EMS Annual Meeting Abstracts
Vol. 20, EMS2023-542, 2023, updated on 06 Jul 2023
EMS Annual Meeting 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Prediction and predictability of hydrological extreme events in the region Berlin-Brandenburg for risk assessment in the project SpreeWasser:N

Clara Hauke, Uwe Ulbrich, and Henning Rust
Clara Hauke et al.
  • Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany (c.hauke@fu-berlin.de)

SpreeWasser:N aims at developing strategies to gain a better understanding of the water cycle and associated hydrological extremes in the region Berlin-Brandenburg and create action plans to reduce related future risks. The goal is to develop long-term concepts regarding drought management, integrated water resources management and improved water storage systems together with water users and policy makers to pave the way for a sustainable and interdisciplinary water resource management course.


The predictability of hydrological extreme events is assessed on time scales ranging from near-term to decadal and predictors acting as potential indicators of imminent risk are inferred from statistical analyses, modeling and literature. Climate projections for different emission scenarios for Brandenburg provide a bigger picture of how climate variables will shift in the future and how this will affect the hydrological balance in the region. Ensemble methods are a helpful tool to assist in some of these tasks, including estimating uncertainties for forecasts and projections. Downscaling methods are used with convection-permitting models to provide data which can be used in hydrological models to improve the forecast of hydrological impacts. In conjunction with the project partners drought warning systems and adaptation strategies are developed. A drought forecast based on k-nearest neighbor regression is being developed using an algorithm that automatically selects those meteorological variables and regions yielding the largest forecast skill as input predictor variables during a hindcast period using reanalysis data. This machine learning approach supports the discovery of underlying physical links in atmospheric phenomena. Using weather patterns as an additional predictor variable, connections between certain states of the atmosphere and hydrological extreme weather events can be detected. Another research focus is the succession of certain weather patterns and its impact on precipitation.

How to cite: Hauke, C., Ulbrich, U., and Rust, H.: Prediction and predictability of hydrological extreme events in the region Berlin-Brandenburg for risk assessment in the project SpreeWasser:N, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-542, https://doi.org/10.5194/ems2023-542, 2023.