Articles | Volume 30, issue 11
https://doi.org/10.5194/hess-30-3597-2026
© 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-30-3597-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Do convection-permitting regional climate models have added value for hydroclimatic simulations? A test case over small and medium-sized catchments in Germany
Climate Service Center Germany (GERICS), Helmholtz-Zentrum hereon, 20095 Hamburg, Germany
Institute of Geography, University of Hamburg, 20146 Hamburg, Germany
Verena Maleska
Chair of Environmental Development and Risk Management, Technical University of Dresden, 01062 Dresden, Germany
Laurens M. Bouwer
Climate Service Center Germany (GERICS), Helmholtz-Zentrum hereon, 20095 Hamburg, Germany
Institute of Geography, University of Hamburg, 20146 Hamburg, Germany
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Hydrol. Earth Syst. Sci., 29, 3687–3701, https://doi.org/10.5194/hess-29-3687-2025, https://doi.org/10.5194/hess-29-3687-2025, 2025
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Our study focuses on filling in missing precipitation data using an advanced neural network model. Traditional methods for estimating missing climate information often struggle in large regions where data are scarce. Our solution, which incorporates recent advances in machine learning, captures the intricate patterns of precipitation over time, especially during extreme weather events. Our model shows good performance in reconstructing large regions of missing rainfall radar data.
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Nat. Hazards Earth Syst. Sci., 25, 77–117, https://doi.org/10.5194/nhess-25-77-2025, https://doi.org/10.5194/nhess-25-77-2025, 2025
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Our research, involving 22 European scientists, investigated drought and heat impacts on forests in 2018–2022. Findings reveal that climate extremes are intensifying, with central Europe being most severely impacted. The southern region showed resilience due to historical drought exposure, while northern and Alpine areas experienced emerging or minimal impacts. The study highlights the need for region-specific strategies, improved data collection, and sustainable practices to safeguard forests.
Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 22, 3015–3039, https://doi.org/10.5194/nhess-22-3015-2022, https://doi.org/10.5194/nhess-22-3015-2022, 2022
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Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
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Short summary
Convection-permitting regional climate models, such as ICON-CLM at 3 km resolution, have great potential for improved hydroclimatic simulations. The studied model run shows lower bias in air temperature and summer global radiation, as well as in the frequency of wind speed over the Weiße Elster catchment in East Central Germany. Due to a pronounced overestimation of the intensity and frequency of heavy rainfall, however the discharge estimates are skewed, with no apparent added value.
Convection-permitting regional climate models, such as ICON-CLM at 3 km resolution, have great...