GIS and Remote Sensing in Hydrology, Water Resources and Environment (Proceedings of ICGRHWE held at the Three Gorges Dam, China, September 2003). IAHS Publ. 289, 2004

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Parameterization using ArcView GIS in medium and large watershed modelling Y. B. LIU1, F. H. DE SMEDT1, L. HOFFMANN2 & L. PFISTER2

1 Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium yongbliu@vub.ac.be

2 Research Unit in Environment and Biotechnologies, Centre de Recherche Public-Gabriel Lippmann, 162a Avenue de la Faïencerie, L-1511 Luxembourg Abstract This paper describes the development of a comprehensive subcatch-ment parameterization method for medium and large-scale watershed modelling based upon the fully distributed WetSpa model. A simplified approach is used in order to take advantage of the existing spatial analysis function within ArcView GIS and its Spatial Analyst and hydrological modelling extensions. The model combines elevation, soil and land use data within a GIS framework, and predicts the flood hydrograph and water balance over a river basin. The method of diffusive wave approximation is applied in tracing water for both overland flow and channel flow, while the water and energy budget are maintained for each very small watershed derived from the high resolution DEM. The method was applied to the Alzette River basin, an 1176-km2 medium-size watershed in the Grand-Duchy of Luxembourg. Good agreement between the predicted and measured hydrograph has been achieved according to the graphical comparison and statistical assessment. Key words Alzette River basin; ArcView; diffusive wave; watershed modelling

INTRODUCTION Along with the rapid development of GIS technology and remote sensing techniques, especially the concomitant availability of high resolution DEM and the advances in integrating GIS with hydrological modelling, flood prediction with distributed models tends to be more advantageous and competent. However, model simulation for long time series on medium and large catchment scales (e.g. drainage areas are greater than 500 km2), with small grid sizes and short time intervals is tedious, costly, and time consuming. This is because the computation time and the use of computer memory are affected by the number of cells involved in the catchment. If the watershed is large or the cell dimension too small, the number of cells increases so that the computation time increases and the free memory of the computer is often insufficient, and therefore the simulation is sometimes difficult or impossible to realize with a personal computer. One approach to cope with this problem is to increase the cell size, which may introduce errors by aggregation of spatial input data and misrepresentation of the true catchment characteristics. Studies have shown that grid size has remarkable effects on the simulation results of both runoff generation and flow routing. The coarser resolution results in a lower peak discharge and earlier time-to-peak, especially for the storm events of short duration and lower antecedent soil moisture condition (Molnar & Julien, 2000). This is in agreement with the argument used by Beven (1995) that the

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aggregation approach towards macro-scale hydrological modelling, using averaged parameter values, is inadequate for representing hydrological processes at a large scale. In addition, terrain information is lost as a result of data aggregation at a larger grid-cell size, and the high-resolution data is not fully utilized in the watershed modelling. In this study, an operational method for the automated physiographic para-meterization of a hydrological model is developed. The purpose is to provide an enabling technology for GIS-based medium and large watershed modelling, utilizing high-resolution information as much as possible. Using this technique, it is possible to derive all necessary model parameters rapidly and accurately, and at different discretizing levels by varying the number of GIS derived subcatchments. A case study in the Alzette River basin, Grand-Duchy of Luxembourg, is presented. METHODOLOGY The modelling approach used in this study is based on the WetSpa (Water and Energy Transfer between Soil, Plant and Atmosphere) model, which was originally developed by Wang et al. (1996) and adapted for flood prediction on an hourly time scale by De Smedt et al. (2000) and Liu et al. (2002). WetSpa is a raster-based simulation model where computations are carried out on a cell-based pattern and at an hourly time step. The model conceptualizes a basin hydrological system being composed of atmosphere, canopy, root zone, transmission zone, and saturation zone layers. The watershed is discretized into a number of grid cells, for which the water and energy balance are maintained. The hydrological processes include: precipitation, interception, depression, surface runoff, infiltration, evapotranspiration, percolation, interflow, overland and channel flow, and groundwater flow. The water budget computations for interception storage, depression storage and soil moisture storage are carried out for each cell, while the water balance for groundwater storage is computed on a small subcatchment scale. A mixture of physical and empirical relationships is used to describe the hydrological processes in the model. The model predicts flow hydro-graphs, which can be defined for any numbers and locations in the channel network, and can simulate the spatial distribution of catchment hydrological characteristics. In order to deal with the computing time and memory problem when applying the fully distributed WetSpa model to a medium or large river basin, the model is modified to a semi-distributed pattern, where the water budget is computed for each very small subcatchment, built up from high resolution DEM data, rather than large grid cells with approximately the same area as the subcatchments. The GIS derived very small subcatchment serves as a relatively homogeneous hydrological unit, with the same precipitation and potential evapotranspiration. The advantage of this method is to maintain the internal drainage structure of the subcatchment, for which surface runoff, interflow and groundwater flow can be estimated at the subcatchment outlet, and the respective water and energy are balanced for each subcatchment. Simulations using large grid cells do not have this advantage, because the cell boundary may not adequately reflect the true drainage boundary. Using GIS ArcView, the meteorological and hydrometrical stations are geo-referenced to the base map and a spatial analysis of the precipitation and potential evapotranspiration is undertaken for the entire catchment with ArcView Thiessen

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Polygons extension. The hourly mean precipitation and mean potential evapotranspira-tion inputs to the model for each subcatchment are then calculated by weighting the data of the neighbouring meteorological stations, for which the weight of a given station is predetermined by its areas percentage of the whole catchment, using a high resolution base map and the ArcView Spatial Analyst extension. In such a way, the spatial distribution of meteorological variables is fairly taken into account in the model. Model parameters such as interception storage capacity, depression storage capacity, potential runoff coefficient, overland roughness coefficient, root depth, soil property parameters, average travel time to the outlet, dispersion coefficient and so on, are firstly calculated for each grid cell using ArcView lookup tables and the high resolution DEM, soil type and land use maps, or a combination of the three base maps. The mean parameters for each subcatchment are obtained by integrating the values from all cells of that subcatchment. These results give a better estimation of the natural properties compared with the method using large grid cells, since a unique value is assigned to each pixel for grid type data and therefore the grid re-sample enumeration may cause errors in reflection of the cell characteristics. In general, coarser spatial resolution tends to produce shorter flow paths, and hence decreases the flow time and the hydrodynamic and geomorphological attenuation of the flood wave. In the modified WetSpa model, the above drawbacks can be overcome, for which the minority categories of land use and soil type are taken into account in each very small subcatchment, and the flow time and dispersion coefficient are calculated based on the high resolution DEM. The unit response functions for each simulation unit at their outlets are then obtained by integration of the cell response functions within that subcatchment weighted by the potential runoff coefficient, whereas the channel response functions from each subcatchment outlet to the basin outlet are obtained by integration of the cell response functions along the stream channel. If the discretization of the watershed is highly intensive, the water may flow out of the subcatchment within the first time step, and the process of flow routing within each subcatchment can be omitted. However, this process is maintained in the model in case of coarse discretization of the catchment or very small time intervals of the model simulation. Using the above predetermined parameters for each subcatchment, the model is operated within the ArcView interface and with the input meteorological data of hourly precipitation and potential evapotranspiration of different gauging stations. The division of the watershed should be performed according to the project purpose and the complexity of the terrain. Dividing the watershed into very small subcatchments is necessary in order to take care of most of the spatial variability of the hydrological processes. However, for a medium sized plain watershed, if the project purpose is flood prediction only at the outlet of the watershed, dividing that watershed into a few hundred or even tens of subcatchments might be sufficient. Then, the modified WetSpa model could be considered as becoming more and more semidistributed as the dimensions of the subcatchments increase. A few simulations are necessary to decide how many subcatchments are necessary to meet various objectives.

APPLICATIONS

One case study of the modified WetSpa model was performed on the Alzette River basin using available hydro-meteorological data from December 1996 to December

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2000, for which topography and soil data were available in GIS form, while the land use data was obtained from remote sensed images. The study area covers an area of 1176 km2 and is located mostly in the Grand-Duchy of Luxembourg, where high-magnitude floods occurr frequently and have caused important damage since the early 1990s. Figure 1(a) shows the location of the study area as well as the river system and monitoring network. The study area generally has a rolling topography, with elevation ranging from 195 to 545 m and an average basin slope of 8.7%. The soil is mostly a sandstone/limestone mixture with areas of clay and marl, and can be classified as texture categories: loamy sand (16.7%), silt loam (8.1%), silt (21.4%), loam (22.7%), sandy clay loam (10.9%), silt clay loam (15.4%) and clay loam (4.8%). The dominant land use types in the catchment are deciduous shrub and forest (33.7%), grassland (30.7%) and cropland (23.3). Urban area (11.2%) is also one of the major land use types in the basin, as the study area is highly developed with several towns; Ettelbruck, Mersch, Luxembourg-city and Esch/Alzette. Other land use types are free water surface (0.2%), and former mining areas (0.9%), which are located on the right bank tributaries of the upstream catchment and disturb the local hydrological behaviour during storm events. Figure 1(b) shows the land use of the watershed. The climate in the region has a northern temperate humid oceanic regime without extremes. Rainfall has a relatively uniform distribution over the year. High runoff occurs in winter and low runoff occurs in summer due to the higher evapotranspiration. The average annual precipitation in the region varies between 800 mm to 1000 mm,

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and the annual potential evapotranspiration is around 550 mm. A dense rainfall and discharge monitoring network has been set up to study the hydrological behaviours of the river basin since 1995 as shown in Fig. 1(a). Potential evapotranspiration is esti-mated using the Penman-Monteith formula with measured meteorological data in Luxembourg-city, and extended to each rainfall Thiessen polygon based on the proportions of different land use type over the polygon (Drogue et al., 2002). The daily areal rainfall interpolated via Thiessen polygons is time disaggregated according to the temporal structure of the four hourly reference raingauges. Based on the point elevation measurements, a 50 × 50 m pixel resolution digital elevation model was generated, and the digital land use and soil type map with the same cell size were converted from the available land use and soil coverage. The cell-based parameters were then identified using ArcView tools and lookup tables, which relate default model parameters to each category of the individual base map, or the combination of the three base maps. Starting from the high resolution DEM, hydrological features including aspect, surface slope, flow direction, flow accumulation, flow length, stream network, and drainage area were delineated. Maps of porosity, field capacity, wilting point, residual moisture, saturated hydraulic conductivity, and pore size distribution index were obtained from the soil type map. Maps of root depth, Manning’s roughness coefficient and interception storage capacity were derived from the land use map. Maps of default runoff coefficient and depression storage capacity were calculated from the slope, soil type and land use class combinations. The average hydraulic radius for each grid cell was calculated using a power law relationship, which relates hydraulic radius with the controlling drained area by a network constant and a geometry scaling exponent (Molnar & Ramirez, 1998) representing the average behaviour of the cell and the channel geometry. By combining the maps of the average hydraulic radius, the Manning’s roughness coefficient and the surface slope, average flow velocity in each cell was calculated using Manning’s equation, which resulted in values of 0.005 m s-1 for overland flow on upland areas in the watershed, and up to 2.7 m s-1 for some parts of the main river. Next, the celerity and dispersion coefficients in each cell were produced according to Henderson (1966). The contributing area was then determined from topographic data for a particular downstream convergence point, i.e. normally the cells corresponding to the stream flow gauging sites and the basin outlet. For each contributing grid cell, the values of mean flow time and its standard deviation were calculated by integration along the flow paths. A spatial distribution of the mean flow time to the basin outlet is shown in Fig. 2(a), in which the average flow time is <10 h for the main river and up to 55 h for the most remote areas. For the simulation of hydrographs both at the basin outlet and the flow monitoring stations inside the catchment, and the interest in the spatial variability of hydrological characteristics over the catchment, the basin was divided into 2276 subcatchments, as shown in Fig. 2(b), corresponding to the threshold value of 100 when delineating the stream network based on the flow accumulation theme. The areas of the GIS derived subcatchments range from 0.025 to 3.8 km2 with an average subcatchment area of 0.52 km2 corresponding to a grid mesh with cell size of 720 m. The flow routing with-in each subcatchment is not important if the GIS derived subcatchments are very small, as is the case in this study, since most excess water may flow out of the subcatchment

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and validation for the period 1999–2000 using ttial evapotranspiration and stream discharge eters that need to be adjusted during model g factor, groundwater flow recession coefficiedistributed model parameters are predeterminedl operation and normally remain constant. eters of potential runoff coefficient, hydraulic raso be adjusted during calibration if necessary. Thmonitoring sites inside the catchment can also bethe simulation results at Ettelbruck, the basin oue 3 shows the observed vs the simulated stream fflow plus groundwater drainage) at Ettelbruck fred in February and March 1997. Three largeg the period, and caused serious floods over th

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g of the flow is therefore mainly wever, flow routing within each nt along with the increasing ing thresholds of 200, 300, 500, of 1207, 797, 455 and 233, ation, for which the predicted ch, but the simulation results for erably affected. Ettelbruck for the period 1997–

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calibration are mainly interflow nt, and plant coefficient, while using GIS ArcView before the The predetermined distributed dius, Manning’s coefficient etc., e predicted hydrographs at other seen as model verifications, but tlet, are presented in this paper. low and the estimated base flow or a compound flood event that storms happened successively e catchment. The total recorded

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rainfall over the catchment during the period was 147.8 mm, with 56.5, 22.8 and 67.4 mm for the three storms, respectively, corresponding to observed peak discharges of 115, 105 and 190 m3 s-1 at Ettelbruck for each individual flood. As can be seen in Fig. 3, the predicted hydrograph fits the measured hydrograph very well. The volume of surface runoff takes about 53% of the total flood volume, while the interflow and groundwater flow is about 47% of the total flood volume. The first storm did not produce a relevant big flood compared with its storm volume, as a large quantity of rainfall was used for saturating the soil, while the second and the third storms generated a higher proportion of runoff. This was demonstrated by the predicted hydrograph, where the calculated surface runoff coefficient was 0.30, 0.35 and 0.39 for the three floods, respectively. With the initial hydrological condition at the end of calibration period, the validation results for the year 1999 and 2000 were in fairly good agreement with the measured discharges. The model can reproduce high flows well, but low flows are a little overestimated. This may be because of the simplification of the groundwater component in the model, or less accurate estimation of the evapo-transpiration from groundwater storage during dry periods. Similar simulation results can be obtained for other flow gauging sites inside the catchment, but the model performance for the stations with small drained area is not as good as the stations with large areas. However, by discretizing the large catchment into very small subcatch-ments, the model is able to consider the precipitation, antecedent moisture and runoff-generating processes in a spatially realistic manner based on topography, land use and soil type, resulting in a fairly high accuracy for high flows, and the general hydrolog-ical trends being well captured by the model. Both numerical and graphical criteria were considered to provide an overall indication of the model’s capabilities. Model biases are within the range of –0.08 to 0.05 for the whole simulation period. The model confidential coefficients are within the range of 0.53 to 0.84, with a mean value of 0.73, and the Nash-Sutcliffe coefficients (Nash & Sutcliffe, 1970) are within the range of 0.47 to 0.87 with mean values of 0.74. It is found that better performance exists for the watershed with large

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areas, while for small watersheds the performance is less satisfied. This indicates that the modified WetSpa model can give a good representation of flow hydrographs for larger catchments. For a small watershed, a fully distributed model based on a high resolution DEM and with smaller time interval is preferable. DISCUSSION AND CONCLUSIONS A practical approach for dividing a large catchment into very small subcatchments, based on high-resolution digital graphic data, is presented in this paper. The modified WetSpa model uses elevation, soil and land use data in a simple way to estimate runoff and soil moisture within root zone on the scale of GIS derived natural units, allowing for the internal drainage structure of WetSpa for each small subcatchment. The dis-cretization of the catchment is implemented according to the model purpose, terrain complexity, and the interest of spatial variable distributions. In the case of fine dis-cretization, channel characteristics are more important in controlling the routing of stream flow, which have to be properly determined. However, flow routing inside the subcatchment has to be taken into account for coarse discretization, in order to predict outflow hydrographs more accurately. The model has been applied successfully to the 1176-km2 Alzette River basin in Luxembourg with 4 years of observed hourly rainfall and stream flow data. Results from the integrated output show that it adequately predicts stream flow at the basin outlet and the flow gauging stations with a relatively large drained area inside the catchment. Likewise, this methodology can also be applied over large watersheds (e.g. drainage areas are >1500 km2) using smaller sub-basins within the large watershed as model elements, so as to account for spatial differences in soils, land use, topography, channel morphology, and weather conditions in a semi-distributed manner. However, application of this method for a large area may introduce some errors, either due to local variations which are not taken into account, or regional variations for model parameters. The use of the modified WetSpa model with its physiographic parameters provided by ArcView GIS has the attraction of reducing the cost and time required for parameterization and model simulation. This advantage is more outstanding when modelling a large watershed with long data series and short time intervals. The implementation of the model entirely within ArcView GIS enables the integration of modelling capabilities within a GIS environment. The method makes full use of the spatially distributed hydrological and geophysical characteristics of the catchment, and is therefore suitable for stream flow forecasting and scenario simulations. REFERENCES Beven, K. J. (1995) Linking parameters across scales—subgrid parameterizations and scale dependent hydrological

models. Hydrol. Process. 9, 507–525. De Smedt, F., Liu, Y.B. & Gebremeskel, S. (2000) Hydrologic modelling on a catchment scale using GIS and remote

sensed land use information. In: Risk Analysis II (ed. by C. A. Brebbia), 295–304. WTI Press, Southampton, Boston, USA.

Drogue, G., Leviandier, T., Pfister, L., El Idrissi, A., Iffly, J. F., Hoffmann, L., Guex, F., Hingray, B. & Humbert, J. (2002) The applicability of a parsimonious model for local and regional prediction of runoff. Hydol. Sci. J. 47(6), 905–920.

Henderson, F. M. (1966) Open Channel Flow. McMillan Company, New York, USA.

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Liu, Y.B., De Smedt, F. & Pfister, L. (2002) Flood prediction with the WetSpa model on catchment scale. In: Flood Defence ‘2002 (ed. by B. S. Wu, Z. Y. Wang, G. Q. Wang, G. H. Huang, H. W. Fang, and J. C. Huang), 499–507. Science Press, New York, USA.

Molnar, D. K. & Julien, P. Y. (2000) Grid-size effects on surface runoff modelling. J. Hydrol. Eng. 5, 8–16. Molnar P. & Ramirez, J. A. (1998) Energy dissipation theories and optimal channel characteristics of river networks.

Water Resour. Res. 34(7), 1809–1818. Nash, J. E. & Sutcliffe, J. V. (1970) River flow forecasting through conceptual models, Part 1: A discussion of principles.

J. Hydrol. 10, 282–290. Wang, Z., Batelaan, O. & De Smedt, F. (1996) A distributed model for water and energy transfer between soil, plants and

atmosphere (WetSpa). Phys. Chem. Earth 21, 189–193.