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SWAT-G, a version of SWAT99.2 modified for application to low mountain range catchments
Physics and Chemistry of the Earth 27 (2002) 641–644 www.elsevier.com/locate/pce
SWAT-G, a version of SWAT99.2 modified for application to low mountain range catchments K. Eckhardt *, S. Haverkamp, N. Fohrer, H.-G. Frede Institut f€ur Landeskultur, Justus-Liebig-Universit€at Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany Received 15 May 2001; received in revised form 22 November 2001; accepted 22 November 2001
Abstract The Soil and Water Assessment Tool (SWAT) is a well established distributed eco-hydrologic model. However, using the example of a mesoscale catchment in Germany it is shown that the version SWAT99.2 is not able to correctly reproduce the runoff generation in a low mountain region. The calculated contribution of the baseflow to the streamflow is far too high whereas the interflow is strongly underestimated. Alternatively, the modified version SWAT-G can be used which, as is demonstrated in this paper, yields far better results for catchments with predominantly steep slopes and shallow soils over hard rock aquifers. In the example, calibrating the model over three hydrologic years of daily streamflow, the model efficiency increases from 0.17 to þ0.76. The modifications in SWAT-G allow hydrological processes to be modelled in low mountain ranges while not restraining the applicability of the model to catchments with other characteristics. Ó 2002 Elsevier Science Ltd. All rights reserved. Keywords: Distributed models; Model verification; SWAT
1. Introduction The model SWAT (Soil and Water Assessment Tool) (Arnold et al., 1998) is used to characterize the influence of land use changes on hydrological processes in a low mountain region, the Lahn-Dill-Bergland, in the federal state of Hesse, Germany (Fohrer et al., this issue). SWAT is a distributed, continuous time model operating on a daily time step. The objective of the model development was to predict the impact of management on water, sediment and agricultural chemical yields in meso- to macroscale basins. Major model components include: weather, hydrology, soil temperature, plant growth, nutrients, pesticides, and land management. In each of the spatial subunits of a catchment model, water balance is represented by several storage volumes: canopy storage, snow, soil profile, shallow aquifer, and deep aquifer. The soil profile can be subdivided into multiple layers. Soil water processes include infiltration, evaporation, plant uptake, lateral flow, and percolation to lower layers. Percolation from the bottom of the soil profile recharges the shallow aquifer. A recession con-
*
Corresponding author. Fax: +49-4101-693857. E-mail address: [email protected] (K. Eckhardt).
stant is used to lag flow from the shallow aquifer to the stream. Other shallow aquifer components include evaporation, pumping withdrawals, and seepage to the deep aquifer. SWAT is currently used by the EPA (United States Environmental Protection Agency), the NOAA (National Oceanic & Atmospheric Administration/USA), universities and environmental consulting firms. It serves, for example, to assess effects of land use changes on hydrological processes (Fohrer et al., this issue) and water quality (Saleh et al., 2000), and to predict impacts of climate change (Fontaine et al., 2001; Rosenberg et al., 1999). The hydrologic components of the model have been validated for numerous watersheds (e.g. Arnold and Allen, 1996). Yet, Peterson and Hamlett (1998) found a poor model performance when calibrating SWAT for a watershed containing fragipan soils. Fragipans impede the percolation. Therefore, on hillslopes much nearsurface lateral flow (interflow) is produced. In this aspect the catchments of the Lahn-Dill-Bergland are similar. With their steep slopes, shallow soils and hard rock aquifers they can be assumed to be typical of many European low mountain ranges. In the present study it is shown that even after a comprehensive calibration, the model version SWAT99.2 is not able to correctly
1474-7065/02/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 4 7 4 - 7 0 6 5 ( 0 2 ) 0 0 0 4 8 - 7
642
K. Eckhardt et al. / Physics and Chemistry of the Earth 27 (2002) 641–644
reproduce the runoff processes in such a catchment. It is demonstrated which subprocesses in the model cause the aberrant simulation result, how the description of these processes can be corrected, and how the model results are subsequently improved.
2. The investigated catchment The Dietzh€ olze catchment is located in the Lahn-DillBergland about 30 km north-west of Giessen/Germany. Its area amounts to 81 km2 with an elevation range from 250 to 685 m a.s.l., and average slope steepness of about 20%. Land use information was derived from Landsat TM5 satellite images (N€ ohles, 2000). The dominating land cover is forest (coniferous forest: 40%, deciduous forest: 21% of the area). Other land use categories are pasture (17%), range and farm land (13%) and settlement (9%). On the slopes and hilltops the soil consists mainly of shallow cambisols (HLUG, 2000) over a bedrock of schist and greywacke. This hard rock base of the soil has to be passed by the percolating water before it can reach the groundwater. In the catchment model it is represented by an additional layer of high bulk density and low hydraulic conductivity underneath the corresponding soils. In the valleys gley- and fluvisols is also found although in only a few percent of the area. As a consequence of the hydrogeological structure of the catchment, the contribution of groundwater (baseflow) to the streamflow is small. An analysis of the low flow characteristics of the Dietzh€ olze showed that this
contribution amounts to only about 20% (Kaviany, 1978). A model of the Dietzh€ olze catchment with a spatial resolution of 5 subbasins and 35 hydrotopes (defined as unique combinations of land cover and soil) has been established and calibrated against three hydrologic years (1991–1993) of daily measured runoff at the catchment outlet. The parameters were optimized by using an automatic calibration algorithm, the Shuffled Complex Evolution algorithm developed by Duan et al. (1992). The calibration strategy was outlined by Eckhardt and Arnold (2001).
3. Results and modifications of the model In Table 1 the calibrated parameters, their upper and lower bounds and their optimized values are listed. The resulting streamflow is shown in Fig. 1. SWAT99.2 is not able to correctly reproduce the runoff dynamics in the catchment. The model efficiency (Nash and Sutcliffe, 1970) is –0.17. The key process responsible for this inacceptable calibration result is the calculation of the vertical and lateral flows in the soil layers as a function of actual soil water content, field capacity, soil temperature, porosity, saturated hydraulic conductivity, slope steepness and slope length. The flow out of the hard rock layer underneath the shallow soils is limited by the low hydraulic conductivity of this layer. As the water content of the hard rock layer approaches its maximum water capacity, the inflow from the undermost porous layer should be reduced in favour of an
Table 1 Calibrated model parameters Parameter Snow melt rate (mm/d/°C) Surface runoff lag time (d) Curve number for coniferous forest Maximum potential interception for coniferous forest (mm) Manning’s ‘‘n’’ value for overland flow (m1=3 s) Groundwater recession coefficient (d1 ) Delay of the groundwater recharge (d) Deep aquifer percolation fraction Thickness of the rocky base of soil no. 202a (mm) Thickness of the rocky base of soil no. 2458b (mm) Density, soil no. 2458b , third layer (g/cm3 ) Density of the bedrock (g/cm3 ) Available water capacity, soil no. 2458b , first layer (mm/mm) Saturated conductivity, soil no. 202a , third layer (mm/h) Saturated conductivity, soil no. 2458b , third layer (mm/h) Anisotropy factorc , soil no. 2458b , third layer Maximum leaf area index for coniferous forest Maximum leaf area index for pasture a
Shallow cambisol on the lower slope. Shallow cambisol on the upper slope. c Only available in SWAT-G. b
Lower bound 1.00 1.00 50.0 3.00 0.20 0.030 1.00 0.00 1000 3000 1.50 2.51 0.16 1.0 10.0 2.00 4.00 1.50
Upper bound 3.00 5.00 60.0 6.00 0.50 0.060 20.00 0.80 5000 10 000 1.60 2.64 0.20 45.0 85.0 8.00 14.00 5.50
Calibration result SWAT99.2
SWAT-G
1.05 1.43 52.4 3.11 0.47 0.054 4.12 0.77 1060 9620 1.56 2.52 0.16 39.3 53.9 5.80 1.69
1.00 1.00 51.0 3.58 0.50 0.031 19.65 0.05 1870 3990 1.60 2.64 0.19 44.8 84.8 8.00 5.95 2.31
K. Eckhardt et al. / Physics and Chemistry of the Earth 27 (2002) 641–644
Fig. 1. Result of the automatic model calibration using SWAT99.2.
enhanced interflow. Yet, this is not the case in SWAT99.2. Water continues to percolate into the hard rock layer the same as before, so that the layer is inadmissibly filled beyond its actual water capacity. Most of this water recharges the groundwater with a marked delay of about three months after maxima of the precipitation. This is best seen in springtime (Fig. 1). The calculated contribution of baseflow to the streamflow amounts to 56%. Thus, it is much too high although the optimized value of the parameter ‘‘deep aquifer percolation fraction’’ is 0.77, expressing that most of the groundwater recharge percolates through the shallow aquifer into the deep aquifer and therefore does not contribute to baseflow. As a consequence, the measured streamflow is underestimated by more than 50% (Table 2). In another calibration, whose results are not shown here, the ‘‘deep aquifer percolation fraction’’ was not optimized but defined to be 0.0. Although this increased the mean calculated streamflow to a level close to the measured mean value, the higher baseflow contribution led to a further decrease in model efficiency because the wrong dynamics of the calculated streamflow were intensified. In SWAT-G, percolation from a layer i is linearly reduced in the same measure as the actual water content of the layer i þ 1 below approaches its maximum water capacity: swði þ 1Þ 0 percðiÞ ¼ perc ðiÞ 1 ð1Þ ulði þ 1Þ
643
with perc(i): amount of percolating water, perc0 ðiÞ: amount of percolating water calculated with SWAT99.2, swði þ 1Þ: actual water content of layer i þ 1, ulði þ 1Þ: water content of layer i þ 1 at saturation. Another modification is based on the observation that soils of Central European low mountain ranges which have experienced solifluction in the interglacial periods are anisotropic with respect to their hydraulic conductivity (Kleber et al., 1998). Therefore, a possibility for defining anisotropy factors (hydraulic conductivity of the soil parallel to the slope divided by the vertical hydraulic conductivity) was introduced and the interflow––which is proportional to the hydraulic conductivity––is calculated as latðiÞ ¼ lat0 ðiÞ anisoðiÞ
ð2Þ
with lat(i): amount of lateral flow in layer i, lat0 (i): amount of lateral flow in layer i calculated with SWAT99.2, aniso(i): anisotropy factor of layer i. As a consequence of these modifications, most of the percolating water flows off as interflow on top of the bedrock (Fig. 2). The calibration result using SWAT-G is shown in Fig. 3. A far better correspondence of the measured and calculated streamflow is now obtained. The model efficiency (Nash and Sutcliffe, 1970) increases from 0.17 to þ0.76 (Table 2). A model validation using the daily streamflow in the three following hydrologic years (1994–1996) confirms the calibration (efficiency: þ0.81). This improvement is not an effect of the different parameterization of the two catchment models as a result of their independent calibration. If the catchment model based on SWAT99.2 is parameterized with the values found by the calibration of the SWAT-G model, the efficiency further decreases to 0.41. Crucial for the better performance when using SWAT-G rather is that with 12% in the calibration and 13% in the validation period, the contribution of the baseflow to the streamflow now is much closer to the expected value.
Table 2 Results of the automatic model calibration
Average streamflow (mm/d) Contribution of the baseflow (%) Model efficiencyb a b
Kaviany (1978). Nash and Sutcliffe (1970).
Observed
Calculated SWAT99.2
SWAT-G
1.18 20a
0.57 56
1.17 12
0.17
0.76 Fig. 2. Schematic of the soil water flow using SWAT99.2 and SWATG.
644
K. Eckhardt et al. / Physics and Chemistry of the Earth 27 (2002) 641–644
Acknowledgements This study has been supported by the Deutsche Forschungsgemeinschaft within the scope of the Collaborative Research Centre (SFB) 299.
References
Fig. 3. Result of the automatic model calibration using SWAT-G.
4. Discussion and conclusions It can be assumed that the Dietzh€ olze catchment possesses many characteristics which are typical for low mountain ranges. Therefore, the inability of SWAT99.2 to correctly reproduce the runoff generation processes in the catchment substantially restrains the applicability of the model. Obviously, Peterson and Hamlett (1998) were confronted with the same problem that SWAT99.2 and older versions of the program simulate too much percolation through soil layers with low permeability. From a hydrograph separation they found a baseflow contribution of only 28% to the measured streamflow in their catchment while their best model fit yielded 46% of baseflow. Alternatively, the modified version SWAT-G can be used which, as has been demonstrated in this paper, shows a far better performance under the specified hydrogeological conditions. Both the comparison of measured and calculated streamflow at the catchment outlet and the analysis of the source code show the improvement of the description of hydrological processes in the model. Further modifications in SWAT-G compared to SWAT99.2 affect the regionalization of precipitation and temperature, the calculation of evapotranspiration, the generation of surface runoff, the simulation of plant growth and crop rotation, as well as the control of reservoirs. While these modifications allow hydrological processes to be modelled in low mountain ranges they do not restrain the applicability of the model to catchments with other characteristics.
Arnold, J.G., Allen, P.M., 1996. Estimating hydrologic budgets for three Illinois watersheds. Journal of Hydrology 176, 55–77. Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., 1998. Large area hydrologic modeling and assessment, Part I: model development. Journal of the American Water Resources Association 34, 73–89. Duan, Q., Sorooshian, S., Gupta, V.K., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28, 1015–1031. Eckhardt, K., Arnold, J.G., 2001. Automatic calibration of a distributed catchment model. Journal of Hydrology 251, 103–109. Fohrer, N., M€ oller, D., Steiner, N., An interdisciplinary modelling approach to evaluate the effects of land use change. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 27, 655–662. Fontaine, T.A., Klassen, J.F., Cruick€ o, T.S., Hotchkiss, R.H., 2001. Hydrological response to climate change in the Black Hills of South Dakota, USA. Hydrologic Sciences Journal––Journal des Sciences Hydrologiques 46, 27–40. HLUG (Hessisches Landesamt f€ ur Umwelt und Geologie), 2000. Digital soil map 1: 50 000. Kaviany, E., 1978. Zur Hydrogeologie im Niederschlagsgebiet der Dill (Hessen). Gießener Geologische Schriften, vol. 19. Kleber, A., Lindemann, J., Schellenberger, A., Beierkuhnlein, C., Kaupenjohann, M., Pfeiffer, S., 1998. Slope deposits and water paths in a spring catchment, Frankenwald, Bavaria, Germany. Nutrient Cycling in Agroecosystems 50, 119–126. Nash, J.E., Sutcliffe, J.E., 1970. River flow forecasting through conceptual models, Part I: A discussion of principles. Journal of Hydrology 10, 282–290. N€ ohles, I., 2000. Landnutzungsklassifikation mit multitemporalen Landsat TM-Szenen in einer kleinstrukturierten Agrarregion. Boden und Landschaft, vol. 32. Peterson, J.R., Hamlett, J.M., 1998. Hydrologic calibration of the SWAT model in a watershed containing fragipan soils. Journal of the American Water Resources Association 34, 531– 544. Rosenberg, N.J., Epstein, D., Wang, J., Vail, L., Srinivasan, R., Arnold, J.G., 1999. Possible impacts of global warming on the hydrology of the Ogallala aquifer region. Climatic Change 42, 677– 692. Saleh, A., Arnold, J.G., Gassman, P.W., Hauck, L.M., Rosenthal, W.D., Williams, J.R., McFarland, A.M.S., 2000. Application of SWAT for the Upper North Bosque River Watershed. Transactions of the ASAE 43, 1077–1087.
SWAT-G, a version of SWAT99.2 modified for application to low mountain range catchments K. Eckhardt *, S. Haverkamp, N. Fohrer, H.-G. Frede Institut f€ur Landeskultur, Justus-Liebig-Universit€at Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany Received 15 May 2001; received in revised form 22 November 2001; accepted 22 November 2001
Abstract The Soil and Water Assessment Tool (SWAT) is a well established distributed eco-hydrologic model. However, using the example of a mesoscale catchment in Germany it is shown that the version SWAT99.2 is not able to correctly reproduce the runoff generation in a low mountain region. The calculated contribution of the baseflow to the streamflow is far too high whereas the interflow is strongly underestimated. Alternatively, the modified version SWAT-G can be used which, as is demonstrated in this paper, yields far better results for catchments with predominantly steep slopes and shallow soils over hard rock aquifers. In the example, calibrating the model over three hydrologic years of daily streamflow, the model efficiency increases from 0.17 to þ0.76. The modifications in SWAT-G allow hydrological processes to be modelled in low mountain ranges while not restraining the applicability of the model to catchments with other characteristics. Ó 2002 Elsevier Science Ltd. All rights reserved. Keywords: Distributed models; Model verification; SWAT
1. Introduction The model SWAT (Soil and Water Assessment Tool) (Arnold et al., 1998) is used to characterize the influence of land use changes on hydrological processes in a low mountain region, the Lahn-Dill-Bergland, in the federal state of Hesse, Germany (Fohrer et al., this issue). SWAT is a distributed, continuous time model operating on a daily time step. The objective of the model development was to predict the impact of management on water, sediment and agricultural chemical yields in meso- to macroscale basins. Major model components include: weather, hydrology, soil temperature, plant growth, nutrients, pesticides, and land management. In each of the spatial subunits of a catchment model, water balance is represented by several storage volumes: canopy storage, snow, soil profile, shallow aquifer, and deep aquifer. The soil profile can be subdivided into multiple layers. Soil water processes include infiltration, evaporation, plant uptake, lateral flow, and percolation to lower layers. Percolation from the bottom of the soil profile recharges the shallow aquifer. A recession con-
*
Corresponding author. Fax: +49-4101-693857. E-mail address: [email protected] (K. Eckhardt).
stant is used to lag flow from the shallow aquifer to the stream. Other shallow aquifer components include evaporation, pumping withdrawals, and seepage to the deep aquifer. SWAT is currently used by the EPA (United States Environmental Protection Agency), the NOAA (National Oceanic & Atmospheric Administration/USA), universities and environmental consulting firms. It serves, for example, to assess effects of land use changes on hydrological processes (Fohrer et al., this issue) and water quality (Saleh et al., 2000), and to predict impacts of climate change (Fontaine et al., 2001; Rosenberg et al., 1999). The hydrologic components of the model have been validated for numerous watersheds (e.g. Arnold and Allen, 1996). Yet, Peterson and Hamlett (1998) found a poor model performance when calibrating SWAT for a watershed containing fragipan soils. Fragipans impede the percolation. Therefore, on hillslopes much nearsurface lateral flow (interflow) is produced. In this aspect the catchments of the Lahn-Dill-Bergland are similar. With their steep slopes, shallow soils and hard rock aquifers they can be assumed to be typical of many European low mountain ranges. In the present study it is shown that even after a comprehensive calibration, the model version SWAT99.2 is not able to correctly
1474-7065/02/$ - see front matter Ó 2002 Elsevier Science Ltd. All rights reserved. PII: S 1 4 7 4 - 7 0 6 5 ( 0 2 ) 0 0 0 4 8 - 7
642
K. Eckhardt et al. / Physics and Chemistry of the Earth 27 (2002) 641–644
reproduce the runoff processes in such a catchment. It is demonstrated which subprocesses in the model cause the aberrant simulation result, how the description of these processes can be corrected, and how the model results are subsequently improved.
2. The investigated catchment The Dietzh€ olze catchment is located in the Lahn-DillBergland about 30 km north-west of Giessen/Germany. Its area amounts to 81 km2 with an elevation range from 250 to 685 m a.s.l., and average slope steepness of about 20%. Land use information was derived from Landsat TM5 satellite images (N€ ohles, 2000). The dominating land cover is forest (coniferous forest: 40%, deciduous forest: 21% of the area). Other land use categories are pasture (17%), range and farm land (13%) and settlement (9%). On the slopes and hilltops the soil consists mainly of shallow cambisols (HLUG, 2000) over a bedrock of schist and greywacke. This hard rock base of the soil has to be passed by the percolating water before it can reach the groundwater. In the catchment model it is represented by an additional layer of high bulk density and low hydraulic conductivity underneath the corresponding soils. In the valleys gley- and fluvisols is also found although in only a few percent of the area. As a consequence of the hydrogeological structure of the catchment, the contribution of groundwater (baseflow) to the streamflow is small. An analysis of the low flow characteristics of the Dietzh€ olze showed that this
contribution amounts to only about 20% (Kaviany, 1978). A model of the Dietzh€ olze catchment with a spatial resolution of 5 subbasins and 35 hydrotopes (defined as unique combinations of land cover and soil) has been established and calibrated against three hydrologic years (1991–1993) of daily measured runoff at the catchment outlet. The parameters were optimized by using an automatic calibration algorithm, the Shuffled Complex Evolution algorithm developed by Duan et al. (1992). The calibration strategy was outlined by Eckhardt and Arnold (2001).
3. Results and modifications of the model In Table 1 the calibrated parameters, their upper and lower bounds and their optimized values are listed. The resulting streamflow is shown in Fig. 1. SWAT99.2 is not able to correctly reproduce the runoff dynamics in the catchment. The model efficiency (Nash and Sutcliffe, 1970) is –0.17. The key process responsible for this inacceptable calibration result is the calculation of the vertical and lateral flows in the soil layers as a function of actual soil water content, field capacity, soil temperature, porosity, saturated hydraulic conductivity, slope steepness and slope length. The flow out of the hard rock layer underneath the shallow soils is limited by the low hydraulic conductivity of this layer. As the water content of the hard rock layer approaches its maximum water capacity, the inflow from the undermost porous layer should be reduced in favour of an
Table 1 Calibrated model parameters Parameter Snow melt rate (mm/d/°C) Surface runoff lag time (d) Curve number for coniferous forest Maximum potential interception for coniferous forest (mm) Manning’s ‘‘n’’ value for overland flow (m1=3 s) Groundwater recession coefficient (d1 ) Delay of the groundwater recharge (d) Deep aquifer percolation fraction Thickness of the rocky base of soil no. 202a (mm) Thickness of the rocky base of soil no. 2458b (mm) Density, soil no. 2458b , third layer (g/cm3 ) Density of the bedrock (g/cm3 ) Available water capacity, soil no. 2458b , first layer (mm/mm) Saturated conductivity, soil no. 202a , third layer (mm/h) Saturated conductivity, soil no. 2458b , third layer (mm/h) Anisotropy factorc , soil no. 2458b , third layer Maximum leaf area index for coniferous forest Maximum leaf area index for pasture a
Shallow cambisol on the lower slope. Shallow cambisol on the upper slope. c Only available in SWAT-G. b
Lower bound 1.00 1.00 50.0 3.00 0.20 0.030 1.00 0.00 1000 3000 1.50 2.51 0.16 1.0 10.0 2.00 4.00 1.50
Upper bound 3.00 5.00 60.0 6.00 0.50 0.060 20.00 0.80 5000 10 000 1.60 2.64 0.20 45.0 85.0 8.00 14.00 5.50
Calibration result SWAT99.2
SWAT-G
1.05 1.43 52.4 3.11 0.47 0.054 4.12 0.77 1060 9620 1.56 2.52 0.16 39.3 53.9 5.80 1.69
1.00 1.00 51.0 3.58 0.50 0.031 19.65 0.05 1870 3990 1.60 2.64 0.19 44.8 84.8 8.00 5.95 2.31
K. Eckhardt et al. / Physics and Chemistry of the Earth 27 (2002) 641–644
Fig. 1. Result of the automatic model calibration using SWAT99.2.
enhanced interflow. Yet, this is not the case in SWAT99.2. Water continues to percolate into the hard rock layer the same as before, so that the layer is inadmissibly filled beyond its actual water capacity. Most of this water recharges the groundwater with a marked delay of about three months after maxima of the precipitation. This is best seen in springtime (Fig. 1). The calculated contribution of baseflow to the streamflow amounts to 56%. Thus, it is much too high although the optimized value of the parameter ‘‘deep aquifer percolation fraction’’ is 0.77, expressing that most of the groundwater recharge percolates through the shallow aquifer into the deep aquifer and therefore does not contribute to baseflow. As a consequence, the measured streamflow is underestimated by more than 50% (Table 2). In another calibration, whose results are not shown here, the ‘‘deep aquifer percolation fraction’’ was not optimized but defined to be 0.0. Although this increased the mean calculated streamflow to a level close to the measured mean value, the higher baseflow contribution led to a further decrease in model efficiency because the wrong dynamics of the calculated streamflow were intensified. In SWAT-G, percolation from a layer i is linearly reduced in the same measure as the actual water content of the layer i þ 1 below approaches its maximum water capacity: swði þ 1Þ 0 percðiÞ ¼ perc ðiÞ 1 ð1Þ ulði þ 1Þ
643
with perc(i): amount of percolating water, perc0 ðiÞ: amount of percolating water calculated with SWAT99.2, swði þ 1Þ: actual water content of layer i þ 1, ulði þ 1Þ: water content of layer i þ 1 at saturation. Another modification is based on the observation that soils of Central European low mountain ranges which have experienced solifluction in the interglacial periods are anisotropic with respect to their hydraulic conductivity (Kleber et al., 1998). Therefore, a possibility for defining anisotropy factors (hydraulic conductivity of the soil parallel to the slope divided by the vertical hydraulic conductivity) was introduced and the interflow––which is proportional to the hydraulic conductivity––is calculated as latðiÞ ¼ lat0 ðiÞ anisoðiÞ
ð2Þ
with lat(i): amount of lateral flow in layer i, lat0 (i): amount of lateral flow in layer i calculated with SWAT99.2, aniso(i): anisotropy factor of layer i. As a consequence of these modifications, most of the percolating water flows off as interflow on top of the bedrock (Fig. 2). The calibration result using SWAT-G is shown in Fig. 3. A far better correspondence of the measured and calculated streamflow is now obtained. The model efficiency (Nash and Sutcliffe, 1970) increases from 0.17 to þ0.76 (Table 2). A model validation using the daily streamflow in the three following hydrologic years (1994–1996) confirms the calibration (efficiency: þ0.81). This improvement is not an effect of the different parameterization of the two catchment models as a result of their independent calibration. If the catchment model based on SWAT99.2 is parameterized with the values found by the calibration of the SWAT-G model, the efficiency further decreases to 0.41. Crucial for the better performance when using SWAT-G rather is that with 12% in the calibration and 13% in the validation period, the contribution of the baseflow to the streamflow now is much closer to the expected value.
Table 2 Results of the automatic model calibration
Average streamflow (mm/d) Contribution of the baseflow (%) Model efficiencyb a b
Kaviany (1978). Nash and Sutcliffe (1970).
Observed
Calculated SWAT99.2
SWAT-G
1.18 20a
0.57 56
1.17 12
0.17
0.76 Fig. 2. Schematic of the soil water flow using SWAT99.2 and SWATG.
644
K. Eckhardt et al. / Physics and Chemistry of the Earth 27 (2002) 641–644
Acknowledgements This study has been supported by the Deutsche Forschungsgemeinschaft within the scope of the Collaborative Research Centre (SFB) 299.
References
Fig. 3. Result of the automatic model calibration using SWAT-G.
4. Discussion and conclusions It can be assumed that the Dietzh€ olze catchment possesses many characteristics which are typical for low mountain ranges. Therefore, the inability of SWAT99.2 to correctly reproduce the runoff generation processes in the catchment substantially restrains the applicability of the model. Obviously, Peterson and Hamlett (1998) were confronted with the same problem that SWAT99.2 and older versions of the program simulate too much percolation through soil layers with low permeability. From a hydrograph separation they found a baseflow contribution of only 28% to the measured streamflow in their catchment while their best model fit yielded 46% of baseflow. Alternatively, the modified version SWAT-G can be used which, as has been demonstrated in this paper, shows a far better performance under the specified hydrogeological conditions. Both the comparison of measured and calculated streamflow at the catchment outlet and the analysis of the source code show the improvement of the description of hydrological processes in the model. Further modifications in SWAT-G compared to SWAT99.2 affect the regionalization of precipitation and temperature, the calculation of evapotranspiration, the generation of surface runoff, the simulation of plant growth and crop rotation, as well as the control of reservoirs. While these modifications allow hydrological processes to be modelled in low mountain ranges they do not restrain the applicability of the model to catchments with other characteristics.
Arnold, J.G., Allen, P.M., 1996. Estimating hydrologic budgets for three Illinois watersheds. Journal of Hydrology 176, 55–77. Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., 1998. Large area hydrologic modeling and assessment, Part I: model development. Journal of the American Water Resources Association 34, 73–89. Duan, Q., Sorooshian, S., Gupta, V.K., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28, 1015–1031. Eckhardt, K., Arnold, J.G., 2001. Automatic calibration of a distributed catchment model. Journal of Hydrology 251, 103–109. Fohrer, N., M€ oller, D., Steiner, N., An interdisciplinary modelling approach to evaluate the effects of land use change. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 27, 655–662. Fontaine, T.A., Klassen, J.F., Cruick€ o, T.S., Hotchkiss, R.H., 2001. Hydrological response to climate change in the Black Hills of South Dakota, USA. Hydrologic Sciences Journal––Journal des Sciences Hydrologiques 46, 27–40. HLUG (Hessisches Landesamt f€ ur Umwelt und Geologie), 2000. Digital soil map 1: 50 000. Kaviany, E., 1978. Zur Hydrogeologie im Niederschlagsgebiet der Dill (Hessen). Gießener Geologische Schriften, vol. 19. Kleber, A., Lindemann, J., Schellenberger, A., Beierkuhnlein, C., Kaupenjohann, M., Pfeiffer, S., 1998. Slope deposits and water paths in a spring catchment, Frankenwald, Bavaria, Germany. Nutrient Cycling in Agroecosystems 50, 119–126. Nash, J.E., Sutcliffe, J.E., 1970. River flow forecasting through conceptual models, Part I: A discussion of principles. Journal of Hydrology 10, 282–290. N€ ohles, I., 2000. Landnutzungsklassifikation mit multitemporalen Landsat TM-Szenen in einer kleinstrukturierten Agrarregion. Boden und Landschaft, vol. 32. Peterson, J.R., Hamlett, J.M., 1998. Hydrologic calibration of the SWAT model in a watershed containing fragipan soils. Journal of the American Water Resources Association 34, 531– 544. Rosenberg, N.J., Epstein, D., Wang, J., Vail, L., Srinivasan, R., Arnold, J.G., 1999. Possible impacts of global warming on the hydrology of the Ogallala aquifer region. Climatic Change 42, 677– 692. Saleh, A., Arnold, J.G., Gassman, P.W., Hauck, L.M., Rosenthal, W.D., Williams, J.R., McFarland, A.M.S., 2000. Application of SWAT for the Upper North Bosque River Watershed. Transactions of the ASAE 43, 1077–1087.