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| Hauptseite > Publikationsdatenbank > Evaluation of the skill of probabilistic seasonal forecasts in predicting droughts with the hydrologic model ParFlow/CLM over central Europe |
| Hauptseite > Publikationsdatenbank > Evaluation of the skill of probabilistic seasonal forecasts in predicting droughts with the hydrologic model ParFlow/CLM over central Europe |
| Conference Presentation (After Call) | FZJ-2026-03984 |
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2026
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Please use a persistent id in citations: doi:10.34734/FZJ-2026-03984
Abstract: Repeated droughts in recent years and their impacts on managed and natural ecosystems have stressed the need for seasonal forecasts of subsurface water resources, including their depletion and their recovery. To address this need, we provide an experimental water resources bulletin with a 7-months forecast of the subsurface water storage anomaly from the surface to 60m depth at the beginning of each meteorological season. Here, we evaluate the skill of eight spring forecasts, each initialised on March 1st between 2018 and 2025, that cover the vegetation period until the end of September. Each forecast consists of a 50-member ensemble simulating the terrestrial water cycle at the surface and subsurface with the physics-based integrated hydrologic model ParFlow/CLM over a central European model domain at 0.6km resolution. The ensemble is driven by the probabilistic seasonal weather forecast SEAS5 from the European Centre for Medium-Range Weather Forecasts. Overall the seasonal forecast clearly outperforms a simple climatological outlook. However, the normalized Spread Skill Score indicates an underdispersed ensemble, especially under abnormally dry conditions, suggesting that the ensemble as a whole has difficulties to represent droughts, confirmed by the Cumulative Rank Probability Skill Score. Nevertheless, a closer look at the ensemble tail (lower quintile and tercile) with the Relative Operating Characteristics Skill Score yields good results, meaning that droughts are well included in the seasonal forecast ensembles. With this system we explore the hydrologic forecasting possibilities strand as part of and towards a high-resolution European ecosystem reanalysis using the latest high performance computers in Europe.
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