Wasserressourcen Management: Von der Wasserverfügbarkeit zur Wiederverwertung

The aim of this project is to investigate whether the "Model Conditional Processor-MCP" uncertianty processor, which is frequently used for the pre- and post-processing of meteorological and hydrological forecasts, provides better results for the post-processing of water levels for 14-day water level forecasts on the federal waterway Rhine with respect to using currently used EMOS method. The theoretical advantage of the method is that the spatial and temporal dependencies can allegedly be better mapped multivariate with the MCP. This needs to be verified in operational practice using a software developed at the chair of Water Resources Management and Climate Imapct Research.