Adjustment of a weather generator to represent actual rain erosivity in the northern Black Forest – Germany

Abstract

To estimate the impact of highly erosive precipitation on existing and planned forest infrastructure we deem the Forest Service WEPP Interfaces, based on the Water Erosion Prediction Project (WEPP), feasible.As a first step towards testing WEPP and especially the implemented weather generator CLIGEN for conditions in Germany we evaluated the application of CLIGEN to calculate rain erosivities from generated time series. CLIGEN parameters were taken from time series of up to 17years with 10-minute resolution from three sites in the Northern Black Forest, southwestern Germany. We assessed a rain kinetic energy function for this region from field measurements with a laser disdrometer to compare CLIGEN performance using the local function to using common kinetic energy functions.We showed that running CLIGEN with unaltered input parameters is not suitable to model climatic conditions and erosivity indices in the Northern Black Forest. R-factors from unaltered model runs deviated extremely from observed R-factors, resulting in just one third of observed values. Model performance and parameter uncertainties do not benefit much from the use of a site specific kinetic energy function. Differences in model errors and sensitivities compared to a well-established kinetic energy function remain negligible. However, model output was improved by empirical calibration of input data. Virtual best parameter sets for best model results could be identified.To reproduce observed rain erosivities the input parameters of CLIGEN have to be manipulated to model a precipitation regime where daily precipitation amounts and maximum precipitation intensities are higher at a lower number of rainy days.We also identified the input parameters to which the model is most sensitive to when manipulated, i.e. precipitation amount and frequency, and maximum peak precipitation. These parameters are especially important when implementing future climate change scenarios.