This paper was peer-reviewed for scientific content. Pages 994-999. In: D.E. Stott, R.H. Mohtar and G.C. Steinhardt (eds). 2001. Sustaining the Global Farm. Selected papers from the 10th International Soil Conservation Organization Meeting held May 24-29, 1999 at Purdue University and the USDA-ARS National Soil Erosion Research Laboratory. Applying the SWAT Model as a Decision Support Tool for Land Use Concepts in Peripheral Regions in Germany N. Fohrer*, K. Eckhardt, S. Haverkamp and H.-G. Frede shallow, poor soils and steep slopes, and good job ABSTRACT alternatives in other sectors of the economy. Thus, the In the Lahn-Dill- Bergland in the hilly midlands of percentage of fallow land is increasing and some landscape Hesse, Germany, agriculture is retreating from functions are endangered, like gaining agricultural income, landscape due to employment alternatives in various habitat properties for certain species, and a sufficient branches of industry and marginal conditions for quantity of groundwater recharge. agricultural production. Thus, the amount of fallow land One group of the SFB 299 analyzes the prevailing biotic is increasing. To stop this development a collaborative and abiotic site conditions and provides input information, research project (SFB 299) with 19 departments involved for instance soil and vegetation data or socio-economic was established at Giessen University in 1997 to develop boundary conditions, for the group responsible for modeling. new concepts of land use and assess their economic and An integrated system of three GIS-linked, raster-based ecological impact. The economic model ProLand (Möller models (Fohrer et al., 1999A; Weber et al., 1999B, Möller et et al., 1999A) is optimizing land use by maximizing al., 1999b) is used to develop and evaluate land use agricultural income. It proposes spatially distributed scenarios in terms of ecology, hydrology, and economy. The land use options which are evaluated in terms of ecology economic model ProLand (Möller et al., 1999a) has two with ELLA (Weber et al., 1999a) and with regard to main tasks. It provides economic key indicators like hydrological changes with the SWAT model (Arnold et agricultural income or labor input. On the other hand it is al., 1993, 1998). All three models are GIS-based and able to predict spatially distributed land use changes, exchange data via GIS. resulting from a particular framework of natural, economic The continuous-time, grid cell watershed model and political characteristics and is therefore used to generate SWAT (Arnold et al., 1993; 1998) was tested and adapted land use scenarios, which serve as input maps for the other to typical conditions in the project region. The Dietzhölze two models. The ecological model ELLA (Weber et al., (81.8 km²) and the Aar watershed (59.8 km²) were used 1999A) is a cellular automaton, which is investigating the to calibrate and validate the model. All relational distribution of key species due to land use changes based on databases which are implemented into SWAT (Arnold et habitat preferences. It is providing information on al., 1993; 1998) e.g. for weather, soil, tillage, and crops biodiversity as a function of land use patterns. Finally the were substituted by regional data sets. hydrological model SWAT (Arnold et al., 1993; 1998) is Two different land use scenarios were proposed by employed to observe the behavior of water balance ProLand (Möller et al., 1999A) for the Aar watershed and components for different land use concepts provided by the SWAT model was applied to evaluate the effect of ProLand (Möller et al., 1999A). Every land use scenario is these land use changes on the water balance. An output evaluated by all three models. Ecological, hydrological and interface was developed to produce spatially distributed economic indicators are provided to a decision making maps of water balance components. group, which consists of scientists (SFB 299), land owners, politicians and citizens of the project region. In a round table INTRODUCTION discussion, competing aims are weighted and compared with In 1997, the joint research center “SFB 299: Land use the model outputs for different concepts. If the results are concepts for peripheral regions” was established at the not satisfactory, a new set of socio-economic measures Giessen University at the faculty of agriculture. Its main (subsidies, support programs) is proposed to ProLand and objective is the development of sustainable land use new scenarios are developed and evaluated. concepts and their evaluation with regard to the effect on ecological and economic landscape functions. Due to the APPLICATION OF THE SWAT MODEL complexity and the enormous variety of landscape functions, FOR DECISION SUPPORT a multidisciplinary approach is indispensable. The The SWAT model (Arnold et al., 1993; 1998) was methodology, which should be transferable to other regions applied in two test watersheds in the Lahn- Dill- Bergland, and valid for various scales, is developed in the “Lahn-Dill- which is situated north of Giessen, in the state of Hesse, Bergland” as a first test region. This region is characterized Germany. The Aar catchment (59.8 km²) and the Dietzhölze by its peripheral features. Agriculture is retreating from this (81.8 km²) were used to calibrate and validate the model for area due to marginal natural production conditions, such as the utilization under the specific conditions of the region. *Department of Agricultural Ecology and Natural Resources Management, Sec. Soil and Water Protection, Giessen University, Heinrich- Buff-Ring, D-35392 Giessen. *Corresponding author: [email protected] Then two ProLand scenarios for the Aar catchment were the level of discretization. The virtual sub-basins were evaluated in terms of water balance effects due to land use derived with the SWATGRASS interface (Srinivasan and changes. Arnold, 1994). In total, the Dietzhölze watershed was subdivided into 58 sub-basins and 256 virtual sub-basins and Description of the study area and model setup the Aar into 21 sub-basins and 125 virtual sub-basins, SWAT (Arnold et al., 1993; 1998) is a spatially respectively. The soil information was based on the soil map distributed, physically based hydrological model, which can of Hesse 1:50.000 (Hessisches Landesamt für operate on a daily time step as well as in annual steps for Bodenforschung, 1998). Measured daily rainfall and long-term simulations up to 100 years. Three different types temperature data were obtained by the German Weather of input data are required. Spatially distributed information service. For the Dietzhölze four rainfall stations in and is necessary for elevation, soil, and land use data. Relational around the catchment were available, while for the Aar there databases such as soil, weather and crop data are provided were two rainfall gages within the watershed. For each for the use within the US. An input interface catchment, one climate station was employed. For flow (SWATGRASS, Srinivasan and Arnold, 1994) links these calibration and validation the stream gauges Dillenburg II data bases with the spatially distributed raster maps, which (Dietzhölze) and Bischoffen (Aar) were used. For the are stored in the GIS system GRASS (U.S. Army, 1988). Dietzhölze stream flow data were available for the Optionally time series of rainfall and temperature data are hydrological years 1985-1995, for the Aar 1979-1987, needed for each model run. They can be generated also by respectively. the implemented weather generator. For validation purposes the catchment should be gauged. Calibration and validation of the model For the use in the SFB 299 the SWAT model was The Aar catchment. Figure 2 shows the time series of modified and the SWATGRASS interface (Srinivasan and observed and simulated monthly stream flow for the Aar Arnold, 1994) was adapted to the regional data bases catchment during the period of 1983-1987. For calibration formats. All US databases where substituted by regional data the hydrological years 1986/87 were analyzed in a daily sets (Fohrer et al., 1999b). A management database for resolution. typical regional cropping systems was also implemented into A base flow separation (Arnold et al., 1995) was carried the model. out to gain more information for calibration purposes. The Spatial information for the model runs was provided in a input variables used for calibration were soil properties and 25 m by 25 m grid. Actual land use information was derived curve number. The curve number (USDA Soil Conservation from Landsat TM5 satellite images for the years 1987 and Service, 1972) was allowed to vary within the range of the 1994. In the Dietzhölze catchment, peripheral features are categories for good and fair hydrologic conditions. The more pronounced than in the Aar catchment (Fig. 1). available water capacity was set within the range of its More than 58% of the Dietzhölze catchment are covered natural uncertainty for the study region. Statistical results for by forest and 36% are grassland. Cropland exists only on the comparison of measured and predicted stream flow can 0.2% of the area. The Aar catchment is also characterized by be found in Table 1. The correlation coefficient for observed a high percentage of forest (42%), but 25% of the area is still vs. predicted monthly stream flow is 0.92. The model under tillage. The grassland portion is 20%. For both efficiency (Nash and Sutcliffe, 1970) is 0.74. For model catchments, a digital elevation model in a 40m*40m grid validation in the period of 1983-1985, the correlation was obtained by the German Land Survey. The software coefficient is 0.85 and the Nash Sutcliffe index 0.53, package TOPAZ (Version 1.2, Gabrecht u. Martz, 1998) was respectively. In general, the model is able to predict the used to delineate sub-basins for the spatial aggregation. The temporal dynamics of total stream flow rather well (Fig.