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Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information

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Steirou,  Eva-Styliani
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/lgerlitz

Gerlitz,  Lars
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Sun,  Xun
External Organizations;

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Apel,  H.
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Agarwal,  Ankit
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Totz,  Sonja
External Organizations;

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Merz,  B.
4.4 Hydrology, 4.0 Geosystems, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Zitation

Steirou, E.-S., Gerlitz, L., Sun, X., Apel, H., Agarwal, A., Totz, S., Merz, B. (2022): Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information. - Scientific Reports, 12, 13514.
https://doi.org/10.1038/s41598-022-16633-1


Zusammenfassung
We investigate whether the distribution of maximum seasonal streamflow is significantly affected by catchment or climate state of the season/month ahead. We fit the Generalized Extreme Value (GEV) distribution to extreme seasonal streamflow for around 600 stations across Europe by conditioning the GEV location and scale parameters on 14 indices, which represent the season-ahead climate or catchment state. The comparison of these climate-informed models with the classical GEV distribution, with time-constant parameters, suggests that there is a substantial potential for seasonal forecasting of flood probabilities. The potential varies between seasons and regions. Overall, the season-ahead catchment wetness shows the highest potential, although climate indices based on large-scale atmospheric circulation, sea surface temperature or sea ice concentration also show some skill for certain regions and seasons. Spatially coherent patterns and a substantial fraction of climate-informed models are promising signs towards early alerts to increase flood preparedness already a season ahead.