| This paper analyses the effect of spatial input data resolution on the
simulated effects of regional scale landuse scenarios using the TOPLATS
model. A data set of 25 m resolution of the central German Dill catchment
(693 km2) and three different landuse scenarios are used for the
investigation. Landuse scenarios in this study are field size scenarios, and
depending 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 used
areas 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 a
20 years time period are calculated for the entire Dill catchment as well as
for 3 subcatchments without any recalibration. Additionally water balances
based on the three landuse scenarios as well as changes between current
conditions and scenarios are calculated. The study reveals that both model
performance measures (for current landuse) as well as water balances (for
current landuse and landuse scenarios) almost remain constant for most of
the aggregation steps for all investigated catchments. Small deviations are
detected at the resolution of 50 m to 500 m, while significant differences
occur at the resolution of 1 km and 2 km which can be explained by changes
in the statistics of the input data. Calculating the scenario effects based
on increasing grid sizes yields similar results. However, the change effects
react more sensitive to data aggregation than simple water balance
calculations. Increasing deviations between simulations based on small grid
sizes and simulations using grid sizes of 300 m and more are observed.
Summarizing, this study indicates that an aggregation of input data for the
calculation of regional water balances using TOPLATS type models does not
lead to significant errors up to a resolution of 500 m. Focusing on scenario
effects the model is more sensitive to input data aggregation as aggregation
effects of current data and scenarios partly sum up. The maximum reasonable
grid size for scenario calculations decreases to 200–300 m. |