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Updated Flood Map for Surface Water

National Scale Surface Water Flood Mapping Methodology

Final Report version 1.0 (May 2013)

ii Updated Flood Map for Surface Water - National Scale Surface Water Flood Mapping Methodology

We are the Environment Agency. We protect and improve the environment and make it a better place for people and wildlife.

We operate at the place where environmental change has its greatest impact on people’s lives. We reduce the risks to people and properties from flooding; make sure there is enough water for people and wildlife; protect and improve air, land and water quality and apply the environmental standards within which industry can operate.

Acting to reduce climate change and helping people and wildlife adapt to its consequences are at the heart of all that we do.

We cannot do this alone. We work closely with a wide range of partners including government, business, local authorities, other agencies, civil society groups and the communities we serve.

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Updated Flood Map for Surface Water - National Scale Surface Water Flood Mapping Methodology iii

Acknowledgements This report has been produced with input from the following individuals:

Neil Hunter, Rob Berry, Amanda Crossley, Duncan Faulkner, Andrew Gubbin, Rosalind Rogers and Simon Waller, JBA Consulting

Ali Cotton, Elliot Gill, Dan Stansfield and Yong Wang, Halcrow

Matt Horritt, Horritt Consulting

Alastair Duncan, Jo Diamond, Shirley Greenwood, Karl Jeans, Kate Marks and Nikki Richardson, Environment Agency

It has also benefited considerably from technical reviews and discussions with the following individuals:

Chris Digman, MWH

Jamie Margetts, Clear Environmental Consultants

Michael Adams, Adam Baylis, Mike Steel, Mark Whitling and Paul Wyse, Environment Agency

David Graham and Sue Humm, Gloucestershire County Council

Andrew Stone, Rhondda Cynon Taf County Borough Council

Francis Comyn, Rochdale Metropolitan Borough Council

iv Updated Flood Map for Surface Water - National Scale Surface Water Flood Mapping Methodology

Contents

1 Introduction 1

2 Data preparation 3

2.1 DP1 - LIDAR/IfSAR composite digital terrain model 3

2.2 DP2 - Ordnance Survey MasterMap and other land cover datasets 4

2.3 DP3 - Soil maps 4

2.4 DP4 - FEH depth-duration-frequency model parameters 4

2.5 DP5 - Local drainage information from LLFAs and WaSCs 5

3 Flood modelling 6

3.1 MOD1 - Improve DTM representation of key topographic controls 8

3.2 MOD2 - Develop initial rainfall hydrology 9

3.3 MOD3 - Run JFlow+ to check model set-up and edit DTM 20

3.4 MOD4 - Finalise DTM and spatially-varying model inputs 22

3.5 MOD5 - Finalise rainfall hydrology 23

3.6 MOD6 - Run final JFlow+ models 23

4 Flood mapping 24

4.1 MAP1 - Post-process depth, velocity and hazard rating model outputs 24

4.2 MAP2 - Undertake flood map validation and confidence rating 37

4.3 MAP4 - Deliver national scale surface water flood map 48

5 Comparison of Environment Agency surface water flood mapping

methodologies 49

References 52

List of abbreviations 54

Table 1 Manning’s n roughness values by Ordnance Survey MasterMap Feature Code (reproduced from Capita Symonds/Scott Wilson (2010, p23)) 22

Table 2 Quadrant analysis formula 27 Table 3 Hazard to People classif ication using FD2320/FD2321 hazard rating formula (Depth x (Velocity + 0.5) + Debris

Factor, where debris factor = 0.5 if depth <= 0.25m and debris factor = 1 if depth > 0.25m) 30 Table 4 Combinat ion of model routing and effective rainfall uncertainties into f inal model output uncertainty - example

values 38 Table 5 Validation evidence for the pioneer locations 41 Table 6 Results of property validation for f looded properties recorded in 2007 summer f loods in suburb of Cheltenham

covered by InfoWorks ICM model 46 Table 7 Results of property validation for internal f lood incidents recorded in RCT, 2002-2011 47 Table 8 Comparison of Environment Agency surface water f lood mapping methodologies 49

Figure 1 Stages in producing the Updated Flood Map for Surface Water 2 Figure 2 Total rainfall depths (in mm) evaluated using FEH DDF model parameters at the centroids of 5km x 5km

modelling grids (shown in red, superimposed on the greyscale DTM) 5 Figure 3 Monitor point w ater level and velocity time series for Test 8A 7 Figure 4 Map of critical storm durations in Birmingham (reproduced from Faulkner, 2010)) 10 Figure 5 Urban-rural land cover mask used to determine net rainfall-runoff 13 Figure 6 Urban runoff processing to generate net rain (1 in 30 probability, 1 hour duration rainfall event) 14

Updated Flood Map for Surface Water - National Scale Surface Water Flood Mapping Methodology v

Figure 7 Sketch of surface runoff and throughflow processes 17 Figure 8 Example application of different net rainfall hyetographs according to urban-rural classif ication for two adjacent

modelling tiles 19 Figure 9 Example differences between total and net rainfall 20 Figure 10 A DTM before and after manual editing 20 Figure 11 Areas (shown in red) which could need a DTM edit and require further investigation 21 Figure 12 Flood mapping data outputs 24 Figure 13 Overview of f lood mapping process 25 Figure 14 Overview of Step 1 - Blending grids together 26 Figure 15 Quadrants for f low direction analysis 27 Figure 16 Areas f illed in (blue) 31 Figure 17 Areas removed (red) 31 Figure 18 Areas on diagonal with an area > 96m2 31 Figure 19 Overview of Step 2 - Creation of cleaned outline 32 Figure 20 Flow direction at 1:500 on a 2m grid 34 Figure 21 Flow direction at 1:1,000 on a 5m grid 34 Figure 22 Flow direction at 1:3,000 on a 10m grid 34 Figure 23 Overview of Step 3 - Clipping of grids to the outline 35 Figure 24 Overview of Step 4 - Creation of outputs 37 Figure 25 Data used to derive the confidence star rating and f inal map output 40 Figure 26 Comparison between known flooding locations (surface water in purple polygons, unknown sources in green)

and national scale map outputs (1 in 30 probability) for two locations in Greater Manchester 42 Figure 27 Comparison between AStSWF (top, 1 in 200 probability), FMfSW (middle, 1 in 30 probability) and latest

national scale map (bottom, 1 in 30 probability) for Manchester city centre 43 Figure 28 Comparison between latest national scale map (top, 1 in 100 probability) and InfoWorks ICM (bottom, 1 in

100 probability). Properties f looded in 2007 are shown in red. 44 Figure 29 Velocities predicted by the InfoWorks ICM (top) and national scale mapping models (bottom) in Cheltenham,

displayed in two classes: blue <1 m/s and red >1 m/s 45 Figure 30 National scale map for 1 in 30 probability, w ith reported f lood incidents as red points, for two locations in RCT47

Updated Flood Map for Surface Water - National Scale Surface Water Flood Mapping Methodology 1

1 Introduction In August 2012, the Environment Agency appointed a contractor team of JBA Consulting, Halcrow and Horritt Consulting to deliver new national scale surface water flood mapping for all England and Wales by February 2013. This mapping is intended to form the basis of a new product, the Updated Flood Map for Surface Water, that will show the worst case flood extents, depths, velocities (both magnitude and direction of flow) and hazard rating associated with the 1 in 30, 1 in 100 and 1 in 1,000 probabilities, in order to:

help Lead Local Flood Authorities (LLFAs) whose areas cover part or all of a Flood Risk Area to meet their obligations for Flood Hazard and Risk Mapping under the Flood Risk Regulations 2009;

provide all LLFAs with better quality datasets to support local flood risk management and spatial and emergency planning activities;

provide a consistent view of surface water flood risk across England and Wales to support the Environment Agency's Strategic Overview/Oversight role, for example, by facilitating better decision making and more effective targeting of the available resources through the Flood Defence Grant-in-Aid (FDGiA) and Long Term Investment Strategy (LTIS) processes;

produce mapping that is useful, locally-credible and accessible to the public to help them understand the hazards of flooding from surface water;

provide models and parameterisation datasets that the Environment Agency and LLFAs will want to re-use and develop further for local studies.

This report sets out the final methodology used to produce the new national scale surface water flood mapping (the "how") and should be read in conjunction with the Guidance on surface water flood mapping for Lead Local Flood Authorities (Environment Agency, 2012a), which sets out the political context and drivers for the project (the "why"). The methodology described herein has been developed and refined in consultation with Environment Agency technical specialists, external peer reviewers (Dr Chris Digman (MWH) and Jamie Margetts (Clear Environmental Consultants)), the Project Board, three "pioneer" LLFAs (Greater Manchester, Gloucestershire and Rhondda Cynon Taf) and delegates who attended the LLFA Capacity Building Workshops hosted by Defra in November 2012.

This report is structured according to the three main stages of producing the new national scale surface water flood map shown in Figure 1:

1. Data preparation (Section 2)

2. Flood modelling (Section 3)

3. Flood mapping (Section 4)

The "LLFA review" component of Stage 4 is described in Reviewing the national scale surface water flood maps (Environment Agency, 2013a) while the approach to "map updating" is currently a work in progress. Finally, Section 5 provides a summary of the methodological differences between the new flood maps and the previous Areas Susceptible to Surface Water Flooding (AStSWF) mapping and the Flood Map for Surface Water (FMfSW).

2 Updated Flood Map for Surface Water - National Scale Surface Water Flood Mapping Methodology

Figure 1 Stages in producing the Updated Flood Map for Surface Water


2 Data preparation The following datasets are of key importance for producing high quality surface water mapping products. These data were acquired, stored and processed by the contractor project team in accordance with the agreed data management plan.

2.1 DP1 - LIDAR/IfSAR composite digital terrain model

The modelling and mapping was undertaken on an updated version of the Environment Agency’s LIDAR/NEXTMap composite digital terrain model (DTM). This DTM provides a continuous description of "bare earth" topography across England and Wales at a horizontal grid resolution of 2m. It includes all the LIDAR data from the recent “urban areas greater than 3km²" campaign but does not include any LIDAR data held by LLFAs or Water & Sewerage Companies (WaSCs). Vertical accuracies for the LIDAR and NEXTMap DTMs are quoted as ±0.15m and ±1m respectively, although the accuracy of Environment Agency LIDAR data has been closer to ±5cm since 2005 (A Duncan, pers. comms.). However, it should be remembered that these data and accuracy statistics will only reflect a "snapshot" of the landscape at the time of data capture and where the landscape has subsequently changed (e.g. due to urban development, natural change, landfill, coastal realignment), then the potential for anomalies is much greater. A GIS file is provided with the final mapping data that shows which areas are covered by LIDAR data.

The first stage in producing the composite DTM was to resample the underlying terrain data – LIDAR data of 2m, 1m, 0.5m or 0.25m resolutions and NEXTMap data of 5m resolution – to a common 2m resolution. This resampling was done using the bilinear method within ArcGIS Spatial Analyst. The resampled data was then mosaiced together into a single DTM, with the LIDAR data taking precedence in areas of common coverage. In order to remove any potential steps at the boundaries of the LIDAR and NEXTMap data, the mosaicing process incorporated a "blending" step which utilised distance-weighted averaging to provide a smooth transition at the DTM edges. Blending is applied across an "overlap distance" of 100m.

The same technique was used to smooth the edges of overlapping LIDAR surveys, albeit with a much shorter overlap distance: a 30m overlap was used for 2m data, a 25m overlap for 1m data, a 20m overlap for 0.5m data and a 15m overlap for 0.25m data. These values were selected after testing various overlap sizes over many different types of terrain and have been found to provide the best fit overall. They have been used for all LIDAR composite products since 2007 without any significant problems (A Duncan, pers. comms.).

It should be noted, however, that blending does not resolve discrepancies in the underlying data – for example, large woodland tracts which have not been removed from the NEXTMap DTM. In these situations, a 100 metre wide “ramp” was produced from the more realistic elevations of the LIDAR DTM up to the false heights (top of woodland canopy) recorded in the NEXTMap DTM. These errors will not be overcome without re-processing all the NEXTMap data with new surface feature removal algorithms or flying the whole of England and Wales with LIDAR.


2.2 DP2 - Ordnance Survey MasterMap and other land cover datasets

Ordnance Survey (OS) MasterMap Topography data (11 June 2011 release) were used to produce maps of building footprints, road layouts and manmade areas using the following attribute queries:

Building footprints:

("FEATCODE" = 10021) AND ("DESTERM" <> 'Archway')

Road layouts:

("FEATCODE" = 10172) AND ( ("MAKE" = 'Manmade') OR ("MAKE" = 'Multiple') OR (("MAKE" = 'Natural') AND ("DESTERM" = 'Track')) )

Manmade areas:

"MAKE" <> 'Natural'

These maps were used as the basis for positively reinforcing important topographic controls on flow within the DTM (see Section 3.1) and defining the spatial variation in runoff and infiltration rates and hydraulic roughness (see Sections 3.2 and 3.4 respectively).

2.3 DP3 - Soil maps

The Environment Agency provided the HOST (Hydrology Of Soil Types) mapping (Boorman et al., 1995), which includes the BFIHOST (Baseflow Index) and SPRHOST (Standard Percentage Runoff) parameters on a 1km grid, under licence from the National Soil Resources Institute (see http://www.landis.org.uk/data/sshydrology.cfm for further details).

Allowing for discretisation errors, these data are as per the Flood Estimation Handbook (FEH) CD-ROM and were used in the calculation of runoff in rural areas (see Section 3.2.3).

2.4 DP4 - FEH depth-duration-frequency model parameters

England and Wales was subdivided into approximately 7,100 5km x 5km "tiles" that provided the basis for rainfall estimation and subsequent hydraulic modelling. For each tile, a model of rainfall depth-duration-frequency (DDF) was constructed using parameters available from the FEH CD-ROM at the tile centroid and the techniques outlined in Volume 2 of the FEH (Faulkner, 1999). Each DDF curve was then used to calculate a tile-specific total rainfall depth for a storm of given duration and probability (see Figure 2).

The DDF parameters (C(1km), D1(1km), D2(1km), D3(1km), E(1km), F(1km)) were extracted automatically from a specially-licensed copy of the FEH CD-ROM version 3. A further FEH catchment descriptor, PROPWET, was also extracted for use in the rural runoff calculations.

http://www.landis.org.uk/data/sshydrology.cfm


Figure 2 Total rainfall depths (in mm) evaluated using FEH DDF model parameters at the centroids of 5km x 5km modelling grids (shown in red,

superimposed on the greyscale DTM)

It is acknowledged that this approach ignores the spatial variation in rainfall that is known to occur across areas smaller than a 5km x 5km tile. As such, it may over predict the amount of rainfall across large areas, and therefore give a very much worst case estimate of runoff, which for some large sub-catchments may over play the flood risk being predicted. The choice of a 5km x 5km tile therefore represents a compromise between the computational limits of the hydraulic modelling software and the need to describe broadscale variations in rainfall patterns without oversampling the underlying DDF methods and data.

2.5 DP5 - Local drainage information from LLFAs and WaSCs

Approximately 10% of LLFAs provided information on drainage rates, critical storm durations and/or infiltration/runoff rates in response to a data request issued by the Environment Agency in Summer 2012 (Environment Agency, 2012b). Following basic sense checks, these data were processed into suitable formats for incorporating within the rainfall hydrology calculations and hydraulic modelling. Where data could not be used "as supplied", inconsistencies were resolved via one-to-one discussions.


3 Flood modelling The flood modelling component of the project was undertaken on a 2m resolution grid for all England and Wales. Although almost certainly an overspecification in many areas (e.g. unpopulated rural locations), a "2m everywhere" approach was felt by the Project Board to be the most efficient and expedient way to offset technical and political concerns over which areas should be modelled at higher resolution. It is, however, acknowledged that the use of sub-2m resolution grids/meshes is now relatively common in 2D urban drainage modelling (J Margetts, pers. comms.). Here finer resolution modelling was not considered compatible with either the available DTM data (which would not support further disaggregation) or the project programme/resource constraints.

Simulations were performed using the JFlow+ 2D hydraulic model (Lamb et al., 2009; Crossley et al., 2010a, 2010b). JFlow+ solves the Shallow Water Equations using a finite volume formulation that combines the Riemann based solver of Roe with an upwind treatment of the source terms. The model is both conservative and shock capturing, and maintains water at rest over irregular topography. JFlow+ is implemented on a regular grid using the supplied DTM and does not require any secondary grid generation process. This simplifies the model set up and allows for direct interpretation of the model results relative to the DTM.

JFlow+ has been designed with the emphasis on easy set up and model specification. Models are configured using databases, and this provides a highly ordered means to store significant quantities of data. The modelling engine is controlled through a web interface, ensuring that projects and models can be set up, run and monitored from any location in which an internet connection is available.

JFlow+ has been benchmarked using the test cases proposed by the Environment Agency in the Science Report SC080035/SR2, Benchmarking of 2D Hydraulic Modelling Packages, and the results have been submitted to the Environment Agency. Results for Test 8A which considers rainfall and point source surface flow in an urban area are shown in Figure 3 (note that the axes used are consistent with those in the SC080035/SR2 report). These plots demonstrate the ability of JFlow+ to deliver robust velocity data for direct rainfall applications.

JFlow+ is commercially available now for use by the Environment Agency or any LLFA, or by any other party on behalf of the Environment Agency or any LLFA. The latest version of the software includes all the modifications required to undertake the modelling work described here.


Water level (depth) time series Velocity time series

Test 8A, point 7

Test 8A, point 7

Test 8A, point 9

Test 8A, point 9

Figure 3 Monitor point water level and velocity time series for Test 8A

The flood modelling approach was based on the "direct rainfall" concept where net or "effective" runoff volumes applied to each grid cell in the hydraulic model are routed across the DTM surface, identifying flooding pathways and areas where ponding will occur. The approach has been successfully implemented by JBA to produce both the previous Environment Agency surface water flood maps and is widely accepted as an appropriate method for analysing higher magnitude, lower probability storms where subsurface drainage systems are likely to be overwhelmed and/or inlet capacities exceeded (Defra, 2010).

As for the Areas Susceptible to Surface Water Flooding (AStSWF) mapping and the Flood Map for Surface Water (FMfSW), England and Wales was subdivided into approximately 7,100 5km x 5km tiles (or model domains). These modelling tiles included a 500m buffer with adjacent tiles (so are actually 6km x 6km) to ensure that modelled areas overlap sufficiently and "edge effects" are not visible in the final maps. For expediency, tile boundaries were aligned to the British National Grid rather than surface water catchments, and this may be significant for underestimating runoff response from missing (i.e. "off-tile") upland areas or inputs to the natural and manmade drainage network that prevent their discharge further downstream, particularly during longer storm events. However, with no national map of surface water catchments available (or reliable means of deriving one automatically) and the use of relatively short storm durations (see Section 3.2.1), there was little option but to acknowledge and accept this compromise. As noted in Section 2.4 above, the choice of a 5km x 5km tile also represented a trade-off between the computational limits of the hydraulic modelling software and the need to describe broadscale variations in rainfall patterns without oversampling the underlying DDF methods and data.


3.1 MOD1 - Improve DTM representation of key topographic controls

3.1.1 Buildings

As directed by the Project Statement of Requirements (SoR), the OS MasterMap data processed during step DP2 was used to explicitly raise the ground level within building footprints (according to the bare earth DTM) by approximately 0.3m. An upstand height of 0.3m was selected because flooding at this depth will certainly exceed the level of any damp-proof course and result in property flooding in many cases.

This requirement was based on the conclusions of the FMfSW Improvements Pilot Studies (Halcrow/JBA Consulting, 2012), which demonstrated the importance of modelling the "deflection effect" of buildings on surface water flows. However, contrary to the recommendations of Allitt (2009, pg12), buildings are also represented in such a way that there can be flow through them once the depth exceeds the height of the upstand. This is consistent with evidence from steeper areas where water is observed to pass through, rather than around, buildings during flood events (N Rookes, A Stone, pers. comms.). It was therefore felt that the upstand approach provided the best compromise currently available for building representation in national scale surface water modelling.

To ensure that buildings deflect flow but do not cut into the DTM, even on the steepest slopes, the following logic was applied:

Where:

Accordingly, each building footprint was assigned a horizontal "floor level" that will ensure more consistent results within individual properties and also minimise the occurrence of partially flooded buildings in the final mapping that can complicate property counting approaches.

3.1.2 Roads

Better definition of kerb features is standard practice in high resolution urban flood modelling (Hankin et al., 2008; Allitt, 2009; WaPUG, 2009; Defra, 2010; Halcrow, 2011). The representation of the road network, which is known to preferentially collect and route storm water when it rains, was therefore improved within the DTM. Road surfaces, selected from OS MasterMap data using the criteria shown in Section 2.2, were lowered by 0.125m (the height of a British Standard kerb) to better delineate these important pathways in the hydraulic modelling and mapping. However, this approach may overestimate the routing effect of roads in rural areas where there are fewer kerb stones or where the kerb height is substantially less because the road has been resurfaced.


Both the topographic modifications described above were quick and easy to apply and ensured that the principal flood pathways between buildings and along roads were better represented in the regular 2m model grids. However, detailed urban drainage modelling often shows that it is subtle changes in local topography that can significantly affect the ultimate direction and extent of the flooding, particularly during higher probability events where depths may be low. As such, the inability to represent other important urban features explicitly within the DTM, such as walls, fences, drop kerbs and speed bumps, should be recognised as a limitation (albeit understandable for a national scale approach).

3.2 MOD2 - Develop initial rainfall hydrology

As part of this step, two pieces of work relating to the rainfall hydrology were undertaken:

1. Conservative rainfall estimates were produced to allow DTM editing (see Section 3.3) to be started as soon as possible. Total rainfall depths were calculated for a 3 hour, 1 in 1,000 probability event on a tile-by-tile basis and then scaled across a 50% summer profile assuming 100% runoff to produce a hyetograph that will be applied uniformly across each hydraulic model domain.

2. New methods were investigated for better representing the spatial variation in rainfall runoff rates resulting from different urban densities, as well as the urban/rural split. While a new approach was developed for use in rural areas (described in Section 3.2.3), it was not possible to improve on the urban loss model used in the previous FMfSW given the time and data available. This method is therefore described again for completeness in Section 3.2.2.

3.2.1 Specification of design events

Rainfall probabilities

As directed by the Project SoR in order to meet the requirements of the Flood Risk Regulations 2009, the modelling and mapping work was undertaken for the 1 in 30, 1 in 100 and 1 in 1,000 rainfall probabilities. Unlike previous Environment Agency surface water flood maps, mapping was not produced for the 1 in 200 probability.

Rainfall storm durations

Originally the Project SoR specified that a single rainfall storm duration of 3 hours should be applied everywhere, except where LLFAs requested that alternative values should be used. However, choosing a single, representative critical storm duration is difficult, because any modelled area will include a number of sub-catchments of different size, steepness and shape. Our physical understanding indicates that a short duration storm (around 1 hour) better represents the type of event that leads to surface water flooding, but there is also evidence that longer storms may be critical in flatter areas (e.g. Halcrow, 2007; Faulkner, 2010).


Figure 4 shows how the distribution of critical durations is strongly linked to the topography. In low-lying areas, near to rivers, the critical duration is long because surface runoff drains into these areas from larger catchments which have a longer time of concentration. There are also isolated areas elsewhere with a long critical duration, which tend to be topographic depressions where water will pond. On hill slopes the critical duration is generally short because the greatest flood depth arises from high intensity rainfall.

It is also recognised that the critical duration for flood extent and depth may not be the same as for velocities (which will increase for shorter, more intense storms).

Figure 4 Map of critical storm durations in Birmingham (reproduced from Faulkner, 2010))


Therefore, in response to the technical concerns of the peer reviewers as well as numerous requests for additional storm durations from LLFAs, the Project Board took the decision to model storm durations of 1, 3 and 6 hours for each rainfall probability (i.e. 9 rainfall scenarios in total). Use of multiple storm durations should also mean that the final mapping is more representative of a 1 in x chance flood, rather than only representing a 1 in x chance rainfall event.

An alternative to the conventional design storm is the "superstorm" concept (Osborne, 2012) which is a long-duration storm that includes within it the critical conditions for shorter duration events. It is intended to avoid the need to run large numbers of different storm durations. It has been applied on some Surface Water Management Plans (SWMPs) and Integrated Urban Drainage (IUD) studies but the long duration of the superstorm proved prohibitive in terms of model run times here.

Storm profile

Total rainfall depths for each rainfall probability/storm duration were then scaled across a standardised storm profile to produce design hyetographs. Two standard profiles are typically recommended: a 75% winter profile, for rural catchments, and a 50% summer profile, for urban catchments. The summer profile is more peaked than the winter profile, because of the prevalence of intense convective storms during the summer, so the intensity is greater in the middle of the storm. Intuitively, therefore, the summer event is more likely to be critical for surface water flooding and it has been selected for previous surface water mapping studies in England and Wales, Scotland and Ireland.

Areal reduction factor

An areal reduction factor (ARF) was not applied to the rainfall because, unlike for fluvial studies, there is no defined catchment into which the rainfall is falling. The direct rainfall approach to surface water mapping can be viewed as merging together many separate point rainfall events during a single model run. Admittedly, in reality the depth of surface water flooding at any location will depend on rainfall over an area which is draining towards that location and local downstream conditions. The size of this contributing area will vary from point to point, but in general it is likely to be small, because when there is a large area draining into a point, the point generally coincides with a river, in which case the flooding is fluvial rather than pluvial. For a rainfall duration of 3 hours, the ARF is above 0.97 for an area of 2km

2 and so it would seem

reasonable to ignore the areal reduction effect for surface water mapping. The surface water mapping methodology divides an area into 5km x 5km tiles for the purpose of running JFlow+. This division is not relevant to the setting of ARF values because it is not likely that any points will receive runoff from the entire 25km2 square.


3.2.2 Urban runoff calculation

Introduction: how does surface water flooding occur in urban areas?

In urban areas, serviced by underground sewerage and highways drainage infrastructure, surface water flooding occurs when either:

Runoff cannot reach inlets (e.g. road gullies and downspouts) to the drainage system; or

Drainage systems or their inlets are at capacity and no further flow can enter; or

Drainage systems are at capacity and flood through gullies, manholes and sometimes within properties through domestic plumbing connections.

In addition, urban areas can be crossed by watercourses, often in culvert, which also flood when capacity is exceeded. These watercourses receive flows from outside of the urban area but also from within it via surface water drainage outfalls or combined sewer overflows or from pluvial flows entering directly along the bank.

The combined effect is a highly complex flooding environment where even close observation of flooding events is uncertain to identify the full mechanism. Complex integrated urban drainage models are used to represent the interaction of these different processes and represent the state of the art in predicting urban surface water flooding. Unfortunately it is not feasible to model flooding at a nationwide scale using these methods because simulations take a long time and models are demanding of detailed asset data. Therefore, the Updated Flood Map for Surface Water uses a simplified approach which can approximate these processes over large geographical areas.

Description and justification of the chosen method

The chosen method generates a net rainfall for urban areas which allows for an urban runoff coefficient (70%) and a default drainage capacity (12 mm/hr). Net rainfall is then assumed to flow and pond freely across the urban area to predict flooding according to topographic controls.

Urban areas were defined using an analysis which considered the land use (based on OS MasterMap information) for grid squares 250m by 250m. Each square was screened to determine the percentage coverage of "manmade" landuse; this included all buildings, roads, paths and other hard-standing. Where the manmade landuse was greater than 50% of the square, the whole square was determined as "urban" and urban runoff rules were applied. All other squares were defined as "rural" and the methods defined in Section 3.2.3 were applied. This method proved successful in delineating the edge of the built-up area whilst excluding large areas of green space (e.g. parks) within cities. Figure 5 is an extract of the results of this analysis for part of the town of Skipton in North Yorkshire. Urban 250m grid squares are coloured red. Rural runoff calculation methods were applied in green squares.


Figure 5 Urban-rural land cover mask used to determine net rainfall-runoff

A runoff coefficient of 70% was chosen for urban areas. This is a good average runoff coefficient for built-up areas including gardens and green verges and a mix of city centre and more suburban land uses. The FEH catchment descriptor assumes a 70% coefficient for urban areas. It is the value for urban runoff used in previous national surface water flood maps in England and Scotland. Viessman and Lewis (2003), a standard hydrology text, quote city centre runoff coefficients in the range 70-95% and suburban runoff coefficients in the range 50-70%. This is also in line with average runoff coefficients in calibrated and verified sewerage hydraulic models.

Making an allowance for drainage systems is more challenging because urban drainage systems vary so much in nature and their effectiveness in different event durations is linked to very local characteristics such as the arrangement and capacity of road gullies and whether drainage is via combined or separate sewerage. This information is not available in a consistent manner over large areas. Therefore, an approach used in the earlier Flood Map for Surface Water was re-used. The method combined estimates of service level (or standard of protection from flooding) for drainage systems (between 1 in 5 and 1 in 30 years, centred around 1 in 10 years) with estimates of critical storm duration (0.5 to 2 hours) with estimates of percentage impermeable area (30% to 80%) with estimates of DDF rainfall parameters to determine typical drainage system capacity expressed in mm/hr using a Monte Carlo method. This is the rate of rainfall in urban areas which is typically conveyed in drainage systems. The range was between 5mm/hr and 54mm/hr with a mode of 12mm/hr. The mode value was adopted as a typical drainage removal rate. In areas of known low or high drainage capacity, alternative values of 6mm/hr or 20mm/hr were substituted.

Assumptions and limitations

Whilst very robust and appropriate for the wide-area assessment of surface water flood risk, the selected method contains a number of limitations which should be noted when interpreting flood maps.


The selected method assumes that runoff generation is even across urban areas and even though large green spaces are accounted for, the method does not differentiate between the higher runoff rates likely in city centres (e.g. >80%) and the lower rates found in more suburban areas (e.g. <60%). Therefore, the approach risks underestimating city centre runoff and over estimating suburban runoff.

It also assumes that drainage is principally provided by underground sewers and that their capacity controls the onset of surface water flooding. This is recognised as a simplification because the role of inlet control structures is likely to be important for short duration, high intensity rain events. It is probable that surface water flooding sometimes occurs before underground drainage systems reach their capacity. Therefore, the chosen method probably underestimates net rainfall (causing flooding) for at least the 60 minute (short duration) events.

The prediction of net rainfall (and hence flooding) for the 1 in 30 rainfall probability is especially sensitive to the assumption for drainage capacity. This is because the drainage system controls most of the flow volume for this size of event and small changes in actual capacity would result in significantly greater or less flooding. Prediction of flooding for the 1 in 100 and 1 in 1,000 probability events are less sensitive to assumptions about drainage capacity because the drainage system plays a less significant role in conveying storm water. However, and especially for the 1 in 100 year event, the drainage system can still play an important role in determining the exact location and timing of flooding. In cities where large underground tunnels convey storm water in directions counter to the natural topography the simplified approach described here is unlikely to accurately predict the location of flooding.

Implementation

Figure 6 illustrates the changes made to rainfall data to generate a net rainfall which is applied in urban areas to predict surface water flooding. First a factor of 0.7 is applied to account for losses to infiltration (effective rain). Second, 12 mm/hr is removed to account for losses to the drainage system (net rain). In the illustration below, the scenario is the 1 in 30 probability, 1 hour duration rainfall event which has a total gross rainfall depth of 31.79 mm, a total effective rainfall depth of 22.25 mm and a total net rainfall depth of 11.81 mm.

0

20

40

60

80

100

120

140

0 10 20 30 40 50 60 70

rain

rat

e (m

m/h

r)

Duration (minutes)

Gross rain (mm/hr)

Effective rain (mm/hr)

Net rain (mm/hr)

Figure 6 Urban runoff processing to generate net rain (1 in 30 probability, 1 hour duration rainfall event)


3.2.3 Rural runoff calculation

Introduction: how does surface water flooding occur in rural areas?

In marked contrast to fluvial flood estimation, there has been little research into methods for estimating design runoff rates for surface water flood modelling. There have been numerous investigations of runoff processes during flood conditions, but many have been focused on the eventual role of runoff in producing stream flow, rather than the way in which surface water flooding is generated. Even the meaning of runoff can be hard to pin down: it is often used to refer to movement of water through the soil and subsoil (throughflow) in addition to overland flow.

A simple conceptual model of surface water flooding would envisage it as resulting from overland flow, due either to infiltration excess (rainfall at a rate higher than can infiltrate) or saturation excess (rainfall on saturated ground). However, flooding that occurs where throughflow emerges at the ground surface (e.g. due to topographic factors or soil heterogeneity) could also be classed as surface water flooding.

Approaches to the estimation of runoff vary from physically-based models (often calibrated from measurements of soil properties at individual points or in laboratory studies) to conceptual or statistical methods in which runoff is taken as one of the components of the process of converting rainfall to river flow. There are pros and cons to all types of approaches.

We have reviewed recent research literature and guidance documents relevant to surface water flood modelling (e.g. WaPUG, 2009; Burton et al., 2010; Defra, 2010; Hurford et al., 2012; Osborne, 2012) and project reports including the runoff calculation methods used for pluvial flood maps in Scotland (2011) 1 and Ireland (2010)2. Methods used for calculating rural runoff include:

a blanket percentage runoff irrespective of soil type;

the FSR/FEH rainfall-runoff method, applied in a variety of ways;

Horton's equation for infiltration;

a combination of initial loss (depression storage) and infiltration.

We believe that the approach outlined below is a significant step forward from methods that have previously been applied for national-scale surface water mapping. However, it does have some limitations and make some assumptions, as discussed in the following sections.

Description and justification of the chosen method

Our approach for calculating losses, and hence runoff, in rural areas used the losses model from the Revitalised Flood Hydrograph (ReFH) rainfall-runoff method (Kjeldsen et al., 2005). An identical approach was applied for calculation of runoff within the green portions of urban areas.

1 HALCROW, 2011. Deriv ation of a National Pluv ial Flood Hazard Database. Report to SEPA.

2 HR WALLINGFORD, 2010. National Pluv ial Screening Project f or Ireland. Report EX6335 f or Office of Public Works.


This is an improvement on the percentage runoff model used in the FSR/FEH rainfall-runoff method. Losses are controlled by a single parameter, the maximum soil moisture storage capacity (Cmax), which can be estimated from two FEH catchment descriptors, BFIHOST and PROPWET. It is also necessary to specify an initial condition, the starting value of soil moisture, Cini, which can also be estimated (when simulating a design flood event) from catchment descriptors.

The ReFH losses model calculates the volume of runoff at each time step as a function of the current soil moisture content, so that the percentage runoff increases as the rainfall continues. This increase is fairly minor for short duration storm events.

Although ReFH was designed for fluvial rather than surface water studies, the same is true of other methods that have been previously applied for national-scale surface water mapping. For example, the Environment Agency's current flood map for surface water uses a runoff percentage for rural areas (39%) derived from consideration of standard percentage runoff values used in the FSR/FEH rainfall-runoff method for estimating fluvial design flows (JBA Consulting, 2009).

Methods such as ReFH (or FSR/FEH rainfall-runoff) have an advantage over more physically-based techniques of calculating runoff (or infiltration) in that they are designed and calibrated from well-established long datasets with national coverage (rainfall, river flow and potential evaporation). No equivalent datasets are available for parameters such as infiltration or surface runoff which are generally measured over small scales and short durations at experimental sites or in the laboratory.

ReFH was developed not only to model runoff from rainfall but specifically to do so in the context of estimating design floods. When used in conjunction with a specified rainfall depth, duration and profile and initial soil moisture, the ReFH model has been calibrated to produce a design river flow with a specified return period.

An alternative to ReFH, as envisaged from the outputs of the FMfSW Improvements Pilot Studies (Halcrow/JBA Consulting, 2012), would be to calculate the volume of runoff from the standard percentage runoff estimated from HOST data, SPRHOST. We suggest that the approach outlined above is preferable, for the following reasons:

Calculation of runoff from SPRHOST comes from the FSR/FEH rainfall-runoff method which has now been superseded by the ReFH method.

SPR is considerably more difficult to calculate than BFI, and thus the relationship between SPR and HOST data is less certain than that between BFI and HOST. For this reason, recent research by CEH has actively sought to avoid using SPRHOST as an explanatory variable in regression models (Kjeldsen et al., 2008).

ReFH allows for the proportion of runoff to increase through the storm, as the soil wets up.

Assumptions and limitations

The application of FEH/ReFH methods for modelling surface runoff is consistent with a recent recommendation from Defra/Environment Agency R&D project SC090031 to use such methods for estimation of greenfield runoff for development control (Faulkner et al., 2012). Essentially this extension of such methods relies on the assumption that the relationship between rainfall and river flow continues to follow a similar form as the catchment becomes smaller and smaller until it no longer contains a watercourse, at which point the flow is occurring over or close to the surface of the ground. In the present case, only one part of the ReFH method is being used: the losses model, which determines the volume of flow. At first sight it may seem a reasonable


assumption that the relationship between the volume of rainfall and the volume of surface runoff is similar to the relationship between the volume of rainfall and the volume of river flow, given that nearly all pluvial flood water can be expected to continue down the catchment and enters a river to become fluvial flood water. However, the assumption does have some potential weaknesses:

There may be additional losses occurring during or after surface water flooding and before the runoff reaches the river (see Figure 7);

Conversely, there may be additional sources of quick flow for fluvial floods, such as interflow, which are not associated with runoff (see Figure 7);

The definition of runoff used in the ReFH model (and other conceptual models) depends on the way the fluvial flood hydrograph is separated into baseflow and quick flow, and does not have any direct physical interpretation.

The assumption is difficult to test due to the lack of extensive long-term records of surface runoff.

River

SW

flooding

Runoff

Rain

Losses

More

losses?

Continued

runoff

Interflow

Baseflow

Figure 7 Sketch of surface runoff and throughflow processes

The way in which we implemented the ReFH losses model was to pre-calculate net rural rainfall before applying it within the hydraulic model. A drawback of this approach was that there was no allowance for ongoing infiltration in places where water ponds for long periods, for example, in topographic depressions. Currently JFlow+ does not represent mechanisms such as infiltration or evaporation and thus there is the potential to over-estimate the duration and depth of flooding in some locations, particularly during prolonged events.


The ReFH losses model has a known limitation on highly permeable soils (BFIHOST>0.65)3. The regression model tends to underestimate the maximum soil moisture storage capacity, Cmax, in such areas4, which (in isolation) would result in overestimation of percentage runoff. The value of the initial soil moisture, Cini, may also be unrealistic5. Difficulties representing the complex flood-generation processes on highly permeable catchments are to be expected for design event methods of flood estimation, and the validity of the FSR/FEH rainfall-runoff method is also limited in such cases

6. In areas where BFIHOST exceeds 0.65 (principally chalk and limestone

geology), it is hoped that LLFAs will review the results of the method and suggest refinements where necessary, for example setting a minimum percentage runoff.

Implementation

To apply the ReFH losses model for simulation of design floods, it is necessary to specify values for the rainfall depth (see Section 2.4), duration and temporal profile and the initial soil moisture, Cini.

The ReFH model can be run with either a summer or winter design event. The summer event has a greater depth of rainfall but a lower value of Cini. The rainfall profile is also more sharp-peaked, so the intensity is greater in the middle of the storm. Intuitively, the summer event is more likely to be critical for surface water flooding but the summer design event in ReFH (in particular, for the present application, the regression equation relating Cini to catchment descriptors) was developed using only seven catchments and so its validity is uncertain. Further testing was not possible within the project timescales and so the recommendation in the ReFH report to use the summer event had to be followed.

The rainfall depth for input to ReFH is calculated by applying a seasonal correction factor to the depth derived from the FEH rainfall frequency model. In the case of the summer event, the seasonal correction factor is quite close to 1, but we appl ied it anyway.

For design runs of the ReFH model, the initial soil moisture Cini is scaled down by an adjustment factor, , which was introduced to ensure that fluvial flood growth curves

produced by the ReFH model matched those estimated directly from flood peak data. For our purposes we were interested only in the losses model, not the other aspects of ReFH. It was not appropriate to apply the factor in this case, which is only relevant

for modelling fluvial flood peaks.

The parameter Cmax and initial soil moisture Cini are estimated from two FEH catchment descriptors: BFIHOST and PROPWET. Both of these descriptors are simply spatial averages of gridded data and thus their definition is not restricted to areas that comprise river catchments: they can be evaluated for any geographic area.

3 FAULKNER, D.S. AND BARBER, S., 2009. Performance of the Rev italised Flood Hy drograph Method. Journal of Flood Risk Management 2, 254-

261.

4 KJELDSEN, T.R., STEWART, E.J., PACKMAN, J.C., FOLWELL, S. AND BAYLISS, A.C., 2005. Revitalisation of the FSR/FEH Rainfall -Runoff

Method. Defra R&D Technical Report FD1913/TR.

5 FAULKNER, D.S, ROBB, K. AND HAYSOM, A., 2008. Return Period Assessment of the Summer 2007 Floods in Central England. BHS 10th

National Hy drology Symposium, Exeter, September 2008, 227-232.

6 WEBSTER, P., 1999. Factors Affecting the Relationship Between the Frequency of a Flood and its Causative Rainf all. In: L. Gottschalk, J.-C.

Oliv ry, D. Reed & D. Rosbjerg, eds. Hydrological Extremes: Understanding, Predicting, Mitigating (Proceedings of the IUGG99 Symposium HS1,

Birmingham, July 1999). IAHS Publication. No. 255. Wallingf ord: IAHS Press, 1999, 251-257.


BFIHOST data was extracted from the HOST mapping at the native 1km resolution (see Section 2.3), where as PROPWET values were obtained from FEH catchment descriptors previously extracted during step DP4 (see Section 2.4). PROPWET is derived from MORECS data evaluated on a 40km x 40km grid and so it will take identical values for sets of nearby 5km tiles.

Net rainfall hyetographs were therefore calculated on a 1km grid and then applied within JFlow+ according to the urban-rural land cover mask shown in Figure 5 to produce runoff for all rural areas or green spaces within urban areas (see Figure 8).

Figure 8 Example application of different net rainfall hyetographs according to urban-rural classification for two adjacent modelling tiles

Figure 9 shows example results from the ReFH losses model: net rainfall hyetographs for a 1.1-hour storm in rural areas with different soil types.


Figure 9 Example differences between total and net rainfall

3.3 MOD3 - Run JFlow+ to check model set-up and edit DTM

The composite DTM developed in steps DP1 and MOD1 will need further processing to provide a suitable topographic basis for direct rainfall modelling. Manual editing is required to provide flow paths through "flyover" features that present unrealistic barriers to known flow routes. These features include road and railway embankments, bridges, subways, and tunnels, and, unless edited, can cause runoff to back up and flood a larger area "upstream" of the obstruction. Similarly, areas "downstream" of the obstruction may be unrealistically shown as being free from flood risk (see Figure 10).

Figure 10 A DTM before and after manual editing

0

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18

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Rain

(m

m)

Time (hours)Gross rainfall

Net rainfall: impermeable, wet soils

Net: Moderate permeability and wetness

Net: permeable, dry soils


The approach adopted requires that a model run is completed and the results from the model run used to identify where water is retained. Edits are then made to the local DTM values and a further model run is undertaken. Further edits may be necessary and the process is repeated until a satisfactory output is achieved. Achieving satisfactory results in rural and urban areas can typically require between 2-3 and 5-10 iterations respectively. All edits are then checked and signed-off by experienced staff.

A range of datasets were used to support this work, including OS MasterMap and 1:10,000 Scale Raster mapping, vertical aerial photography (Google Earth, Bing Maps) and 360-degree, street-level imagery (Google Street View), but as can be seen from Figure 11, DTM editing is a very time-consuming and highly subjective task that requires experienced judgement to determine which blockages should be removed and how.

In total, approximately 91,000 edits were made to the DTM as part of this project.

Information useful for this task also exists within asset registers held by various organisations (e.g. Highways Agency, Network Rail and local authority highway and drainage departments) but it was not possible to collect and collate these data within constraints of the project programme. It is hoped, however, that this information will be used to check the mapping at key asset locations during the LLFA review process.

Figure 11 Areas (shown in red) which could need a DTM edit and require further investigation


3.4 MOD4 - Finalise DTM and spatially-varying model inputs

Following step MOD3, the DTM was now ready to undertake the final model runs for each of the three flood probabilities required.

As well as topography, maps that describe the spatial variability of hydraulic roughness were also required. Here these maps were based on the land cover information provided by the OS MasterMap data which was then converted to Manning’s n values using look-up tables developed during the recent Drain London project 7 (see Table 1 below).

Table 1 Manning’s n roughness values by Ordnance Survey MasterMap Feature Code (reproduced from Capita Symonds/Scott Wilson (2010, p23))

Feature Code

Descriptive Group Comment Manning's n value

9999 0.035

10021 Building 0.015 (Depth <= 30mm) 0.500 (Depth > 30mm)

10053 General Surface Residential yards 0.040 10054 General Surface Step 0.025

10056 General Surface Grass, parkland 0.030 10062 Building Glasshouse 0.015 (Depth <=

30mm) 0.500 (Depth > 30mm)

10076 Land; Heritage And Antiquities

0.500

10089 Water Inland 0.035

10093 0.040 10096 0.040

10099 0.040 10111 Natural Environment

(Coniferous / NonConiferous Trees)

Heavy woodland and forest

0.100

10119 Roads Tracks And Paths

Manmade 0.020


Tarmac or dirt tracks 0.025

10167 Rail 0.050


Tarmac 0.020

10183 Roads Tracks And Paths (roadside)

Pavement 0.020

10185 Structures Roadside structure 0.030 10187 Structures Generally on top of

buildings 0.015 (Depth <= 30mm) 0.500 (Depth > 30mm)

7 CAPITA SYMONDS AND SCOTT WILSON, 2010. Drain London - Data and Modelling Framework. Report version 1.0 (10 December 2010).


Feature Code

Descriptive Group Comment Manning's n value

10193 0.040 10203 Water Foreshore 0.040

10210 Water Tidal water 0.035 10217 Land (unclassified) Industrial yards, car

parks 0.035

As can be seen from Table 1, the Manning's n value associated with certain MasterMap Feature Codes varies with depth. The conceptual argument for this approach is presented in Capita Symonds/Scott Wilson (2010) and is implemented here at the request of the Environment Agency.

3.5 MOD5 - Finalise rainfall hydrology

The sensed-checked local information on drainage rates and infiltration/runoff coefficients provided by LLFAs was used to update the parameters of the rainfall-runoff methods described in Sections 3.2.2 and 3.2.3.

3.6 MOD6 - Run final JFlow+ models

Using the inputs derived and finalised in the previous steps, the final model runs were undertaken for each of the nine rainfall scenarios. As requested in the Project SoR, the following outputs were produced for each hydraulic model run:

Maximum flood depth, velocity and hazard rating

Direction of flow at maximum hazard rating and at maximum velocity.

To ensure that the peaks in flood depth, velocity, and hazard rating are captured during the simulation, a model simulation period of the storm duration plus 3 hours was used throughout (i.e. if the storm duration was 6 hours then the hydraulic model was run for 9 hours).

As part of the quality assurance (QA) process, model diagnostic outputs such as numerical convergence and mass balance were checked. Where QA checks did not satisfy predetermined criteria based on the contractor project team's extensive experience of similar modelling studies, the model run was flagged for user intervention and re-run when resolved.

On project completion, a full set of electronic model input and output files in widely supported formats (e.g. .xls, .csv or .txt) will be provided for re-use in JFlow+ or alternative 2D hydraulic models.


4 Flood mapping GIS post-processing is required to improve the usability of the model output data for identifying areas and receptors at risk of surface water flooding. Here the specification for the flood mapping (i.e. post-processing thresholds, size of wet and dry ‘islands’ and topology rules) was developed in close consultation with future users of the data. A number of initial options were developed during face-to-face sessions with Environment Agency technical specialists and the three pioneer LLFAs, which were then discussed and voted on by attendees at the nine Capacity Building Workshops hosted by Defra in November 2012. As well as reaching a strong consensus on what the final product should look like, the process also highlighted the appetite of risk management authorities to re-use and develop further the models and parameterisation datasets within local studies.

The Project SoR also required pragmatic, easily understood methods for validating and rating the confidence in the final flood map products to be developed. It is intended that the default confidence scores produced via the automated methods described in Section 4.2 will be confirmed or updated by LLFAs using locally-held flood risk knowledge and data during the subsequent review phase.

4.1 MAP1 - Post-process depth, velocity and hazard rating

model outputs

4.1.1 Process overview

The flood map production process takes the JFlow+ modelling outputs and processes them into usable maps to aid decision making processes.

The JFlow+ output data can be summarised by output types, rainfall probabilities and storm durations (see Figure 12). In total, when combined together, there are 45 different outputs that require post processing.

Figure 12 Flood mapping data outputs


The flood mapping process has been split into four stages to facilitate the efficient post process of data. These stages are shown in Figure 13 and are discussed in more detail in subsequent sections:

1. Blend (Section 4.1.2)

2. Clean (Section 4.1.3)

3. Clip (Section 4.1.4)

4. Output (Section 4.1.5)

Figure 13 Overview of flood mapping process

In addition to splitting the post-processing into stages, the maps are produced on a 50km x 50km tile basis (as per the OS 50k reference frame, i.e. SK1, SK2, SK3, SK4, etc) to facilitate the transfer of the maps and to keep the maps to a practical size for end users. There are 93 50km x 50km tiles which cover England and Wales.

Map processing has been undertaken using the tools and functionality built into ESRI ArcGIS. The process was automated using the ArcPy scripting language, which was parallelised to increase processing performance. The entire process was further automated through a job scheduler which controlled the processing of maps and the allocation of jobs to processing machines.

4.1.2 Step 1 - Blending grids together

Combination (blend) of JFlow model tiles

Each JFlow+ model is run on a 6km x 6km tile which includes a 0.5km overlap of results with neighbouring tiles. This generates an overlap with adjoining tiles so that the results from each model can be combined.

Step 1 of the mapping process is to combine (blend) the JFlow+ model outputs for each OS 50km tile. The process is described in Figure 14. For the blend process the model output tiles are selected for the OS tile to be processed. In addition a set of surrounding edge model tiles from adjacent OS tiles are also selected to ensure that a smooth blend between OS tiles can be achieved.


Figure 14 Overview of Step 1 - Blending grids together

The approach to blending grids together varies depending on the output type:

Depth, velocity and hazard output types:

To create a complete depth, velocity and hazard flood maps, we use a weighted blend on the overlapping areas to join results together. The output cell value of the overlapping areas is a blend of values that overlap; this blend value is determined from an algorithm that is weight based and dependent on the distance from the pixel to the edge within the overlapping area.

Flow direction output types:

For the creation of flow direction grids, a blend methodology is not appropriate. A flow direction of 359o and 1o would result in an average of 179o in the central portion of the overlap, or some weighted variation in other parts. A quadrant based averaging approach is instead used, which uses different formulas based on the quadrant where the flow directions in each overlapping tile lie. Figure 15 shows the quadrants and Table 2 shows the formulae used to calculate the average flow direction in each case.


Figure 15 Quadrants for flow direction analysis

Table 2 Quadrant analysis formula

Scenario Layer 1 (L1) quadrant

Layer 2 (L2) quadrant

Direction calculation

1 1 1 (L1 + L2) / 2

2 1 2 (L1 + L2) / 2 3 1 3 If L2 - L1 < 180:

(L1 + L2) / 2 Else: L2 + ((360 – L2 + L1) / 2)

4 1 4 If ((L1 + (360 – L2)) / 2) + L2 >= 360: ((L1 + (360 – L2)) / 2) + L2 – 360 Else: (L1 + (360 – L2)) / 2 + L2

5 2 1 (L1 + L2) / 2

6 2 2 (L1 + L2) / 2 7 2 3 (L1 + L2) / 2

8 2 4 If L2 - L1 < 180: (L1 + L2) / 2 Else: L2 + ((360 – L2 + L1) / 2) - 360

9 3 1 If L1 - L2 < 180: (L1 + L2) / 2 Else: L1 + ((360 – L1 + L2) / 2)

10 3 2 (L1 + L2) / 2

11 3 3 (L1 + L2) / 2 12 3 4 (L1 + L2) / 2

13 4 1 If ((L2 + (360 – L1)) / 2) + L1 >= 360: ((L2 + (360 – L1)) / 2) + L1 – 360 Else: (L2 + (360 – L1)) / 2 + L1

14 4 2 If L1 - L2 < 180: (L1 + L2) / 2 Else: L2 + ((360 – L2 + L1) / 2) - 360

15 4 3 (L1 + L2) / 2

16 4 4 (L1 + L2) / 2


Each of the outputs of the blending process is stored in a master spatial database. In total the process creates 45 blended output type grids. The file naming convention for the outputs is:

XXX_YYY_HHH_T_MOS

Where:

XXX = OS 50k tile reference, e.g. TR3

YYY = P1000, P100, P30

HHH = 1HR, 3HR, 6HR

T = D (depth), V (velocity), H (hazard), VDMH (flow direction at maximum hazard), VDMV (flow direction at maximum velocity)

Example:

SD1_P1000_1HR_D_MOS

TR3_P100_3HR_V_MOS

Maximum outputs

For each of the blended output types and rainfall probability grids, a grid of maximums across the different storm durations is calculated to identify the worst case result in every grid cell. These maps are then clipped in Step 3 of the process to give the final outputs which appear on the LLFA review website (www.ufmfsw.com).

For the depth, velocity and hazard grids, a simple cell by cell maximum is calculated.

For the flow direction grids, the maximum value for each grid cell is selected from the same storm duration that produced the worst case magnitude result.

In total the process creates 15 maximum output type grids. The file naming convention is:

XXX_YYY_MAX_T_MOS

Where:


YYY = P1000, P100, P30


Example:

SD1_P1000_MAX_D_MOS

TR3_P100_MAX_V_MOS

http://www.ufmfsw.com/


Critical storm duration

As part of the flood mapping process, critical storm duration outputs are generated. These are useful in providing a visual check of the where the values from the maximum outputs are obtained from and they are also used in the creation of the maximum flow direction grids.

The critical storm duration process is undertaken on the depth, velocity and hazard output types and in total it generates 9 maximum output type grids. The file naming convention for the outputs is:

XXX_YYY_CSD_T_MOS

Where:


YYY = P1000, P100, P30

T = D (depth), V (velocity), H (hazard)

Example:

SD1_P1000_CSD_D_MOS

TR3_P100_CSD_V_MOS

4.1.3 Step 2 - Creation of cleaned outline

Threshold grids

The outputs which are generated from Step 1 contain complete coverage of flooding and do not possess a conventional wet/dry flood outline. To create an outline, a "cookie cutter" approach is used where a thresholded depth, velocity or hazard rating grid is used as a mask to extract results from the other two datasets. A number of possible options were considered:

where surface water flooding is greater than 0.1m deep, extract the corresponding velocity and hazard rating results

where surface water flooding is quicker than 0.25m/s, extract the corresponding depth and hazard rating results

where surface water hazard rating is greater than 0.555 (see Table 3 below), extract the corresponding depth and velocity results

where surface water hazard rating is greater than 0.575 (see Table 3 below), extract the corresponding depth and velocity results

where surface water hazard rating is greater than 0.75 (i.e. in the "danger for some" category, see Table 3 below), extract the corresponding depth and velocity results


Table 3 Hazard to People classification using FD2320/FD2321 hazard rating formula (Depth x (Velocity + 0.5) + Debris Factor, where debris factor = 0.5 if

depth <= 0.25m and debris factor = 1 if depth > 0.25m)

0.05 0.10 0.20 0.25 0.30 0.50 0.75 1.00 2.00 3.00 4.00 5.00

0.05 0.528 0.555 0.610 0.638 1.165 1.275 1.413 1.550 2.100 2.650 3.200 3.750

0.10 0.530 0.560 0.620 0.650 1.180 1.300 1.450 1.600 2.200 2.800 3.400 4.000

0.20 0.535 0.570 0.640 0.675 1.210 1.350 1.525 1.700 2.400 3.100 3.800 4.500

0.25 0.538 0.575 0.650 0.688 1.225 1.375 1.563 1.750 2.500 3.250 4.000 4.750

0.30 0.540 0.580 0.660 0.700 1.240 1.400 1.600 1.800 2.600 3.400 4.200 5.000

0.40 0.545 0.590 0.680 0.725 1.270 1.450 1.675 1.900 2.800 3.700 4.600 5.500

0.50 0.550 0.600 0.700 0.750 1.300 1.500 1.750 2.000 3.000 4.000 5.000 6.000

0.75 0.563 0.625 0.750 0.813 1.375 1.625 1.938 2.250 3.500 4.750 6.000 7.250

1.00 0.575 0.650 0.800 0.875 1.450 1.750 2.125 2.500 4.000 5.500 7.000 8.500

2.00 0.625 0.750 1.000 1.125 1.750 2.250 2.875 3.500 6.000 8.500 11.000 13.500

3.00 0.675 0.850 1.200 1.375 2.050 2.750 3.625 4.500 8.000 11.500 15.000 18.500

4.00 0.725 0.950 1.400 1.625 2.350 3.250 4.375 5.500 10.000 14.500 19.000 23.500

5.00 0.775 1.050 1.600 1.875 2.650 3.750 5.125 6.500 12.000 17.500 23.000 28.500

Hazard Rating Key

Less than 0.75

0.75 to 1.25

1.25 to 2.0

More than 2.0

Danger for most – includes the general public

Danger for all – includes the emergency services

Depth (m)

Velocity (m/s)

Danger for some – includes children, the elderly and the infirm

Very low hazard – caution

Hazard to People Classification

In consultation with stakeholders, it was decided that the maps should be thresholded based on a hazard rating of 0.575. This covers shallow flows ≥ 1m/s and all depths ≥ 0.15m. This value was used to create a polygon from the hazard output type rasters.

To aid the processing of this data individual isolated pixels ≤ 4m2 (1 cell) were removed

from the analysis. This process creates 3 feature classes per OS 50k tile and they are called:

XXX_YYY_MAX_H_MOS_MASK

Where


YYY = P1000, P100, P30

Example:

SD1_P1000_MAX_H_MOS_MASK

TR3_P100_MAX_H_MOS_MASK

Clean up of outline

Several algorithms have been used to clean the flood polygons generated from the hazard threshold:

Fill of areas smaller than 48m2 (Figure 16)

Removal of polygons less than 96m2 unless they touch a larger than 96m2 area polygon (Figure 17)

Polygons kept where they touch other polygon(s) where their combined areas are greater than 96m2 (Figure 18)


Figure 16 Areas filled in (blue)

Figure 17 Areas removed (red)

Figure 18 Areas on diagonal with an area > 96m2


This process creates 3 feature classes per OS 50k tile and they are called:

XXX_YYY_MAX_H_MOS_OUTLINE

Where:


YYY = P1000, P100, P30

Example:

SD1_P1000_MAX_H_MOS_OUTLINE

TR3_P100_MAX_H_MOS_OUTLINE

Figure 19 Overview of Step 2 - Creation of cleaned outline


4.1.4 Step 3 - Clipping of grids to the outline

Clipping of rasters

A constant raster clipping mask is generated from the flood outline and this is clipped to the OS 50k tile boundary and the coastline. The mask is then used to clip the blended grids for maximum depth, velocity, hazard and flow direction x 2, and critical storm duration for depth, velocity and hazard.

This process is shown in Figure 20. It creates 24 grids for each tile and the clipped grids are called:

XXX_YYY_ZZZ_T_MOS_CLIP

Where:


YYY = P1000, P100, P30

ZZZ = MAX or CSD


Example:

SD1_P1000_MAX_V_MOS_CLIP

TR3_P100_MAX_VDMH_MOS_CLIP

Resampling of flow direction

Some thinning of the 2m data is required to produce useful maps of flow direction at different zoom scales. Figure 20, Figure 21 and Figure 22 show that at:

1:500 scale a 2m grid is appropriate

1:1,000 scale a 5m grid is appropriate

1:3,000 scale a 10m grid is appropriate


Figure 20 Flow direction at 1:500 on a 2m grid

Figure 21 Flow direction at 1:1,000 on a 5m grid

Figure 22 Flow direction at 1:3,000 on a 10m grid


On completion of clipping, the flow direction grids are re-sampled from 2m to 5m, 10m and 20m grids for their anticipated future use. This creates a further 18 grids for each tile and they are called:

XXX_YYY_MAX_T_MOS_CLIP_SSS

Where:


YYY = P1000, P100, P30


SSS = resampled grid resolution, either 5M, 10M, 20M

Example:

SD1_P1000_MAX_ VDMV_MOS_CLIP_5M

TR3_P100_MAX_VDMH_MOS_CLIP_20M

Figure 23 Overview of Step 3 - Clipping of grids to the outline


4.1.5 Step 4 - Creation of outputs

Flood maps are output from the master spatial database into the following formats:

ESRI File GeoDatabase (version 9.3.2)

GeoTIFF images of clipped grids (compatible in either ESRI or MapInfo software)

ASCII files of clipped grids (compatible in either ESRI or MapInfo software)

Figure 24 shows an overview of the datasets that are outputted to each format. These 117 files are produced on an OS 50k tile basis (i.e. SK1, SK2, SK3, SK4, etc). In summary there are:

27 x blended raster grids of depth, velocity, hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities and 1hr, 3hr and 6hr storm durations

9 x maximum grids of depth, velocity, hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities

9 x critical storm duration grids of depth, velocity, hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities

18 x blended raster grids of direction of flow at maximum velocity and direction of flow at maximum hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities and 1hr, 3hr and 6hr storm durations

6 x maximum grids of direction of flow at maximum velocity and direction of flow at maximum hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities

3 x hazard polygon masks for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities

3 x cleaned hazard polygon masks for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities

24 x clipped 2m grids of depth, velocity, hazard, direction of flow at maximum velocity and direction of flow at maximum hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities and 1hr, 3hr and 6hr storm durations

18 x resampled clipped grids of direction of flow at maximum velocity and direction of flow at maximum hazard for the 1 in 30, 1 in 100 and 1 in 1,000 probabilities at 2m, 5m and 20m

Due to the number of grids and the size of files, each dataset occupies a sizeable amount of storage space. The average file sizes for an OS tile with complete coverage are:

ESRI File GeoDatabase - 137Gb

GeoTIFF images of clipped grids - 4Gb

ASCII files of clipped grids - 83Gb

Approximately 15Tb of storage space is required to hold the complete set of maps.


Figure 24 Overview of Step 4 - Creation of outputs

4.2 MAP2 - Undertake flood map validation and confidence

rating

The aim of the validation and confidence mapping work was to provide users with guidance and information which allows them to view the map with an appropriate level of confidence. This was a technically challenging aspect of the project. Previous projects have not provided a clear lead in how surface water model predictions can be validated to assess confidence in their outputs, and as noted in the project start up meeting "effectively communicating the level of confidence/certainty in the new mapping will be difficult".

The system used for classifying confidence is based on a star rating from 1 to 5 stars, with 1 star representing lowest confidence, and 5 stars representing the highest. For the national scale flood mapping, the star rating is expected to be in the range 1-3, with a 4 star rating possible if detailed local modelling is included in the Updated Flood Map for Surface Water. This means future improvements in national scale mapping and local modelling can be included in the same system as confidence moves towards a 5 star rating.

The star rating system is also being used as part of a project to map confidence in maps of flooding from rivers and the sea, as described in Environment Agency (2013b). Using a single system for flooding from rivers, the sea and surface water will make understanding confidence easier for users and allow them to understand the differences in confidence in maps of different sources of flooding.


A star rating has been derived for the national scale mapping. This varies between locations, and is produced as a grid of 50m x 50m cells. The confidence star rating is based on the quality of input data, topography (steep/flat) and the land cover type (urban/rural). Validation has been carried out for the 3 pioneer locations, to confirm that the national scale mapping is capable of adequately reproducing observed flooding. More details of how the star rating has been derived and validation results can be found in Environment Agency (2013c).

Users will have the opportunity to review and change the star rating, and provide an appropriate star rating for local modelling uploaded as part of the Updated Flood Map for Surface Water. Detailed guidance for users on how to do this, and appropriate uses of the surface water flood maps based on the confidence star rating can be found in Environment Agency (2013a).

An overview of the confidence rating system, and the validation process used to inform it, are given in the following sections.

4.2.1 Confidence rating

Based on the findings of the FMfSW Improvements Pilot Studies (Halcrow/JBA Consulting, 2012), the method recognises that the sources of uncertainty in model outputs are in two broad classes: related to the effective rainfall input into the model, and related to how water is routed by the model. These uncertainties are used to derive the confidence star rating; where uncertainty is higher, the confidence star rating will be lower.

Effective rainfall uncertainty arises from uncertainty in the total rainfall (derived from FEH depth-duration-frequency relationships) and the infiltration/drainage model. Uncertainty will be greater in urban areas where no local drainage information has been supplied, where the default drainage rate of 12 mm/hr is used. For areas where local drainage capacity information has been provided, or for rural areas which are unaffected by drainage systems, uncertainty is lower.

Uncertainty in how water is routed by the model is based on the key influences of DTM source (LIDAR or NEXTMap), topographic steepness, and influence of buildings. Uncertainty will be higher in areas with NEXTMap-derived DTM, because the NEXTMap data has less vertical accuracy than LIDAR. Uncertainty will be higher in flatter topography because errors in the DTM will be greater in relative terms than the underlying topography which influences patterns of flooding; for steeper areas DTM errors matter less because the topographic variations are larger. Uncertainty will be higher in rural areas where the chief control on flow paths is the DTM, and lower in urban areas where building and street patterns have a significant influence on flow paths.

Table 4 Combination of model routing and effective rainfall uncertainties into final model output uncertainty - example values

Urban LLFA Urban Default Rural

DTM NEXTMap Flat Steep

LIDAR Flat Steep


In practice, these effects have been combined in a scoring system, based on sensitivity analysis and the outputs of previous surface water mapping projects (see Environment Agency 2013c) for more details). The inputs to the scoring system are:

Polygons of LIDAR and NEXTMap coverage

Polygons of urban areas derived from the Office for National Statistics England and Wales Urban Area Boundaries (2001) dataset

A raster map of topographic steepness, derived by calculating the topographic slope, in degrees, from a 25m resolution DTM, then taking the median value of this slope over 5km x 5km tiles. Steep areas are defined as those giving a median value greater than 2°. This gives a map which is representative of steeper and flatter areas across England and Wales.

Polygons of areas where LLFAs have provided local drainage information

These data sources, and the results of applying the scoring system, are shown in Figure 25. The map reflects what would be expected from our understanding of the factors affecting confidence in national scale map outputs. There are large areas of 3 star rated outputs in Wales, the West Country and the North West, where topography is steeper and there is good LIDAR coverage. There are large areas of 1 star output in the South East, where NEXTMap coverage coincides with flatter topography.


Figure 25 Data used to derive the confidence star rating and final map output


4.2.2 Validation

The purpose of the validation component of this project was to assess how well the new national scale map reproduces true flooding and how the well the discrepancies between the national scale map and observed flooding are reflected by the confidence rating. This process has been limited by difficulty of accessing good quality validation data sets; finding validation data for the 10 locations required in the Project SoR was not possible.

Effort has instead been focussed on producing a robust but easily followed local review process, to allow LLFAs and other partners to provide feedback on the quality of national scale mapping outputs based on their understanding of local flooding issues. The review process is described in more detail in Environment Agency (2013a).

Limited validation has been undertaken for the 3 pioneer locations of Greater Manchester, Gloucestershire and Rhondda Cynon Taff (RCT), using both qualitative methods and quantitative analysis of flooded properties identified by the national scale map. The results are described fully in the separate validation report (Environment Agency, 2013c); an overview and summary of the conclusions is given below. Evidence for improvements in the national scale map outputs over previous versions (AStSWF and FMfSW) is also included.

Table 5 summarises the validation data available for the three pioneer locations. Gloucestershire and RCT have some good quality evidence; data for Greater Manchester is less useful, with only data from Bolton being useable.

Table 5 Validation evidence for the pioneer locations

Location Data Source Number and Date

Greater Manchester Polygons of flooding recorded in Bolton, with source noted as surface water

62 records from 1946-2009

Polygons of wet spots recorded in Bolton

29 records, no dates

Gloucestershire Points of recorded flood incidents

3424 possible surface water incidents, summer 2007

Output grid from InfoWorks ICM model for 1 in 100 probability event

Approximately equivalent to summer 2007 event

Rhondda Cynon Taff Point records of flooded buildings

946 points from 2002-2011

Points records of external flooding

1874 points from 2002-2011


Greater Manchester

The lack of good quality evidence for Greater Manchester makes it difficult to draw conclusions about model performance. The national scale map correctly identifies some flooding locations, but not all and there are areas of significant flooding in the map not associated with a known flooding location. An example is shown in Figure 26; the results are similar for the rest of Bolton.

Figure 26 Comparison between known flooding locations (surface water in purple polygons, unknown sources in green) and national scale map outputs (1

in 30 probability) for two locations in Greater Manchester

There is little evidence for an improvement in results compared to previous generations. The results for FMfSW and the latest national scale map are very similar (see Figure 27), predicting isolated areas of flooding which roughly coincide in the two maps. This is to be expected, given the similarity of the modelling approaches used in the two generations. AStSWF predicts much more flooding, as is expected since it makes no allowance for drainage, and uses a more extreme design storm.

Most of Bolton and Manchester are assigned a confidence rating of 3*, with some areas of 2*, by the confidence rating method described in the previous section. The validation data is of insufficient quality to assess whether this is appropriate.


Figure 27 Comparison between AStSWF (top, 1 in 200 probability), FMfSW (middle, 1 in 30 probability) and latest national scale map (bottom, 1 in 30

probability) for Manchester city centre


Gloucestershire

National scale map outputs have been validated against a high quality InfoWorks ICM model of Cheltenham. This model represents sub-surface drainage through a linked sewer model, with hydraulics based on physical properties (pipe network location and dimensions) rather than an assumed level of performance. It should therefore provide a good test of national scale map quality as independent validation evidence.

Results are shown in Figure 28. The correspondence between the two models is close, and both pick out groups of buildings which were reported as flooding in 2007 (which had a probability of approximately 1 in 100). The flood extent for the ICM outputs appears larger, but this is mainly due to large areas of very shallow water which are not regarded as significant.

A direct comparison of depths predicted by the two models has also been made. The national scale mapping predicts significantly deeper water than InfoWorks, and the two sets of point depth predictions match approximately within a factor of 2. The two models also predict approximately the same areas of lower and higher velocity (Figure 29).

Figure 28 Comparison between latest national scale map (top, 1 in 100 probability) and InfoWorks ICM (bottom, 1 in 100 probability). Properties flooded

in 2007 are shown in red.


Figure 29 Velocities predicted by the InfoWorks ICM (top) and national scale mapping models (bottom) in Cheltenham, displayed in two classes: blue <1 m/s

and red >1 m/s

The dataset of properties flooded in summer 2007 allows a quantitative comparison of properties predicted as flooding to be made, with the results summarised in Table 6 for floods recorded in Cheltenham. Properties were identified as flooded by buffering the building footprint by 2m; taking the maximum depth around the buffered footprint; and thresholding this at 0.3m. Property counts are sensitive to buffering and thresholds applied, and further investigation of property counting methods is being undertaken at the time of writing.

The national scale map correctly identifies 76 out of 100 properties that were observed to flood, but there is also significant overprediction with a further 813 properties predicted as flooding which were not reported as doing so. This result is consistent with previous studies, which also found significant overestimation of property counts by factors of 10 or more. The national scale map and InfoWorks ICM perform similarly; InfoWorks ICM identifies fewer of the flooded properties and halves the number of properties mapped as flooded that have no record of flooding.

The previous versions of the Environment Agency's surface water flood maps show similar levels of performance, both identifying >80% of flooded properties with significant overprediction in the number of flooded properties. In the case of FMfSW this will be partly due to it modelling a 1 in 200 probability event rather than the 1 in 100


event observed. This indicates that identifying flooded properties is relatively easy for this site.

The area covered by the InfoWorks ICM model has been assigned a confidence rating of 3 stars for the majority of its area. This agrees well with the outputs of the validation study, which indicates that the national mapping should be associated with a high confidence. For the wider Cheltenham area where property count validation has been carried out, the confidence rating is approximately half 2* and half 3*. These confidence ratings are broadly appropriate given the number of properties correctly identified.

In summary, the national scale map does well in reproducing observed patterns of flooding (including at individual properties), with performance equalling that of a more detailed local model. The map appears to overestimate numbers of flooded properties by a factor of 10.

Table 6 Results of property validation for flooded properties recorded in 2007 summer floods in suburb of Cheltenham covered by InfoWorks ICM model

Hits Misses Mapped but no record of flooding

Total predicted

Total observed

National scale map 1 in 100

76 24 813 889 100

InfoWorks ICM 1 in 100

56 44 413 469 100

AStSWF Less category

84 16 995 1079 100

FMfSW 1 in 200

94 4 1680 1774 100

Rhondda Cynon Taff

Surface water flood incidents have been recorded over a period of approximately 10 years in RCT, and these should correspond most closely with the 1 in 30 probability event. However, Figure 30 shows that there is little correlation between the national flood map and the locations of flood incidents, which are scattered throughout the urban areas with no obvious clustering. The rest of RCT shows similar patterns.

Table 7 shows the results for property count validation for RCT. All 3 generations of modelling show similar performance, identifying ~1/3 of the 1,000 flooded buildings correctly. The models identify 20-30 thousand properties in the 1 in 30 probability flood outline, meaning that on average ~10 thousand properties might be expected to flood in a 10 year period. The models therefore appear to be overpredicting the number of flooded properties by a factor of ~30 (although the exact figure may change with any updated property counting method as discussed in the previous section).

The majority of RCT is assigned a confidence rating of 3 stars, reflecting the steep topography and LIDAR DTM. The validation outputs indicates that this is not appropriate, given the discrepancies between observed and predicted patterns of flooding.

In summary, the performance of the national scale map in RCT is not as good as for Cheltenham, either in terms of predicting patterns of flooding, or property counts.


Table 7 Results of property validation for internal flood incidents recorded in RCT, 2002-2011

Figure 30 National scale map for 1 in 30 probability, with reported flood incidents as red points, for two locations in RCT

Hits Misses FA Total predicted

Total observed

National scale map 1 in 30

367 572 26326 26693 939

AStSWF Less category

319 620 24586 24905 939

FMfSW 1 in 30

294 645 21359 21653 939


4.3 MAP4 - Deliver national scale surface water flood map

At the end of the project, the contractor team will deliver all national scale surface water flood mapping output data and default confidence ratings for the coverages specified in the Project SoR to the Environment Agency. All data will be supplied with appropriate EU INSPIRE-compliant metadata.

The contractor team will also provide:

A full set of electronic model input and output files in JFlow+ format

A full set of electronic model input files in widely supported formats (e.g. xls, .csv or .txt) for re-use in alternative 2D hydraulic models by/for you, LLFAs or their contractors. All model grids (e.g. DTM, rainfall-runoff, Manning's n) will be supplied in ESRI File GeoDatabase, GeoTIFF and ASCII raster formats.


5 Comparison of Environment Agency surface water flood mapping methodologies A summary of the methodological differences between the AStSWF, FMfSW and the new national scale surface water flood mapping is provided in Table 8 below.

Table 8 Comparison of Environment Agency surface water flood mapping methodologies

Item AStSWF FMfSW uFMfSW

Modelling approach

Hydraulic modelling

2D overland flow modelling



Hydrological modelling

Direct Rainfall approach with no allowances made for the sewer network and infiltration:

Direct Rainfall approach with allowances for the sewer network and infiltration:

Direct Rainfall approach with allowances for the sewer network and infiltration: - England and Wales classified as either rural or urban based on manmade area coverage as defined in OS MasterMap Topography Layer on 250m x 250m grid. - In rural areas, ReFH losses model is applied and is parameterised using NSRIs ‘SERIES Hydrology’ data. - In urban areas, a default loss rate of 12mm/hr and 70% runoff coefficient is applied. - Runoff parameters adjusted by local drainage information where available

Software JFlow-DW (diffusion wave-based)

JFlow-DW (diffusion wave-based)

JFlow+ (Shallow Water Equation-based)



Hydraulic model parameters

Digital elevation model

Infoterra bare earth LIDAR and GeoPerspectives DTM provided in 2007.

Bare earth LIDAR/NEXTMap composite DTM at 5m horizontal resolution provided by Geomatics in 2010.

Bare earth LIDAR/NEXTMap composite DTM at 2m horizontal resolution provided by Geomatics in 2012.

Grid size 5m regular grid 5m regular grid 2m regular grid

Representation of buildings

Not represented Represented explicitly as unfloodable objects in the DTM. Building footprints, as defined in OS MasterMap data, raised by 5m.

- Use of an approximately 0.3m “up-stand” and depth-varying roughness coefficients within the OS MasterMap building footprint (as per Drain London specification). - Complex rules were developed to ensure that building footprints did not “cut” into the DTM but did always protrude at the upstream face of the building.

Representation of structures

DTM was manually edited in over 5,000 locations to improve flow through ‘flyover’ features, such as rail/road embankment culverts, bridges etc.



Representation of other features

N/A N/A Areas of the DTM covered by the road network were lowered by 0.125m.

Manning’s n values

0.1 0.1 rural, 0.03 urban Varied by OS MasterMap Topography Layer Feature Code as per Drain London specification

Mass balance Not recorded ±1% 0% (JFlow+ is mass conservative by the nature of their numerical formulation)

End of simulation criteria

Dynamic stopping condition. Models will stop running if the number of wet cells is unchanged over a 1 hour period.

Dynamic stopping condition. Models will stop running if the number of wet cells is unchanged over a 1 hour period.

Rainfall event duration + 3hrs



Rainfall and hydrology

Design rainfall FEH depth-duration-frequency parameters defined on a regular 5km grid: - 1 in 200 rainfall probability. - A storm duration of 6.25hrs was used for all scenarios. - 50% summer storm profile used - No aerial reduction factor applied

FEH depth-duration-frequency parameters defined on a regular 5km grid: - 1 in 30 and 1 in 200 rainfall probabilities. - A storm duration of 1.1hrs was used for all scenarios. - 50% summer storm profile used - No aerial reduction factor applied

FEH depth-duration-frequency parameters defined on a regular 5km grid: - 1 in 30, 1 in 100, 1 in 1,000 rainfall probabilities. - Storm durations of 1, 3 and 6hrs were used for all scenarios. - 50% summer storm profile used - No aerial reduction factor applied

Inflows from outside of study area

None None None

Downstream boundary conditions

Free overflow Free overflow Free overflow

Validation of results and sensitivity testing

Validation High-level evaluation of the potential uses and quality of the AStSWF. Some qualitative comparison against historical observations and local modelling data (see Halcrow, 2008).

Undertaken for 11 areas using historical observations and local modelling data (see Halcrow, 2010).

Undertaken for three pilot areas using historical observations and local modelling data (see Environment Agency, 2013c).

Sensitivity testing

None None None


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List of abbreviations ARF Areal Reduction Factor

AStSWF Areas Susceptible to Surface Water Flooding

BFIHOST Baseflow Index (estimated from soil type)

DDF Depth-Duration-Frequency

DTM Digital Terrain Model

FDGiA Flood Defence Grant-in-Aid

FEH Flood Estimation Handbook

FMfSW Flood Map for Surface Water

FSR Flood Studies Report

HOST Hydrology Of Soil Types

IfSAR Interferometric Synthetic Aperture Radar

IUD Integrated Urban Drainage

LIDAR Light Detection And Ranging

LLFAs Lead Local Flood Authorities

LTIS Long Term Investment Strategy

NRD National Receptors Dataset

OS Ordnance Survey

QA Quality Assurance

ReFH Revitalised Flood Hydrograph

SoR Statement of Requirements

SPRHOST Standard Percentage Runoff (estimated from soil type)

SWMP Surface Water Management Plan

WaSCs Water & Sewerage Companies

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