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Environmental Modelling & Software 39 (2013) 24e38

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Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

A fully automated and integrated multi-scale forecasting scheme for emergencypreparednessq

Muhammad Akbar a,*, Shahrouz Aliabadi a,1, Reena Patel b,2, Marvin Watts a,3

aNorthrop Grumman Center for High Performance Computing of Ship Systems Engineering, Jackson State University, MS e-Center, Box 1400, 1230 Raymond Road, Jackson,MS 39204, USAb Information Technology Laboratory, US Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180-6199, USA

a r t i c l e i n f o

Article history:Received 11 February 2011Received in revised form27 September 2011Accepted 28 December 2011Available online 21 March 2012

Keywords:Multi-scale hurricane simulationWater surgeOverland flowFinite elementParallel computationFully automated through scripting

q Thematic Issue on the Future of Integrated Mode* Corresponding author. Tel.: þ1 601 979 1825.

E-mail addresses: muhammad.k.akbar@jsums.ejsums.edu (S. Aliabadi), reena.r.patel@usace.army.mijsums.edu (M. Watts).

1 Tel.: þ1 601 979 1821.2 Tel.: þ1 601 634 5430.3 Tel.: þ1 601 979 1839.

1364-8152/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.envsoft.2011.12.006

a b s t r a c t

In this paper, we present one multi-scale integrated simulation technology for emergency preparednesswith a holistic approach in hurricane, related storm surge and flood forecasting; infrastructure assess-ment; and emergency planning. This is an emergency management tool to aid the decision-makers andfirst responders in preparation for the appropriate response to an impending hurricane disaster.

Three primary models, hurricane forecasting, storm surge, and overland flooding, are executed insequence to generate the necessary results for the proposed integrated emergency planning andpreparedness tool. Two of these are open source codes in the public domain and the overland floodingmodel is an “in-house” code developed by the authors. Using the results of the primary models, twosecondary models are executed to assess local infrastructure vulnerability and to determine the optimalevacuation routes for impacted inhabitants. The results from each model are post-processed and saved asKeyhole Markup Language (KML) files that are viewable in Google Earth for overlay analysis anddecision-making. Hurricane Katrina (2005) in the Mississippi coastal area is chosen as a case study tovalidate the developed tool.

The models are run in sequence to generate the layers of data necessary during an actual event. Thesequence is fully automated using Python and Shell scripts, which allow users to interact with eachmodel through a series of Graphical User Interfaces. The development of technology described herewould not only satisfy the scope of the project, but also be of great significance to national homelandsecurity in the area of emergency preparedness and response.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

August 2005 saw the development of Hurricane Katrina, whichwill likely be remembered as one of the most costly and destructivestorms in US history for generations to come. Katrina, at one stagea category 5 hurricane, ultimately made landfall in Louisiana andMississippi with category 3 strength. While loss of life did notapproach the magnitude of the Galveston Hurricane of 1900(6000e12,000 deaths), it nonetheless caused more than 1300deaths and has by far incurred the highest damage costs of any

ling Science and Technology.

du (M. Akbar), saliabadi@l (R. Patel), marvin.d.watts@

All rights reserved.

hurricane in history. The devastation caused by hurricane Katrinaunderlined the dire need for operational research to developtechnology that is capable of predicting storm surge and flooddisasters for better emergency preparedness.

Hurricane induced storm surges, which are generated byextreme wind stress acting on shallow, continental shelf seas canlead to severe storm surge and coastal floods, particularly whenthey coincide with a high tide and result in overtopping andbreaching of sea defenses (Pugh, 1987). It may result in substantialeconomic and social impacts, including loss of life, damage toproperty, and disruption of essential services (Knabb et al., 2005;Wilkinson, 2006; Gram-Jensen, 1991; Tsuchiya and Shuto, 1995;Danard et al., 2003). Again, hurricane Katrina is a perfect exampleof such a disastrous scenario.

Although many aspects of evacuation, such as the uncertaintiesassociated with human behavior, seem uncontrollable and practi-cally impossible to plan for, many other components of evacuationcan be controlled by having an efficient and prioritized evacuationstrategy. To achieve such a plan, we need comprehensive

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 25

knowledge about hurricanes, storm surge and flooding. A timelyand accurate prediction of the critical events, e.g. landfall, infra-structure failure, and transportation network failure, is crucial toestablishing an effective evacuation.

In the event of a hurricane, the emergency plans start with thedetermination of the track path and intensity of the hurricane usinga forecast model. The hurricane forecast model is a computerprogram that uses meteorological data to forecast the path, motionand intensity of hurricanes. There are three types of models:statistical, dynamical, or combined statistical-dynamic (HurricaneAlley, 2008). Among several other track models, GFS (Global Fore-cast System), CLIPER (CLImatology and PERsistence),WRF (WeatherResearch and Forecasting) are well known. WRF (Michalakes et al.,1998), an open source parallel model, is extensively used by theNational Center for Atmospheric Research (NCAR), the NationalOceanic and Atmospheric Administration (NOAA), and theacademic community, and is used in the present study.

Once the hurricane track path and intensity is known, stormsurge models are executed. Storm surge models are based on theshallow water equations (SWE), which are numerically solved ina domain containing ocean and coastal regions. The storm surgemodels need wind stresses and pressure force terms that areobtained from the hurricane forecast models. Among several stormsurge models, Sea, Lake, and Overland Surge from Hurricanes(SLOSH) (Jelesnianski et al., 1992), is a well-developed computer-ized model used by the Federal Emergency Management Agency(FEMA), United States Army Corps of Engineers (USACE), and theNational Weather Service (NWS). The SLOSHmodel uses structuredgrid, which limits its use in resolving complicated coastlines andthus severely restricts its capability for accurate simulation offlooding. Moreover, SLOSH neglects the advection terms in themomentum equations, which influences the accuracy of thesimulation. The ADvanced CIRCulation (ADCIRC) (Luettich et al.,1992; Westerink et al., 1994) model is another semi-open sourceparallel storm surge model, which employs an unstructured gridand is able to resolve the complex coastline and the bathymetry ofshallow water quite well. ADCIRC is a finite element-based modeland extensively used by the academic community and federalagencies such as the US Army Corps of Engineers, NOAA, and theNaval Research Laboratory. ADCIRC is used in the present study.

If the storm surge model supports wet-dry components, such asADCIRC, the same model can be used for overland flooding in thecoastal region as well, provided the domain mesh contains theoverland region. Alternately, a standalone flood model can be usedto predict the overland flood. A number of standalone flood modelshavebeen reported in theopen literature (BatesandAnderson,1993;Stelling et al.,1998; Bates andHervouet,1999;Makhanovet al.,1999;Beffa and Connell, 2001;Hsu et al., 2000; Brown, 2004;Mignot et al.,2006; Tayefi et al., 2007; Yu and Lane, 2006a, 2006b; Braschi andGallatti, 1989). These include applications of the two-dimensional(2-D) diffusion equation to flooding from storm drains (Hsu et al.,2000), and applications of the full Saint-Venant equations tocoastal flooding (Brown, 2004; Mignot et al., 2006).

Based on the hurricane and related storm surge and floodprediction, evacuation plans are setup. Currently, there area number evacuation programs that are in use (Lindell and Prater,2007), such as HURREVAC (Hurrevac, 2010) and HURRTRAK (PCWeather Products, 2010). HURREVAC (Hurricane Evacuation) isused to enable tracking hurricanes and assist in evacuationdecision-making. It is a restricted-use Internet based computerprogram, which includes an ETIS (Environment TransportIntegrated planning System) module which allows for inclusionand access to real-time traffic information by emergency managers.

Although there are a number of individual predictive models,integration of these models to get the comprehensive destruction

scenario of the whole event to setup evacuation plans is verychallenging. The data assimilation and portability between modelsis one of themajor challenges one has to deal within the integrationprocess. The coupling of models typically has different meshes, andthe models may pass information via external files (Bunya et al.,2010; Pandoe and Edge, 2008; Funakoshi et al., 2008; Dietrichet al., 2010). Interpolation may be needed to transfer data fromone model to another repeatedly. An emerging practice is to couplemodels through a generic framework, such as the Earth SystemModeling Framework (ESMF) (Hill et al., 2004; Collins et al., 2005),the Open Modeling Interface (OpenMI) Environment (Moore andTindall, 2005; Gregersen et al., 2005) or the Modeling CouplingToolkit (MCT) (Warner et al., 2008). These frameworks managewhen and how the individual models are run, interpolateinformation between models if necessary, and make transparentthe coupling to developers and users.

In this study, we have developed a comprehensively integratedhurricane and storm surge model for the southeastern coastalregion of the United States. Our integrated emergency planning andpreparedness tool has two sets of models: primary and secondarymodels. The primary models simulate hurricane from its inceptionin the deep ocean to landfall and associated water surge andflooding in the coastal regions. The secondary models assess theinfrastructure vulnerability due to the hurricane wind, storm surgeand inland flooding; and then establish the evacuation routes. It isa detailed plan-of-actionwhich involves the development of a fullyintegrated ensemble modeling suite that will not only benefitMississippi, but the entire Gulf Coast. There has been some recentefforts to create similar integrated models, such as Coastal andInland FLooding Observation and Warning (CI-FLOW, 2011), whichunderlines the importance of such tools. Our integrated scheme isfully automated via computer programs and scripts and usersinteract with the tool using a Graphical Users Interface (GUI). Thescheme is seamless to the point that any emergency personnel withmoderate training can execute it and produce results successfully.

We organize the rest of the paper as follows. We present thetechnical details about the different models in Section 2. Weprovide the details of the model integration in Section 3. After thatin Section 4 we provide some results to demonstrate theperformance of the integrated scheme. Finally in Section 5, wesummarize this paper with final concluding remarks.

As a case study in the present research, we have chosen hurri-cane Katrina (2005).

2. Integrated model details

The simulation process starts with the acquisition of the latestmeteorological data. Using the meteorological data, the groundwind speed, pressure, rain, etc. are predicted with the WeatherResearch and Forecasting (WRF) model (Michalakes et al., 1998).The hurricane wind speed and pressure data obtained from WRFare then used as input to ADvanced CIRCulation (ADCIRC) stormsurge model (Westerink et al., 1994). ADCIRC results are thenprovided as input to model the coastal area flooding phenomenausing the finite element-based CaMEL Overland flow solver (Akbarand Aliabadi, 2009; Akbar et al., 2009a,b; Aliabadi, 2010) developedat Northrop Grumman Center (NGC) for High PerformanceComputing at Jackson State University. The water surge valuessimulated from ADCIRC along the shoreline are used as Dirichletboundary conditions to CaMEL Overland. The rain data predictedfrom WRF can be used as a source term in the solver.

Using the results from our primary models e WRF, ADCIRC, andCaMEL Overland e two secondary models are executed. The firstsecondary model assesses the building and infrastructure vulner-ability due to the hurricane wind and associated storm surge and

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3826

inland flooding. The second one is the emergency planning andpreparedness model.

The graphical representation in Fig. 1 shows the integration ofthe full system. Fig. 1(a) displays the ‘Primary Models’, and Fig. 1(b)shows the ‘Secondary (Emergency Response) Models’ that aredependent on the output from the primary models.

More details about the models are presented below.

2.1. Primary models

2.1.1. Weather Research Forecasting (WRF)WRF model is used to generate wind data from raw meteoro-

logical data input. It is a next-generation numerical weatherprediction model designed to serve both operational forecastingand atmospheric research needs (Michalakes et al., 1998, 2007). It iscapable of operational weather forecasting, regional climateprediction, air-quality simulation, and dynamical studies at gridscales ranging from 1 km to tens of kilometers. It allows researchersto conduct simulations reflecting either real data or idealizedconfigurations. WRF employs a structured grid discretization.Through the use of “nesting”, which is a form of mesh refinement,costly higher resolution computation over a region of interest canbe obtained for more accurate simulations. WRF simulation isknown to generate reasonable details of a hurricane.

WRF needs meteorological data, which can be downloadedfrom University Corporation for Atmospheric Research website(http://www2.ucar.edu/). Data assimilation to the WRF mesh is

Fig. 1. Graphical representation of the integrated modeling scheme, (a) Primarymodels, (b) Secondary (emergency response) models.

done within the WRF model before the model starts the weatherforecasting procedure.

2.1.2. ADCIRCADCIRC (ADvanced Multi-Dimensional CIRCulation Model for

Shelves, Coasts and Estuaries) is a multi-dimensional, depthintegrated, barotropic time-dependent long wave, finite element-based hydrodynamic circulation code to solve the equations ofmoving fluid on a rotating earth. The water elevation is obtainedfrom the solution of the depth-integrated continuity equation inGWCE (Generalized Wave-Continuity Equation) form. The depth-averaged velocity is obtained from the solution of verticallyintegrated momentum equations. All nonlinear terms are kept inthese equations (Luettich et al., 1992).

ADCIRC model primarily depends on the wind velocity andpressure data generated from WRF or similar hurricane forecastmodels. In the present study, WRF and Planetary Boundary Layer(PBL)models are used. Asmentioned earlier, theWRFmodel is usedto generate wind data from raw meteorological data input. The PBLmodel is used to generate similar wind data from the hurricane besttrack files. A typical PBL model estimates surface (10 m) windspeeds over earth surface as a function of atmospheric stabilityconditions and surface stress (Powell, 1980; Cardone et al., 1992).The PBL wind file generator model, “P15” (ADCIRC User Manual,2010), is used in the present study for Katrina.

The tidal forcing is necessary in ADCIRC to capture the periodiclong waves. Tidal parameters can be extracted from ADCIRC TidalDatabase (2008), Version ec2001_v2d.

2.1.3. CaMEL OverlandFollowing the ADCIRC simulation, the coastal area flooding is

modeled using an “in-house” code developed by researchers at theNorthrop Grumman Center (NGC) for High Performance Computingat Jackson State University, CaMEL Overland code. Overland flowconsists of a low depth water runoff over the ground surface. Theoverland flow can be modeled using the two-dimensional shallowwater equations. The shallow water equations are derived from thedepth-integrated continuity and NaviereStokes equations usingkinematic boundary conditions (Beinhorn and Kolditz, 2005;Kolditz, 2002). This simplified version of the shallow waterequation is also called the Richard’s equation. CaMEL Overlandsolves a diffusive wave or Richard’s equation by the Galerkin finiteelement method (Akbar and Aliabadi, 2009; Akbar et al., 2009a,b;Aliabadi, 2010). The resulting discretization leads to a linearequation systemwith the stiffness matrix as the coefficient matrix.The system is solved using a matrix-free GMRES solver. The CaMELmodel is fully implicit, and is able to use large time steps. Theunderlying matrix-free finite element method has been discussedin detail in the solution of Poisson equation in Tu and Aliabadi(2007). To save memory, the global stiffness matrix is not formed.Instead, a matrix-free implementation is followed (Tu and Aliabadi,2007; Aliabadi, 1994; Tu et al., 2006).

The notation of the water-land terrain system used in CaMELOverland is shown in Fig. 2. Here H, h, and Z are water depth, waterheight measured from geoid reference point, and ground elevation,respectively. Note in this context, h ¼ H þ Z. The mesh generationcomponent of the model generates a very fine mesh based on thebathymetry of the domain.

2.2. Secondary models

These secondary models assess the infrastructure vulnerabilitydue to hurricane in the coastal region, and sets up dynamic evac-uation plan based on the result of primary models in the wake of animpending hurricane. These secondary models are critical parts of

Fig. 2. A typical water-land terrain system.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 27

the project, which establish the direct connection of the projectwith the public and emergency management. The WRF modelprovides estimated hurricane touchdown time, as well as, windspeed and pressure. ADCIRC and CaMEL Overland provide oceanwater surge and inland flooding results, respectively. Using theseoutput data sets, the Secondary Models developed by partnerorganizations, predict infrastructure failure and optimal evacuationroutes for the affected area.

2.2.1. InfrastructureFor the infrastructure risk assessment FISTRAP, a model devel-

oped by Engineer Research and Development Center (ERDC)researchers, is used. Infrastructures that are assessed in the presentstudy are hospital, schools, churches, bus terminals, airports, dams,bridges, emergency centers, and casinos. Primarily depending onthe water elevation and velocity magnitude, the model determines

Fig. 3. Evacuee ‘centroids’ (yellow tags) in the coastal Mississippi area. (For interpretation oof this article.)

the risk of infrastructure failure. For detail of the FIRSRAP model,please refer to Aliabadi (2010).

2.2.2. Evacuation setupThe transportation and evacuation model, developed by the

NGC partners Iteris, Inc. and University of Houston, generatesevacuation routes for the affected area (Lim et al., 2009a,b).Evacuation is assumed to start approximately 3 days beforehurricane touchdown. The affected coastal area is considered to bethe “danger zone”. Coastal counties are divided into severalsubzones. Each subzone has one ‘centroid’ and subzonal evacueesare assumed to be clustered in their own ‘centroid’. Fig. 3 displaysthe centroids in the coastal Mississippi area. Any area north ofInterstate-10 is considered to be the “safe zone”. An optimizationmodel is run to generate evacuation paths from each centroid tothe safe zone, and uploaded to Google Earth for evacuees, emer-gency personnel, and concerned citizens. In the evacuation plan,priority is given to the immediate danger subzones, determinedby proximity to the hurricane landfall location. However, evacu-ation paths dynamically change based on current traffic condi-tions, which is reflected on Google Earth. For detail of the dynamicevacuation plan developed by partners, please refer to Aliabadi(2010).

3. Model integration and automation

The models used in the project are multi-scale and multi-physics. There are a number of challenges in running these as anintegrated unit in pre-processing, execution, and post-processing.The models are executed in sequential manner to facilitate outputfrom one model is seamlessly transferred over as input to thesubsequent model. The automatic data transfer from amodel to thenext one requires data compatibility transformation, interpolation,

f the references to colour in this figure legend, the reader is referred to the web version

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3828

and data assimilation. Data dependencies among the models aregraphically summarized in the Fig. 1.

3.1. Integration using graphical user interface

To facilitate optimal interaction for the end user, a GUI has beendeveloped to integrate and automate the project components e

both primary and secondary models. The GUI was developed usingCþþ Tcl/Tk libraries, Python and Shell scripts; and is currentlyinstalled on NGCs SUN cluster, with 520 AMD processor cores. TheGUI is designed in such a manner that the end user, with minimaltraining, can successfully use it. The buttons on the GUI arearranged in a logical manner, so that the user can click on therelevant model to be run one by one. The end user can enter verysimple parameters like the date of an approaching hurricane andthe number of days the simulation needs to be carried out. Fig. 4(a)

Fig. 4. The integration of the models, (a) flowchart of the primary models,

and (b) show the flowcharts for the primary and secondary modes.Fig. 4(c) displays a snapshot of the GUI front pages for both primaryand emergency models. The general procedure of a model isdescribed below as an example.

All the models need pre-processing before the models can beexecuted. For example, WRF needs meteorological data, which canbe downloaded from University Corporation for AtmosphericResearch website (http://www2.ucar.edu/). The forecast startingdate and hour, and number of forecasted days are necessary in the“Create Input” deck on the “WRF GUI”. Once the input data isprovided, the user clicks the “Pre-processing” button to initiatea shell script to download the desired meteorological data. Afterdata is downloaded, the data assimilation and initial andboundary condition assignment to the WRF mesh is done throughthe same shell script. The model is ready to be executed after that.The pre-processing for other primary models is done by clicking

(b) flowchart of the emergency (secondary) models, (c) GUI snapshot.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 29

on “Create Input” and/or “Pre-processing” as well. The pre-pro-cessing for the secondary models is done through the post-pro-cessing of the primary models.

Once the pre-processing is complete, execution of the model isdone by clicking on “Run”, which calls a shell script in the back-ground. For example, the WRF execution is done by clicking on the“RunWRF” button. The job is submitted in the cluster based on thenumber of processor assigned by the user in the input deck earlier.After the run of a model is finished, the output file is ready forpost-processing.

Post-processing of the models includes creating images,animations, data assimilation and input file creation for subse-quent models. At this stage, it is a natural step and is convenient tocreate the input files needed for the subsequent models. Perhapsthe most challenging part in the post-processing is creatingimages and animations from the raw data that are viewable inGoogle Earth. Traditionally the image processing is done interac-tively using a commercial code, which is not an option for thecurrent automated GUI system. Some details of the image pro-cessing, and animation procedure is described in the followingtwo subsections.

3.2. Automatic image creation

To facilitate customized automated post-processing, codes aredeveloped to create images by using open source code PyPNG(PyPNG, 2010). The images are created using RGBA color channelsbased on the desired output variables of a model. The briefprocedure is: the image domain is selected according to the zone ofinterest on the mesh. Then the pixel resolution of the image is setbased on the quality desired. The typical pixel resolution is2000� 2000. The data from the original mesh is interpolated to the

Fig. 4. (cont

image pixel nodes. The pixel data values are scaled as desired andthe RGBA color scales are set. Then a python script is written usingthe pixel RGBA data, which is compatible with PyPNG code. Thepython script is then executed using PyPNG to generate the image.Fig. 5 displays the image creation technique. A sample python scriptto generate a simple color image is presented in Appendix A.

The alpha channel of the color code is used to filter out locationsthat are not important. For example, the dry coastal area or oceanregion without significant water surge is not important in thepresent study. Those areas are made transparent by setting alphachannel on. Hence the images superimposed on Google Earthshows only the portion viewers need to see. The rest of the image istransparent and displays the Geographical properties of the GoogleEarth. Fig. 6 demonstrates the necessity of alpha channel in theimages in the present study.

Interpolation of data is required from the model mesh to theimage domain at the pixel nodes. A fast interpolation algorithm isdeveloped to facilitate the processing of dozens of high qualityimages within minutes.

For the fast interpolation, the portions of a model mesh that areoutside the image domain are excluded from interpolation proce-dure. The remaining model mesh is then subdivided into hundredsof strips to minimize the search area for the pixel nodes. To find thelocation of the pixel nodes on the simulation domain, either finiteelement shape functions or particle-to-the-left algorithm (Zhouand Leschziner, 1999) can be used. Shape function based searchmethod is fast for triangular mesh, but needs iterative solution forstructured mesh. Therefore, shape function method is used fortriangular meshes (ADCIRC and Overland), and particle-to-the-leftalgorithm is used for structured mesh (WRF) in the present study.

If a point (x, y) is located inside an element with vertices (x1, y1),(x2, y2), and (x3, y3), then the following condition must be true

inued).

Fig. 5. Flowchart of automatic image creation from raw data.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3830

0 � r � 10 � s � 1

�(1)

where,

r ¼ ðx� x3Þðy2 � y3Þ � ðy� y3Þðx2 � x3Þðx2 � x3Þðy1 � y3Þ � ðy2 � y3Þðx1 � x3Þ

(2)

s ¼ ðx� x3Þðy1 � y3Þ � ðy� y3Þðx1 � x3Þðx2 � x3Þðy1 � y3Þ � ðy2 � y3Þðx1 � x3Þ

: (3)

Once found, the model elements are tagged with correspondingpixel nodes along with calculated interpolation coefficients. Theinterpolation can be done either using inverse distance technique(Tu and Aliabadi, 2007) or using shape functionmethod coefficientsgiven above. The interpolated value u can be calculated based onthe values at the vertices u1, u2, and u3 as

u ¼ u1r þ u2sþ u3ð1� r � sÞ; (4)

As described earlier, the interpolated values are scaled withinthe desired range and then RGBA color coded, and further pro-cessed using PyPNG to create the images.

3.3. KMZ animation creation and usage

After the images are created, the zipped-KML, or KMZ, anima-tion can be created from those. The procedure is to assign appro-priate timestamps on the image series and project the image to thecorrect geographic location on the Google Earth. One python scriptis run to facilitate automatic KMZ creation process. After KMZ filecreation, it can be loaded on the Google Earth for visualization. (Thepython script is sharable with the reader via email.).

This real-time data can be provided to the end users, such as theemergency management personnel as well as the general public inthe form of KMZ files. These KMZ files can be opened on anycomputer on which Google Earth has been downloaded andinstalled. The end user should be able to visualize the approachinghurricane and also make a scientifically informed decision as towhich evacuation route should be optimal in case of evacuation.The user can enter their current address on Google Earth. Whenthey are zoomed in to their location they can click on the nearestpushpin as shown in Fig. 2 and get an idea of the evacuation routeto take. As the hurricane approaches the shore, the models can bererun and the most accurate KMZ files can be made available witha mere click of button through this automated and integrated GUI.

4. Results and discussion

The simulation of hurricane Katrina in the Gulf of Mexico ischosen as a case of study in the paper. This is primarily due to thevoluminous amount of data available for Katrina. It facilitates moreaccurate simulation and comparison with observed data recordedduring or after Katrina.

4.1. WRF results

A single domain WRF mesh with 300 grid points in east-westand 220 grid points in south-north is used, which contains theentire Gulf of Mexico and surrounding areas. Each segment is 8 km.

Fig. 7 shows the comparison of Katrina simulation and actualtrack path. Fig. 7(a), (b), (c), and (d) show the WRF simulated trackpaths starting from Aug 26 e 00 A.M., Aug 27 e 00 A.M., Aug 27 e

12 P.M., and Aug 29 e 00 A.M., respectively. Fig. 7(e) shows theactual track path obtained by using the Planetary Boundary Layer(PBL). Note that the PBL method generated the wind information

Fig. 6. Effect of alpha channel on images superimposed on Google Earth. (a) Google Earth regional image, (b) Storm surge image with alpha channel turned off, (c) Storm surge andoverland images with Alpha channel turned off, (d) Storm surge and overland images with alpha channel turned on.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 31

from the published meteorological data for Katrina after it hadalready past. Katrina track information is downloaded from theNational Hurricane Center (2010) website. The published track pathof Katrina is shown in Fig. 7(f). Fig. 7(a) and (b), when comparedwith either Fig. 7(e) or Fig. 7(f), show that these WRF simulationwere started too early. These landfall locations are somewhat eastof the actual one. Fig. 7(c) appears to have the best result. AlthoughFig. 7(d) had the latest meteorological data, the hurricane wasalready too close to the land and it appears to subside. These resultshighlight the importance of running WRF repeatedly, typicallyevery 6 h, as soon as new meteorological data is available in theevent of an actual hurricane.

WRF seems to work best with latest meteorological data, whilethe hurricane is still at least 24 h away from landfall. The hurricanemay take unexpected turns, which only the latest meteorologicaldata may reflect. The accuracy of WRF results propagate intoADCIRC and CaMEL Overland simulations through the wind andrain input.

4.2. ADCIRC results

The ADCIRC grid used in our simulation is the same as Mukaiet al. (2001), which consists of 254,565 nodes and 492,179elements containing the entire Gulf of Mexico and deep Atlantic

Ocean. The computational grid for ADCIRC is displayed in Fig. 8. Thetotal model period is 7.25 days with the time step of 1 s. Atmo-spheric wind and pressure fields for Katrina are generated usingPBL code for different durations. As mentioned earlier, PBL preparesa wind data file from the hurricane best track information, which isavailable for a past event. Although the best track information isavailable for the whole Katrina period, we used WRF predictiveresults for the duration of Aug 27, 12 P.M. to Aug 31, 0 A.M. togenerate wind data to test the integrated scheme. Therefore,a combined PBL and WRF wind data from Aug 23, 6 P.M. to Aug 31,0 A.M. is used for our simulation. The merger process is automatedin the GUI.

Zero-flux boundary conditions are used on the land boundary,and tidal conditions are used in the ocean boundary. ADCIRC TidalDatabase (2008), Version ec2001_v2d, is used to extract tide dataduring Katrina period. The 2DDI ADCIRC simulation starts fromscratch. The weighting factor in generalized wave continuityequation and the time weighting factor are default values. Wetting/drying function is turned on. The hybrid nonlinear bottom frictionformulation is used to represent the increase of the drag coefficientas the water depth decreases in shallow water, and the defaultvalues are used for the drag coefficients.

Katrina oceanwater elevation plots from ADCIRCwith differentwind speed and pressure input from WRF and/or Planetary

Fig. 7. Katrina wind pressure plots (a) WRF: Aug 26, 00 A.M.eAug 31, 00 A.M, (b) WRF: Aug 27, 00 A.M.eAug 31, 00 A.M, (c) WRF: Aug 27, 12 P.M.eAug 31, 00 A.M, (d) WRF:Aug 29,00 A.M.eAug 31, 00 A.M, (e) PBL: Aug 23, 06 P.M.eAug 31, 00 A.M, (f) Published track.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3832

Boundary Layer (PBL) are displayed in Fig. 9. Fig. 9(a)e(c) use WRFwind input with different starting date and time. Note that PBLwind data is used at beginning part of the simulation until WRFresults kick in. Fig. 9(d) uses actual Katrina wind data provided byPBL for the whole simulation duration. From the comparison ofFig. 9(a)e(c) with Fig. 9(d), it is evident that the starting date ofWRF simulation has huge impact on the results. It is because of thefact that the latest meteorological data in WRF generates moreaccurate wind speed, pressure, and landfall location of hurricane.Hurricane landfall location has a huge impact on ocean watersurge. Experience suggests that due to the converging funneleffect of complicated land structure water rises rapidly if thehurricane hits Louisiana coast, which could increase the stormsurges by 20e40% (Day et al., 2006). On the contrary, hurricanehitting the Alabama coast is most likely to cause much lesserwater surge.

4.3. CaMEL Overland results

The overland computation domain covers primarily Mississippicoastal area and the mesh contains 505,358 nodes and 1,005,369elements. The domain has the matching shoreline boundary withthe ADCIRC mesh. However, to get a refined mesh in the overlandshoreline area, additional nodes are inserted along the boundary.Water height values for those inserted nodes are interpolated fromtwo neighboring boundary nodes, which are overlapping withADCIRC boundary nodes.

The overland computational domain is two-dimensional.Dirichlet condition applied in one boundary, which is along theshoreline. Zero-flux conditions are applied in other three bound-aries. The time-dependent water surge values simulated fromADCIRC along the shoreline are used as Dirichlet boundary input inthe model. The overland mesh boundary is coupled with the

Fig. 8. ADCIRC meshe(a) Whole domain, (b) Region of interest (zoomed in).

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 33

ADCIRC shoreline boundary to facilitate the data transfer fromADCIRC to CaMEL Overland. The time step of the overland simu-lation is 1 s. When the simulation times of CaMEL Overland andADCIRCmodels are not matching, linear interpolation between twoADCIRC output files is done. Initially zero water height, i.e., h ¼ 0,condition is applied in the domain. Water propagates from theDirichlet boundary side into the domain. The rain data predictedfrom WRF can be used as the source term in the model, which isignored in the present study.

Simulated High Water Mark (SHWM) or maximum waterelevation that happens in the overland domain anytime during theentire simulation period is displayed in Fig. 10. This is one of themost important metrics in establishing the evacuation plan andother similar planning measures in the coastal regions. Fig. 11displays the observatory data collected after hurricane Katrinausing the High Water Mark (HWM) remained on the structures bythe flood water (USGS, 2011). Fig. 11(a) shows the observationstations on the domain (zoomed). Fig. 11(b) shows the comparisonof simulated ADCIRC and overland results with the HWM data. The

comparison is reasonably good. The differences may be attributedwith several well-known deficiencies in the storm surge models,such as the lack of near shore wave, lack of refined mesh and finetuning of the bathymetry-specific bottom friction, not accountingfor the decreased wind drag over water, erroneous values for localwater depth and land height in HWM measurement, etc. It shouldbe also noted that since the scheme is integrated, any error inmeteorological data will propagate to the WRF and followingmodels. Therefore, some kind of margin in the model results mustbe assumed since this model will be used primarily as a predictivetool at a time when the exact wind and pressure information of thehurricane will not be available.

In an actual hurricane event WRF, ADCIRC, and CaMEL Overlandcodes must be executed in sequence two to three days before itslandfall, most likely every 6e12 h. Repeated simulation of the codesallows the use of more recent and accurate meteorological data inthe WRF initialization, which helps minimizing the relevant errorpropagation from one model to the next.

4.4. Infrastructure assessment

The Simulated High Water Mark (SHWM) results of overlandmodel are used to predict whether any building or structure in thedomain will be flooded, damaged, or unaffected. A Katrina floodingimage created from our simulation with 3D buildings super-imposed on it for Biloxi area is shown in Fig.12. The height of the 3Dbuildings is arbitrary and uniform for all buildings. Water height ispresented by the color variation. Both ADCIRC and CaMEL Overlandresults are included in the image. The gray interface along theshoreline, where ADCIRC and overland mesh boundaries overlap, isvisible due to the resolution limitations.

Using the storm surge and flood water elevation and velocitycriteria, infrastructures are categorized as flooded, damaged, orundamaged. Fig. 13 displays the failure status of church buildings inthe coastal Mississippi area.

4.5. Evacuation setup

Based on the hurricane track path, wind speed, and floodingassessment, the coastal areas are prioritized for evaluation. Thecoastal region is divided into several zones. The evacuees areassumed to be concentrated in the centroid of each zone. Theevacuation paths are drawn from each zone centroid to the safezone. Any area north of I-10 is considered to be a safe zone. Theevacuation paths are optimized based on many different parame-ters including travel time, current traffic condition, populationdistribution, etc; and a particular path may not be the shortestpossible (Lim et al., 2009a,b). The paths are dynamically updatedbased on the traffic condition. Fig. 14 displays a few sample evac-uation paths from selected centroids to the safe zone. Evacueepopulation is displayed in Google Earth as dynamic bars in bothdanger and safe zones. As the evacuees reach the safe zone, the safebar increases, while the danger bars decrease, as displayed inFig. 14.

4.6. General remarks

The Southeast Region Research Initiative is a ground breakingprogram of the US Department of Homeland Security to assist local,state and tribal leaders in developing the tools and methodsrequired to anticipate and forestall terrorist events and to enhancedisaster response. SERRI combines science and technology withvalidated operational approaches to address regionally uniquerequirements and suggest regional solutions with potentialnational implications. Program imperatives include a regional and

Fig. 9. Comparison of ADCIRC simulation for different wind speed and pressure data as input. (a) PBL: from Aug 23, 6 P.M. þ WRF from Aug 26, 2005, 0 A.M., (b) PBL from Aug 23, 6P.M. þ WRF from Aug 27, 2005, 0 A.M., (c) PBL from Aug 23, 6 P.M. þ WRF from Aug 27, 2005, 12 hr (d) PBL from Aug 23, 6 P.M. to Aug 31, 0 A.M.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3834

local homeland security focus and research results that culminatein integrated product development with a clear path to usabilityand interoperability across the region. As such, the integratedsystem has been calibrated for use in the Gulf Coast region ofMississippi. The models that comprise the developed system areapplicable to other regions of the country affected by hurricanes,thus the integrated system can be calibrated to other regions aswell. However, this portability process is not currently automated.The system has been tested using hurricane Katrina data byNorthrop Grumman Center researchers in a hindcast study. It hasnot been tested for a forecasted hurricane event to date, but theresearchers of the Northrop Grumman Center are monitoring the2011 Atlantic hurricane season for opportunities to execute theintegrated system in response to a possible hurricane in the GulfCoast region.

Fig. 10. CaMEL Overland model simulated maximum water elevation results in theMississippi coastal region due to Katrina.

Uninterrupted internet access and computer power are requiredduring the meteorological data download and execution of thesystem. It is often the case that the evacuation process takes placeapproximately 2 days ahead of the forecasted hurricane landfall. Inaddition, multiple modes of communication are typically used inthe field level, i.e., there is usually a backup mode in case one fails(USDOT, 2011).

Currently the models are integrated both temporally andphysically. The data transfer, interpolation, and conversion havebeen hardwired in the system since it is known exactly what datato be used and transported from one model to another. It greatlyhelps the operator and minimizes the chances of operationalmistakes.

There are two major types of errors that can happen in thisintegrated system: error due to the invalid input by the operator,and output error related to the forecasting and model uncertainty.To minimize operator mistakes, the number of input values to themodel has been greatly reduced. For most of the cases, the modelsdisplay warning signs to caution the operator about an invalid inputvalue. Each model used in the scheme has its own well-developedexception/error handling protocol. The main source of forecastingand model error in such systems is the uncertainty in meteoro-logical data, which is difficult to overcome due the currenttechnological limitations. The hurricane path, wind speed, etc. areforecasted from the latest meteorological data. To minimize theuncertainty of the forecast, the systemmust run every 6 hwhen thelatest meteorological data is available. The evacuation plan will

Fig. 11. Comparison of High Water Mark (HWM) of Katrina with the simulated results of ADCIRC and CaMEL Overland models. (a) Station locations on the mesh, (b) Comparison.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 35

automatically reflect the latest model results. It is well known thatthe forecast/storm surge/flood models have some error margins,which could be as high as þ/�20%, as reported by SLOSH stormsurge model (SLOSH, 2011). Therefore, decision-makers andemergency management usually apply safety factors and tolerancecriteria for interpreting system results. The current system does notassume any error margin, but it is relatively easy to incorporate ifneeded. Those details are to be implemented based on the needsand suggestions of emergency management personnel andagencies.

Emergency management agencies are the primary users of thissystem. All models are executed in Linux/Unix platform, whichshould be located in a high performance computing center. Theprimary models require a parallel environment. The secondarymodels can be run either in Linux or Windows platform. Mostsoftware used in the system is freely available and require onetime installation. It is assumed that a trained technician willoperate the system under the direct supervision of an experi-enced manager, who has a good understanding of the models andknows how these models operate and how to interpret theresults.

The simulation starts with the meteorological data. Themeteorological data must be complete for the forecast periodprovided in the WRF model input deck. If WRF data is complete,

the remaining models should automatically have complete data.However, errors/warnings may happen if invalid input is used inany of the models, and the operator should be trained to detectthese warnings. The primary models must run in sequence sinceoutput from the earlier model is needed as input in the followingone. The secondary models can be run in batch. All time calcula-tions start from the time stamp the meteorological data contains.That time and date usually is given in the Universal TimeCoordinated (UTC) format. The forecast continues for the numberof hours or days used in the input deck. The output reporting iscontrolled through the output frequency option provided in theinput deck.

The evacuation process could be dynamic. The traffic data isdynamically collected and fed into the system to reflect theevacuation routes. Multiple agencies work together to make theemergency evacuation a success (USDOT, 2011). If there is a breach,such as levee overtopping, the model can predict it and send anevacuation alert for the affected area. However, the current setupdoes not dynamically adjust for the relevant structural modificationin the mesh.

Again, the system is currently setup for the Gulf Coast region ofMississippi. In principle, the framework is usable and transferablefor other parts of the country. It can be employed for other regionswith a proper mesh and input parameter modifications.

Fig. 12. Simulated flood in Biloxi with superimposed 3D buildings.

Fig. 13. Flood assessment for church buildings in the coastal Mississippi area. (For interpretation of the references to colour in this figure legend, the reader is referred to the webversion of this article.)

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3836

Fig. 14. Evacuee population bars and sample evacuation paths from centroids to the safe zones. (For interpretation of the references to colour in this figure legend, the reader isreferred to the web version of this article.)

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e38 37

5. Conclusions

In this paper a multi-scale and multi-physics integrated schemeis presented to forecast hurricane and water surge in the Gulf ofMexico to aid emergency preparedness. Three primary models andtwo secondary models are used in the scheme. The primary modelsconstitute the integrated modeling scheme of a hurricane from itsapproach to landfall and associated water surge and flooding in thecoastal regions. The scheme is appropriate for both forecast andhindcast, depending on whether forecasted or past event meteo-rological or wind data used as input. WRF is used to simulate thehurricane path and strength. ADCIRC is used to simulate the oceanicstorm surge. Then the NGC CaMEL Overland code is used to modelthe inland flooding.

The simulations results are compared with the availableobserved Katrina data. In general, the comparison is comparableto the available observed data. The WRF hurricane model heavilydepends on the meteorological data and model physics parame-ters. Meteorological data is available every 6 h and the wholescheme should be run every 6 h to make the hurricane simulationreliable and feasible. The most accurate WRF results can be ob-tained just 2e3 days before the hurricane touchdown. Theaccuracy of WRF data is propagated to the ADCIRC storm surgemodel. ADCIRC run should be done for 5e7 simulation days tocatch both short and long waves generated from wind and tide,respectively. The ADCIRC shoreline water elevation data is used inCaMEL overland code. This kind of integrated scheme can mostlikely provide the best and worst case scenario e not an exactpicture. Therefore, it is imperative that some kind of error margin

is used for the hurricane prediction schemes because of theintegrated nature of the models.

The results from primary models are used subsequently in thesecondary models for infrastructure risk assessment and evacua-tion response planning. Collectively, the hurricane-surge models,infrastructure assessment model, and evacuation planning modelshave been integrated into a single system.

To facilitate optimal interaction for the end users, a GUI usingPython and Shell scripts is developed to fully automate all projectcomponents, including pre-process, execution, post-processes,image and animation creation, and visualization in Google Earth.The image creation component of the scheme efficiently generateshigh quality model images and animations without any external/commercial software. The data portability and interpolationcomponents are also developed for efficient and better communi-cation between models.

Acknowledgments

This work is sponsored by the U.S. Department of HomelandSecurity (DHS) through the South East Regional Research Initiative(SERRI). Authors would like to thank DHS for their support.

Appendix. Supplementary material

Supplementary material associated with this article can befound, in the online version, at doi:10.1016/j.envsoft.2011.12.006.

M. Akbar et al. / Environmental Modelling & Software 39 (2013) 24e3838

References

ADCIRC Tidal Database, 2008. http://www.unc.edu/ims/ccats/tides/tides.htm.ADCIRC User Manual Compile Time Operations, 2010. http://adcirc.org/

documentv49/Compile_time_operations.html.Akbar, M.K., Aliabadi, S., 2009. Integrated Hurricane and Overland FlowModeling in

Parallel Platform 21st International Conference on Parallel Computational FluidDynamics e (ParCFD 2009), May 18e22, 2009, Moffett Field, CA.

Akbar, M.K., Aliabadi, S., Patel, R., 2009a. Finite Element Modeling of Overland Flowin Mississippi Coastal Region Including Precipitation, River Flow, and BuildingStructures Abstract of USACM’s 10th US National Congress for ComputationalMechanics, July 16e19, 2009, Columbus, Ohio.

Akbar, M.K., Aliabadi, S., Wan, T., Patel, R., 2009b. Overland Flow Modeling ofMississippi Coastal Region Using Finite Element Method 19th AIAA Computa-tional Fluid Dynamics Conference, June 22e25, San Antonio, TX.

Aliabadi, S., 2010. Multi-Purpose, Multi-Scale Storm Surge and Flood Forecasting forPlanning and Preparedness. SERRI Report 70004-01. Oak Ridge National Labo-ratory, Oak Ridge, TN.

Aliabadi, S., 1994. Parallel finite element computations in aerospace applications.Ph.D. Thesis, University of Minnesota.

Bates, P.D., Anderson, M.G., 1993. A two-dimensional finite element model for riverflow inundation. Proc. R. Soc. Lond. A 440, 481e491.

Bates, P.D., Hervouet, J.M., 1999. A new method for moving-boundary hydrody-namic problems in shallow water. Proc. R. Soc. Lond. A 455, 3107e3128.

Beffa, C., Connell, R.J., 2001. Two-dimensional flood plain flow: 1. Model description.J. Hydrol. Eng. 6, 397e405.

Beinhorn, M., Kolditz, O., 2005. Overland Flow Theory and Implementation. GeoSyse Preprint. Tubingen March 2005. Version 2005.1.1. ZAG Publisher.

Braschi, G., Gallatti, M., 1989. Simulation of a level-breaking submersion of planesand urban areas. In: Proceedings of the International Conference on Compu-tational Modeling and Experimental Methods in Hydraulics. Hydrocomp,Elsevier, New York.

Brown, J.D., 2004. Uncertainty propagation through a numerical model of stormsurge flooding. Ph.D. thesis, Univ. of Cambridge, Cambridge, U.K., 530 pp.

Bunya, S., Dietrich, J.C., Westerink, J.J., Ebersole, B.A., Smith, J.M., Atkinson, J.H., et al.,2010. A high resolution coupled riverine flow, tide, wind, wind wave and stormsurge model for southern Louisiana and Mississippi: part I e model develop-ment and validation. Mon. Wea. Rev. 138, 345e377.

Cardone, V.J., Greenwood, C.V., Greenwood, J.A., 1992. Unified Program for theSpecification of Hurricane Boundary Layer Winds Over Surfaces of SpecifiedRoughness. Contract Report CERC-92-1. U.S. Army Engineer Waterways Exper-iment Station, Vicksburg, MS.

CI-FLOW, 2011. Coastal and Inland-Flooding Observation and Warning. http://www.nssl.noaa.gov/ciflow/.

Collins, N., Theurich, G., DeLuca, C., Suarez, M., Trayanov, A., Balaji, V., et al., 2005.Design and implementation of components in the Earth System ModelingFramework. Int. J. High Perform. Comput. Appl..

Danard, M., Munro, A., Murty, T., 2003. Storm surge hazards in Canada. Nat. Hazards28, 407e431.

Day, J., Ford,M., Kemp, P., Lopez, J., December 4 2006.MisterGoMust GoeAGuide forthe Army Corps Congressionally-Directed Closure of the Mississippi River GulfOutlet. http://www.edf.org/documents/5665_Report%20-%20Mister%20Must%20Go.pdf.

Dietrich, J.C., Bunya, S., Westerink, J.J., Ebersole, B.A., Smith, J.M., Atkinson, J.H., et al.,2010. A high resolution coupled riverine flow, tide, wind, wind wave and stormsurgemodel FORsouthern Louisiana andMississippi: part IIe synoptic descriptionand analyses of Hurricanes Katrina and Rita. Mon. Wea. Rev. 138, 378e404.

Funakoshi, Y., Hagen, S.C., Bacopoulos, P., 2008. Coupling of hydrodynamic andwave models: case study for Hurricane Floyd (1999) hindcast. ASCE J.Waterway, Port, Coastal, Ocean Eng. 134 (6), 321e335.

Gram-Jensen, I., 1991. Sea Floods. Dan. Meteorol. Inst., Copenhagen.Gregersen, J.B., Gijsbers, P.J.A., Westen, S.J.P., Blind, M., 2005. OpenMI: the essential

concepts and their implications for legacy software. Adv. Geosci. 4, 37e44.Hill, C., DeLuca, C., Balaji, V., Suarez, M., da Silva, A., 2004. Architecture of the Earth

System Modeling Framework. Comput. Sci. Eng. 6 (1).Hsu, M.H., Chen, C.H., Chang, T.J., 2000. Inundation simulation for urban drainage

basin with storm sewer system. J. Hydrol. 234, 21e37.Hurricane Alley, 2008. Hurricane Models Information. http://www.hurricanealley.

net/hurmdls.htm.Hurrevac, 2010. HURREVAC: A Quick Reference Guide from: http://www.hurrevac.

com/documents/HurrevacGuide2010.pdf.Jelesnianski, C.P., Chen, J., Shaffer, W.A., 1992. SLOSH: Sea, Lake, and Overland Surges

from Hurricanes, NOAA Technical Report, NWS 48, U.S. Department ofCommerce, 71 pp. [Available from Meteorological Development Laboratory,1325 East West Highway, Silver Spring, MD 20910.].

Knabb, R.D., Rhome, J.R., Brown, D.P., 20 December 2005. Tropical Cyclone ReportHurricane Katrina 23e30 August 2005. National Hurricane Center.

Kolditz, O., 2002. Computational Methods in Environmental Fluid Mechanics.Graduate Text Book. Springer Science Publisher, Berlin.

Lim, G.J., Zangeneh, S., Baharnemati, M.R., Assavapokee, T., 2009a. A Network FlowOptimization Approach for a Short Notice Evacuation Planning. ConferenceProceedings, ICOVACS Conference, October 2009.

Lim, G.J., Zangeneh, S., Baharnemati, M.R., Assavapokee, T., 2009b. A sequentialsolution approach for short notice evacuation scheduling and routing. In:Proceedings of the IIE Annual Conference, pp. 874e879. IERC 2009.

Lindell, M.K., Prater, C.S., 2007. Critical behavioral assumptions in evacuation timeestimate analysis for private vehicles: examples from hurricane research andplanning. J. Urban Plann. Dev..

Luettich Jr., R.A., Westerink, J.J., Scheffner, N.W., 1992. ADCIRC: An Advanced Three-Dimensional Circulation Model for Shelves, Coasts and Estuaries. Technicalreport DRP-92e6, Report 1: Theory and methodology of ADCIRC-2DDI andADCIRC-3DL. U.S. Army Engineer Waterways Experiment Station.

Makhanov, S.S., Vannakrairojn, S., Vanderperre, E.J., 1999. 2D numerical model offlooding in east Bangkok. J. Hydraul. Eng. 125, 407e414.

Michalakes, J., Dudhia, J., Gill, D., Klemp, J., Skamarock, W., 1998. Design of a Next-Generation Regional Weather Research and Forecast Model: Towards Ter-acomputing. World Scientific, River Edge, New Jersey, 117e124 pp.

Michalakes, J., Henderson, T., Gill, D., Duda, M., June 2007. WRF Software. 8th WRFUsers’ Workshop, Boulder CO.

Mignot, E., Paquier, A., Haider, S., 2006. Modeling floods in a dense urban area using2D shallow water equations. J. Hydrol. 327 (1e2), 186e199.

Moore, R.V., Tindall, I., 2005. An overview of the open modelling interface andenvironment (the OpenMI). Environ. Sci. Policy 8, 279e286.

Mukai, A.Y., Westerink, J.J., Luettic Jr., R.A., Mark, D., 2001. A Tidal ConstituentDatabase for Western North Atlantic, Gulf of Mexico, and Caribbean Sea. U.S.Army Corps of Engineers. ERDC/CHL TR-02-24.

National Hurricane Center (NHC), 2010. http://www.nhc.noaa.gov/.Pandoe, W.W., Edge, B.L., 2008. Case study for a cohesive sediment transport model

for Matagorda Bay, Texas, with coupled ADCIRC 2D-transport and SWAN wavemodels. ASCE J. Hydraul. Eng. 134 (3), 303e314.

PC Weather Products, 2010. Tropical Cyclone Tracking Software Products from:http://www.pcwp.com/products.html.

Powell, M.D., 1980. Evaluations of diagnostic marine boundary layer models appliedto hurricanes. Mon. Wea. Rev. 108, 757e766.

Pugh, D.T., 1987. Tides, Surges and Mean Sea Level. John Wiley, Chichester, U. K.PyPNG Documentation, 2010. http://packages.python.org/pypng/index.html.SLOSH Model, 2011. http://www.nhc.noaa.gov/HAW2/english/surge/slosh.shtml.Stelling, G.S., Kernkamp, H.W.J., Laguzzi, M.M., 1998. Delft flooding system:

a powerful tool for inundation assessment based upon a positive flow simu-lation. Hydroinformatics, 449e456.

Tayefi, V., Lane, S.N., Yu, D., Hardy, R.J., 2007. Comparison of 1D and 2D methods forpredicting flood inundation over complex rural floodplains. Hydrol. Processes21 (23), 3190e3202.

Tsuchiya, Y.Y., Shuto, N., 1995. Tsunami: Progress in Prediction, Disaster Preventionand Warning. Kluwer Acad., Dordrecht, Netherlands.

Tu, S., Aliabadi, S., 2007. Development of a hybrid finite volume/element solver forincompressible flows. Int. J. Numer. Methods Fluids 55, 177e203.

Tu, S., Watts, M., Fuller, A., Patel, R., Aliabadi, S., November 06 2006. Developmentand performance of CaMEL Aero, a truly matrix-free, parallel and vectorizedunstructured finite volume solver for compressible flows. In: Proceedings of the25th Army Science Conference, Orlando, FL.

US Department of Transportation, 2011. http://www.fhwa.dot.gov/reports/hurricanevacuation/index.htm.

U.S. Geological Survey, 2011. http://www.usgs.gov/.Warner, J.C., Perlin, N., Skyllingstad, E.D., 2008. Using the model coupling toolkit to

couple earth system models. Environ. Modell. Softw. 23, 1240e1249.Westerink, J.J., Blain, C.A., Luettich, R.A., Scheffner, N.W., 1994. ADCIRC: An

Advanced Three-Dimensional Circulation Model for Shelves, Coasts, and Estu-aries; Report 2, User’s Manual for ADCIRC-2DDI. Technical Report DRP-92-6.U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS.

Wilkinson, C., 13 Aug, 2006. Crawling from the Wreckage. Observer.Yu, D., Lane, S.N., 2006a. Urban fluvial flood modelling using a two dimensional

diffusion-wave treatment: 1. Mesh resolution effects. Hydrol. Processes 20,1541e1565.

Yu, D., Lane, S.N., 2006b. Urban fluvial flood modelling using a two dimensionaldiffusion-wave treatment: 2. Development of a sub-grid scale treatment.Hydrol. Processes 20, 1567e1583.

Zhou, Q., Leschziner, M.A., 1999. An improved particle-locating algorithm forEulerianeLagrangian computations of two-phase flows in general coordinates.Int. J. Multiphase Flow 25, 813e825.