SWAT model calibration of a grid-based setup

The eco-hydrological model SWAT (Soil and Water Assessment Tool) is a useful tool to simulate the effects of catchment processes and water management practices on the water cycle. For each catchment some model parameters (e.g. ground water delay time, ground water level) remain constant and therefore are used as constant values; other parameters such as soil types or land use are spatially variable and thus have to be spatially discretized. SWAT setup interfaces process input data to fit the data format requirements and to discretize the spatial characteristics of the catchment area. The primarily used configuration is the sub-watershed discretization scheme. This spatial setup method, however, results in a loss of spatial information which can be problematic for SWAT applications that require a spatially detailed description of the catchment area. At present no SWAT interface is available which provides the management of input and output data based on grid cells. To fill this gap, the authors developed a grid-based model interface. To perform hydrological studies, the SWAT user first calibrates the model to fit to the environmental and hydrological conditions of the catchment. Compared to the sub-watershed approach, the grid-based setup significantly increases model computation time and hence aggravates calibration according to established calibration guidelines. This paper describes how a conventional set of sub-watershed SWAT parameters can be used to calibrate the corresponding grid-based model. The procedure was evaluated in a sub-catchment of the River Elbe (Northern Germany). The simulation of daily discharge resulted in Nash-Sutcliffe efficiencies ranging from 0.76 to 0.78 and from 0.61 to 0.65 for the calibration and validation period respectively; thus model performance is satisfactory. The sub-watershed and grid configuration simulate comparable discharges at the catchment outlet (R2 = 0.99). Nevertheless, the major advantage of the grid-based set-up is an enhanced spatial description of landscape units inducing a more realistic spatial distribution of model output parameters.