Predicting storm runoff from different land-use classes using a geographical information system-based distributed model

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1 HYDROLOGICAL PROCESSES Hydrol. Process. 2, (26) Published online 18 October 25 in Wiley InterScience ( DOI: 1.12/hyp.592 Predicting storm runoff from different land-use classes using a geographical information system-based distributed model Y. B. Liu, 1 * S. Gebremeskel, 1 F. De Smedt, 1 L. Hoffmann 2 and L. Pfister 2 1 Department of Hydrology and Hydraulic Engineering, Vrije Universieit Brussel, Brussels, Belgium 2 Research Unit in Environment and Biotechnologies, Centre de Recherche Public Gabriel Lippmann, 162a Avenue de la Faïencerie, L-1511 Luxembourg, Grand Duchy of Luxembourg Abstract: A method is presented to evaluate the storm runoff contributions from different land-use class areas within a river basin using the geographical information system-based hydrological model WetSpa. The modelling is based on division of the catchment into a grid mesh. Each cell has a unique response function independent of the functioning of other cells. Summation of the flow responses from the cells with the same land-use type results in the storm runoff contribution from these areas. The model was applied on the Steinsel catchment in the Alzette river basin, Grand Duchy of Luxembourg, with 52 months of meteo-hydrological measurements. The simulation results show that the direct runoff from urban areas is dominant for a flood event compared with runoff from other land-use areas in this catchment, and this tends to increase for small floods and for the dry-season floods, whereas the interflow from forested, pasture and agricultural field areas contributes to recession flow. It is demonstrated that the relative contribution from urban areas decreases with flow coefficient, that cropland relative contribution is nearly constant, and that the relative contribution from grassland and woodland increases with flow coefficient with regard to their percentage of land-use class areas within the study catchment. Copyright 25 John Wiley & Sons, Ltd. KEY WORDS flood generation; storm runoff partitioning; land use; GIS; WetSpa INTRODUCTION The appearance of the soil surface plays a key role for the infiltration process, which is influenced by a variety of factors, including crusting, compaction and sealing at the soil surface, the extent and type of vegetation cover, and the surface topography (Bronstert et al., 22). Slope shares an intuitive relationship with runoff volume, as steep slopes lead to larger amounts of surface flow and gentle slopes result in more infiltration. Soil affects the amount of water that can be absorbed and the rate at which water can percolate to the groundwater system or drain as lateral interflow. For example, sandy soils allow higher infiltration rates and better drainage than silty or clayey soils. The influence of land use on storm runoff generation is more complicated, as land use and soil cover have an effect on interception, surface retention, evapotranspiration, and resistance to overland flow. For instance, cropland and urban land yield more flood volumes, higher peak discharges and shorter flow travel times than grassland or woodland. Increased runoff from cropland is mainly due to the removal of native vegetation and soil compaction, which decrease soil infiltration capacity. Increased runoff from urban areas results from impervious surfaces that prevent infiltration of water into soils (Brun and Band, 2). Urban land uses also reduce the surface roughness and, therefore, shorten the overland flow retention time. * Correspondence to: Y. B. Liu, Pleinlaan 2, Brussels, Belgium. yongbliu@vub.ac.be Received 5 January 24 Copyright 25 John Wiley & Sons, Ltd. Accepted 18 January 25

2 534 Y. B. LIU ET AL. In contrast, less runoff is produced from undisturbed grassland and woodland areas. This is due to factors such as interception of precipitation by the vegetation canopy, the dense network of roots that increase infiltration capacity and soil porosity, as well as the accumulated organic debris on the surface that increases depression storage capacity and overland flow retention time. Moreover, dense vegetation causes higher evapotranspiration and affects the long-term water and energy balance. Evidently, areas with a high percentage of cropland or urban land use yield more storm runoff than the areas of similar soils and topography with grassland or woodland. One of the recent thrusts in hydrological modelling is the assessment of the effects of land-use and land-cover changes on water resources, and their influences on storm runoff generation. In general, conceptual rainfall-runoff models usually consider the entire catchment or subcatchment as one unit, and describe the transformation of rainfall to runoff with simple concepts. Owing to limitations in the model conceptualizations of the hydrological processes involved, lumped catchment models must be used carefully in predicting the impacts of land-use change on catchment runoff (Kuczera et al., 1993). Physically based distributed models, which use theoretical equations and measurable parameters, provide a dynamic explanation of catchment behaviour, but they require too much information and are too complex to be easily used at a catchment scale. Physical conceptual distributed or semi-distributed models try to overcome the limits of the previous types, while keeping their advantages by simplifying the dynamic approach and discretization using new concepts (Ambroise, 1999). These models have the advantage of reflecting the effects of spatially distributed model parameters such as land use on streamflow, and the assessment can reflect the variability of the hydrological parameter at a required scale. Moreover, the present-day availability of spatially distributed data such as digital elevation models (DEMs), land use and soil type, and the recent advances in computer hardware and geographic information system (GIS) technology make the use of distributed models much easier and more practical. In this regard, many previous studies have been done on GIS-based hydrological modelling (e.g. Sui and Maggio, 1999; Brun and Band, 2; Bronstert, 24) and on the assessment of land-use hydrological impact using distributed models (e.g. Doe et al., 1996; Bronstert et al., 22; Klocking and Haberlandt, 22). The objective of this paper is to study the storm runoff contribution from different land-use areas within a catchment using a GIS-based distributed hydrological model, i.e. WetSpa. The application was performed on the Steinsel catchment in the Alzette river basin, Grand Duchy of Luxembourg, for which topography and soil data were available in a GIS form, and the land-use data were obtained from remote-sensed images. Streamflow measurements on an hourly scale for 4 years at different stations were used for model calibration and validation. Based on the results of model simulation, the important runoff processes that contribute to the storm runoff are identified, and the factors which influence storm runoff partitioning from different land-use areas are also discussed. DESCRIPTION OF THE WETSPA MODEL The WetSpa model is a grid-based physical conceptual hydrological model for simulating water and energy transfer between soil, plants and atmosphere. It was originally developed by Wang et al. (1996) and adapted for flood prediction on an hourly time step by De Smedt et al. (2) and Liu et al. (23). For each grid cell, the vertical direction is divided in to four layers: vegetation zone, root zone, transmission zone and saturated zone. The hydrological processes considered in the model are precipitation, interception, depression, surface runoff, infiltration, evapotranspiration, percolation, interflow, groundwater flow, and water balance in the root zone and the saturated zone. The total water balance for a raster cell is composed of the water balance for the vegetated, bare-soil, open water and impervious parts of each grid cell. This allows one to account for the non-uniformity of the land use per cell, which is dependent on the resolution of the raster cell. The processes in each grid cell are set in a cascading way, which means that an order of occurrence of the processes is assumed after precipitation. Figure 1 shows the schematic diagram of WetSpa on the cell scale. Inputs to the model are precipitation and potential evapotranspiration series interpolated by the Thiessen polygon method.

3 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES 535 Evapotranspiration Precipitation Evaporation Streamflow Interception store Throughfall Infiltration Land surface Soil moisture store Depression store Surface runoff Interflow Percolation Groundwater store Groundwater flow Figure 1. Schematic diagram of the WetSpa model on the cell scale The outputs are flow hydrographs (which can be defined for any number and location in the channel network) and the spatial distribution of hydrological variables within the catchment. To calculate the amount of surface runoff and infiltration, a modified rational method is used. This allows the actual runoff coefficient to vary with time, rainfall intensity, rainfall duration, soil moisture and the cell characteristics. The equation can be expressed as V D c r P I s / s D s 1 where V [LT 1 ] is the surface runoff in depth over time, P [LT 1 ] is the rainfall intensity, I s [LT 1 ]isthe interception storage, D s [LT 1 ] is the depression storage calculated with the equation suggested by Linsley (1982), [L 3 L 3 ] is soil moisture content of the root zone, s [L 3 L 3 ] is the soil porosity, and c r is a potential runoff coefficient that depends upon the slope, soil type, land use and the proportions of bare soil and impervious areas in the grid cell. Default runoff coefficients are interpolated from the values collected from the literature. A look-up table linking values to slope, soil type and land-use classes is then generated based on physical and statistical analyses. Owing to the lack of field evidence, a correction factor is introduced to account for this simplification, which can be determined during model calibration. Therefore, c r values are not necessarily the real runoff coefficient, but a measure reflecting rainfall partitioning capacities or relative runoff contributions from cells with different combinations of slope, soil type and land use. The state variable is determined based on the intermediate soil moisture of the time step. A first trial is performed using of the last step to estimate excess rainfall and infiltration, and the soil moisture at the end of this time step is calculated by means of the water balance of the cell. The intermediate soil moisture is then estimated as the arithmetic mean of the moisture content at the end of the last step and the result of the first trial. Thereafter, excess rainfall and soil moisture content at the end of the time step are recalculated. In this way, is controlled not only by the antecedent soil moisture, but also by the rainfall intensity and rainfall duration. Hence, high rainfall intensity or rainfall with long duration tends to give a greater percentage of runoff, and vice versa. / s is time dependent; its value is unity when the soil is saturated, reflecting potential surface runoff conditions, and approaches zero when soil moisture tends to the residual moisture content. The product of c r and / s forms the actual runoff coefficient, which varies both with time (depending upon rainfall intensity and the antecedent soil moisture) and space (depending upon the physical characteristics of the cell). This allows one to compute runoff for each time step during the model simulation. The actual runoff coefficient is set to one under the condition where saturation happens from below and groundwater resurgence occurs.

4 536 Y. B. LIU ET AL. The water balance in the root zone is modelled continuously by equating inputs and outputs: D1/1t D P I V E R F 2 where D [L] is the root depth, 1 [L 3 L 3 ] is the change in soil moisture, 1t [T] is the time interval, I D I s C D s [LT 1 ] is the initial abstraction including interception and depression losses within time 1t, E [LT 1 ] is the actual evapotranspiration from the soil, R [LT 1 ] is the percolation out of the root zone, and F [LT 1 ] is the amount of interflow in depth over time. Evapotranspiration from soil and vegetation is calculated as a function of potential evapotranspiration, vegetation type, stage of growth and soil moisture content. Potential evapotranspiration data can be obtained from field measurements, estimated from the historical records through statistical analysis, or calculated with the Penman Monteith equation when hourly meteorological data of net radiation, air temperature, relative humidity and wind speed are available. The actual evapotranspiration for the root-zone layer is computed as the area-weighted mean of the land-use percentage, where transpiration happens from the vegetated parts, evaporation happens from the bare soil, and there is no evaporation on impervious areas. Based on water balance studies within the Alzette river basin (Pfister et al., 22), groundwater may rise to a level close to the root zone or even to the surface in the area close to the river channel under heavy rains. Therefore, the evapotranspiration from groundwater is accounted for in this study; this is calculated as a portion of the remaining potential evapotranspiration after the abstraction from soil and land surface depending on the groundwater storage. Finally, the total evapotranspiration is calculated as the sum of evaporation from interception storage, depression storage, and the evapotranspiration from soil and groundwater storage. Percolation and interflow are assumed to be gravity driven. The percolation out of the root zone is equated as the hydraulic conductivity corresponding to the moisture content as a function of the soil pore size distribution index (Eagleson, 1978). Shallow subsurface fluxes (interflow), which contribute to surface runoff, are also evaluated at each time step. Interflow is assumed to occur in the root zone after percolation and becomes significant only when the soil moisture is higher than field capacity. Darcy s law and a kinematic wave approximation are used to estimate the amount of interflow generated from each cell (as a function of hydraulic conductivity, the moisture content, slope angle, and the root depth), and a scaling parameter that depends on land use was modified to account for the effects of river density and the effects of organic matter on the horizontal hydraulic conductivity in the top soil layer. The routing of overland flow and channel flow is implemented by the method of the diffusive wave approximation. This method has been used in some recent GIS-based flood models (Olivera and Maidment, 1999; Fortin et al., 21). An approximate solution using a two-parameter response function, termed average flow time and the standard deviation of the flow time proposed by Liu et al. (23), is applied in this study. The flow time and its variance are determined by the local slope, surface roughness and the hydraulic radius for each grid cell. The flow path response function at the outlet of the catchment or any other downstream convergence point is calculated by convoluting the responses of all cells located within the drainage area in the form of the probability density function of the first passage time distribution. This routing response serves as an instantaneous unit hydrograph, and the total discharge is obtained by a convolution integral of the flow response from all spatially distributed precipitation excess. Because groundwater movement is much slower than the movement of water in the surface and nearsurface water system, and little is known about the bedrock, groundwater flow is simplified as a lumped linear reservoir on the small GIS-derived subcatchment scale. Considering the river damping effect for all flow components, overland flow and interflow are first routed from each grid cell to the main channel, and joined with groundwater flow at the subcatchment outlet. Then the total hydrograph is routed to the basin outlet by the channel response function. The total discharge is the sum of overland flow, interflow and groundwater flow, and is obtained by a convolution integral of the flow responses from all grid cells. One advantage of this approach is that it allows the spatially distributed runoff and hydrological parameters of the basin to be used as inputs to the model. Therefore, the runoff contribution to the flood hydrograph from a certain land-use

5 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES 537 Table I. Evaluation criteria for the assessment of model performance Code Criteria Description CR 1 CR 2 CR 3 CR 4 N Q si / N Q oi 1 id1 id1 1 N Q si Q oi 2 / N Q oi Q o 2 id1 id1 1 N [ln Q si ln Q oi ] 2 / N [ln Q oi ln Q o ] 2 id1 id1 1 N Q oi C Q o Q si Q oi 2 / N Q oi C Q o Q oi id1 Q o 2 id1 Model bias for evaluating the ability of reproducing water balance Model efficiency for evaluating the ability of reproducing the time evolution of flows Model efficiency for evaluating the ability of reproducing the time evolution of low flows Model efficiency for evaluating the ability of reproducing the time evolution of high flows category can be obtained by a convolution integral of the flow responses from the cells in this category, and the sum of partitions from different land-use areas forms the total flow at the catchment outlet. For the assessment of the model performance and model efficiency, four evaluation criteria were selected, as listed in Table I. In all equations, Q s and Q o are the simulated and observed streamflows at time step i, Q o is the mean observed streamflow over the simulation period, and N is the number of time steps. CR 1 is the model bias, for which the value zero represents a perfect simulation of the flow volume. The model efficiency is measured by the Nash Sutcliffe coefficient (Nash and Sutcliffe, 197), expressed as CR 2.CR 2 D 1 indicates a perfect fit, and a negative CR 2 means that the prediction is worse than simply using the observed mean. CR 3 is a logarithmic transformed Nash Sutcliffe criterion, used to evaluate the quality of low-flow simulations (Smakhtin et al., 1998). An arbitrarily small value may be introduced to the discharges in the case of zero flows for which the logarithm does not exist. Moreover, an adapted version of the Nash Sutcliffe criterion CR 4, proposed by Guex (21) is used in this study. As seen in the formula, more weight is given to high discharges; therefore, the CR 4 criterion can be used for evaluating model efficiency for high flows. SITE DESCRIPTION AND DATA The model was applied to the Steinsel catchment, located in the upstream part of the Alzette river basin. The study area is situated in the southern part of the Grand Duchy of Luxembourg, with a small part in the south located in France (Figure 2). The elevation in the 47 km 2 watershed ranges from 225 to 45 m, with an average basin slope of 7% derived from a 5 ð 5 m 2 pixel-resolution DEM. The local topography is characterized by a natural sandstone bottleneck, located near Luxembourg city. The valley is up to 2Ð5 km wide upstream of the bottleneck, and only 75 m in the Luxembourg sandstone, which extends approximately 8 m into the ground (Pfister et al., 2). The dominant soil types are loamy sand (29Ð1%) in the upstream part, silt (37Ð7%) in the middle, and silt clay loam (13Ð3%) in the downstream part. Other soils are sandy clay loam (1Ð2%) and clay loam (9Ð5%), which are mainly located in the river valley. The soil cover was obtained from the digital land-use map of Luxembourg and France derived from remote-sensed images. The original land-use map was classified to 14 classes for use in the WetSpa model, and reclassified into five hydrological land-use classes for simulation of storm runoff contributions from different land-use class areas, i.e. urban areas, cropland, grassland, woodland, and open water, as shown in Figure 3. The watershed is characterized by high urbanization and extensive agricultural activity. Urban areas cover about 2Ð5% of the catchment, with Luxembourg city in the downstream and Esch-Alzette city in the upstream part (Figure 2). Agricultural fields take about 23Ð2% of the total area, mainly close to the river valleys with main crops of maize and wheat. Forest (29Ð%) and grass (24Ð3%) are

6 538 Y. B. LIU ET AL. Town Clervaux Rain gauge Stream gauge BELGIUM Wiltz Ettelbruck GERMANY River Study area Alzette basin Luxembourg Ell Mersch Echternach Esch/Alzette Steinsel Pfaffenthal Luxembourg-city Hesperange Livange Remich FRANCE N W E S 1 2 km Figure 2. Location and monitoring network of the study area Table II. Description of the area, slope and main soil types for each land-use class Land use Area (km 2 ) Relative area (%) Average slope (%) Main soil types Cropland 94Ð 23Ð1 5Ð77 Silt, loamy sand Grassland 97Ð3 23Ð9 4Ð73 Silt, clay loam, sandy clay loam Woodland 115Ð6 28Ð4 1Ð1 Silt, loamy sand, silt clay loam Mining area 1Ð2 2Ð5 11Ð5 Loamy sand Urban 83Ð4 2Ð5 5Ð93 Silt, silt clay loam Water surface 6Ð5 1Ð6 1Ð31 Clay loam, silt clay loam Total Ð1 Silt, loamy sand, silt clay loam predominant in the river valleys and high terrain, intermixed with urban areas and cultivated land. There are also some former mining areas located on higher terrains in the upstream catchment, which cover about 2Ð5% of the catchment. Surface runoff is seldom generated here. The watershed is well drained with a dense stream network. Open water occupies about 1Ð6% of the total area. A general description of the area, the average slope and the main soil types for each land-use class are given in Table II. The climate in the region has a northern temperate humid oceanic regime without extremes. The mean annual temperature is around 9Ð3 C, with average temperature of 1Ð2 C injanuaryand18ð5 C injuly(pfisteret al., 22). Rainfall has a relatively uniform distribution throughout the year. High runoff occurs in winter and low runoff in summer due to the high evapotranspiration. Winter storms are strongly influenced by the westerly

7 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES 539 Cropland Grassland Woodland Mining area Urban area River Boundary N W E S 5 1 km Figure 3. Land-use map of the study area atmospheric fluxes that bring humid air masses from the Atlantic Ocean (Pfister et al., 2), and floods happen frequently because of the saturated soils and low evapotranspiration. The average annual precipitation in the region varies between 8 and 1 mm. Precipitation generally exceeds potential evapotranspiration, except for 4 months in the growing season. A dense hydrological observation network has been set up in the Alzette river basin (Figure 2), where four stream gauges, namely Steinsel, Pfaffenthal, Hesperange and Livange, are located on the main stem of the river recording water levels at a 15 min time step. Ten rain gauges are located within and around the catchment and record at an hourly or daily time step. Daily rainfall was disaggregated into hourly rainfall series according to the nearest hourly reference rain gauges. Potential evapotranspiration was estimated using the Penman Monteith formula (Monteith and Unsworth, 199) with daily meteorological data measured at Luxembourg airport, and extended to each rainfall Thiessen polygon based on the proportions of different land-use type over the polygon (Drogue et al., 22). A total of 52 months of hourly rainfall, discharge and potential evapotranspiration data from December 1996 to March 21 are available for model application. The average flow at Steinsel during the monitoring period is 5Ð6 m 3 s 1, with flows ranging from Ð7 to 4Ð7 m 3 s 1, and the measured maximum hourly rainfall intensity of 23 mm h 1 occurred on 2 July 2. MODEL CALIBRATION The application procedures for the WetSpa model include database development, watershed delineation, model calibration and validation. Spatial model parameters include terrain features derived from the DEM (such as slope, flow direction, flow accumulation, stream network and drainage area), soil properties (such as soil porosity, field capacity, wilting point, residual moisture content, saturated hydraulic conductivity and pore size distribution index), land-use-based parameters (such as root depth, Manning s roughness coefficient, leaf

8 54 Y. B. LIU ET AL. area index and interception storage capacity), and compound parameters obtained from the combination of the three base maps (such as potential runoff coefficient, depression storage capacity, flow velocity, average travel time and its standard deviation). These parameters are identified using GIS tools and look-up tables, which relate default model parameters to the base maps, or the combination of the base maps within a GIS framework. Some of the model parameters (including evapotranspiration correction factor, interflow scaling factor, groundwater flow recession constant and the runoff coefficient correction factor) are difficult to define in a spatial way using the available digital maps. These parameters are set as scalar values in the WetSpa model, and are calibrated using automated and manual procedures during model calibration. Impervious areas have significant influences on runoff production in a watershed, because they can generate direct runoff even during small storms. Owing to the model s 5 ð 5 m 2 grid size, cells may not be 1% impervious in urban areas. In practice, the percentage of impervious area in a grid cell is computed based on land-use classes, with 3% for residential area, 7% for commercial and industrial area and 1% for streams, lakes and bare exposed rock. Default potential runoff coefficients for these areas are calculated by adding the impervious percentage with a grass runoff coefficient multiplied by the remaining percentage. This leads to runoff coefficients of 4 to 1% in urban areas, whereas other areas have much smaller values, down to 3% for forests in valleys with practically zero slopes. The model was calibrated against hourly streamflow measurements at the four stations for the time period December 1996 to December The data in the period January 2 to March 21 were used for model validation. The initial soil moisture was set to the field capacity of the soil type during model calibration, and the simulated moisture content at the end of 1999 was used as the initial moisture content during model validation. The calibration was carried out for the scalar parameters only. Other spatial model parameters were set to values obtained from the literature, which have been shown to yield reliable results in previous model applications (De Smedt et al., 2; Liu et al., 23). Calibration of the evapotranspiration factor can be performed independently by comparing the calculated and observed flow volume for a long time series. The shape of the recession limb of the simulated hydrograph is affected by the delayed response related to interflow and groundwater flow (Figure 4). Therefore, the interflow scaling factor can be calibrated by matching the computed discharge with the observed discharge on the recession limb of the flood hydrograph. However, the peak flow is affected considerably by this parameter. The groundwater flow recession coefficient can be obtained by the analysis of recession curves at discharge gauging stations. Refinement of this coefficient is necessary to get a better fit for the low flows. 6 Hourly discharge (m 3 /s) Calculated surface runoff Calculated interflow Calculated baseflow Precipitation Measured stream flow Hourly precipitation (mm/h) 2/2/97 12/2/97 22/2/97 4/3/97 14/3/97 24/3/97 3/4/97 2 Figure 4. Measured and calculated hourly flows at Steinsel for the flood in February 1997

9 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES 541 Model performances for calibration and validation were evaluated through qualitative and quantitative measures, involving both graphical comparisons and statistical analysis for hourly, daily and monthly values. In addition to the above comparisons, the water balance components for individual land uses were reviewed by displaying model results including precipitation, surface runoff, interflow, baseflow, actual evapotranspiration and groundwater recharge. Although observed values were not available for each of the water balance components listed above, the average annual values must be consistent with expected values for the region, depending upon the individual land-use categories. This is a separate consistency check with data independent of the modelling to ensure that the land-use categories and the overall water balance reflect local conditions. Finally, the spatial outputs of simulated hydrological variables were used to assess the reasonability of hydrological processes distribution, where the processes of surface runoff, soil moisture, interflow, and percolation etc. would be spatially distributed depending upon the cell s physical characteristics. Figure 4 gives a graphical comparison between simulated and observed hourly streamflows at Steisel for a sequence of floods that occurred in February and March The total rainfall was 184Ð3 mm, with measured runoff of 17Ð8 mm and simulated runoff of 111Ð mm. A small flood happened in early February, followed by three large floods successively. The simulated hydrographs of surface runoff, interflow and baseflow correspond respectively to 38%, 27% and 35% of the predicted total flood volume, values which were obtained by summation of the flow responses from all contributing cells. The figures show a very good agreement between the predicted and measured hydrograph, in which the rising and high water limbs are dominated by surface runoff, whereas interflow is delayed a few hours and mainly contributes to recession flow. Groundwater discharge forms the baseflow of the total hydrograph. Owing to the high antecedent soil moisture content and groundwater storage, the amount of interflow and groundwater flow is abundant in these flood events, being 62% in total of the whole flood volume, which is typical for this catchment during the wet season. Figure 5 presents the graphical comparisons of calculated and measured daily flows at Steinsel for the validation year 2. The year 2 was a very high flow year in the region, with an annual precipitation of 14 mm and an annual mean discharge of 6Ð61 m 3 s 1 at Steinsel, which is 1Ð4 times the average flow for the previous 3 years. Floods happened both in winters (due to the saturated soils and the high groundwater storage) and in summer (due to high rainfall intensity). With the initial hydrological condition at the end of 4 Discharge (m 3 /s) 3 2 Precipitation Measured Calculated 3 6 Precipitation (mm/d) Figure 5. Measured and calculated daily flows at Steinsel for the year 2 12

10 542 Y. B. LIU ET AL. Table III. Model performance for the calibration and validation period Station Area (km 2 ) Urban (%) Crop (%) Grass (%) Forest (%) Period Mean flow (m 3 s 1 ) CR 1 CR 2 CR 3 CR 4 Livange Ð6 28Ð9 22Ð9 24Ð7 Calibration 2Ð92 Ð2 Ð78 Ð83 Ð82 Validation 3Ð92 Ð4 Ð75 Ð78 Ð8 Hesperange Ð8 27Ð4 25Ð3 25Ð4 Calibration 3Ð71 Ð3 Ð83 Ð78 Ð87 Validation 5Ð99 Ð2 Ð79 Ð81 Ð84 Pfaffenthal 35 19Ð2 25Ð4 26Ð8 25Ð2 Calibration 3Ð96 Ð2 Ð81 Ð82 Ð92 Validation 6Ð34 Ð3 Ð8 Ð76 Ð87 Steinsel 47 2Ð5 23Ð2 24Ð3 29Ð Calibration 4Ð69 Ð1 Ð85 Ð83 Ð85 Validation 7Ð87 Ð3 Ð84 Ð82 Ð86 the simulation period, the validation results for the year 2 are in fairly good agreement with the measured daily discharges. This indicates that the model is able to take into account precipitation, antecedent moisture and runoff generation processes in a spatially realistic manner based on topography, land use and soil type, giving the simulation a fairly high degree of precision, and general hydrological trends are well captured by the model. The model performance was evaluated both qualitatively (by visual comparison of the simulated and observed hydrographs) and quantitatively (using the four statistical indexes at Steinsel and the other three stations located within the catchment). The model performance is found to be satisfactory, as illustrated in Table III, which shows the results of the four assessment criteria for both the calibration and the validation periods on an hourly scale. Model biases, CR1, are within the range Ð4 to Ð2 and the average values for the three efficiency criteria CR2, CR3 and CR4 are Ð81, Ð8 and.85, respectively. This indicates that the model has a high confidence and can give a fair representation of both low-flow and high-flow hydrographs for the study catchment. However, in this study all the gauging stations are on the main stem, and the land-use compositions upstream of each station are similar. This may reduce significantly the additional value of the internal validation, which is commonly used to assess the model validity in hydrological modelling. RESULTS AND DISCUSSION For assessing runoff partitioning from different land-use class areas of the catchment, the calibrated WetSpa model is run for the whole simulation period, and the flow components for different land-use classes are calculated at each time step. Figure 6 gives a graphical presentation for the same flood event used in the model calibration, but shows storm runoff contributions from different land-use class areas. Clearly, surface runoffs from urban areas, cropland and grassland form the high water peak of the hydrograph, representing 39Ð1%, 11Ð6% and 9Ð% respectively of the storm runoff (excluding baseflow). Interflows from woodland, grassland and cropland yield 16Ð7%, 8Ð8% and 7Ð5% respectively of the storm runoff, which are essential components of the storm runoff for this flood event. Other storm runoff components in the figure are mainly surface runoff from water surfaces and forested areas, accounting to about 7Ð2% of the storm runoff, whereas surface runoff and interflow from mining areas and interflow from urban areas are negligible for this flood event. To analyse the controlling factors on runoff at the basin outlet from different land-use class areas, 18 flood hydrographs with peak discharges higher than 2 m 3 s 1 within the simulation period are selected, and the partitions and the flow coefficient for the flood event and the relative errors in flood volume and peak discharges are calculated individually. Table IV contains the simulated runoff contributions from different

11 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES 543 Table IV. Simulated runoff contributions from different land-use classes for the selected storms No. Flood period Precipitation Contribution (%) Observed Error on Flow Observed Error on (mm) flood volume flood volume coefficient peak discharge peak discharge Urban Cropland Grassland Woodland Other (mm) (%) (m 3 s 1 ) (%) 1 1 Feb 6 Mar Ð1 2Ð9 23Ð8 16Ð1 5Ð1 85Ð1 1Ð6 Ð55 4Ð2 12Ð Dec Ð2 32Ð 22Ð 22Ð1 19Ð 4Ð9 26Ð5 9Ð4 Ð61 27Ð5 7Ð Jan Ð7 22Ð6 21Ð7 25Ð1 26Ð2 4Ð5 45Ð1 1Ð Ð8 33Ð9 8Ð Jan Ð 22Ð2 23Ð1 25Ð2 25Ð1 4Ð4 29Ð7 17Ð8 Ð85 31Ð5 12Ð Oct 7 Nov Ð7 29Ð3 22Ð8 22Ð1 2Ð8 4Ð9 76Ð3 6Ð Ð44 4Ð5 15Ð Apr Ð4 39Ð9 23Ð7 18Ð7 12Ð7 5Ð1 19Ð9 1Ð6 Ð41 32Ð9 15Ð Dec Ð4 35Ð1 24Ð1 22Ð1 13Ð5 5Ð1 31Ð5 1Ð Ð4 38Ð9 16Ð Dec 2 Jan 2 67Ð 22Ð9 22Ð3 24Ð2 25Ð8 4Ð8 49Ð1 18Ð1 Ð73 39Ð9 6Ð Mar 2 19Ð4 23Ð8 23Ð 25Ð5 23Ð1 4Ð6 15Ð9 13Ð2 Ð82 32Ð6 1Ð Jul 2 25Ð3 42Ð 24Ð1 22Ð7 6Ð 5Ð1 1Ð2 1Ð8 Ð4 24Ð8 14Ð Jul 2 41Ð6 38Ð3 22Ð9 19Ð3 14Ð3 5Ð2 12Ð7 18Ð1 Ð31 21Ð4 16Ð Jul 2 63Ð3 35Ð2 23Ð7 22Ð4 13Ð7 4Ð9 2Ð 13Ð5 Ð32 23Ð8 16Ð Aug 2 43Ð4 53Ð7 22Ð4 11Ð8 5Ð9 6Ð3 8Ð6 18Ð6 Ð2 24Ð3 1Ð Oct 2 42Ð 35Ð7 23Ð5 22Ð1 14Ð 4Ð6 18Ð2 9Ð3 Ð43 25Ð8 5Ð Nov 2 37Ð5 26Ð9 22Ð6 24Ð3 21Ð6 4Ð6 2Ð3 5Ð9 Ð54 26Ð1 17Ð Nov 2 49Ð4 22Ð7 22Ð6 24Ð8 25Ð4 4Ð4 3Ð 2Ð7 Ð61 28Ð6 7Ð Jan 21 7Ð3 24Ð 22Ð5 23Ð9 25Ð1 4Ð6 39Ð7 5Ð Ð56 4Ð7 14Ð Jan 21 63Ð3 22Ð7 23Ð4 24Ð6 24Ð8 4Ð5 41Ð1 1Ð5 Ð65 32Ð6 11Ð8 Mean 62Ð 31Ð3 22Ð8 22Ð5 18Ð5 4Ð87 31Ð1 32Ð2 Ð53 31Ð4 2Ð

12 544 Y. B. LIU ET AL. 6 Discharge (m 3 /s) Urban surface Cropland surface Grassland surface Other runoff components Woodland interflow Grassland interflow Cropland interflow Baseflow Precipitation Measured stream flow Precipitation (mm/h) 2/2/97 12/2/97 22/2/97 4/3/97 14/3/97 24/3/97 3/4/97 2 Figure 6. Storm runoff partitioning at Steinsel for the flood in February 1997 land-use classes for the selected storm events occurring at Steisel and the statistics of the simulation errors for each flood event. The flow coefficient for a storm event defined in Table IV is the ratio of the outflow water volume at the catchment outlet to the volume of water precipitated over the catchment during this event. The simulated flow coefficients can be computed in a similar manner by incorporating the simulated flow volume at the basin outlet and should be close to those of the observed flow coefficient. A series of plots is presented in Figure 7 showing the event-to-event variations of the different runoff contributions, the evolution of each contribution normalized by the percentage of its land-use class area, the error on flood volume and the error on peak discharge with respect to the flow coefficient. As can be seen in Table IV and Figure 7a, both flow coefficient and runoff partitioning from different landuse areas vary from one storm event to another, which demonstrates the effects of antecedent soil moisture, storm characteristic and groundwater storage on the flood behaviour of the catchment. Urban contribution to storm runoff decreases with the flow coefficient, with mean of 1Ð53 and standard deviation of Ð43 (Figure 7b). The relative contribution value is larger than unity, indicating that its contribution is higher than its percentage of land-use class area of the catchment. Cropland relative contribution is almost constant for different storms, with mean of Ð99 and standard deviation of Ð4 (Figure 7c); this indicates that its contribution is moreor-less equal to the cropland percentage of the catchment. The relative contribution from grassland increases slightly with the flow coefficient, with mean of Ð94 and standard deviation of Ð14 (Figure 7d). The relative contribution from woodland areas increases significantly with the flow coefficient, with mean of Ð64 and standard deviation of Ð24 (Figure 7e). The relative contribution values from the grass and forest land-use class areas are less than unity, indicating that their contributions are less than the percentage of their land-use class areas. Other contributions (mining plus water surface) decrease with the flow coefficient, with mean of 1Ð19 and standard deviation of Ð11 (Figure 7f), which is mainly due to the change of runoff contribution from water surface. The relative error on the flood volume strongly decreases with the flow coefficient (Figure 7g). This indicates that the Thiessen polygon method may not give a precise estimate of the rainfall distribution for localized storms. The relative error on the peak discharge is presented in Figure 7h, with mean of 1Ð5 m 3 s 1 and standard deviation of 12Ð8 m 3 s 1, which indicates that the model gives a fairly good prediction of the flood peaks.

13 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES (a) Cropland relative contribution (c) Flow coefficient Land-use classes contributions Urban Grassland Others 5 Event Cropland Woodland Woodland relative contribution (e) Flow coefficient 2.5 Urban relative contribution (b) 2.5 Flow coefficient Grassland relative contribution (d) 2.5 Flow coefficient Other relative contribitions (f) Flow coefficient 2 Error on flood volume 2 Error on peak discharge (g) Flow coefficient (h) Flow coefficient Figure 7. (a) Event-to-event variations of the different runoff contributions; (b) normalized relative runoff contribution from urban, (c) cropland, (d) grassland, (e) woodland, (f) other areas, all with respect to flow coefficient; (g) error on flood volume and (h) error on peak discharge, both with respect to flow coefficient Variations in runoff partitioning are directly tied to soil moisture and groundwater storage. Among the 18 storm events listed in Table IV, 13 occurred during the winter season, and five events occurred between April and November. Winter storms are usually characterized by high flow coefficients, due to the high soil moisture content and high groundwater storage, causing high baseflow, interflow and saturation overland flow. Under such conditions, an amount of the river discharges is generated from natural areas. This can be illustrated by the two flood events that occurred in January 1998 and the flood that occurred in March 2, in which flow

14 546 Y. B. LIU ET AL. coefficients were ½Ð8 and in which the runoff contributions from urban, agricultural, pasture and forested areas were of the same magnitude. This indicates that groundwater drainage plays an important role in the winter season, which is mostly produced by previous storms. For instance, the simulated baseflow accounts for 3% of the total volume for the flood event in March 2, and 38% for the flood in January However, the urban contribution increases greatly if we consider only the storm runoff excluding baseflow. For instance, the simulated urban contribution increases from 22Ð6% of the total runoff to 31Ð2% of the storm runoff for the first flood in January 1998, and from 22Ð2% to 36Ð5% for the second flood in the same month. On the contrary, summer storms usually have low flow coefficients, due to the low soil moisture content and low groundwater storage. Runoff from urban areas is dominant in all flood hydrographs of this catchment, whereas other contributions are relatively small, especially the runoff from forested areas. An extreme example is the storm event that occurred in August 2, for which the total rainfall was 43Ð3 mm, causing a peak discharge of 24Ð3 m 3 s 1 at Steinsel and a flood volume of 8Ð3 mm with a flow coefficient of 2%. The calculated runoffs from urban, agricultural, pasture and forested areas were 53Ð7%, 22Ð4%, 11Ð8% and 5Ð9% respectively. The soil was very dry before the storm, and most rainfall infiltrated in the soil, resulting in rather small runoff contributions to the flood event. Storm characteristics, such as volume, duration, intensity and shape, have a big influence on the flow coefficient and the runoff contributions from different land-use areas. Large storms with long duration and high intensity produce more runoff under similar antecedent soil moisture conditions, but the flow coefficient may not respond positively due to the lower baseflow. For small rainfall events, most runoff is generated from impermeable areas and open water surfaces, whereas runoff from natural areas can be ignored. For the three storm events in July 2, for example, the rainfall volume for each storm event was 25Ð3 mm, 41Ð6 mm and 63Ð3 mm, resulting in urban runoff of 42Ð%, 38Ð3% and 35Ð2% respectively, whereas the contributions from other natural land-use classes increased with storm volume accordingly. In addition, runoff also varies with time and storm shape. The flow at the start of the flood stems exclusively from direct runoff from urban areas and water surfaces because of the quick response and short travel times. Other runoff contributions join the flow afterwards, but the magnitude of these depends strongly upon soil moisture conditions. Figure 8 shows the simulated contributions to the monthly flow at Steinsel from different land-use classes from December 1996 to March 21. The runoff contributions from urban (29Ð3%), agricultural (22Ð8%), 15 Runoff (mm/m) Water surface Cropland Woodland Precipitation Mining area Grassland Urban Mearsured runoff Precipitation (mm/m) /96 1/97 8/98 6/99 4/ 2/1 1 Figure 8. Contributions of monthly flow at Steinsel from different land-use classes

15 PREDICTING STORM RUNOFF FROM DIFFERENT LAND-USE CLASSES 547 pasture (22Ð2%), forested (21Ð5%), mining (1Ð5%) and water surfaces (2Ð7%) are indicated separately. The runoff contribution from urban areas is the highest, contributing mainly to direct flow. Contributions from agricultural, pasture and forested areas are more-or-less equal, contributing to surface runoff, interflow and baseflow, whereas the contribution from mining areas is the smallest, contributing only to baseflow. CONCLUSIONS An application of a GIS-based spatially distributed model, WetSpa, to predict storm runoff contributions from different land-use class areas is presented. The model uses elevation, soil and land-use data in a simple way to predict outflow hydrographs and the spatial distribution of hydrological characteristicswithin a river catchment. Surface runoff is calculated based on rainfall intensity and soil moisture status, and is calculated as a function of the potential runoff coefficient, which depends upon slope, land use and soil type combinations. The use of the spatially distributed unit response functions enables the routing of the excess water from individual cells to the basin outlet along the DEM-derived flow paths. Therefore, the model is suitable for evaluating storm runoff contributions from different land-use class areas within a river catchment. Compared with other GISbased complex models, WetSpa s specifications closely relate runoff with cell physical properties, requiring relatively few model parameters, and allow the model to operate at different spatio-temporal scales. Moreover, the use of GIS techniques in the WetSpa model to keep track of all parameters and state variables makes it easy for parameter calibration and model simulation under different conditions. The model was applied on the Steinsel catchment in the Alzette river basin, Grand Duchy of Luxembourg, with 52 months of observed hourly rainfall and discharge data. Model calibration and validation show the model s level of representativeness to be quite satisfactory. The outflow at the catchment outlet has been especially well reproduced. It was demonstrated in this study that the land-use composition and soil moisture condition play an important role in generating flood hydrographs at the basin outlet. Simulations show that the flow coefficient and the runoff partitioning from different land-use class areas vary from one storm event to another due to the differences in soil moisture and storm characteristics. The relative runoff contribution from urban areas decreases with the flow coefficient. Cropland relative runoff contribution tends to be a constant, being more-or-less equal to the cropland area percentage of the catchment. The relative runoff contributions from grassland and woodland increase with flow coefficient, and are close to their percentage of land-use class areas of the catchment for large storms. Simulation results show that the important runoff processes contributing to storm runoff in this catchment are mainly surface runoff from urban areas and partly from cropland and grassland for large storms. Interflow from woodland, grassland and cropland forms the recession of the flood hydrograph, but also contributes considerably to the peak discharge for floods in the wet season. Other land-use class areas with high infiltration and depression storage capacity contribute very little to the storm runoff. It can be concluded that the runoff from urban areas is dominant for a flood event, compared with other land-use classes in this catchment, and tends to increase for small floods and for flood events with low antecedent soil moisture. Other runoff contributions tend to increase for large storms and for storm events with high antecedent soil moisture. Interflow and baseflow from natural areas are important during the wet season, but not for small floods during the dry season. REFERENCES Ambroise B Streamflow generation within small rural catchments in a temperate environment: 2 systemic and dynamic modelling. Journal of Water Science 12: Bronstert A. 24. Rainfall-runoff modelling for assessing impacts of climate and land-use change. Hydrological Processes 18: Bronstert A, Niehoff D, Burger G. 22. Effects of climate and land-use change on storm runoff generation: present knowledge and modelling capabilities. Hydrological Processes 16: Brun SE, Band LE. 2. Simulating runoff behavior in an urbanizing watershed. Computers, Environment and Urban Systems 24: 5 22.

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