Physically based river basin modelling within a GIS: the LISFLOOD model

HYDROLOGICAL PROCESSESHydrol[ Process[ 03\ 0870Ð0881 "1999#

Copyright © 2000 John Wiley & Sons, Ltd.Received 6 March 1998

Accepted 1 July 1999

Physically based river basin modelling within a GIS: theLISFLOOD model

A[ P[ J[ De Roo\0� C[ G[ Wesseling1 and W[ P[ A[ Van Deursen1

0 Joint Research Centre\ Space Applications Institute\ ARIS Unit\ Natural Hazards Project\ TP 152\ 10919 Ispra "Va#\ Italy1 PCRaster Environmental Software\ P[O[ Box 316\ 2499 AK Utrecht\ The Netherlands

Abstract]Although many geographical information systems "GISs# are very advanced in data processing and display\

current GIS are not capable of physically based modelling[ Especially\ simulating transport of water and

pollutants through landscapes is a problem in a GIS environment[ A number of speci_c routing methods are

needed in a GIS for hydrologic modelling\ amongst these are the numerical solutions of the Saint!Venant

equations\ such as the kinematic wave approximation for transport of surface water in a landscape[ The

PCRaster Spatial Modelling language is a GIS capable of dynamic modelling[ It has been extended recently

with a kinematic wave approximation simulation tool to allow for physically based water ~ow modelling[ The

LISFLOOD model is an example of a physically based model written using the PCRaster GIS environment[

The LISFLOOD model simulates river discharge in a drainage basin as a function of spatial data on topography\

soils and land cover[ Although hydrological modelling capabilities have largely increased\ there is still a need

for development of other routing methods\ such a di}usion wave[ Copyright Þ 1999 John Wiley + Sons\ Ltd[

KEY WORDS ~oods^ GIS^ PCRaster^ LISFLOOD^ hydrological modelling^ catchment^ kinematic wave

INTRODUCTION

Recently\ dramatic ~ooding occurred in several regions of the world] Bangladesh "0877#\ Vaison La Romaine"France\ 0881#\ Mississippi River "USA\ 0882#\ Meuse "Netherlands\ 0882#\ Piemonte "Italy\ 0883#\ Rhineand Meuse "Netherlands\ Belgium and Germany\ 0884#\ Biescas "Spain\ 0885#\ and the most recent ~oodsof the Oder in The Czech Republic\ Poland and Germany "0886#[ To assess the causes of these and other~ood events\ hydrological modelling at the scale of a large river basin is a very useful tool[

Many modelling approaches exist to simulate ~oods and ~ood runo}[ The choice of the simulationapproach depends on the questions to be answered[ If design is the main aim\ simple models using adequateobserved data may perform better than quasi!physically based models "Pilgrim and Cordery\ 0882#[ However\many simple simulation approaches will not explain the reasons behind a ~ood[ Because our research aimsare investigating the in~uence of land use on ~oods and _nding the source areas that contributed to the~ood\ spatially distributed modelling is needed[ Disadvantages of using empirical methods such as the SoilConservation Service method\ using Curve Numbers\ are that little quantitative information is available onthe database from which it was developed\ and the manner in which this database was used in the developmentmodels "Pilgrim and Cordery\ 0882#[ Also\ the application of this method outside the USA might bequestioned[ Hydrological models such as TOPMODEL "Beven and Kirkby\ 0868# and HBV "Bergstrom\0884^ Lindstrom et al[\ 0886# were not selected here because a large number of the parameters needed to run

� Correspondence to] A[ P[ J[ De Roo\ Joint Research Centre\ Space Applications Institute\ ARIS Unit\ Natural Hazards Project\ TP152\ 10919 Ispra "Va#\ Italy[

A[ P[ J[ DE ROO\ C[ G[ WESSELING AND W[ P[ A[ VAN DEURSEN

Copyright Þ 1999 John Wiley + Sons\ Ltd[ Hydrol[ Process[ 03\ 0870Ð0881 "1999#

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those models need to be found through calibration[ Therefore\ as much as possible\ a physically based andspatially distributed approach is chosen using input parameters that can be measured[

Physically based modelling at the scale of an entire river basin requires large input databases[ Therefore\a geographical information system "GIS# is a very useful environment modelling because of its advantagesof data storage\ display and maintenance "De Roo et al[\ 0878^ Burrough and McDonnell\ 0887#[ Thus\linking or integrating models with a GIS provides an ideal environment for modelling processes in alandscape[ Three approaches exist for modelling within a GIS environment] loose coupling\ tight couplingand embedded coupling "Wesseling et al[\ 0885#[ In loose coupling the GIS is used to preprocess the spatialdata into the desired model input!_le format and post!process the model output\ such as used by De Roo et

al[ "0878#\ De Roo "0885# and Kite et al[ "0885#[ In tight coupling models and GIS\ model input andoutput can be addressed directly by the GIS[ In embedded coupling the model is written in an integratedprogramming language[ The advantage of this is that the user can construct his or her own models asrequired[ However\ not many GISs are capable of physically based modelling[ Some GISs have somemodelling capabilities\ such as the AML language and the cell!based modelling tools of the ARC:INFOGIS "such as the FlowAccumulation command# and the drain command in the GRASS GIS[ Using thesetools\ some transport modelling can be done\ but no physically based transport modelling[

At present\ most GISs provide catchment analysis tools for delineation of catchments and de_nition ofdrainage networks[ These tools\ however\ are not su.cient for dynamic modelling^ they are not capable ofsolving transport operations through the de_ned network[ Also\ simulations in time are often not possible\such as simulating the catchment response to a rainfall time!series[ Ideally\ for transport modelling oneshould be able to control the amount and velocity of water "or any other material# through the network]one would want to have {access| to the algorithms describing this transport[

Therefore\ there is a need for GIS modelling environment that is capable of physically based transportmodelling[ This paper describes such a GIS and\ as an example\ the LISFLOOD river ~ooding model[ Anearlier example of embedded modelling is the LISEM catchment erosion model\ which is described in DeRoo et al[ "0885#[

THE PCRASTER SPATIAL MODELLING ENVIRONMENT

The PCRaster spatial modelling language "Van Deursen\ 0884^ Wesseling et al[\ 0885# is an extension of theideas behind Map Algebra "Tomlin\ 0889# and the Cartographic Modelling Language proposed by Berryand Tomlin "Berry\ 0876^ Tomlin\ 0889# but includes ideas of iterations used in dynamic modelling andconcepts for de_ning transport equations along the drainage network "in PCRaster this is referred to asLocal Drain Direction network#[ Thus\ PCRaster allows for the development of physically based transportmodels[

PCRaster contains cartographic modelling functions\ dynamic modelling functions and geostatisticalmodelling functions[ Dynamic modelling is modelling of processes over time] new attributes are computedas a function of attribute changes over time[ The cartographic modelling functions consist of point operations"arithmetic and boolean functions#\ neighbourhood operations "operations in windows\ local drain directionoperations\ spread operations\ transport of material over a local drain direction map\ visibility analysis#\area operations "averaging\ clumping\ etc[# and map operations "MAPMAXIMUM\ MAPTOTAL\ etc[#[ Thedynamic modelling functions consist of tools to build a sequential model\ using the operators TIMEINPUT

"retrieving dynamic data from the database#\ TIMEOUTPUT "storing dynamic data in the database# andREPORT "stores the result of an operation in the database#[ The user de_nes grid size and time!step usedin the sequential model scripts[ All values in the grids are updated before the next time!step[ The geostatisticalmodelling functions consist of calculating variograms\ _tting variogram models\ inverse distance and severalkriging interpolation methods\ cross!validation functions\ and functions for Gaussian simulation and indi!cator simulation of spatial patterns of map variables[

PHYSICALLY BASED RIVER BASIN MODELLING


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The syntax of the language is based on mathematical equations\ where each equation assigns the value ofan expression to a single output[ For example\ an equation for sediment transport capacity of a water ~owderived by Kirkby "0865#

G�C×Qd×sin"S#×09−2

is applied to the raster maps CoverFactor "C#\ Flowvolume "Q# and Slope "S# with an exponent "d# of 0=6using the command

TransportCapacity�CoverFactor�FlowVolume��0=6�sin"Slope#�09−2

The command above is a point operation\ where a new value for each location\ a grid cell\ is derived fromdi}erent attribute values on that same location only[ Global operators\ where a new value for each locationis derived from di}erent attribute values on "possible# di}erent locations\ are also modelled as mathematicalfunctions[

The set of available global operators in PCRaster is very extensive in comparison to the range of operationsgenerally considered as Map Algebra[ A rich suite of geomorphological and hydrological operators isavailable[ These include functions for hillslope and catchment analysis\ and the de_nition of topology formodelling transport "drainage# of material over the local drain direction map with routing functions[

PCRaster is further illustrated by an example where the routing function ACCUFLUX is used to calculatethe upstream area of one or more catchments[ The operator ACCUFLUX calculates the accumulated amountof material that ~ows out of the cell into its neighbouring downstream cell[ This accumulated amount is thematerial in the cell itself plus the amount of material in all upstream cells of the cell[ The topological linkagesde_ning upstream and downstream neighbours is given by a local drain direction map as the _rst argument[The second argument of ACCUFLUX is\ in general\ a map with material to be transported\ such as surfacewater as in the next example]

WaterFlow�accu~ux"Ldd\ WaterAmount#^

Dynamic models are constructed by writing scripts containing series of statements "Van Deursen\ 0884#[The language has no explicit structures for iteration\ although dynamic models do iterate in time[ Instead\there are di}erent sections that are controlled by the de_nition of a timer[

The TIMER section regulates the duration and time!slice of the model through three parameters\ START!

TIME\ ENDTIME and TIMESLICE[The INITIAL section sets the initial conditions for the model\ including maps and non!spatial attributes[

These values may be de_ned with one or more PCRaster operations[ The DYNAMIC section de_nes theoperations for each time!step\ which result in a map of values for that time!step[ Each time!step consists ofone or more PCRaster operations\ which are performed sequentially[

The next example shows a simpli_ed model script that incorporates precipitation\ in_ltration and overland~ow[ In this example\ rainfall is the dynamic output[ RainTimeSeries is a timetable with the precipitationmeasured at several meteorological stations[ RainZones does not represent the location of the stations butthe area of pixels for which the measurement at that station is the best estimation of the actual precipitationat each pixel[ Such a map can be computed easily from a station location map\ as done in the initial sectionwith the application of SPREADZONE[ The RainZones map denotes for each pixel the column number inthe timetable[ At each time!step\ TIMEINPUTSCALAR reads a row associated with the current time!stepand returns a map containing the column values as de_ned by RainZones[ Note that the current time!stepis an implicit argument to all dynamic functions\ such as TIMEINPUTSCALAR and TIMEOUTPUT[



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è9 this symbol is typed at the start of a comment linetimer0 17 0^ è 17 time!steps of 5 hours

initialè coverage of meteorological stations for the whole areaRainZones�spreadzone "Rainstations\9\0#^è create a map of in_ltration capacity "mm:5 h#\è based on a soil mapIn_ltrationCapacity� lookupscalar"SoilIn_ltrationTable\SoilType#^

dynamicè add rainfall to surface water "mm:5h#SurfaceWater� timeinputscalar"RainTimeSeries\RainZones#^è compute both the runo} and actual in_ltrationRuno}\ In_ltration�accuthreshold~ux\ accuthresholdstate"Ldd\SurfaceWater\In_ltrationCapacity#^

è output runo} at each timestep for selected locationsreport SampleTimeSeries� timeoutput"SamplePlaces\Runo}#^

The second operation in the DYNAMIC section transports the SurfaceWater over the local drain directionmap Ldd[ This is done with the ACCUTHRESHOLD operator\ which is one of the transport functions"named accu!operators# that accommodate transports\ restricted by certain transport functions[ The ACCU!

THRESHOLD operator transports water only once the in_ltration capacity\ given on the map In_l!trationCapacity\ is exceeded[ It results in two maps] a map with the actual in_ltration "In_ltration# and amap with the amount of overland ~ow "Runo}#[ The maps In_ltrationCapacity is calculated in the initialsection on the basis of a soil map "SoilType# and a cross!table "SoilIn_ltrationTable# that gives for each soiltype the in_ltration capacity[

The last statement of the example creates time!series from certain locations in the Runo} map[ TheSamplePlaces map identi_es these locations[ Each non!zero value in the SamplePlaces map stands for acolumn in the time!series "SampleTimeSeries#[ A PCRaster script that contains a dynamic section does notwrite any results to the database unless the keyword report is added[ This prevents the surplus storage ofintermediate results[ Thus\ typing the report before a statement such as Run!o}�ACCUTHRESHOLDFLUX"[ [ [# creates a stack of raster maps for each time!step[

Other functions in the {accu!family| include functions for limited transport capacity\ or functions fortransporting only a fraction of the input material[ Experiments are carried out with travel!time approachesas well "Van Deursen\ 0884#[

EXTENDING PCRASTER FOR PROCESS!BASED HYDROLOGICAL MODELLING

The {accu!approach| is not su.cient when the time!step of the model approaches the average travel timethrough a cell\ or whenever the number of pixels along the transport path is large[ In this case the PCRastermodels become numerically very unstable[ A more robust approach is needed for solving the di}erentialequations underlying this transport process[ This approach can be found in the traditional algorithms for~ood routing\ such as the kinematic wave approximation and the more complex di}usion wave model "Chowet al[\ 0877^ Fread\ 0882^ Singh\ 0885#[ Recently\ the KINEMATIC wave has been implemented in PCRasterusing the following syntax]

Q1�kinematic"Ldd\Q0\ q\ALPHA\ betaDTSEC\DX#^



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The new discharge Q1 at a certain location within the ~ow network "de_ned with Ldd# is calculated fromthe discharge Q0 from the previous time!step at the same location and using Q1 from neighbouring upstreampixels[ The kinematic wave can be used for hydrological catchments models for overland ~ow routing ofruno} and for channel routing under certain conditions[ The kinematic wave cannot be used in case ofbackwater e}ects in rivers[ It has been developed in PCRaster as the _rst physically based routing methodbefore the more complex methods such as the dynamic wave[

The following code gives an example of the use of the kinematic wave algorithm implemented in aPCRaster model script[ The model script simulates water transport in a catchment with 0999 m pixels\ usinga 04 min "899 s# time!step[ These parameters are given in {BINDING| section of the script[ Available waterfor transport is given in the initial map WHO[ The Flow Network is de_ned in Idd[map\ and slope GRADientis de_ned in slope[map[ Several catchment outlets and suboutlets are given in outlet[map[ As stated in the{TIMER| section\ 499 time!steps are simulated "014 h#[

In the DYNAMIC section of the script the alpha variable is calculated _rst using cross!section information"water depth and pixel size "DX##\ Manning|s n and slope gradient[ When a pixel is a channel cell\ the cellsize DX is replaced by the width of the channel[ The next step is to calculate discharge[ Subsequently thenew discharge is calculated using the KINEMATIC wave function[ Then\ the new water depth is derivedfrom Q1 using the old alpha[ This step is repeated to reduce numerical errors[

The results of this simple ~ood simulation model code are those that contain the {REPORT| statement[Flood hydrographs of several "sub#!outlets are presented in the _le Q1TSS and are plotted "Figure 0#[ Forthis example the drainage network of the Meuse catchment has been used[ The outlet stations for whichoutput is produced represent discharge gauging stations\ such that validation of simulated discharge ispossible[ For any pixel inside the simulated catchment\ a ~ood hydrograph can be generated[ These pixelsshould be de_ned in the OUTFLOWPOINT map called {outlet map|[ A map of overland ~ow and channelwater depths\ represented by the WH variable\ is also produced "Figure 1#[

Using the KINEMATIC wave function allows for a more robust method for solving transport equations[However\ as backwater e}ects are not represented\ other routing methods such as the di}usion wave needto be implemented in the PCRaster GIS to further expand its capabilities for physically based modelling[

THE LISFLOOD MODEL

To investigate the origin and causes of ~ooding and the in~uence of land use\ soil characteristics andantecedent catchment saturation\ the distributed catchment model LISFLOOD has been developed using asimilar approach as shown above[ The LISFLOOD model simulates runo} and ~ooding in large river basinsas a consequence of extreme rainfall[ LISFLOOD is a distributed rainfallÐruno} model taking into accountthe in~uences of topography\ precipitation amounts and intensities\ antecedent soil moisture content\ landuse type and soil type[

The LISFLOOD model is built using the PCRaster Dynamic Modelling Language[ Thus\ the model usessquare grids to represent the landscape[ The grid size is user de_ned and the maximum number of grids islimited by computer memory only[ Also\ the user can de_ne the simulation time!step[ Full basin!scalesimulations can be carried out\ such that in~uences of land use\ spatial variations of soil properties andspatial precipitation di}erences are taken into account[ Processes simulated are precipitation\ interception"Von Hoyningen!Huene method^ Von Hoyningen!Huene\ 0870#\ soil freezing "degree!day method#\ snowmelt"degree!day method#\ evapotranspiration "PenmanÐMonteith method\ as applied in the WOFOST model#"Supit et al[\ 0883^ Van Der Goot\ 0886#\ and the PriestleyÐTaylor method for forested areas#\ in_ltration"SmithÐParlange equation\ Smith and Parlange\ 0866#\ percolation and capillary rise\ groundwater ~ow andsurface runo} "Figure 2#[

For surface runo} and channel routing\ which are routed separately\ a GIS!based kinetic wave routingmodule has been developed within PCRaster\ as described above\ using the Manning equation[ The usercan choose both the spatial and temporal resolution of the model[ The channel routing part contains a



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Figure 0[ Flood hydrographs of several outlets in the Meuse catchment simulated using the PCRaster script[ Water input to the modelis a constant 4 cm layer of water in the entire catchment[ Values do not represent reality



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Figure 1[ A map showing one of the outputs of the PCRaster script] the spatial distribution of water depths in the catchment 0 dayafter {applying| 4 cm of water to the catchment

simple solution to account for ~oodplain storage and ~ow[ Inundation extent will be simulated by extra!polating predicted water levels onto a DEM\ or LISFLOOD will be linked to an existing two!dimensionalor three!dimensional model for detailed ~oodplain routing[ Models built with the PCRaster DynamicModelling Language\ such as LISFLOOD\ have a very ~exible model structure and are easy adaptable bypeople with little programming knowledge[ The LISFLOOD model contains around 599 lines of code"excluding lines with comments#[

The LISFLOOD model uses rainfall\ temperature\ actual vapour pressure\ sunshine duration\ cloud coverand windspeed time!series as input[ Data from a large amount of meteorological stations can be used[ Themeteorological data from stations are spatially interpolated with an inverse distance method using the threenearest stations[ In order to account for orographic in~uences\ rainfall\ temperature and vapour pressuremaps are corrected for altitude using a DEM[



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Figure 2[ Flowchart of the LISFLOOD model

Outputs of LISFLOOD are time!series of discharge at user!de_ned catchment outlets and suboutlets[Furthermore\ _nal maps of source areas of water\ total rainfall\ total interception\ total in_ltration\ etc[\ canbe produced\ as well as a series of maps showing changes in time of certain variables\ such as the waterdepth in each pixel[

APPLICATIONS OF LISFLOOD

In two pilot transnational European river basins the ~ooding problem is investigated using the LISFLOODmodel] the Meuse catchment\ covering parts of France\ Belgium and The Netherlands\ and the Oder basin\covering parts of The Czech Republic\ Poland and Germany[ The Meuse su}ered from extreme ~ooding inDecember 0882 and JanuaryÐFebruary 0884[ The Oder area was ~ooded in July 0886[ In these catchments\LISFLOOD is tested\ calibrated and validated before a detailed analysis of the causes of ~ooding can beexamined[

At present\ a 29!arc second digital elevation model is used as input\ resulting in grids of 0999 by 0999 m[A 64 m resolution commercially available DEM for both the Meuse and Oder catchment is used forcomparison in selected subcatchments and for slope calculations[ The actual stream network is {burned| intothe DEM for better ~ow direction calculations[ Slope gradient is also calculated\ although interpretation ofslope gradient at this scale is di.cult[

Furthermore\ hourly "Belgium\ The Netherlands# and dialy "France# rainfall data of 46 rain gauges areused and interpolated using an inverse distance method using the three nearest stations[ A correction is



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applied for altitude\ to include orographic e}ects[ Daily temperature data from European weather stationsare taken from the MARS meteorological database available at JRC Ispra[

Soils information from the European Soils database "King et al[\ 0886# is used to obtain maps of soildepth and soil texture[ The scale of these data is 0 ] 0 999 999[ Work is in progress to use 0 ] 149 999 soil data[Based on the HYPRES database "Lilly\ 0886^ J[ H[ M[ Wo�sten\ Personal communication\ 0887# maps ofsaturated hydraulic conductivity and soil water content at saturation of topsoil and subsoil are constructed\based on average data for soil texture classes[ In the near future\ values will be based on individual soilmapping units[ At present\ the soils data used in the project have the poorest scale\ and work is in progressto improve that[

The CORINE land use database of Europe "099 m pixels# is used to obtain maps of land!use type[ Adi.cult issue here is how to de_ne and use the in~uence of land use on in_ltration behaviour[ Data in theHYPRES database\ like many other soil physical measurements\ are carried out mostly in vegetation!freeareas[ There is a real lack of knowledge on the speci_c in~uence of vegetation on these soil physicalproperties[ As for now in this study\ a correction factor has been introduced for saturated hydraulicconductivity to account for vegetation in~uences based on limited literature data[ This correction factor"ranging from 9 to 1\ depending on the land use of that pixel# is used to multiply the hydraulic conductivityin each pixel\ which depends on soil texture[

Information on soil cover by vegetation and leaf area index\ used for interception and evapotranspirationcalculations\ are derived from satellite images "NOAA!AVHRR and IRS!0C WiFS images\ with respectively\a 0=0 km resolution and a 079 m resolution#[

VALIDATION OF LISFLOOD

In the Meuse pilot project\ LISFLOOD is validated by stream~ow data of gauging stations along the mainRiver Meuse and data of several tributaries[ These data are obtained from the National and Regional WaterAuthorities in The Netherlands\ France and Belgium[ The SAR images will be used to validate the "maximum#extent of the ~ooded area in the ~oodplain[

The development\ testing\ calibration and validation is in full progress[ First results are shown in Figure3\ showing source areas of water\ and Figure 4\ showing a comparison between measured and simulateddischarge[

No conclusions can be made from Figure 4\ because not all input data are available\ such as cross!sectiongeometry[ However\ the results indicate that the ~oods of 0882 and 0884 can be simulated reasonably wellusing LISFLOOD[ In principle\ the advantage of LISFLOOD over other\ mostly lumped\ models is itspossibility to evaluate spatial di}erences in soil and land!use types[ The LISFLOOD model can use readilyavailable landscape data\ such as digital elevation models\ Corine Land Cover and the European Soilsdatabase[ In order to integrate all these di}erent data sources for large catchments\ working on a regulargrid is a lot easier than working with a triangular tesselation of a _nite!element system[ The size of the grid!database is still reasonable[ However\ the computation time needed for running the code on large catchmentsis still considerable "about 09 h on a 299 Mhz PC#[ Therefore\ at the moment using stochastic proceduresand uncertainty analysis by Monte Carlo type simulations is not feasible

CONCLUSIONS

Embedded models in a GIS environment have many advantages over traditional loosely linked models andGISs[ However\ current GISs lack tools for developing physically based models[ Especially\ simulatingtransport of water and pollutants through landscapes is a problem in a GIS environment[

The PCRaster Spatial Modelling language is a GIS capable of dynamic modelling[ Grid!based modelsusing input time!series data such as changing rainfall amounts with time can be used\ and output time!seriesresults such as discharge data can be produced[ PCRaster contains several concepts for de_ning transport



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Figure 3[ A map showing simulated source areas of water in the Meuse catchment\ during the 0884 ~ood[ Preliminary result\ based onBelgian rain data only and using a preliminary version of LISFLOOD[ Values shown are amount of water "mm# contributing to the

~ood during a 2!month period[ Negative values are net in_ltrating areas

equations along the drainage network[ It recently has been extended with a kinematic wave approximationsimulation tool to allow for physically based water ~ow modelling[ The LISFLOOD model is an exampleof a physically based model written using the PCRaster GIS environment[ LISFLOOD simulates riverdischarge in a drainage basin as a function of spatial data on topography\ soils and land cover[ TheLISFLOOD model is currently being tested and validated in two transnational European catchments] theMeuse and the Oder[ Although hydrological modelling capabilities have largely increased\ there is still aneed for development of other routing methods\such as di}usion wave\ to allow simulating rivers withbackwater e}ects[

ACKNOWLEDGEMENTS

Dr Luca Montanarella "JRC Ispra#\ Christine Le Bas "INRA\ Orleans# and Dr Henk Wo�sten "WinandStaring Centre\ Wageningen# are thanked for their help with the soils and HYPRES data[ Stephen Peedell



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Figure 4[ Measured and simulated discharge in the Meuse catchment during the ~ood of January and February 0884[ Day 0 represents0 January 0884

"JRC Ispra# is thanked for his valuable help on ARCFLO[ Dr Thierry Ubeda\ Dr David Price\ Dr FrancescaSomma\ Mr Johan Van Der Knij} and Mrs Marleen Stam "JRC Ispra# are thanked for their help with theLISFLOOD simulations[ Dr Paul Bates "University of Bristol# is thanked for his contributions on ~oodrouting[ Dr Bart Parmet and Ir[ Hans Bakker "RWS\ Arnhem:Maastricht# are thanked for providing GISand discharge data on the Meuse catchment[ Finally\ Dr Guido Schmuck is thanked for his contributionsto the ~ood research at JRC[

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Chichester^ 126Ð139[Kite GW\ Ellehoj E\ Dalton A[ 0885[ GIS for large!scale watershed modelling[ In Geo`raphical Information Systems in Hydrolo`y\

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Documents

Physically based river basin modelling within a GIS: the LISFLOOD model