Inhaltszusammenfassung

Study region: Lowland Ems catchment in North-west Germany Study focus: Alterations of streamflow caused by climate change can impact the riverine ecosystems, socio-economic development and the effectiveness of flood protection measures. Therefore, hydrological climate impact studies are crucial for foresighted water management. In addition to conceptual and physical hydrological models, machine learning based models are frequently used in streamflow modelling. However, the applicability of ... Study region: Lowland Ems catchment in North-west Germany Study focus: Alterations of streamflow caused by climate change can impact the riverine ecosystems, socio-economic development and the effectiveness of flood protection measures. Therefore, hydrological climate impact studies are crucial for foresighted water management. In addition to conceptual and physical hydrological models, machine learning based models are frequently used in streamflow modelling. However, the applicability of machine learning models for climate impact studies remains relatively unexplored. In this study, we use a standard ensemble of climate change projections of the German Weather Service for the Ems catchment. We compare the climate change signals of two conceptual hydrological models to those of two different long shortterm memory model approaches. Based on long-term simulations, climate change impact on different streamflow indices is assessed. New hydrological insights for the region: The results indicate heterogenous alterations of streamflow depending on the applied model and its model-specific input data. Further analysis reveals that the application of the Haude formula for calculation of potential evaporation is questionable for climate change impact assessment despite its successful application in the study region in recent climate. For a successful application of LSTM in climate change impact studies, physical restrictions need to be defined to simulate a plausible catchment behavior also for climate conditions beyond the range of training data.» weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
3.46 - Wasserforschung

DDC Sachgruppe:
Naturwissenschaften