Abstract
Water resources planning and management are every day more crucial due to climate change influences. More frequent and powerful extreme events feature them in the short term. Therefore a good forecasting model is essential to deal with proper management of water resources. It allows to reduce the hydrogeological risk, to optimize the energy production and to ensure a balance between the various uses of water. This water-food-energy nexus approach needs precise prediction to avoid wrong decisions. Hydrological short term forecasting (up to 5 days) is the main topic of this work. This lead period is crucial to manage the energy production of hydropower plants and to address flood hazards. Uncertainties are common in forecasting models. Three types of uncertainties are usually considered in hydrological models: input data, model parameters and model structure.This contribution aims to validate the ICON forecasting regional climate model (RCM) as input data in hydrological modelling. The validation is carried out on an Alpine basin in Alto Adige Province (Passirio/Passer basin). Alpine regions are even more affected by climate change due to their intrinsic complexity. A dense spatial and temporal meteorological input is crucial and DWD ICON regional climate model seems to satisfy these requirements. The entire Alto Adige region is the benchmark for a preliminary meteorological comparison of precipitation and temperature data sets. The high number of weather stations in this area allows to build a reliable gridded observational data set. The comparison validates the goodness of DWD ICON and it identifies the biases. Passirio/Passer basin is the benchmark of the hydrological model. The calibration is carried out with the observational data set. The validation is done on three extreme events. At this step, a gridded version of ICON meteorological outputs are used as precipitation and temperature input for the hydrological model. The performance of the meteorological-hydrological chain is evaluated with a comparison between the modeled and the measured streamflow. On this comparison a post processing analysis is carried out to understand the goodness of this provider as input for hydrological models in Alpine regions. The validation aims to understand how the hydrological model propagates the uncertainty of the input data (precipitation and temperature) to the output (streamflow). The streamflow forecasting can be used to organize the alerts and to manage the energy production for the next few days. The uncertainty confidence bands allow one to get a visual understanding of the results goodness.