Articles | Volume 30, issue 4
https://doi.org/10.5194/hess-30-1165-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-1165-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Mar 2026
Research article |
| 02 Mar 2026
| 02 Mar 2026
Climate change effects on river droughts in Bavaria using a hydrological large ensemble
Benjamin Poschlod
CORRESPONDING AUTHOR
Research Unit Sustainability and Climate Risk, Earth and Society Research Hub (ESRAH), Universität Hamburg, Hamburg, 20144, Germany
Laura Sailer
Department of Geography, Ludwig-Maximilians-Universität München, Munich, 80333, Germany
Alexander Sasse
Department of Geography, Ludwig-Maximilians-Universität München, Munich, 80333, Germany
Anastasia Vogelbacher
Institute of Geo-Hydroinformatics, Hamburg University of Technology, Hamburg, 21073, Germany
United Nations University Hub on Engineering to Face Climate Change at the Hamburg University of Technology, United Nations University Institute for Water, Environment and Health (UNU-INWEH), Hamburg, 21073, Germany
Ralf Ludwig
Department of Geography, Ludwig-Maximilians-Universität München, Munich, 80333, Germany
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Preprint archived
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Julia Miller, Andrea Böhnisch, Ralf Ludwig, and Manuela I. Brunner
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Short summary
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We assess the impacts of climate change on fire danger for 1980–2099 in different landscapes of central Europe, using the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by 2099. High fire danger (FWI > 21.3) becomes the mean condition by 2099 under an RCP8.5 scenario. This study highlights the potential for severe fire events in central Europe from a meteorological perspective.
Florian Zabel and Benjamin Poschlod
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Short summary
Short summary
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This study applies artificial neural networks to predict drought occurrence in Munich and Lisbon, with a lead time of 1 month. An analysis of the variables that have the highest impact on the prediction is performed. The study shows that the North Atlantic Oscillation index and air pressure 1 month before the event have the highest importance for the prediction. Moreover, it shows that seasonality strongly influences the goodness of prediction for the Lisbon domain.
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Benjamin Poschlod, Ralf Ludwig, and Jana Sillmann
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This study provides a homogeneous data set of 10-year rainfall return levels based on 50 simulations of the Canadian Regional Climate Model v5 (CRCM5). In order to evaluate its quality, the return levels are compared to those of observation-based rainfall of 16 European countries from 32 different sources. The CRCM5 is able to capture the general spatial pattern of observed extreme precipitation, and also the intensity is reproduced in 77 % of the area for rainfall durations of 3 h and longer.
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Short summary
Europe was hit by severe droughts in recent years resulting in extreme low flow conditions in rivers. Here, we investigate future climate change effects on river droughts in Bavaria. We find increasing severity for the low peak discharge and low flow duration in a warmer climate. This is caused by hotter and drier summers, where the joint occurrence of heat and drought intensifies. Further, we show that conditions in the year before the drought gain more importance in a warmer climate.
Europe was hit by severe droughts in recent years resulting in extreme low flow conditions in...
