flooding of a large partof the Netherlands (Gerritsen 2005) and to a slighter degreein the UK and Germany This catastrophe was responsiblefor 1836 deaths (Gerritsen 2005 Wolf and Flather 2005) Inthe last fifteen years in France the storms Martin and Xyn-thia (Bertin et al 2012a) hit the western coast and causedthe flooding of large coastal areas Xynthia (February 2010)was responsible for 47 deaths and at least 12 billion eurosin damages in France (Lumbroso and Vinet 2011) Of these47 deaths 41 occurred in the study area
Beside loss of human lives and material damages thechanges of environmental conditions in coastal habitats asso-ciated with marine floods cause a cascade of direct and indi-rect ecological responses that range from immediate to long-term For example inundation of fresh marshes by storm-driven seawater tends to damage or kill all the vegetationsometimes for several years (Morton and Barras 2011)
This study is focused on Xynthia and the associated surgebecause for the first time the flooded areas were accuratelymapped in this region of France Following this storm a re-gional storm surge modeling system was developed (Bertin etal 2012a) and accurate LiDAR (Light Detection and Rang-ing) data were obtained in order to identify vulnerable coastalareas Previous topographic data could not be used for suchapplication because they were not accurate enough to rep-resent coastal defenses and sedimentary barriers Indeed Li-DAR is able to measure ground elevation with a horizontalresolution (sim 1 m) and a vertical accuracy (sim 10ndash15 cm) thatare adequate for many flood mapping applications (Gallienet al 2011) The airborne LiDAR-derived Digital ElevationModels (DEMs) are commonly used to evaluate vulnerabil-ity to sea-level rise (Chust et al 2009 2010 Webster 2010Webster et al 2006) coastal flood risks (Bernatchez et al2011 Webster et al 2006) and also fluvial flood risks (Cookand Merwade 2009 Haile and Rientjes 2005)
This study aims to evaluate the benefits and limita-tions of a raster-based static method and a semi-dynamicflood modeling method based on high accuracy LiDAR-derived DEMs Such methods are commonly used to de-lineate areas vulnerable to flooding like the Coastal FloodRisk Prevention Plans (PPR-SMhttpwwwrisquesgouvfrrisques-naturelsinondation) in France the Flood Insurance
Rate Map (FIRM) from the Federal Emergency Manage-ment Agency (FEMAhttpwwwfemagov) in the USA orflood maps from the UK environment agency (httpwwwenvironment-agencygovuk)
The originality of this study stems from the analysis of awide diversity of flooded areas for which the extension of theflooding was accurately delineated More than 40 separatedareas were flooded and mapped allowing linking the skill ofstatic modeling methods with geomorphological character-istics of flooded areas The performance of static modelingmethods is evaluated against generic morphological parame-ters from which this study concludes on the applicability ofsuch methods for other vulnerable coastal environments
2 Study area
21 Geomorphologic setting
The study area is located along the Atlantic Coast of Francenorthward of the Gironde Estuary (Fig 1) The coastlineis irregular and characterized by large embayments (locallynamed ldquoPertuis Charentaisrdquo) corresponding to three drownincised-valley (IV) segments (from north to south the Lay-Sevre IV the Charente IV and the Seudre IV (Chaumillon etal 2008) bounded by the Arvert Peninsula Re and OleronIslands and the south Vendee coastline (Fig 1) The max-imum water depth is 43 m below the 0 NGF (French ver-tical datum (Nivellement General de la France) resultingfrom mean sea level observations at the Marseille tide gaugebetween the 2 February 1885 and the 31 December 1896)within the Charente IV and 61 m within the Lay-Sevre IVNevertheless because 65 of the Pertuis Charentais seafloor area is less than 10 m deep the marine part of the studyarea can be considered as shallow
The landward part of those embayments displays exten-sive intertidal mudflats that can reach 5 km width In thepast (from millenaries to centuries) the seaward parts of thoseonshore incised-valley segments were flooded by the sea (Al-lard et al 2008 Billeaud et al 2004 Chaumillon et al2008 Pawlowski 1998) The rapid siltation and sediment-fill of those IV segments led to the development of extensivecoastal marshes one of them corresponding to the largestcoastal marsh of France (the Poitevin Marsh no 27 Fig 1)The natural infilling of those marshes was enhanced by an-thropogenic activities mainly deforestation (Poirier et al2011) and land reclamation (Allard et al 2008 Bertin et al2005 Chaumillon et al 2004)
Those marshes are bounded by rocky outcrops corre-sponding to the interfluves of the IVs The elevation of alarge part of these marshes is commonly below the high-est sea levels reached during spring tides Considering acoastal area spanning from 10 km inland to the coastlinebetween 45 and 50 is below the highest astronomi-cal tides (Table 1) To prevent marine flooding extensive
J F Breilh et al Assessment of static flood modeling techniques 1597
Fig 1 LiDAR derived Digital Terrain Model (DTM) of the study area Elevation is shown in meter NGF and the horizontal projection is inmeters of Lambert 93 projected coordinate system Circled numbers 1 to 27 are the studied marshes The red dotted line shows the extensionof the observed flooded areas caused by the Xynthia storm
dykes levees (approximately 240 km) and locks have beenbuilt over the last centuries (6 m) Due to the construction ofall these flood management measurements wetlands are dis-connected from the sea During high tides locks are closedpreventing saltwater incursion and during low tides locksare opened allowing the drainage of marshes
22 Hydrodynamic setting
The study area is a mixed tide- and wave-dominated sys-tem Tides are semi-diurnal with amplitude ranging fromless than 2 m (neap tides) to more than 6 m (spring tides)Mean annual offshore (about 120 km offshore Oleron Island
1598 J F Breilh et al Assessment of static flood modeling techniques
Table 1Percentages of the 10 km band land area (from the present-day coastline to 10 km inshore) below (1) the sea level of the mean highwater neaps (MHWN) (2) the sea level of the mean high water springs (MHWS) and (3) the sea level of highest astronomical tide (HAT)
Corresponding tide MHWN MHWS HAT HAT+ Storm surge
Water elevation (m NGF) 1 15 2 25 3 35 4 45
of below sea level surface area of the10 km wide coastal band
1 3 11 32 45 50 54 56
Fig 1) wave conditions are characterized by significantwave heights of 2 m and peak periods ranging from 8 to12 s coming predominantly from the W to NW althoughwinter storms can episodically produce waves higher than9 m (Bertin et al 2008)
Four small coastal rivers contribute to moderate fresh-water input the Lay and Sevre Niortaise rivers that flowinto the Pertuis Breton and Aiguillon Cove and the Char-ente and Seudre rivers that flow into the Marennes-OleronBay (Fig 1) The analysis of available fluvial dischargedata (Banque Hydro 2012) reveals that fluvial dischargesduring Xynthia were close to yearly-mean conditions (Ta-ble 2) which are too weak to induce any freshwater flood
23 The Xynthia storm and the associated damages
Xynthia was a windstorm that hit the coasts of France dur-ing the night of the 27th to the 28th of February 2010The sea-level pressure reached its minimum at 969 mbarSouthern to southwestern winds ranging from 25 to 35 msminus1
(hourly mean at 10 m elevation) blew over the southern partof the Bay of Biscay (Fig 1) and maximum gusts reach-ing 45 msminus1 were recorded on Re Island (Fig 1) (Bertin etal 2012a) Xynthia generated a storm surge that reached itsmaximum in the central part of the Bay of Biscay (Bertinet al 2012a) Storm surges during Xynthia were estimatedby comparing the predicted astronomical tide to the mea-sured sea level and this comparison showed that the stormsurge in La Pallice harbor exceeded 150 m (Fig 2) Thisstorm surge was in phase with a high spring tide caus-ing an extreme water level of 45 m NGF Considering thework of Simon (2008) on extreme water levels this valuewould be associated with a return period larger than 100 yrMany natural barriers and sea-walls were submerged andorbreached causing the flooding of very large areas (approxi-mately 400 km2 in the study area)
Xynthia was one of the costliest and deadliest storms toever strike France in modern history Tourism farming andaquaculture are three major economic activities of this partof France Saltwater flooding of farmlands was disastrous tothe farming industry firstly because the saltwater-inundatedlands can remain contaminated by salt for several years ren-dering them impossible to crop Secondly hundreds of cattlewere drowned Many aquaculture infrastructures located inmarshes as well as tourism infrastructures were destroyed
In term of loss of life some were due to urbanized heavily-inhabited areas also being flooded Thus the total number offatalities directly related to Xynthia exceeded 40 on the west-ern coast of France and the material damages were estimatedto be more than 12 billion euros
24 Classification based on geomorphology and exten-sion of flooded areas
For this study only the inundated marshes with surface areaslarger than 005 km2 are considered The 27 correspondingmarshes (Fig 1) display a huge variety in terms of shapesand surface areas In order to quantify the surface area ofthose marshes the 5 m NGF isoline is considered the arbi-trary landward boundary of the marshes and the coastlinedefined as the maximum landward inundation during high-est astronomical tides is considered as the seaward bound-ary of the marshes The marshes are arbitrarily classified ac-cording to their size (Fig 1 and Table 3) These parametersallow 3 classes of marshes small marshes (lt 30km2) largemarshes (gt 30km2 andlt 500km2) and very large marshes(gt 500km2)
3 Data and methods
31 Sea level during Xynthia
Sea level measurements during the Xynthia storm at La Pal-lice tide gauge (Fig 1) were collected from the REFMAR(wwwrefmarshomfr) database The maximum sea levelreached at this tide gauge during the storm was about 45 mNGF (Fig 2) In order to investigate the spatial variationsof the maximum sea level during the Xynthia storm a newmodeling system was developed and implemented over thenortheast Atlantic Ocean This modeling system realizes thecoupling in two horizontal dimensions between the circula-tion model SELFE (Zhang and Baptista 2008) and the spec-tral wave model WaveWatch III (Tolman 2009) SELFE usesa combination of finite volume and finite element methods tosolve the shallow water equations and employs a Lagrangianmethod to treat the advective terms which guaranties goodstability even when using large time steps WWIII uses finitedifferences on regular grids to solve the spectral wave actionequation A detailed description of this modeling system andits application can be found in Bertin et al (2012a) These
J F Breilh et al Assessment of static flood modeling techniques 1599
Table 2Mean maximum and daily for the day of Xynthia discharges of the four main rivers of the study area
River Lay Sevre Niortaise Charente Seudre
Period of measure 2003ndash2012 1969ndash2012 1998ndash2012 1998ndash2012Mean discharge for all period ( m3sminus1) 12 116 69 15Maximum daily discharge ( m3sminus1) 214 255 1037 19Daily discharge 28022010 ( m3sminus1) 31 62 120 15
Fig 2 Predicted tide (blue line) observed water level at the La Pallice tide gauge (black circles) and modeled water level from Bertin etal (2012a) storm surge modeling system (red line) in meter NGF during the Xynthia storm
authors showed that the storm surge associated with Xyn-thia could only be predicted accurately if the wind stress wascomputed using a wave-dependent parameterization Thisbehavior was explained by a particular sea state during Xyn-thia characterized by young and steep wind waves whichenhanced the ocean roughness and thereby the wind stress
From the model results it appeared that the maximum sealevel reached during Xynthia showed significant spatial vari-ations Maximum sea level varied from 4 m NGF at the en-trances of the Pertuis de Maumusson and Pertuis drsquoAntiocheto almost 5 m NGF within the Aiguillon Cove (Fig 3)
32 Topographic and bathymetric datasets
The high resolution topographic datasets originate from bothLiDAR and RTKndashGPS (Real-Time Kinematic ndash Global Po-sitioning System) measurements LiDAR is a mapping tech-nology that is increasingly used for coastal geomorphologicstudies This technology is appropriate for such analysessince it provides spatially dense and accurate topographicdata (Chust et al 2008 Goff et al 2009 Haile and Rientjes2005 Mazzanti et al 2009 Poulter and Halpin 2008 Web-ster 2010 Young et al 2011) The LiDAR is a laser altime-ter that measures the range from a platform with a positionand altitude determined from GPS and an inertial measure-ment unit (IMU) Basically it uses a scanning device thatdetermines the distance from the sensor to a set of groundpoints roughly perpendicular to the direction of flight (Chust
et al 2008) In 2010 the French National Geographic In-stitute (IGN) carried out the topographic mapping of the en-tire coastal area of the Pertuis Charentais four months af-ter Xynthia using the LiDAR technology The aerial flightswere carried out between low- and mid-tide A terrestrialDEM was generated from the LiDAR data with a resolutionof 1 m and a vertical accuracy of 015 m (root mean squareerror hereafter RMSE) in low vegetated and gently slopingareas The accuracy was assessed by IGN in test zones us-ing GPS receivers with RTK system In this study a ground(bare-earth ie excluding objects such as buildings treesand shrubs) DTM obtained from the DEM was used In or-der to better represent some key topographic features such asdykes levees and seawalls additional measurements basedon RTKndashGPS were included The theoretical vertical accu-racy of our devices (Topcon hyperpro) is 002 m but the op-erational accuracy which includes uncertainties related tothe measurement would rather be of the order of 005 mThis data could locally improve the reliability of the LiDARDTM as shown by Gallien et al (2011)
The bathymetric datasets shown in Fig 1 is a combinationfrom different sources The bathymetry of intertidal areaswas determined using the LiDAR technology between low-and mid-tide For subtidal areas the bathymetry originatesfrom the SHOM (French Navy Hydrographic and Oceano-graphic Service) and was measured with echo sounders Inareas where there was a lack of data between the intertidalLiDAR data and the subtidal bathymetry complementary
1600 J F Breilh et al Assessment of static flood modeling techniques
Table 3 The 27 marshes of the study area classified according to size Surface area of the flooded area during Xynthia and maximum sealevel during this storm computed by the Bertin et al (2012a) storm surge modeling system
Marsh no Marsh name Marsh area(km2)
Observed flooded area(km2)
Modeled maximum sealevel at the seawardboundary of the marshduring Xynthia (m)
1 La Flotte 006 004 4562 Port des Minimes 013 010 4443 CG17 butte de tir 023 030 4444 Rivedoux-Goguette 027 008 4305 Golf de la Pree 031 029 4706 Fouras 042 029 4457 Port des Barques Ouest 042 017 4468 Coup de Vague 048 044 4759 Port Neuf 050 034 44310 Pampin 051 037 46011 Aix 052 046 44312 Ile Madame 054 047 44513 Parc La Rochelle 130 012 44614 Loix Est 175 152 44315 Port du Plomb 190 140 46116 Saint-Trojan 269 038 41017 La Rochelle Centre 586 056 44618 Aytre-Angoulins 815 338 44419 Loix Ouest Couarde 1380 913 44120 Chateau drsquoOleron 1403 788 44021 Re Nord 2115 1070 45322 Boyardville 6450 1680 44423 Charente 8300 4825 44624 Brouage 12000 2875 44325 Seudre Estuary 12500 8831 41726 Chatelaillon-Yves 16000 1400 44527 Poitevin Marsh 99700 15821 475
bathymetric measurements were carried out by our team us-ing a single beam echo sounder mounted with the sameRTKndashGPS as used for topographic surveys
33 Observed flooded areas related to the Xynthia storm
The area flooded by Xynthia in the northern part of the studyarea ie marsh no 27 northward of the Sevre Niortaise Es-tuary was determined using flood inundation maps from theSERTIT (regional service of image processing and remotesensing) combining images from SPOT 4 (10 m resolutiontaken two days after the storm) ENVISAT ASAR (125 mresolution taken two days after the storm) and RADARSAT2 (6 m resolution taken 4 days after the storm) satellites Forall other flooded areas field observations were carried out bySOGREAH a French consulting agency (DDTM-17 2011)In situ limits of storm deposits physical marks or markersand damages to vegetation were observed to determine hori-zontal and vertical water limits By compiling all these datain a GIS the polygons of the inundated areas (Fig 1) werethen obtained Considering the delay between the storm and
the satellite images it is not possible to assess the polygonextension accuracy for the northern part of the marsh no 27Nevertheless SERTIT inundation maps were compared withSOGREAH field observations for areas where both datasetswere available These comparisons showed a good agreementbetween the two datasets Considering this difficulty to accu-rately assess the horizontal accuracy of maximum water lim-its we arbitrarily set it to 10 m for urbanized flooded areasand 100 m for marshes without any structures These poly-gons were considered as the observed flooded areas for Xyn-thia and were used to evaluate the simulated flooded areas
34 Flooding methods
The following methods are presented according to three lev-els of increasing complexity (1) method SM1 is a static floodmodeling method that uses the maximum sea level recordedduring the storm at La Pallice tide gauge (Fig 2) (2) methodSM2 is a static flood modeling method considering the space-varying maximum sea levels extracted from the modelingsystem of Bertin et al (2012a) (Fig 3) and (3) method SO
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1597
Fig 1 LiDAR derived Digital Terrain Model (DTM) of the study area Elevation is shown in meter NGF and the horizontal projection is inmeters of Lambert 93 projected coordinate system Circled numbers 1 to 27 are the studied marshes The red dotted line shows the extensionof the observed flooded areas caused by the Xynthia storm
dykes levees (approximately 240 km) and locks have beenbuilt over the last centuries (6 m) Due to the construction ofall these flood management measurements wetlands are dis-connected from the sea During high tides locks are closedpreventing saltwater incursion and during low tides locksare opened allowing the drainage of marshes
22 Hydrodynamic setting
The study area is a mixed tide- and wave-dominated sys-tem Tides are semi-diurnal with amplitude ranging fromless than 2 m (neap tides) to more than 6 m (spring tides)Mean annual offshore (about 120 km offshore Oleron Island
1598 J F Breilh et al Assessment of static flood modeling techniques
Table 1Percentages of the 10 km band land area (from the present-day coastline to 10 km inshore) below (1) the sea level of the mean highwater neaps (MHWN) (2) the sea level of the mean high water springs (MHWS) and (3) the sea level of highest astronomical tide (HAT)
Corresponding tide MHWN MHWS HAT HAT+ Storm surge
Water elevation (m NGF) 1 15 2 25 3 35 4 45
of below sea level surface area of the10 km wide coastal band
1 3 11 32 45 50 54 56
Fig 1) wave conditions are characterized by significantwave heights of 2 m and peak periods ranging from 8 to12 s coming predominantly from the W to NW althoughwinter storms can episodically produce waves higher than9 m (Bertin et al 2008)
Four small coastal rivers contribute to moderate fresh-water input the Lay and Sevre Niortaise rivers that flowinto the Pertuis Breton and Aiguillon Cove and the Char-ente and Seudre rivers that flow into the Marennes-OleronBay (Fig 1) The analysis of available fluvial dischargedata (Banque Hydro 2012) reveals that fluvial dischargesduring Xynthia were close to yearly-mean conditions (Ta-ble 2) which are too weak to induce any freshwater flood
23 The Xynthia storm and the associated damages
Xynthia was a windstorm that hit the coasts of France dur-ing the night of the 27th to the 28th of February 2010The sea-level pressure reached its minimum at 969 mbarSouthern to southwestern winds ranging from 25 to 35 msminus1
(hourly mean at 10 m elevation) blew over the southern partof the Bay of Biscay (Fig 1) and maximum gusts reach-ing 45 msminus1 were recorded on Re Island (Fig 1) (Bertin etal 2012a) Xynthia generated a storm surge that reached itsmaximum in the central part of the Bay of Biscay (Bertinet al 2012a) Storm surges during Xynthia were estimatedby comparing the predicted astronomical tide to the mea-sured sea level and this comparison showed that the stormsurge in La Pallice harbor exceeded 150 m (Fig 2) Thisstorm surge was in phase with a high spring tide caus-ing an extreme water level of 45 m NGF Considering thework of Simon (2008) on extreme water levels this valuewould be associated with a return period larger than 100 yrMany natural barriers and sea-walls were submerged andorbreached causing the flooding of very large areas (approxi-mately 400 km2 in the study area)
Xynthia was one of the costliest and deadliest storms toever strike France in modern history Tourism farming andaquaculture are three major economic activities of this partof France Saltwater flooding of farmlands was disastrous tothe farming industry firstly because the saltwater-inundatedlands can remain contaminated by salt for several years ren-dering them impossible to crop Secondly hundreds of cattlewere drowned Many aquaculture infrastructures located inmarshes as well as tourism infrastructures were destroyed
In term of loss of life some were due to urbanized heavily-inhabited areas also being flooded Thus the total number offatalities directly related to Xynthia exceeded 40 on the west-ern coast of France and the material damages were estimatedto be more than 12 billion euros
24 Classification based on geomorphology and exten-sion of flooded areas
For this study only the inundated marshes with surface areaslarger than 005 km2 are considered The 27 correspondingmarshes (Fig 1) display a huge variety in terms of shapesand surface areas In order to quantify the surface area ofthose marshes the 5 m NGF isoline is considered the arbi-trary landward boundary of the marshes and the coastlinedefined as the maximum landward inundation during high-est astronomical tides is considered as the seaward bound-ary of the marshes The marshes are arbitrarily classified ac-cording to their size (Fig 1 and Table 3) These parametersallow 3 classes of marshes small marshes (lt 30km2) largemarshes (gt 30km2 andlt 500km2) and very large marshes(gt 500km2)
3 Data and methods
31 Sea level during Xynthia
Sea level measurements during the Xynthia storm at La Pal-lice tide gauge (Fig 1) were collected from the REFMAR(wwwrefmarshomfr) database The maximum sea levelreached at this tide gauge during the storm was about 45 mNGF (Fig 2) In order to investigate the spatial variationsof the maximum sea level during the Xynthia storm a newmodeling system was developed and implemented over thenortheast Atlantic Ocean This modeling system realizes thecoupling in two horizontal dimensions between the circula-tion model SELFE (Zhang and Baptista 2008) and the spec-tral wave model WaveWatch III (Tolman 2009) SELFE usesa combination of finite volume and finite element methods tosolve the shallow water equations and employs a Lagrangianmethod to treat the advective terms which guaranties goodstability even when using large time steps WWIII uses finitedifferences on regular grids to solve the spectral wave actionequation A detailed description of this modeling system andits application can be found in Bertin et al (2012a) These
J F Breilh et al Assessment of static flood modeling techniques 1599
Table 2Mean maximum and daily for the day of Xynthia discharges of the four main rivers of the study area
River Lay Sevre Niortaise Charente Seudre
Period of measure 2003ndash2012 1969ndash2012 1998ndash2012 1998ndash2012Mean discharge for all period ( m3sminus1) 12 116 69 15Maximum daily discharge ( m3sminus1) 214 255 1037 19Daily discharge 28022010 ( m3sminus1) 31 62 120 15
Fig 2 Predicted tide (blue line) observed water level at the La Pallice tide gauge (black circles) and modeled water level from Bertin etal (2012a) storm surge modeling system (red line) in meter NGF during the Xynthia storm
authors showed that the storm surge associated with Xyn-thia could only be predicted accurately if the wind stress wascomputed using a wave-dependent parameterization Thisbehavior was explained by a particular sea state during Xyn-thia characterized by young and steep wind waves whichenhanced the ocean roughness and thereby the wind stress
From the model results it appeared that the maximum sealevel reached during Xynthia showed significant spatial vari-ations Maximum sea level varied from 4 m NGF at the en-trances of the Pertuis de Maumusson and Pertuis drsquoAntiocheto almost 5 m NGF within the Aiguillon Cove (Fig 3)
32 Topographic and bathymetric datasets
The high resolution topographic datasets originate from bothLiDAR and RTKndashGPS (Real-Time Kinematic ndash Global Po-sitioning System) measurements LiDAR is a mapping tech-nology that is increasingly used for coastal geomorphologicstudies This technology is appropriate for such analysessince it provides spatially dense and accurate topographicdata (Chust et al 2008 Goff et al 2009 Haile and Rientjes2005 Mazzanti et al 2009 Poulter and Halpin 2008 Web-ster 2010 Young et al 2011) The LiDAR is a laser altime-ter that measures the range from a platform with a positionand altitude determined from GPS and an inertial measure-ment unit (IMU) Basically it uses a scanning device thatdetermines the distance from the sensor to a set of groundpoints roughly perpendicular to the direction of flight (Chust
et al 2008) In 2010 the French National Geographic In-stitute (IGN) carried out the topographic mapping of the en-tire coastal area of the Pertuis Charentais four months af-ter Xynthia using the LiDAR technology The aerial flightswere carried out between low- and mid-tide A terrestrialDEM was generated from the LiDAR data with a resolutionof 1 m and a vertical accuracy of 015 m (root mean squareerror hereafter RMSE) in low vegetated and gently slopingareas The accuracy was assessed by IGN in test zones us-ing GPS receivers with RTK system In this study a ground(bare-earth ie excluding objects such as buildings treesand shrubs) DTM obtained from the DEM was used In or-der to better represent some key topographic features such asdykes levees and seawalls additional measurements basedon RTKndashGPS were included The theoretical vertical accu-racy of our devices (Topcon hyperpro) is 002 m but the op-erational accuracy which includes uncertainties related tothe measurement would rather be of the order of 005 mThis data could locally improve the reliability of the LiDARDTM as shown by Gallien et al (2011)
The bathymetric datasets shown in Fig 1 is a combinationfrom different sources The bathymetry of intertidal areaswas determined using the LiDAR technology between low-and mid-tide For subtidal areas the bathymetry originatesfrom the SHOM (French Navy Hydrographic and Oceano-graphic Service) and was measured with echo sounders Inareas where there was a lack of data between the intertidalLiDAR data and the subtidal bathymetry complementary
1600 J F Breilh et al Assessment of static flood modeling techniques
Table 3 The 27 marshes of the study area classified according to size Surface area of the flooded area during Xynthia and maximum sealevel during this storm computed by the Bertin et al (2012a) storm surge modeling system
Marsh no Marsh name Marsh area(km2)
Observed flooded area(km2)
Modeled maximum sealevel at the seawardboundary of the marshduring Xynthia (m)
1 La Flotte 006 004 4562 Port des Minimes 013 010 4443 CG17 butte de tir 023 030 4444 Rivedoux-Goguette 027 008 4305 Golf de la Pree 031 029 4706 Fouras 042 029 4457 Port des Barques Ouest 042 017 4468 Coup de Vague 048 044 4759 Port Neuf 050 034 44310 Pampin 051 037 46011 Aix 052 046 44312 Ile Madame 054 047 44513 Parc La Rochelle 130 012 44614 Loix Est 175 152 44315 Port du Plomb 190 140 46116 Saint-Trojan 269 038 41017 La Rochelle Centre 586 056 44618 Aytre-Angoulins 815 338 44419 Loix Ouest Couarde 1380 913 44120 Chateau drsquoOleron 1403 788 44021 Re Nord 2115 1070 45322 Boyardville 6450 1680 44423 Charente 8300 4825 44624 Brouage 12000 2875 44325 Seudre Estuary 12500 8831 41726 Chatelaillon-Yves 16000 1400 44527 Poitevin Marsh 99700 15821 475
bathymetric measurements were carried out by our team us-ing a single beam echo sounder mounted with the sameRTKndashGPS as used for topographic surveys
33 Observed flooded areas related to the Xynthia storm
The area flooded by Xynthia in the northern part of the studyarea ie marsh no 27 northward of the Sevre Niortaise Es-tuary was determined using flood inundation maps from theSERTIT (regional service of image processing and remotesensing) combining images from SPOT 4 (10 m resolutiontaken two days after the storm) ENVISAT ASAR (125 mresolution taken two days after the storm) and RADARSAT2 (6 m resolution taken 4 days after the storm) satellites Forall other flooded areas field observations were carried out bySOGREAH a French consulting agency (DDTM-17 2011)In situ limits of storm deposits physical marks or markersand damages to vegetation were observed to determine hori-zontal and vertical water limits By compiling all these datain a GIS the polygons of the inundated areas (Fig 1) werethen obtained Considering the delay between the storm and
the satellite images it is not possible to assess the polygonextension accuracy for the northern part of the marsh no 27Nevertheless SERTIT inundation maps were compared withSOGREAH field observations for areas where both datasetswere available These comparisons showed a good agreementbetween the two datasets Considering this difficulty to accu-rately assess the horizontal accuracy of maximum water lim-its we arbitrarily set it to 10 m for urbanized flooded areasand 100 m for marshes without any structures These poly-gons were considered as the observed flooded areas for Xyn-thia and were used to evaluate the simulated flooded areas
34 Flooding methods
The following methods are presented according to three lev-els of increasing complexity (1) method SM1 is a static floodmodeling method that uses the maximum sea level recordedduring the storm at La Pallice tide gauge (Fig 2) (2) methodSM2 is a static flood modeling method considering the space-varying maximum sea levels extracted from the modelingsystem of Bertin et al (2012a) (Fig 3) and (3) method SO
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1598 J F Breilh et al Assessment of static flood modeling techniques
Table 1Percentages of the 10 km band land area (from the present-day coastline to 10 km inshore) below (1) the sea level of the mean highwater neaps (MHWN) (2) the sea level of the mean high water springs (MHWS) and (3) the sea level of highest astronomical tide (HAT)
Corresponding tide MHWN MHWS HAT HAT+ Storm surge
Water elevation (m NGF) 1 15 2 25 3 35 4 45
of below sea level surface area of the10 km wide coastal band
1 3 11 32 45 50 54 56
Fig 1) wave conditions are characterized by significantwave heights of 2 m and peak periods ranging from 8 to12 s coming predominantly from the W to NW althoughwinter storms can episodically produce waves higher than9 m (Bertin et al 2008)
Four small coastal rivers contribute to moderate fresh-water input the Lay and Sevre Niortaise rivers that flowinto the Pertuis Breton and Aiguillon Cove and the Char-ente and Seudre rivers that flow into the Marennes-OleronBay (Fig 1) The analysis of available fluvial dischargedata (Banque Hydro 2012) reveals that fluvial dischargesduring Xynthia were close to yearly-mean conditions (Ta-ble 2) which are too weak to induce any freshwater flood
23 The Xynthia storm and the associated damages
Xynthia was a windstorm that hit the coasts of France dur-ing the night of the 27th to the 28th of February 2010The sea-level pressure reached its minimum at 969 mbarSouthern to southwestern winds ranging from 25 to 35 msminus1
(hourly mean at 10 m elevation) blew over the southern partof the Bay of Biscay (Fig 1) and maximum gusts reach-ing 45 msminus1 were recorded on Re Island (Fig 1) (Bertin etal 2012a) Xynthia generated a storm surge that reached itsmaximum in the central part of the Bay of Biscay (Bertinet al 2012a) Storm surges during Xynthia were estimatedby comparing the predicted astronomical tide to the mea-sured sea level and this comparison showed that the stormsurge in La Pallice harbor exceeded 150 m (Fig 2) Thisstorm surge was in phase with a high spring tide caus-ing an extreme water level of 45 m NGF Considering thework of Simon (2008) on extreme water levels this valuewould be associated with a return period larger than 100 yrMany natural barriers and sea-walls were submerged andorbreached causing the flooding of very large areas (approxi-mately 400 km2 in the study area)
Xynthia was one of the costliest and deadliest storms toever strike France in modern history Tourism farming andaquaculture are three major economic activities of this partof France Saltwater flooding of farmlands was disastrous tothe farming industry firstly because the saltwater-inundatedlands can remain contaminated by salt for several years ren-dering them impossible to crop Secondly hundreds of cattlewere drowned Many aquaculture infrastructures located inmarshes as well as tourism infrastructures were destroyed
In term of loss of life some were due to urbanized heavily-inhabited areas also being flooded Thus the total number offatalities directly related to Xynthia exceeded 40 on the west-ern coast of France and the material damages were estimatedto be more than 12 billion euros
24 Classification based on geomorphology and exten-sion of flooded areas
For this study only the inundated marshes with surface areaslarger than 005 km2 are considered The 27 correspondingmarshes (Fig 1) display a huge variety in terms of shapesand surface areas In order to quantify the surface area ofthose marshes the 5 m NGF isoline is considered the arbi-trary landward boundary of the marshes and the coastlinedefined as the maximum landward inundation during high-est astronomical tides is considered as the seaward bound-ary of the marshes The marshes are arbitrarily classified ac-cording to their size (Fig 1 and Table 3) These parametersallow 3 classes of marshes small marshes (lt 30km2) largemarshes (gt 30km2 andlt 500km2) and very large marshes(gt 500km2)
3 Data and methods
31 Sea level during Xynthia
Sea level measurements during the Xynthia storm at La Pal-lice tide gauge (Fig 1) were collected from the REFMAR(wwwrefmarshomfr) database The maximum sea levelreached at this tide gauge during the storm was about 45 mNGF (Fig 2) In order to investigate the spatial variationsof the maximum sea level during the Xynthia storm a newmodeling system was developed and implemented over thenortheast Atlantic Ocean This modeling system realizes thecoupling in two horizontal dimensions between the circula-tion model SELFE (Zhang and Baptista 2008) and the spec-tral wave model WaveWatch III (Tolman 2009) SELFE usesa combination of finite volume and finite element methods tosolve the shallow water equations and employs a Lagrangianmethod to treat the advective terms which guaranties goodstability even when using large time steps WWIII uses finitedifferences on regular grids to solve the spectral wave actionequation A detailed description of this modeling system andits application can be found in Bertin et al (2012a) These
J F Breilh et al Assessment of static flood modeling techniques 1599
Table 2Mean maximum and daily for the day of Xynthia discharges of the four main rivers of the study area
River Lay Sevre Niortaise Charente Seudre
Period of measure 2003ndash2012 1969ndash2012 1998ndash2012 1998ndash2012Mean discharge for all period ( m3sminus1) 12 116 69 15Maximum daily discharge ( m3sminus1) 214 255 1037 19Daily discharge 28022010 ( m3sminus1) 31 62 120 15
Fig 2 Predicted tide (blue line) observed water level at the La Pallice tide gauge (black circles) and modeled water level from Bertin etal (2012a) storm surge modeling system (red line) in meter NGF during the Xynthia storm
authors showed that the storm surge associated with Xyn-thia could only be predicted accurately if the wind stress wascomputed using a wave-dependent parameterization Thisbehavior was explained by a particular sea state during Xyn-thia characterized by young and steep wind waves whichenhanced the ocean roughness and thereby the wind stress
From the model results it appeared that the maximum sealevel reached during Xynthia showed significant spatial vari-ations Maximum sea level varied from 4 m NGF at the en-trances of the Pertuis de Maumusson and Pertuis drsquoAntiocheto almost 5 m NGF within the Aiguillon Cove (Fig 3)
32 Topographic and bathymetric datasets
The high resolution topographic datasets originate from bothLiDAR and RTKndashGPS (Real-Time Kinematic ndash Global Po-sitioning System) measurements LiDAR is a mapping tech-nology that is increasingly used for coastal geomorphologicstudies This technology is appropriate for such analysessince it provides spatially dense and accurate topographicdata (Chust et al 2008 Goff et al 2009 Haile and Rientjes2005 Mazzanti et al 2009 Poulter and Halpin 2008 Web-ster 2010 Young et al 2011) The LiDAR is a laser altime-ter that measures the range from a platform with a positionand altitude determined from GPS and an inertial measure-ment unit (IMU) Basically it uses a scanning device thatdetermines the distance from the sensor to a set of groundpoints roughly perpendicular to the direction of flight (Chust
et al 2008) In 2010 the French National Geographic In-stitute (IGN) carried out the topographic mapping of the en-tire coastal area of the Pertuis Charentais four months af-ter Xynthia using the LiDAR technology The aerial flightswere carried out between low- and mid-tide A terrestrialDEM was generated from the LiDAR data with a resolutionof 1 m and a vertical accuracy of 015 m (root mean squareerror hereafter RMSE) in low vegetated and gently slopingareas The accuracy was assessed by IGN in test zones us-ing GPS receivers with RTK system In this study a ground(bare-earth ie excluding objects such as buildings treesand shrubs) DTM obtained from the DEM was used In or-der to better represent some key topographic features such asdykes levees and seawalls additional measurements basedon RTKndashGPS were included The theoretical vertical accu-racy of our devices (Topcon hyperpro) is 002 m but the op-erational accuracy which includes uncertainties related tothe measurement would rather be of the order of 005 mThis data could locally improve the reliability of the LiDARDTM as shown by Gallien et al (2011)
The bathymetric datasets shown in Fig 1 is a combinationfrom different sources The bathymetry of intertidal areaswas determined using the LiDAR technology between low-and mid-tide For subtidal areas the bathymetry originatesfrom the SHOM (French Navy Hydrographic and Oceano-graphic Service) and was measured with echo sounders Inareas where there was a lack of data between the intertidalLiDAR data and the subtidal bathymetry complementary
1600 J F Breilh et al Assessment of static flood modeling techniques
Table 3 The 27 marshes of the study area classified according to size Surface area of the flooded area during Xynthia and maximum sealevel during this storm computed by the Bertin et al (2012a) storm surge modeling system
Marsh no Marsh name Marsh area(km2)
Observed flooded area(km2)
Modeled maximum sealevel at the seawardboundary of the marshduring Xynthia (m)
1 La Flotte 006 004 4562 Port des Minimes 013 010 4443 CG17 butte de tir 023 030 4444 Rivedoux-Goguette 027 008 4305 Golf de la Pree 031 029 4706 Fouras 042 029 4457 Port des Barques Ouest 042 017 4468 Coup de Vague 048 044 4759 Port Neuf 050 034 44310 Pampin 051 037 46011 Aix 052 046 44312 Ile Madame 054 047 44513 Parc La Rochelle 130 012 44614 Loix Est 175 152 44315 Port du Plomb 190 140 46116 Saint-Trojan 269 038 41017 La Rochelle Centre 586 056 44618 Aytre-Angoulins 815 338 44419 Loix Ouest Couarde 1380 913 44120 Chateau drsquoOleron 1403 788 44021 Re Nord 2115 1070 45322 Boyardville 6450 1680 44423 Charente 8300 4825 44624 Brouage 12000 2875 44325 Seudre Estuary 12500 8831 41726 Chatelaillon-Yves 16000 1400 44527 Poitevin Marsh 99700 15821 475
bathymetric measurements were carried out by our team us-ing a single beam echo sounder mounted with the sameRTKndashGPS as used for topographic surveys
33 Observed flooded areas related to the Xynthia storm
The area flooded by Xynthia in the northern part of the studyarea ie marsh no 27 northward of the Sevre Niortaise Es-tuary was determined using flood inundation maps from theSERTIT (regional service of image processing and remotesensing) combining images from SPOT 4 (10 m resolutiontaken two days after the storm) ENVISAT ASAR (125 mresolution taken two days after the storm) and RADARSAT2 (6 m resolution taken 4 days after the storm) satellites Forall other flooded areas field observations were carried out bySOGREAH a French consulting agency (DDTM-17 2011)In situ limits of storm deposits physical marks or markersand damages to vegetation were observed to determine hori-zontal and vertical water limits By compiling all these datain a GIS the polygons of the inundated areas (Fig 1) werethen obtained Considering the delay between the storm and
the satellite images it is not possible to assess the polygonextension accuracy for the northern part of the marsh no 27Nevertheless SERTIT inundation maps were compared withSOGREAH field observations for areas where both datasetswere available These comparisons showed a good agreementbetween the two datasets Considering this difficulty to accu-rately assess the horizontal accuracy of maximum water lim-its we arbitrarily set it to 10 m for urbanized flooded areasand 100 m for marshes without any structures These poly-gons were considered as the observed flooded areas for Xyn-thia and were used to evaluate the simulated flooded areas
34 Flooding methods
The following methods are presented according to three lev-els of increasing complexity (1) method SM1 is a static floodmodeling method that uses the maximum sea level recordedduring the storm at La Pallice tide gauge (Fig 2) (2) methodSM2 is a static flood modeling method considering the space-varying maximum sea levels extracted from the modelingsystem of Bertin et al (2012a) (Fig 3) and (3) method SO
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
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Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
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Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
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Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
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Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1599
Table 2Mean maximum and daily for the day of Xynthia discharges of the four main rivers of the study area
River Lay Sevre Niortaise Charente Seudre
Period of measure 2003ndash2012 1969ndash2012 1998ndash2012 1998ndash2012Mean discharge for all period ( m3sminus1) 12 116 69 15Maximum daily discharge ( m3sminus1) 214 255 1037 19Daily discharge 28022010 ( m3sminus1) 31 62 120 15
Fig 2 Predicted tide (blue line) observed water level at the La Pallice tide gauge (black circles) and modeled water level from Bertin etal (2012a) storm surge modeling system (red line) in meter NGF during the Xynthia storm
authors showed that the storm surge associated with Xyn-thia could only be predicted accurately if the wind stress wascomputed using a wave-dependent parameterization Thisbehavior was explained by a particular sea state during Xyn-thia characterized by young and steep wind waves whichenhanced the ocean roughness and thereby the wind stress
From the model results it appeared that the maximum sealevel reached during Xynthia showed significant spatial vari-ations Maximum sea level varied from 4 m NGF at the en-trances of the Pertuis de Maumusson and Pertuis drsquoAntiocheto almost 5 m NGF within the Aiguillon Cove (Fig 3)
32 Topographic and bathymetric datasets
The high resolution topographic datasets originate from bothLiDAR and RTKndashGPS (Real-Time Kinematic ndash Global Po-sitioning System) measurements LiDAR is a mapping tech-nology that is increasingly used for coastal geomorphologicstudies This technology is appropriate for such analysessince it provides spatially dense and accurate topographicdata (Chust et al 2008 Goff et al 2009 Haile and Rientjes2005 Mazzanti et al 2009 Poulter and Halpin 2008 Web-ster 2010 Young et al 2011) The LiDAR is a laser altime-ter that measures the range from a platform with a positionand altitude determined from GPS and an inertial measure-ment unit (IMU) Basically it uses a scanning device thatdetermines the distance from the sensor to a set of groundpoints roughly perpendicular to the direction of flight (Chust
et al 2008) In 2010 the French National Geographic In-stitute (IGN) carried out the topographic mapping of the en-tire coastal area of the Pertuis Charentais four months af-ter Xynthia using the LiDAR technology The aerial flightswere carried out between low- and mid-tide A terrestrialDEM was generated from the LiDAR data with a resolutionof 1 m and a vertical accuracy of 015 m (root mean squareerror hereafter RMSE) in low vegetated and gently slopingareas The accuracy was assessed by IGN in test zones us-ing GPS receivers with RTK system In this study a ground(bare-earth ie excluding objects such as buildings treesand shrubs) DTM obtained from the DEM was used In or-der to better represent some key topographic features such asdykes levees and seawalls additional measurements basedon RTKndashGPS were included The theoretical vertical accu-racy of our devices (Topcon hyperpro) is 002 m but the op-erational accuracy which includes uncertainties related tothe measurement would rather be of the order of 005 mThis data could locally improve the reliability of the LiDARDTM as shown by Gallien et al (2011)
The bathymetric datasets shown in Fig 1 is a combinationfrom different sources The bathymetry of intertidal areaswas determined using the LiDAR technology between low-and mid-tide For subtidal areas the bathymetry originatesfrom the SHOM (French Navy Hydrographic and Oceano-graphic Service) and was measured with echo sounders Inareas where there was a lack of data between the intertidalLiDAR data and the subtidal bathymetry complementary
1600 J F Breilh et al Assessment of static flood modeling techniques
Table 3 The 27 marshes of the study area classified according to size Surface area of the flooded area during Xynthia and maximum sealevel during this storm computed by the Bertin et al (2012a) storm surge modeling system
Marsh no Marsh name Marsh area(km2)
Observed flooded area(km2)
Modeled maximum sealevel at the seawardboundary of the marshduring Xynthia (m)
1 La Flotte 006 004 4562 Port des Minimes 013 010 4443 CG17 butte de tir 023 030 4444 Rivedoux-Goguette 027 008 4305 Golf de la Pree 031 029 4706 Fouras 042 029 4457 Port des Barques Ouest 042 017 4468 Coup de Vague 048 044 4759 Port Neuf 050 034 44310 Pampin 051 037 46011 Aix 052 046 44312 Ile Madame 054 047 44513 Parc La Rochelle 130 012 44614 Loix Est 175 152 44315 Port du Plomb 190 140 46116 Saint-Trojan 269 038 41017 La Rochelle Centre 586 056 44618 Aytre-Angoulins 815 338 44419 Loix Ouest Couarde 1380 913 44120 Chateau drsquoOleron 1403 788 44021 Re Nord 2115 1070 45322 Boyardville 6450 1680 44423 Charente 8300 4825 44624 Brouage 12000 2875 44325 Seudre Estuary 12500 8831 41726 Chatelaillon-Yves 16000 1400 44527 Poitevin Marsh 99700 15821 475
bathymetric measurements were carried out by our team us-ing a single beam echo sounder mounted with the sameRTKndashGPS as used for topographic surveys
33 Observed flooded areas related to the Xynthia storm
The area flooded by Xynthia in the northern part of the studyarea ie marsh no 27 northward of the Sevre Niortaise Es-tuary was determined using flood inundation maps from theSERTIT (regional service of image processing and remotesensing) combining images from SPOT 4 (10 m resolutiontaken two days after the storm) ENVISAT ASAR (125 mresolution taken two days after the storm) and RADARSAT2 (6 m resolution taken 4 days after the storm) satellites Forall other flooded areas field observations were carried out bySOGREAH a French consulting agency (DDTM-17 2011)In situ limits of storm deposits physical marks or markersand damages to vegetation were observed to determine hori-zontal and vertical water limits By compiling all these datain a GIS the polygons of the inundated areas (Fig 1) werethen obtained Considering the delay between the storm and
the satellite images it is not possible to assess the polygonextension accuracy for the northern part of the marsh no 27Nevertheless SERTIT inundation maps were compared withSOGREAH field observations for areas where both datasetswere available These comparisons showed a good agreementbetween the two datasets Considering this difficulty to accu-rately assess the horizontal accuracy of maximum water lim-its we arbitrarily set it to 10 m for urbanized flooded areasand 100 m for marshes without any structures These poly-gons were considered as the observed flooded areas for Xyn-thia and were used to evaluate the simulated flooded areas
34 Flooding methods
The following methods are presented according to three lev-els of increasing complexity (1) method SM1 is a static floodmodeling method that uses the maximum sea level recordedduring the storm at La Pallice tide gauge (Fig 2) (2) methodSM2 is a static flood modeling method considering the space-varying maximum sea levels extracted from the modelingsystem of Bertin et al (2012a) (Fig 3) and (3) method SO
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
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Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1600 J F Breilh et al Assessment of static flood modeling techniques
Table 3 The 27 marshes of the study area classified according to size Surface area of the flooded area during Xynthia and maximum sealevel during this storm computed by the Bertin et al (2012a) storm surge modeling system
Marsh no Marsh name Marsh area(km2)
Observed flooded area(km2)
Modeled maximum sealevel at the seawardboundary of the marshduring Xynthia (m)
1 La Flotte 006 004 4562 Port des Minimes 013 010 4443 CG17 butte de tir 023 030 4444 Rivedoux-Goguette 027 008 4305 Golf de la Pree 031 029 4706 Fouras 042 029 4457 Port des Barques Ouest 042 017 4468 Coup de Vague 048 044 4759 Port Neuf 050 034 44310 Pampin 051 037 46011 Aix 052 046 44312 Ile Madame 054 047 44513 Parc La Rochelle 130 012 44614 Loix Est 175 152 44315 Port du Plomb 190 140 46116 Saint-Trojan 269 038 41017 La Rochelle Centre 586 056 44618 Aytre-Angoulins 815 338 44419 Loix Ouest Couarde 1380 913 44120 Chateau drsquoOleron 1403 788 44021 Re Nord 2115 1070 45322 Boyardville 6450 1680 44423 Charente 8300 4825 44624 Brouage 12000 2875 44325 Seudre Estuary 12500 8831 41726 Chatelaillon-Yves 16000 1400 44527 Poitevin Marsh 99700 15821 475
bathymetric measurements were carried out by our team us-ing a single beam echo sounder mounted with the sameRTKndashGPS as used for topographic surveys
33 Observed flooded areas related to the Xynthia storm
The area flooded by Xynthia in the northern part of the studyarea ie marsh no 27 northward of the Sevre Niortaise Es-tuary was determined using flood inundation maps from theSERTIT (regional service of image processing and remotesensing) combining images from SPOT 4 (10 m resolutiontaken two days after the storm) ENVISAT ASAR (125 mresolution taken two days after the storm) and RADARSAT2 (6 m resolution taken 4 days after the storm) satellites Forall other flooded areas field observations were carried out bySOGREAH a French consulting agency (DDTM-17 2011)In situ limits of storm deposits physical marks or markersand damages to vegetation were observed to determine hori-zontal and vertical water limits By compiling all these datain a GIS the polygons of the inundated areas (Fig 1) werethen obtained Considering the delay between the storm and
the satellite images it is not possible to assess the polygonextension accuracy for the northern part of the marsh no 27Nevertheless SERTIT inundation maps were compared withSOGREAH field observations for areas where both datasetswere available These comparisons showed a good agreementbetween the two datasets Considering this difficulty to accu-rately assess the horizontal accuracy of maximum water lim-its we arbitrarily set it to 10 m for urbanized flooded areasand 100 m for marshes without any structures These poly-gons were considered as the observed flooded areas for Xyn-thia and were used to evaluate the simulated flooded areas
34 Flooding methods
The following methods are presented according to three lev-els of increasing complexity (1) method SM1 is a static floodmodeling method that uses the maximum sea level recordedduring the storm at La Pallice tide gauge (Fig 2) (2) methodSM2 is a static flood modeling method considering the space-varying maximum sea levels extracted from the modelingsystem of Bertin et al (2012a) (Fig 3) and (3) method SO
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1601
Fig 3Maximum sea level during the Xynthia storm in meter NGF calculated from the storm surge numerical model of Bertin et al (2012a)
is a surge overflowing method where the water volume dis-charge over the dykes is computed based on time series ofmodeled water levels thereby consisting of a semi-dynamicmethod For the two first methods (SM1 and SM2) the cellsof the DTM are considered as flooded if their elevation is be-low the maximum sea level and only if they are connected toan adjacent cell that is flooded or connected to open water
341 Static flood modeling (methods SM1 and SM2)
The first step of the static flood modeling was to isolate the27 marshes by extracting DTM cells below a 5 m NGF limitFor each of the 27 obtained DTM two ldquowater surface rastersrdquowere created (1) a first based on the maximum water levelvalue measured at La Pallice tide gauge (SM1) and (2) a sec-ond based on space-varying maximum water levels retrieved
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
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Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
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Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1602 J F Breilh et al Assessment of static flood modeling techniques
from the storm surge modeling system (SM2) To computedifferences between marsh DTMs and their associated wa-ter surface rasters the Environmental Systems Research In-stitutersquos (ESRIrsquos) ArcGIS 10 software along with the SpatialAnalyst extension was used The raster calculator functionwas used to compute cell by cell the differences betweenmarshes DTMs and water surface rasters From these result-ing rasters polygons surrounding the negative value regionswere then created and only those directly connected to theopen sea were kept representing the flooded areas identifiedfrom static flood modeling Two rules of pixels connectiv-ity in rasters exist the ldquofour-side rulerdquo where the grid cellis connected if any of its cardinal directions is adjacent toa flooded cell and the ldquoeight-side rulerdquo where the grid cellis connected if its cardinal and diagonal directions are con-nected to a flooded grid cell (Poulter and Halpin 2008) Inthis study the connectivity was preserved using an eight-siderule
342 The surge overflowing discharge and volume ondykes (method SO)
A semi-dynamic approach based on the computation ofsurge overflowing discharges and volumes over the dykes(method SO) was applied to two marshes where the twoSM methods strongly overestimate flooding predictions Thismethod was based on an approach validated by the CETMEF(French marine and fluvial technical study center) usinga hydrodynamic numerical modeling system in a marshflooded during Xynthia (CETMEF 2010) The computationof discharges over the dykes uses the rectangular weir dis-charge equation of Kindsvater and Carter (1957)
Q = microL(2g)12h32 (1)
whereQ is the water discharge in m3 sminus1micro is the adimen-sional discharge coefficient (equal to 04)L is the lengthof overflowed dyke in mg is the acceleration of gravity inmsminus2 andh is the water depth over the dyke in m calculatedby subtracting the dyke crest height to time series of modeledsea level at the closest computational node This method isvery sensitive to the length of overflowed dyke and is lim-ited to marshes bounded by straight dykes Discharges werecomputed every ten minutes in order to take into account thetemporal variations ofh The resulting discharges were thenused to compute the total overflowing water volume Sincethe objective was to delineate the flooded areas those over-flowing water volumes had to be spread within the marshesWith this aim iterative static flood modeling was performedincreasing step by step the water level until the correspond-ing water volume matched the overflowing water volume
35 Accuracy assessment of flood models
There are many ways to evaluate the performance of floodinundation models in terms of flood extent (Schumann et
al 2009) Among these the following are widely used thefirst one compares modeled and observed flood surface ar-eas (Aronica et al 2002 Bates et al 2005 Horritt 2006Gallien et al 2012 Smith et al 2011) the second one com-pares water levels at the observed and modeled flood outlines(Mason et al 2009) The comparison of water levels at theobserved and modeled flood outlines is not suitable becausethe topography of the studied marshes is almost flat Therebychanges in flood outlines are not necessarily associated withchanges in topography and the use of water levels at modeledand observed flood outlines is not relevant The comparisonbetween modeled and observed surface areas was preferredIn this study the fit measurement (F ) described by Aronicaet al (2002) and Horritt (2006) was used
F = A(A + B + C) (2)
In this equationA is the area correctly predicted asflooded by the modelB is the area predicted as floodedwhile being dry in the observation (overprediction) andC
is the flooded area not predicted by the model (underpre-diction) F is equal to 1 when observed and predicted areascoincide exactly and equal to 0 when no overlap betweenpredicted and observed areas exists Gallien et al (20112012) described several fit measures based on surface areasWe selected Eq (2) which is generally recommended forboth deterministic and uncertain calibration because it con-siders underprediction and overprediction equally undesir-able (Schumann et al 2009) We arbitrarily defined good fitmeasurements for F-valuesge 07 intermediate fit measure-ments for 05 le F-valueslt 07 and bad fit measurements forF-valueslt 05
A multiple linear regression analysis (MLRA) was carriedout in order to investigate the relationship between morpho-logical parameters and land uses and the F-values Five pa-rameters that seemed to be a priori the most relevant werechosen (1) the maximum distance between the coastline andthe landward boundary of the marsh (D) (2) the surfacearea of the marsh (3) the mean topography of the marsh(4) the urbanization rate computed for each marsh using theCorine land cover database (wwweeaeuropaeu) and (5) aland reclamation rate since 1824 calculated using a coastlinedating from 1824
4 Results
41 Fit measurements for static flood modeling (SM1and SM2)
Fit measurements for the modeled flooded areas using meth-ods SM1 and SM2 show a wide variability (Table 4) Forthe 21 small marshes 7 have good 6 intermediate and 8bad F-values when using method SM1 with correspondingF-values ranging from 0 to 088 Method SM2 slightly im-proves the prediction with 8 good 6 intermediate and 7 bad
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1603
F-values (ranging from 010 to 088) For the 5 large marshesF-values range from 009 to 075 using method SM1 andfrom 009 to 078 using method SM2 Good F-values areobtained for 2 marshes and bad F-values are obtained for3 marshes using method SM1 and SM2 For the only verylarge marshF is equal to 016 (bad value) using both SM1and SM2 methods
The performances of both methods (SM1 and SM2) withrespect to the size of the marshes are summarized in Table 5where mean F-values are calculated for small large and verylarge marshes and finally for all marshes Best F-values areobserved for small marshes using method SM2 while SM1and SM2 give bad F-value for the very large marsh
42 Multiple linear regression analyses
In order to investigate the relationship between morphologi-cal parameters and land uses and the F-values distributiona multiple linear regression analysis was realized for theF-values computed using method SM2 The result of theMLRA shows that the 5 parameters considered (distance be-tween the coastline and the landward boundary of the marsh(D) surface area mean topography urbanization rate andland reclamation rate) explain 57 of the variance of theF-values After analyzing the impact of the parameters sep-arately it appears that only two of them have a significantinfluence on F variance the distance between the coastlineand the landward boundary of the marsh (D) which is themore significant parameter and the surface area of the marshThese two parameters explain 44 of the variance of F-values This analysis reveals that best F-values occur formarshes with a small (D) andor a small surface area Otherparameters (mean topography coastline migration rate andurbanization) are not significantly correlated with F-values(Fig 4b d e)
43 Focus on examples
As the 27 studied marshes include small large and very largemarshes we focus on representative examples of each cate-gory For small and large marshes two examples are selectedrespectively showing good (Ile Madame no 12 Seudre Estu-ary no 25) and bad F-values (Coup de Vague no 08 Brouageno 24) for SM methods The SO method is only applied tomarsh examples where the SM1 and SM2 methods resultedin poor flooding predictions (Brouage no 24 and PoitevinMarsh no 27)
431 Two examples of well-predicted flood extent usingstatic flood modeling
The Ile Madame Marsh (no 12 Fig 5) is a small marsh (054km2) emplaced on a small island located immediately to thesouth of the Charente River mouth The observed floodedarea during Xynthia at Ile Madame Marsh was 047 km2Modeled flooded surface areas are 052 km2 by using SM1
(450 m NGF maximum water level) and SM2 (445 m NGFmaximum water level) For the fit measurement calculationthe surface area correctly predicted as flooded by the model(A) is 046 km2 the overprediction (B) is 005 km2 and theunderprediction (C) is 001 km2 using both methods SM1and SM2 The resulting F-values are 088 for SM1 and SM2
The Seudre Estuary Marsh (no 25 Fig 6) is a large marsh(125 km2) bordering the Seudre River estuary Accordingto the observations 8831 km2 of the surface area of thismarsh was flooded during Xynthia The flooded surface ar-eas estimated by the static flood modeling are 118 km2 and111 km2 using SM1 (450 m NGF maximum water level) andSM2 (414 m NGF maximum water level) respectively Us-ing SM1 the fit measurement shows a 8804 km2 surface areacorrectly predicted (A) a 2947 km2 surface area overpre-dicted (B) and a 027 km2 surface area underpredicted (C)Using SM2 A B and C are equal to 8755 km2 2376 km2
and 076 km2 respectively The F-values are 075 and 078using SM1 and SM2 respectively
432 Improvement of flooding prediction using spatialvariations of sea level from a storm surgemodeling system (SM2)
The Coup de Vague Marsh (no 8 Fig 7) located in thenorthern part of the study area is a small marsh (048 km2)where the observed flooded surface area during Xynthia was044 km2 While method SM1 (450 m NGF maximum wa-ter level) does not flood this marsh at all (no black dot-ted line on Fig 7) 043 km2 are supposed to be floodedfollowing the result of method SM2 Therefore the result-ing fit measurement for method SM1 is 0 (A=B=0 km2
C=044 km2) Method SM2 (475 m NGF maximum wa-ter level) gives correctly-predicted overpredicted and under-predicted flooded surface areas of 039 km2 004 km2 and005 km2 respectively Thus method SM2 considerably in-creases the F-value for this marsh (from 0 to 082)
433 Improvement of flooding predictions using surgeoverflowing method (SO)
The results of the MLRA revealed that static flood model-ing gives bad fit measurement values for marshes character-ized by a large distance between the coastline and the land-ward boundary of the marsh and a large surface area Animprovement of flooding predictions is tentatively applied totwo marshes bounded by straight dykes (Brouage no 24 andPoitevin Marsh no 27) The comparison between fit mea-surements from SM1 SM2 and SO methods shows that theSO method significantly improves flooding predictions (Ta-ble 6)
The Brouage Marsh (no 24 Fig 8) is a large marsh(120 km2) located on the eastern side of a tidal bay theMarennes-Oleron Bay Here the observed flooded surfacearea during Xynthia was 2875 km2 Static flood modeling
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1604 J F Breilh et al Assessment of static flood modeling techniques
Table 4Results of fit measurements computation for the 27 marshes classified into three classes small marshes (S) large marshes (L) andvery large marshes (XL) using methods SM1 and SM2
Fit measurement from method SM1 Fit measurement from method SM2
Marsh no Marsh classes A (km2 ) B (km2 ) C (km2 ) F A (km2 ) B (km2 ) C (km2 ) F
1 S 004 001 000 072 004 001 000 0722 S 008 002 002 065 007 002 003 0623 S 014 002 015 046 014 001 016 0444 S 007 014 001 032 006 011 002 0345 S 025 001 004 084 026 002 003 0856 S 025 002 004 079 024 002 005 0777 S 016 021 001 042 016 021 001 0438 S 000 000 044 000 039 004 005 0829 S 025 012 009 055 023 010 010 05410 S 035 012 001 074 036 012 001 07311 S 039 010 007 069 039 009 008 06912 S 046 005 001 088 046 005 001 08813 S 011 098 001 010 011 096 001 01014 S 144 013 008 087 142 012 009 08715 S 137 032 002 080 137 035 002 07916 S 038 202 000 016 038 156 000 01917 S 022 031 033 026 021 030 034 02518 S 326 418 012 043 325 407 013 04419 S 909 422 003 068 908 413 004 06920 S 780 521 008 060 779 495 010 06121 S 1061 992 009 051 1061 993 008 05122 L 1672 3890 009 030 1670 3792 010 03123 L 4691 1915 133 070 4684 1884 140 07024 L 2861 9063 013 024 2859 8975 016 02425 L 8804 2947 027 075 8755 2376 076 07826 L 1356 13910 032 009 1354 13853 034 00927 XL 15622 78963 199 017 15680 80456 141 016
Table 5Mean F-values for all marshes and for the three surface area classes
Marsh classes Mean F-value usingmethod SM1
Mean F-value usingmethod SM2
all marshes 051 054small marshes 055 058large marshes 041 042very large marsh 017 016
results show a 11924 km2 flooded surface area using SM1(450 m NGF maximum water level) and a 11835 km2
flooded surface area using SM2 (443 m NGF maximum wa-ter level) Fit measurements reveal that both methods clearlyoverpredict the flood (Fig 8) The area correctly predictedas flooded by the model (A) is 2861 km2 the overprediction(B) is 9063 km2 and the underprediction (C) is 013 km2 us-ing method SM1 and A B and C are equal to 2859 km28975 km2 and 016 km2 using method SM2 The bad F-values (024 for SM1 and SM2) are thus explained by thislarge overprediction Equation (1) allows for computing a2456times 106 m3 overflowing water volume (Table 2) After
the spread of this water volume in the marsh method SOallows for increasing the F-value to 040 with an A-valueof 1988 km2 a B-value of 2128 km2 and a C-value of887 km2
The Poitevin Marsh (no 27 Fig 9) is the largest marsh(997 km2) in the study area where the Lay and the SevreNiortaise rivers flow During Xynthia 15821 km2 of thismarsh were flooded According to the static flood modeling94585 km2 and 96136 km2 are predicted as flooded usingmethods SM1 (450 m NGF maximum water level) and SM2(475 m NGF maximum water level) respectively The resultof the fit measurement between surface areas using method
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1605
Fig 4 F-values computed using method SM2 for the 27 marshes regarding(A) surface area(B) mean topography(C) distance betweenthe coastline and the landward boundary of the marsh(D) (D) urbanization rate(E) land reclamation rate
Table 6 Results of fit measurements computation for Brouage and Poitevin marshes using method SO and best F-values using methodsSM1and SM2
Marsh no Surge overflowing wa-ter volume (106 m3)
Flooded area usingsurge overflowing overdykes (km2)
A(km2)
B(km2)
C(km2)
F usingmethodSO
F using method SM1 orSM2
24 2156 4116 1988 2128 887 041 024
27 6289 9604 7138 2466 8683 039 017
SM1 gives a 15622 km2 correctly predicted surface area (A)a 78963 km2 overpredicted surface area (B) and a 199 km2
underpredicted surface area (C) while the method SM2 givesA B and C respectively equal to 15680 km2 80456 km2
and 140 km2 Once again the bad Fndashvalues (017 for SM1
and 016 for SM2) are explained by these large overpredic-tions As for the Brouage Marsh case after the spread of a6289times 106 m3 water volume computed from Eq (1) (Ta-ble 2) method SO gives a higher F-value of 039 The surfacearea correctly predicted is 7138 (A) while the overpredicted
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1606 J F Breilh et al Assessment of static flood modeling techniques
Fig 5Digital Terrain Model (DTM) of the Ile Madame Marsh (no 12) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
surface area is 2466 km2 and the underpredicted surface areais 8683 km2
5 Discussion
The availability of high-resolution LiDAR elevation datatogether with accurate observations of post Xynthia stormflooded areas provided the opportunity to evaluate raster-based flood modeling methods on a wide variety of coastallow lands areas that were flooded during this storm
51 Added value of space-varying maximum sea levelsextracted from the modeling system
Considering the spatial variability of maximum water lev-els reached during the Xynthia storm (about 1 m Fig 3)one could expect that using sea level measured at La Pal-lice tide gauge (SM1) would appear as a strong weaknesscompared to using space-varying modeled sea levels (SM2)On the contrary F-values only increased drastically at onemarsh and no significant changes can be observed for theothers marshes when using modeled space-variable sea lev-els The only example where flood predictions are consider-ably improved with the SM2 method is the Coup de Vague
Marsh (no 8 Table 4 and Fig 7) This better prediction withthe SM2 method is related to the water level value used forthe prediction which is slightly below the dyke minimumheight (460 m NGF) in SM1 (45 m NGF) and slightly abovein SM2 (475 m NGF Table 3) This study would suggestthat spatial variations of maximum sea level elevation havea limited impact on the prediction of the flooding Neverthe-less this conclusion may be valid only for the present casestudy where maximum water level in front of the floodedmarshes varies from less than 05 m Other studies have re-ported much larger spatial variability of sea levels for ex-ample along the coastlines of Florida Alabama Mississippiand Louisiana (Fritz et al 2007) South Carolina (Peng etal 2006) or Texas (Rego and Li 2010) Under such condi-tions using spatial variable sea level may improve floodingprediction significantly
52 Applicability of the static flood modeling methodsaccording to the morphology of the marshes
The MRLA analysis showed that the high variability ofF-values obtained using static flood modeling methodswas related to morphological parameters of the consideredmarshes Among the morphological and land use parameters
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1607
Fig 6 Digital Terrain Model (DTM) of the Seudre Estuary Marsh (no 25) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
Fig 7 Digital Terrain Model (DTM) of the Coup de Vague Marsh (no 8) showing the observed flooded area (hatched grey lines) themodeled flooded area using method SM1 (black dotted line) and the modeled flooded area using method SM2 (white line)
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1608 J F Breilh et al Assessment of static flood modeling techniques
Fig 8 Digital Terrain Model (DTM) of the Brouage Marsh (no 24) showing the observed flooded area (hatched grey lines) the modeledflooded area using method SM1 (black dotted line) the modeled flooded area using method SM2 (white line) and the modeled flooded areausing method SO (hatched blue lines)
only two of them explain 44 of the F-values variance thedistance between the coastline and the landward boundaryof the marsh (D) and the surface area of the marsh (Fig 4aand c) The correlation between F-values and D is explainedbecause static flood modeling methods do not take into ac-count the kinematics of the flow and are based on the as-sumption that the flooding is instantaneous In the case ofsmall marshes the flooding volume is small and the marsh isfilled after a short period of time Moreover in the study areamarshes are usually bounded by steep paleo-coastlines corre-sponding to ancient sea cliffs Such morphology for the innerboundary of marshes implies that once completely floodedincrease in water level will lead to very small variationsin flooded surface areas In the case of large marshes withestuaries the distance between the coastline and the land-ward boundary of the marsh (D) is reduced and the length ofoverflowing (L from Eq 1) is important leading to a largesurge overflowing volume In those cases the flooding is fast
and can be considered as nearly instantaneous Consequentlystatic flood modeling methods perform well for this kind oflarge marshes
In the case of large marshes without estuaries or with anestuary but characterized by a long distance between thecoastline and the landward boundary of the marsh (D) thepotential flooded volume is large in comparison to the ob-served surge overflowing volume because the length of over-flowing (L) is small with respect to the marsh surface area Inaddition the distance between the coastline and the landwardboundary of the marsh (D) is long Thus the duration neededto flood the entire marsh area located below the sea levelis considerably longer than the overflowing duration duringthe Xynthia storm For instance the flooding of the dykeslasted less than a few hours because of the tide-induced sealevel variations Consequently static flood modeling whichconsiders the flooding as instantaneous considerably over-predicts the extension of flooded areas as already shown by
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1609
Fig 9Digital Terrain Model (DTM) of the Poitevin Marsh (no 27) showing the observed flooded area (hatched grey lines) and the modeledflooded area from methods SM1 (dashed black line) SM2 (solid white line) and SO (hatched blue lines)
Apel et al (2009) Bates and De Roo (2000) or Gallien etal (2011)
From this study it appears that static methods seem to besuitable for small marshes (Fig 4a) and for large marshesdrained by an estuary with a small distance between thecoastline and the landward boundary of the marsh (Fig 4c)The common morphological parameter for those marshes isthe small distance between the coastline and the landwardboundary of the marsh This result can be generalized tocoastal low lands at a global scale In the case of narrowlow lands commonly found along active margins and upliftedcoastlines and in the case of estuaries or back barrier lagoonsbounded by narrow marshes static flood modeling methodsmay be suitable In contrast this method will fail in predict-ing flood extension in cases of wide low lands such as thosefound in deltas and large land reclamation areas
53 Advantages and limitations of surge overflowingcalculation
Neglecting the kinematics aspect of the flooding is the mainweakness of static inundation techniques To overcome thislimitation a surge overflowing method (SO) was proposedThis method was applied to Brouage (no 24) and PoitevinMarshes (no 27) which are respectively examples of largeand very large marshes with an estuary where static methodsare not suitable In both cases this semi-dynamic method im-proves the prediction of the flooded areas (Table 6 Figs 8and 9) However modeled flooded surface areas remainunderestimated compared to observations for the PoitevinMarsh Nevertheless the storm surge modeling system em-ployed in this study was developed to investigate storm
surges at the scale of continental shelves in the NE AtlanticOcean (sim 1000 m maximum resolution along the shoreline)Results recently obtained with a much higher spatial reso-lution (sim 25 m along the shoreline) and a fully coupled ap-proach suggest that nearshore wave-induced processes canlocally rise water level by 02 to 04 m (Bertin et al 2012b)Such differences may explain why SO method underpre-dicts the flooding in marshes exposed to large wind wavesas in the case of the Poitevin Marsh facing a relatively largefetch in the southwest direction (Fig 1) The Brouage Marshshows contrasted results since the modeled flooded surfacearea from SO method is overestimated compared to the ob-served flooded area This could be explained by the verycomplex multiple dyke system in this marsh (Fig 8) In ad-dition the simple Eq (1) used to compute overflowing dis-charge (Kindsvater and Carter 1957) was designed for anidealized rectangular weir and cannot take into account thecomplexity of the dyke system in the Brouage Marsh
The results obtained with the surge overflowing methodsuggest that this method can improve the flooding predictionsignificantly in the case of straight dykes if water levels areaccurately predicted along the shoreline
6 Conclusions
The aim of this study was to assess a raster-based static floodmodeling method and a semi-dynamic method using surgeoverflowing volumes on a wide diversity of marshes thatwere flooded during Xynthia in the Pertuis Charentais Thecomparison between predictions and observations (delin-eation of post-storm flooded areas) demonstrates that static
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
1610 J F Breilh et al Assessment of static flood modeling techniques
methods can accurately map flooding under certain con-ditions Thus well predicted flooded areas by static floodmodeling methods correspond to small marshes and largemarshes drained by an estuary with a small distance betweenthe coastline and the landward boundary of the marsh In-deed the underlying hypothesis of the static method accord-ing to which the flooding is instantaneous holds in thosecases because the distance between the coastline and thelandward boundary of the marsh is small (less than 3 km)On the contrary static flood modeling methods failed to re-produce flooded areas in the case of large marshes withoutestuaries or large marshes with a long distance between thecoastline and the landward boundary of the marsh Indeed inthose kinds of marshes the instantaneous flooding hypothe-sis of the static method is unacceptable as the distance be-tween the coastline and the landward boundary of the marshis large (more than 10 km) Under these conditions the com-puting of surge overflowing volumes can improve the flood-ing prediction significantly
The raster-based methods assessed in this study are fastdeploying methods much lighter in terms of computation re-sources compared to the high resolution hydrodynamic stormsurge and flood modeling system that requires massive paral-lel techniques (eg Bunya et al 2010 Dietrich et al 2011)In the case of narrow low lands and estuaries or back barrierlagoons bounded by narrow marshes the methods assessedin this study may be attractive alternatives to design marineflooding early warning systems
AcknowledgementsThis work was supported by FEDER 34146-2010 and Conseil General de Charente-Maritime We would liketo thank Frederic Pouget Frederic Rousseaux Cecilia Pignon-Mussaud Dorothee James and Jerome Faucillon for their helpon GIS Thanks also go to Nicolas Bruneau for his help on theextraction of sea level elevations from the storm surge numericalmodel We also would like to thank Clement Poirier for his supporton statistical analysis IGN is thanked for 2010 LiDAR dataSONEL (wwwsonelorg) and REFMAR are thanked for sea leveltide gauge measurements Finally the authors appreciated thecomments of Guillem Chust as well as those of the anonymousreviewer which greatly improved this manuscript
Edited by S TintiReviewed by G Chust and three anonymous referees
References
Allard J ChaumillonE Poirier C Sauriau P-G and WeberO Evidence of former Holocene sea level in the Marennes-Oleron Bay (French Atlantic coast) C R Geosci 340 306ndash314doi101016jcrte200801007 2008
Apel H Aronica G T Kreibich H and Thieken A H Floodrisk analysesndashhow detailed do we need to be Nat Hazards 4979ndash98 doi101007s11069-008-9277-8 2009
Aronica G Bates P D and Horritt M S Assessing the uncer-tainty in distributed model predictions using observed binary pat-
tern information within GLUE Hydrol Process 16 2001ndash2016doi101002hyp398 2002
Bates P and De Roo A P A simple raster-based modelfor flood inundation simulation J Hydrol 236(1ndash2) 54ndash77doi101016S0022-1694(00)00278-X 2000
Bates P D Dawson R J Hall J W Horritt M S NichollsR J Wicks J and Hassan M A A M Simplified two-dimensional numerical modelling of coastal flooding and exam-ple applications Coastal Eng 52(9) 793ndash810 2005
Benavente J Del Rıo L Gracia F and Martınez-del-Pozo JCoastal flooding hazard related to storms and coastal evolutionin Valdelagrana spit (Cadiz Bay Natural Park SW Spain) ContShelf Res 26 1061ndash1076 2006
Bernatchez P Fraser C Lefaivre D and Dugas S In-tegrating anthropogenic factors geomorphological indicatorsand local knowledge in the analysis of coastal floodingand erosion hazards Ocean Coast Manage 54 621ndash632doi101016jocecoaman201106001 2011
Bertin X Chaumillon E Sottolichio A and Pedreros R Tidalinlet response to sediment infilling of the associated bay and pos-sible implications of human activities the Marennes-Oleron Bayand the Maumusson Inlet France Cont Shelf Res 25 1115ndash1131 doi101016jcsr200412004 2005
Bertin X Castelle B Chaumillon E Butel R and QuiqueR Longshore transport estimation and inter-annual variabil-ity at a high-energy dissipative beach St Trojan beachSW Oleron Island France Cont Shelf Res 28 1316ndash1332doi101016jcsr200803005 2008
Bertin X Bruneau N Breilh J-F Fortunato A B andKarpytchev M Importance of wave age and resonance in stormsurges The case Xynthia Bay of Biscay Ocean Model 42 16ndash30 doi101016jocemod201111001 2012a
Bertin X Li K Roland A Breilh J-F and ChaumillonE Contributions des vagues dans la surcote associee a latempete Xynthia fevrier 2010 909ndash916 Editions Paraliahttpwwwparaliafrjngcgc1299 bertinpdf (last accessed 22 June2012b) 2012b
Billeaud I Chaumillon E and Weber O Evidence of a majorenvironmental change recorded in a macrotidal bay (Marennes-Oleron Bay France) by correlation between VHR seismic pro-files and cores Geo-Mar Lett 25 1ndash10 doi101007s00367-004-0183-0 2004
Blake E S The deadliest costliest and most intense United Statestropical cyclones from 1851 to 2006 (and other frequently re-quested hurricane facts) NOAA Technical Memorandum NWSTPC 5 43 2007
Brown J M Souza A J and Wolf J An 11-year valida-tion of wave-surge modelling in the Irish Sea using a nestedPOLCOMS-WAM modelling system Ocean Model 33 118ndash128 2010
Bunya S Dietrich J C Westerink J J Ebersole B A SmithJ M Atkinson J H Jensen R Resio D T Luettich R ADawson C Cardone V J et al A High-Resolution CoupledRiverine Flow Tide Wind Wind Wave and Storm Surge Modelfor Southern Louisiana and Mississippi Part I Model Devel-opment and Validation Mon Weather Rev 138(2) 345ndash377
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
J F Breilh et al Assessment of static flood modeling techniques 1611
doi1011752009MWR29061 2010CETMEF (French Centre for Maritime and Fluvial Techni-
cal Studies) Analyse de lrsquoevenement Xynthia Evaluationdes volumes entrants par modelisationhttphttpwwwcetmefdeveloppement-durablegouvfr 2010
Chaumillon E Tessier B Weber N Tesson M and Bertin XBuried sandbodies within present-day estuaries (Atlantic coast ofFrance) revealed by very high resolution seismic surveys MarGeol 211 189ndash214 doi101016jmargeo200407004 2004
Chaumillon E Proust J-N Menier D and Weber N Incised-valley morphologies and sedimentary-fills within the inner shelfof the Bay of Biscay (France) A synthesis Ocean Bay Biscay72 383ndash396 doi101016jjmarsys200705014 2008
Chust G Galparsoro I BorjaA Franco J and Uriarte ACoastal and estuarine habitat mapping using LIDAR height andintensity and multi-spectral imagery Estuar Coast Shelf Sci78 633ndash643 doi101016jecss200802003 2008
Chust GAngel Borja Liria P Galparsoro I Marcos M Ca-ballero A and Castro R Human impacts overwhelm the ef-fects of sea-level rise on Basque coastal habitats (N Spain) be-tween 1954 and 2004 Estuar Coastal Shelf Sci 84 453ndash462doi101016jecss200907010 2009
Chust G Caballero A Marcos M Liria P Hernandez Cand Borja A Regional scenarios of sea level rise and im-pacts on Basque (Bay of Biscay) coastal habitats throughoutthe 21st century Estuarine Coastal Shelf Sci 87 113ndash124doi101016jecss200912021 2010
Cook A and Merwade V Effect of topographic data geometricconfiguration and modeling approach on flood inundation map-ping J Hydrol 377 131ndash142 2009
DAS P K Prediction Model for Storm Surges in the Bay of Ben-gal Nature 239 211ndash213 doi101038239211a0 1972
DDTM-17 Elements de memoire sur la tempete Xyn-thia du 27 et 28 Fevrier 2010 en Charente-Maritimehttpwwwcharente-maritimeequipementgouvfrelements-de-memoire-xynthia-r157html 2011
Dietrich J Zijlema M Westerink J Holthuijsen L DawsonC Luettich Jr R Jensen R Smith J Stelling G and StoneG Modeling hurricane waves and storm surge using integrally-coupled scalable computations Coast Eng 58 45ndash65 2011
Fritz H M Blount C Sokoloski R Singleton J Fuggle AMcAdoo B G Moore A Grass C and Tate B HurricaneKatrina storm surge distribution and field observations on theMississippi Barrier Islands Estuar Coast Shelf Sci 74 12ndash20doi101016jecss200703015 2007
Gallien T W Schubert J E and Sanders B F Predict-ing tidal flooding of urbanized embayments A modelingframework and data requirements Coastal Eng 58 567ndash577doi101016jcoastaleng201101011 2011
Gallien T W Barnard P L Van Ormondt M Foxgrover AC and Sanders B F A Parcel-Scale Coastal Flood Forecast-ing Prototype for a Southern California Urbanized EmbaymentJ Coastal Res doi102112JCOASTRES-D-12-001141 2012
Gerritsen H What happened in 1953 The Big Flood in theNetherlands in retrospect Philos Trans R Soc London SerA 363 1271ndash1291 doi101098rsta20051568 2005
Goff J R Lane E and Arnold J The tsunami geomorphol-ogy of coastal dunes Nat Hazards Earth Syst Sci 9 847ndash854doi105194nhess-9-847-2009 2009
Haile A T and Rientjes T Effects of LiDAR DEM resolution inflood modelling a model sensitivity study for the city of Teguci-galpa Honduras 36 168ndash173 Enschede the Netherlands 2005
Horritt M S A methodology for the validation of uncer-tain flood inundation models J Hydrol 326 153ndash165doi101016jjhydrol200510027 2006
IPCC Climate Change 2007 Synthesis Report Contribution ofWorking Groups I II and III to the Fourth Assessment Report ofthe Intergovernmental Panel on Climate Change IPCC 2007
Kennedy A B Westerink J J Smith J M Hope M E Hart-man M Taflanidis A A Tanaka S Westerink H CheungK F Smith T Hamann M Minamide M Ota A and Daw-son C Tropical cyclone inundation potential on the Hawai-ian Islands of Oahu and Kauai Ocean Model 52ndash53 54ndash68doi101016jocemod201204009 2012
Kindsvater C and Carter R Discharge characteristics of rectan-gular thin-plate weirs J Hydraul Div ASCE 83 1ndash36 1957
Lumbroso D M and Vinet F A comparison of the causes effectsand aftermaths of the coastal flooding of England in 1953 andFrance in 2010 Nat Hazards Earth Syst Sci 11 2321ndash2333doi105194nhess-11-2321-2011 2011
Mason D C Bates P D and Dallrsquo Amico J T Cal-ibration of uncertain flood inundation models using re-motely sensed water levels J Hydrol 368(1ndash4) 224ndash236doi101016jjhydrol200902034 2009
Mazzanti P and Bozzano F An equivalent fluidequivalentmedium approach for the numerical simulation of coastal l and-slides propagation theory and case studies Nat Hazards EarthSyst Sci 9 1941ndash1952 doi105194nhess-9-1941-2009 2009
Morton R A and Barras J A Hurricane Impacts on CoastalWetlands A Half-Century Record of Storm-Generated Fea-tures from Southern Louisiana J Coastal Res 275 27ndash43doi102112JCOASTRES-D-10-001851 2011
Nicolle A Karpytchev M and Benoit M Amplification ofthe storm surges in shallow waters of the Pertuis Charentais(Bay of Biscay France) Ocean Dynam 59 921ndash935doi101007s10236-009-0219-0 2009
Pawlowski A Geographie historique des cotes Charentaises LeCroix vif (Ed) Paris 235 pp 1998
Peng M Xie L and Pietrafesa L J A numerical study onhurricane-induced storm surge and inundation in CharlestonHarbor South Carolina J Geophys Res 111 C08017doi1010292004JC002755 2006
Perillo G M E Chapter 2 Definitions and Geomorphologic Clas-sifications of Estuaries in Geomorphology and Sedimentologyof Estuaries 53 17ndash47 ElsevierhttpwwwsciencedirectcomsciencearticlepiiS0070457105800226 1995
Poirier C Chaumillon E and Arnaud F Siltation of river-influenced coastal environments Respective impact of lateHolocene land use and high-frequency climate changes MarGeol 290 51ndash62 doi101016jmargeo201110008 2011
Poulter B and Halpin P N Raster modelling of coastal flood-ing from sea-level rise Int J of Geogr Inf Sci 22 167ndash182doi10108013658810701371858 2008
Rego J L and Li C On the importance of the forward speed ofhurricanes in storm surge forecasting A numerical study Geo-phys Res Lett 36 L07609 doi1010292008GL036953 2009
Rego J L and Li C Storm surge propagation in Galve-ston Bay during Hurricane Ike J Mar Syst 82 265ndash279
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
Schumann G Bates P D Horritt M S Matgen P andPappenberger F Progress in integration of remote sensingndashderived flood extent and stage data and hydraulic modelsRev Geophys 47 doi1010292008RG000274 httpwwwaguorgpubscrossref20092008RG000274shtml (last access6 November 2012) 2009
Simon B Statistiques des niveaux marins extremes de pleinemer en Manche et Atlantique CD-Rom edited by SHOM andCETMEF 2008 (in French)
Smith R A E Bates P D and Hayes C Evaluation of a coastalflood inundation model using hard and soft data Environ Mod-ell Softw doi101016jenvsoft201111008 available fromhttplinkinghubelseviercomretrievepiiS1364815211002635(last access 6 November 2012) 2011
Tolman H L User manual and system documentation ofWAVEWATCH-IIITM version 314 Technical note MMABContribution (276) 2009
Wachter J Babeyko A Fleischer J Haner R HammitzschM Kloth A and Lendholt M Development of tsunami earlywarning systems and future challenges Nat Hazards Earth SystSci 12 1923ndash1935 doi105194nhess-12-1923-2012 2012
Webster T L Flood Risk Mapping Using LiDAR for Annapo-lis Royal Nova Scotia Canada Remote Sens 2 2060ndash2082doi103390rs2092060 2010
Webster T L Forbes D L MacKinnon E and Roberts DFlood-risk mapping for storm-surge events and sea-level rise us-ing lidar for southeast New Brunswick Can J Remote Sens 32194ndash211 2006
Wolf J Coastal flooding impacts of coupled wavendashsurgendashtidemodels Nat Hazards 49 241ndash260 doi101007s11069-008-9316-5 2008
Wolf J and Flather R A Modelling waves and surges during the1953 storm Phil Trans R Soc London Ser A 363 1359ndash1375doi101098rsta20051572 2005
Young A P Guza R T OrsquoReilly W C Flick R E andGutierrez R Short-term retreat statistics of a slowly erod-ing coastal cliff Nat Hazards Earth Syst Sci 11 205ndash217doi105194nhess-11-205-2011 2011
Zhang Y and Baptista A M SELFE A semi-implicitEulerianndashLagrangian finite-element model for cross-scale ocean circulation Ocean Model 21(3ndash4) 71ndash96doi101016jocemod200711005 2008
