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This paper analyses the effect of spatial input data resolution on thesimulated effects of regional scale landuse scenarios using the TOPLATSmodel. A data set of 25 m resolution of the central German Dill catchment(693 km<sup>2</sup>) and three different landuse scenarios are used for theinvestigation. Landuse scenarios in this study are field size scenarios, anddepending on a specific target field size (0.5 ha, 1.5 ha and 5.0 ha)landuse is determined by optimising economic outcome of agricultural usedareas and forest. After an aggregation of digital elevation model, soil map,current landuse and landuse scenarios to 50 m, 75 m, 100 m, 150 m, 200 m,300 m, 500 m, 1 km and 2 km, water balances and water flow components for a20 years time period are calculated for the entire Dill catchment as well asfor 3 subcatchments without any recalibration. Additionally water balancesbased on the three landuse scenarios as well as changes between currentconditions and scenarios are calculated. The study reveals that both modelperformance measures (for current landuse) as well as water balances (forcurrent landuse and landuse scenarios) almost remain constant for most ofthe aggregation steps for all investigated catchments. Small deviations aredetected at the resolution of 50 m to 500 m, while significant differencesoccur at the resolution of 1 km and 2 km which can be explained by changesin the statistics of the input data. Calculating the scenario effects basedon increasing grid sizes yields similar results. However, the change effectsreact more sensitive to data aggregation than simple water balancecalculations. Increasing deviations between simulations based on small gridsizes and simulations using grid sizes of 300 m and more are observed.Summarizing, this study indicates that an aggregation of input data for thecalculation of regional water balances using TOPLATS type models does notlead to significant errors up to a resolution of 500 m. Focusing on scenarioeffects the model is more sensitive to input data aggregation as aggregationeffects of current data and scenarios partly sum up. The maximum reasonablegrid size for scenario calculations decreases to 200–300 m

Effects of grid size and aggregation on regional scale landuse scenario calculations using SVAT schemes