Enhancing the seasonal forecasts from large-scale hydro-climate services to better meet the local conditions

A key challenge in continental and global hydro-climate services deals with the incomplete (or even lack of) incorporation of local knowledge and data from the users. Here, we demonstrate the regional skill of seasonal forecasts from large-scale hydro-climate services, while we present a framework that accounts for local data and with the use of machine-learning enhances the seasonal forecasts by better capturing the local information. Five European case studies subject to different hydro-climate conditions and user needs are selected. We test our framework using the E-HYPE hydrological model forced by bias-adjusted ECMWF SEAS5 seasonal meteorological forecasts. We firstly assess the skill of seasonal hydrological forecasts using pseudo-reality and “real” local observations as reference. The skill assessment is driven by the local needs and hence it is conducted for different target hydro-climatic variables and conditions (i.e. floods and droughts). This first evaluation sets the benchmark for quantifying the added value from a machine-learning enhanced hydro-climate service. We next introduce a post-processing workflow to take advantage of the available local observations and potentially improve the forecasting skill. Here, quantile mapping and machine-learning post-processors are tested in the case study areas to further tune the output from the European hydro-climate service towards the local observations. Results from these hybrid seasonal forecasts show potentials to meet the local conditions and consequently address the user expectations from the service. The current work is highlighting the way forward for machine-learning enhanced services that allow tailoring large-scale hydro-climate services using local knowledge and data.