Composition, frequency, and severity of future rain-on-snow floods in Germany
- 1Goethe University Frankfurt (Frankfurt am Main, Germany)
Abstract
In Germany, severe widespread winter floods are often generated by rain-on-snow (ROS) phenomena. Under suitable conditions, when rain falls on the snow cover, the snow can melt and produce extreme amounts of runoff. In a warming climate, the frequency of ROS events is expected to change locally depending on elevation and regionally based on the general climate conditions. Consequently, the characteristics of ROS-driven winter floods are also anticipated to change. To investigate these changes, streamflow for multiple gauge stations in Germany was simulated using an ensemble of Long Short-Term Memory models based on an ensemble of downscaled climate projections. Germany, as a representative mid-latitude country with a considerable portion of historical floods generated by ROS, offers extensive spatial and temporal coverage of hydrological observations spanning long temporal scales, and hence warranting efficient training of the deep learning model. We used an explainable artificial intelligence method, integrated gradients, to examine flood-generating processes, focusing primarily on ROS, for every simulated flood peak. Changes in frequency, feature importance, and a ranking based severity of ROS flood events were assessed for individual streamflow gauges and for widespread floods affecting multiple catchments across four major river basins in Germany. We found that with regard to the ensemble median, the frequency of ROS floods will mostly decrease at the scale of individual gauges by up to 60 %, as well as at the widespread scale for the Rhine, Elbe, and Weser River basins but increase at gauges located in the Danube River basin for the corresponding widespread floods. For all regions, the snowmelt component during ROS floods becomes less relevant, whereas the contribution of rainfall to these events increases. Interestingly, the severity of the mean and the most extreme widespread ROS floods is projected to increase compared to the historical period in all major river basins in Germany. Only for individual gauges the severity may decrease in the future. Despite the overall agreement in the trends of the input features across climate models, the resulting trends in ROS floods are considerably disparate. This discrepancy is primarily attributed to complex model chains in the climate models leading to variability in rainfall and snowmelt, and to different melting seasons.
Versions
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BibTeX
@article{czakay2026composition,
title = {{Composition, frequency, and severity of future rain-on-snow floods in Germany}},
author = {Christian Czakay and Larisa Tarasova and Bodo Ahrens},
journal = {Natural Hazards and Earth System Sciences},
year = {2026},
month = nov,
doi = {10.5194/nhess-26-5609-2026},
url = {https://nhess.copernicus.org/articles/26/5609/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Composition, frequency, and severity of future rain-on-snow floods in Germany
AU - Christian Czakay
AU - Larisa Tarasova
AU - Bodo Ahrens
PY - 2026
DA - 2026/11/05
JO - Natural Hazards and Earth System Sciences
DO - 10.5194/nhess-26-5609-2026
UR - https://nhess.copernicus.org/articles/26/5609/2026/
AB - In Germany, severe widespread winter floods are often generated by rain-on-snow (ROS) phenomena. Under suitable conditions, when rain falls on the snow cover, the snow can melt and produce extreme amounts of runoff. In a warming climate, the frequency of ROS events is expected to change locally depending on elevation and regionally based on the general climate conditions. Consequently, the characteristics of ROS-driven winter floods are also anticipated to change. To investigate these changes, streamflow for multiple gauge stations in Germany was simulated using an ensemble of Long Short-Term Memory models based on an ensemble of downscaled climate projections. Germany, as a representative mid-latitude country with a considerable portion of historical floods generated by ROS, offers extensive spatial and temporal coverage of hydrological observations spanning long temporal scales, and hence warranting efficient training of the deep learning model. We used an explainable artificial intelligence method, integrated gradients, to examine flood-generating processes, focusing primarily on ROS, for every simulated flood peak. Changes in frequency, feature importance, and a ranking based severity of ROS flood events were assessed for individual streamflow gauges and for widespread floods affecting multiple catchments across four major river basins in Germany. We found that with regard to the ensemble median, the frequency of ROS floods will mostly decrease at the scale of individual gauges by up to 60 %, as well as at the widespread scale for the Rhine, Elbe, and Weser River basins but increase at gauges located in the Danube River basin for the corresponding widespread floods. For all regions, the snowmelt component during ROS floods becomes less relevant, whereas the contribution of rainfall to these events increases. Interestingly, the severity of the mean and the most extreme widespread ROS floods is projected to increase compared to the historical period in all major river basins in Germany. Only for individual gauges the severity may decrease in the future. Despite the overall agreement in the trends of the input features across climate models, the resulting trends in ROS floods are considerably disparate. This discrepancy is primarily attributed to complex model chains in the climate models leading to variability in rainfall and snowmelt, and to different melting seasons.
SN - 1684-9981
ER -