Comparing SWAT and WetSpa on the River Grote Laak, Belgium

Comparing SWAT and WetSpa on the River Grote Laak, Belgium ossent J. and W. Bauwens Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (Free University Brussels), Belgium * Abstract SWAT and WetSpa are two hydrologic simulators that can be used for the simulation of discharges. In order to improve the performance of the simulators in the future, we compared them on the models of the catchment of the low land river Grote Laak. This basin has an area of 56 km² and is situated in Belgium. An automatic calibration (SCE-UA for SWAT and PEST for WetSpa) was performed on a period of four continuous years. Both models provided acceptable results, although SWAT showed slightly better calibration and WetSpa better validation results. Both were not able to simulate an extreme dry event. KEYWORDS: distributed modelling, semi-distributed modelling, model comparison, SWAT, WetSpa. Introduction Hundreds of hydrologic simulators have been developed in the past, all with different features and with different advantages and limitations. Distributed models, like WetSpa (Liu et al., 003, Gebremeskel et al., 00) and SHE (Abbott et al., 1986a,b), largely maintain a physical background because of the high level of detail. Modelling these details, however, requires a lot of computer time and data. Due to the spatial lumping, semi-distributed models, like SWAT (Arnold et al., 1993) and TOPMODEL (Beven and Freer, 001), reduce the calculation time and the data requirements significantly, when compared to distributed models. As a result of the lumping, however, the modelling of the processes becomes less detailed and the relation with reality becomes weaker. A typical example of the latter is the use of the equations of de Saint Venant for river routing in distributed models and the use of hydrologic routing methods in semi-distributed models. As a consequence of these differences, the user must find a compromise between the computation time and the data requirements on the one hand and the detail of the representation of the system and the processes on the other hand. While it is generally recognised that such a compromise will be case and problem specific, no objective criteria exist so far to select one or another approach. In order to shed some light on the problem, this paper presents a comparison of a distributed model (WetSpa) and a semi-distributed model (SWAT) for a watershed in Belgium, i.e. the watershed of the river Grote Laak. Based on the information provided in this paper, future research should be performed on the processes that are included in the two models and that lead to the resulting output. In that way, the performance of the models can be improved, when adapted to these findings. * ir. Jiri ossent, Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (Free University Brussels), Pleinlaan, B-1050 Brussels, Belgium, Phone: +3--693036, Fax: +3--6930, jnossent@vub.ac.be Prof. Dr. ir. Willy Bauwens, Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel (Free University Brussels), Pleinlaan, B-1050 Brussels, Belgium, Phone: +3--693038, Fax: +3-- 6930, wbauwens@vub.ac.be

Materials and methods Model description SWAT SWAT (Soil and Water Assessment Tool) is a physically based, time continuous, semi-distributed river basin scale model, developed by the USDA Agricultural Research Service (ARS) in order to quantify the impact of land management practices on water quantity, sediment and water quality in large complex watersheds with varying soil, land use and management conditions over long periods of time. The semi-distributed characteristics of the model are linked to the division of the catchment into subcatchments, which are divided into HRU s (Hydrological Response Units): portions of the subbasin containing a unique combination of land use and soil. The processes are lumped at the HRU level and the discharge of the subcatchments is routed through the river network to the main channel and the basin outlet (eitsch et al., 00). We used the most recent version of SWAT (SWAT005) for the modelling presented in this paper. WetSpa WetSpa is a physically based, distributed hydrological model for predicting the Water and Energy Transfer between Soil, Plants and Atmosphere on regional or basin scale and was developed at the Vrije Universiteit Brussel, Belgium. The model conceptualizes a basin hydrological system as being composed of atmosphere, canopy, root zone, transmission zone and saturation zone. The processes within WetSpa are based on the division of the catchment into grid cells to maintain the distributed characteristics (Wang et al., 1996). For the research presented in this paper, we used the WetSpa Extension. This version is capable of predicting outflow hydrograph at basin outlet or any converging point in a watershed with a variable time step (De Smedt et al., 000). Site and Data description Site and maps The river Grote Laak is a typical lowland river and is situated in the orth Eastern part of Belgium. Grote Laak is a sub-catchment of the ete basin, a part of the larger Schelde catchment and has a surface of 56 km². The mean elevation is 36.6 m with a maximum of 65 m and a minimum of 17 m. A specific feature of the basin are the canals that cross the catchment. The catchment s yearly precipitation is around 900 mm. The topography of the study area was digitised from 1/10,000 maps and divided into grid cells of 50 by 50 m. Based on the resulting Digital Elevation Model (DEM), we performed an initial delineation of the watershed with the ArcView GIS SWAT user interface (Di Luzio et al., 00), with an outlet close to the gauging station of Laakdal. Figure 1 presents the topography of the area, with the main river courses and measuring stations.

Figure 1: Topography of the study area. Based on the mask, created out of this generated watershed, we defined the study area for the use in WetSpa. Because of the small differences in altitude and the small slopes in the catchment of the Grote Laak, WetSpa was not able to generate the correct main river channel and some areas were not routing towards the outlet of the basin. In order to get a better relation between the digitised and the modelled streams, we created a secondary elevation map. We performed a burn in of the streams on this second elevation map with the CRWRprepro -tool (Olivera, 1998) by increasing the elevation of all cells but those that coincide with the digitised streams. We also added an artificial wall around the area to achieve a better delineation and a total coverage of the area with subwatersheds, in WetSpa. The original elevation map was used to generate the correct value of the slopes and the elevations. The secondary map was used for the stream order, stream network and subwatersheds. Wetspa generated 185 subwatersheds in this way; in SWAT we only defined three subcatchments. The predominant soils in the catchment have a sandy texture and the predominant land use classes are residential (high density), pasture, agricultural land (corn) and mixed forest. Figure shows both the land use and soil map, adapted to the extent of the catchment, which are based on maps ordered from GIS Vlaanderen (GIS-Vlaanderen, 001) and were resampled to four soiltypes (landdune, sand, sandyloam, clay) and five land use classes (corn, mixed forest, pasture, urban, water). Figure : Land use and Soil map of the study area

Table 1 presents the distribution of soil and land use over the study area. In SWAT we performed a redistribution on subcatchment scale, leaving out the land uses accounting for % or less of the area and 8% or less for the soil classes. This resulted in a total number of 3 HRU s with varying sizes between 0. and 6.9 km². Table 1: Distribution of soil and landuse over the study area Soil types Land use Class Clay Landdune Sand Sandyloam Corn Forest Pasture Urban Water Percentage [a] 8.9 1.11 84.19 6.41 3.8 8.89 16.39 1.71 0.73 Percentage [b] 8.36 0 87.5 4.11 3.51 9.11 16.50 1.87 0 [a] Actual percentage of soil and landuse in the area. [b] Percentage after redistribution in SWAT (not used in WetSpa). Hydrometeorological data For the gauging station of Laakdal, discharge data is available from October 1998 until December 003 from the HYDROET database (IVA-VMM, 006). We used the discharge data from January 000 on for calibration of the models and the year 1999 for validation. The main reason for this choice is that we wanted to include the very dry year 003 (precipitation around 700 mm) in the calibration period. Meteorological data are all available from the Royal Meteorological Institute (KMI). WetSpa uses daily time series of precipitation, potential evapotranspiration and average temperature data. For SWAT we provided daily time series of precipitation, minimum and maximum temperature, relative humidity, solar radiation and windspeed data in order to use the Penman-Monteith equation. Table presents the available data from January 1996 until December 003 for different measuring stations. Table : Available meteorological data Ukkel [a] Kleine Brogel [a] Lichtaart [a] Turnhout [a] Geel [a] Precipitation X X X X X PET X Av. temperature X X X Min.-Max. temperature X Windspeed X Solar radiation X Rel. Humidity X [a] All stations are located outside the catchment, with a maximum distance for Ukkel: ± 60 km to the center of the catchment Calibration techniques Theoretically, the parameters of physically based models do not need to be calibrated. However, due to uncertainty on the model input and structure and because of the spatial variability in both horizontal and vertical direction, we performed a calibration on discharge data for a selected set of parameters for both models. For SWAT we used the model incorporated LH-OAT method (Latin Hypercube- One factor At a Time) (van Griensven and Meixner, 003, Holvoet et al., 005) to perform a sensitivity analysis. We performed an automatic calibration with the SCE-UA-algorithm (Shuffled Complex Evolution), also incorporated in SWAT (Eckhardt and Arnold, 001), on nine sensitive parameters and a manual calibration on three other parameters afterwards, in order to limit the calculation time of the automatic calibration. Table 3 shows the most sensitive 1 and the calibrated parameters. 1 Parameters related to snowfall and snowmelt are negligible for this catchment and not taken into account.

Table 3: SWAT parameters Parameter Sensitivity Automatic Manual Calibration Calibration C SCS runoff Curve umber 1 HRU split [a] ALPHA_BF Baseflow alpha factor HRU split [a] SURLAG Surface runoff lag coefficient 3 X CH_K Effective hydraulic conductivity (main channel) 4 X GWQM Threshold depth of water for return flow to occur 5 HRU split [a] CH_ Manning coefficient for channel 6 X CAMX Maximum canopy index 7 X RCHRG_DP Groundwater recharge to deep aquifer 8 X SLSUBBS Average slope length 9 X ESCO Plant evaporation compensation factor 10 X SOL_AWC Available water capacity 11 X SLOPE Average slope steepness 1 X [a] HRU split: the parameter was calibrated for the different HRU s separately. We performed a manual and an automatic calibration, using PEST (Doherty, 001), on the WetSpa model on eight (out of eleven) global model parameters. The three global model parameters related to snowfall and snowmelt were not taken into account. Criteria for the evaluation of the model performance Although graphical presentation of the overall shape of the time series of discharges (simulated vs. observed) can be useful for interpreting the model results, objective and quantitative information is needed for the evaluation of the model performance and the comparison of models. In this study, we evaluated the performance compared to measured data by following criteria: 1. SE (ash-sutcliffe efficiency): The ash-sutcliffe efficiency (ash and Sutcliffe, 1970) describes how well the stream flows are simulated by the model. A value of one for the SE indicates a perfect fit between simulated and observed hydrographs, a value of zero indicates that the average measured stream flow would have been as good a predictor as the modelled flow. Equation 1 presents the SE equation: SE Qsi Qoi 1 (1) Qoi Qo where Qs i is the simulated stream flow, mean observed stream flow. Qo i is the observed stream flow and Qo is the. LSE (Logarithmic version of ash-sutcliffe efficiency): This logarithmic transformed ash-sutcliffe efficiency is a criterion for the quality of the low flow simulations (Smakhtin et al., 1998). A value of one gives a perfect representation. Equation presents the LSE equation LSE ln Qsi ln Qoi 1 () ln Qoi ln Qo

Q (m3/s) precipitation (mm) 3. ASE (Adapted version of ash-sutcliffe efficiency): An adapted version of the ash-sutcliffe efficiency is presented in equation 3. It gives a criterion for the evaluation of the high flow simulations. A value of one gives a perfect representation (Liu and De Smedt, 004). ASE Qoi Qo Qsi Qoi 1 (3) Qoi Qo Qoi Qo Results and Discussion Figure 3 presents the comparison of simulated and observed discharges for the calibration period using both models. As one can see, both models have comparable and acceptable results for the daily discharges. Table 4 states these findings, although it can be noticed that the quantitative performance of SWAT is slightly higher than that of WetSpa, especially on the low flows. The water balance was closed in both cases, with corresponding components (Table 5). 6.00 0 5.00 4.00 precipitation SWAT WetSpa observations 0 40 60 80 3.00 100 10.00 140 1.00 0.00 1/01/000 19/07/000 4/0/001 3/08/001 11/03/00 7/09/00 15/04/003 1/11/003 Figure 3: Calibration of SWAT and WetSpa (000-003) 160 180 00 Table 4: Model performance after calibration (000-003) SE LSE ASE SWAT [a] 0.739 0.658 0.788 WetSpa (manual) [a] 0.701 0.588 0.75 WetSpa (automatic) [a] 0.701 0.588 0.75 [a] One day of observed discharge is adapted to a known flooding. Table 5: Water balance components (000-003) Precipitation (mm/y) Runoff (mm/y) Evapotranspiration (mm/y) SWAT 918.1 466.0 434.6 WetSpa 90.8 464.1 476.

Although SWAT performs better in this case, it is not possible to extrapolate this conclusion to other basins. Due to the limited area of the catchment and the availability of sufficient discharge data for calibration, scale effects are probably less important for the semi-distributed model (Refsgaard, 1997). It will be necessary to extend the study in the future to other catchments with other properties to reveal this. The lower performances on low flows (for SWAT and WetSpa) are partly caused by the very dry year 003. It can be seen in the previous figure that both SWAT and WetSpa are underestimating the baseflow during this extreme event. The previous table also shows that the results for the manual and automatic calibration in WetSpa are identical. Due to the little number of parameters, WetSpa is more stable and less prone to equifinality. Besides the calibration method that was used, the number of parameters also has its consequences on the calculation time for the calibration: for WetSpa it took less than four hours to calibrate the model; in SWAT more than four days were needed to get these results. evertheless, the calculation time for one run in WetSpa was four times higher than in SWAT, but still acceptable and workable, because of the limited area of the catchment. The stability of the model with respect to the parameters also has its effects on the quantitative performance of WetSpa for the validation (Table 6). The latter performance is significantly higher than for SWAT and even better than during the calibration period. Although the previously discussed dry year 003 influenced the results of the calibration, one can still conclude that due to the stable performance, WetSpa is better suited to make predictions on future events. Here again future research should confirm this. Conclusions Table 6: Model performance after validation (1999) SE LSE ASE SWAT 0.563 0.580 0.809 WetSpa 0.75 0.690 0.919 SWAT and WetSpa have acceptable and comparable model results for the basin of the Grote Laak, although they both have problems predicting extreme dry events and resulting low flows. The stability of WetSpa with respect to the parameters leads to a better validation and capability to make predictions and has its effects on the calibration time. Future studies on other catchments with other properties are needed to extend the findings of this study. References Abbott, M.B., J.C. Barthurst, J.A. Cunge, P.E. O Connel and J. Rasmussen. 1986a. An introduction to the European Hydrological Systems-Système Hydrologique Européen, SHE. 1. History and philosophy of a physically based distributed modelling system. J. Hydrology 87, 45-60. Abbott, M.B., J.C. Barthurst, J.A. Cunge, P.E. O Connel and J. Rasmussen. 1986b. An introduction to the European Hydrological Systems-Système Hydrologique Européen, SHE.. Structure of a physically based distributed modelling system. J. Hydrology 87, 61-78. Arnold, J.G., P.M. Allen and G. Bernhardt. 1993. A comprehensive surface-groundwater flow model. J. Hydrology 14 (1-4), 47-69. Beven, K. and J. Freer. 001. A dynamic TOPMODEL. Hydrological Processes 15, 1993-011.

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