EGU25-10567, updated on 15 Mar 2025
https://doi.org/10.5194/egusphere-egu25-10567
EGU General Assembly 2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
AI-based seasonal streamflow forecasts across Europe’s hydro-climatic gradient
Claudia Bertini1, Yiheng Du2, Schalk Jan van Andel1, and Ilias Pechlivanidis2
Claudia Bertini et al.
  • 1IHE Delft, Hydroinformatics and Socio-Technical Innovation, Delft, Netherlands (c.bertini@un-ihe.org)
  • 2Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden

Despite the advances in hydro-meteorological forecasting systems, challenges to accurately forecast streamflow at seasonal time horizons still remain, especially when models are applied across a strong hydro-climatic gradient. In this work, we explore the potential of AI-based approaches combined with the output of process-based hydrological models and meteorological forecasts from Numerical Weather Prediction models to enhance seasonal streamflow forecasts, with lead-times up to 30 weeks. We employ the multi-time scale Long Short-Term Memory (MTS-LSTM) model trained with a combination of reanalysis data from the process-based pan-European E-HYPE hydrological model, in-situ observations from GRDC, and bias-adjusted seasonal meteorological forecasts from the ECMWF SEAS5 prediction system. The MTS-LSTM is developed at the pan-European scale, using more than 500 catchments over Europe, which lie in 11 different clusters according to their hydrological regime. We then compare the AI-based forecast performance against streamflow climatology and the E-HYPE streamflow forecasts. Our results show that the streamflow forecasts based on MTS-LSTM outperform the E-HYPE ones in catchments characterised by highly variable and flashy hydrological response and snow-dominated catchments with high seasonality. However, the MTS-LSTM underperform compared to E-HYPE results in catchments with highly variable streamflow regimes and long recessions. These preliminary findings highlight the potential of AI approaches to enhance streamflow predictability at seasonal lead-times across Europe’s strong hydro-climatic gradient, having both scientific and operational added value.

How to cite: Bertini, C., Du, Y., van Andel, S. J., and Pechlivanidis, I.: AI-based seasonal streamflow forecasts across Europe’s hydro-climatic gradient, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10567, https://doi.org/10.5194/egusphere-egu25-10567, 2025.