AI-based seasonal probabilistic hydrological forecasts across Europe

Artificial Intelligence (AI) approaches are nowadays well-established tools to make hydro-meteorological forecasts. While several AI-based models are available to provide probabilistic meteorological (e.g. Lang et al., 2024) or hydrological (Nevo et al., 2022) short-range predictions at global scale, seasonal hydrological probabilistic forecasts at large scale are still lagging behind. Here, we present the updated results of our AI-based seasonal hydrological forecasts across the European hydro-climatic gradient (Bertini et al., 2025). We use an Encoder-Decoder model trained at the pan-European scale with a combination of in-situ hydrological observations, reanalysis data from the process-based E-HYPE hydrological model, and bias-adjusted seasonal meteorological forecasts from the ECMWF SEAS5 prediction system. The model is trained over 500 catchments across Europe, grouped in 11 clusters based on their hydrological regime (Pechlivanidis et al., 2020), and the predictions are compared against both climatology and the E-HYPE streamflow forecasts. Compared to our previous results, the updated Encoder-Decoder model provides improved deterministic and probabilistic performances, proving once again the potential of AI approaches for operational hydrological forecasting.

 

Lang, S., Alexe, M., Chantry, M., Dramsch, J., Pinault, F., Raoult, B., ... & Rabier, F. (2024). AIFS--ECMWF's data-driven forecasting system. arXiv preprint arXiv:2406.01465.

Nevo, S., Morin, E., Gerzi Rosenthal, A., Metzger, A., Barshai, C., Weitzner, D., ... & Matias, Y. (2022). Flood forecasting with machine learning models in an operational framework. Hydrology and Earth System Sciences, 26(15), 4013-4032.

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.

Pechlivanidis, I.G., Crochemore, L., Rosberg, J., & Bosshard, T. (2020). What are the key drivers controlling the quality of seasonal streamflow forecasts? Water Resources Research, 56, e2019WR026987. https://doi.org/10.1029/2019WR026987