Exploring the use of European weather regimes for improving user-relevant hydrological forecasts at the sub-seasonal scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and post-processing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multi-model approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve sub-seasonal hydro-meteorological forecasts in a hybrid forecasting system in an operational mode.