EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Current and future flood risk assessment in the Danube region

Kai Schröter1, Michel Wortmann2, Stefan Lüdtke1, Ben Hayes3, Martin Drews4, and Heidi Kreibich1
Kai Schröter et al.
  • 1German Research Centre for Geosciences GFZ, Section Hydrology, Potsdam, Germany (
  • 2Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 3OASIS lmf, London, United Kingdom
  • 4Technical University of Denmark DTU, Lyngby, Denmark

Severe hydro-meteorological hazards have been increasing during recent decades and, as a consequence of global change, more frequent and intense events are expected in the future. Climate informed planning of adaptation actions needs both consistent and reliable information about future risks and associated uncertainties, and appropriate tools to support comprehensive risk assessment and management. 
The Future Danube Model (FDM) is a multi-hazard and risk model suite for the Danube region which provides climate information related to perils such as heavy precipitation, heatwaves, floods and droughts under recent and future climate conditions. FDM has a modular structure with exchangeable components for climate input, hydrology, inundation, risk, adaptation and visualisation. FDM is implemented within the open-source OASIS Loss Modelling Framework, which defines a standard for estimating ground-up loss and financial damage of disaster events or event scenarios. 
The OASIS lmf implementation of the FDM is showcased for the current and future fluvial flood risk assessment in the Danube catchment. We generate stochastic inundation event sets for current and future climate in the Danube region using the output of several EURO-CORDEX models as climate input. One event set represents 10,000 years of daily climate data for a given climate model, period and representative concentration pathway. With this input, we conduct long term continuous simulations of flood processes using a coupled semi-distributed hydrological and a 1.5D hydraulic model for fluvial floods. Flood losses to residential building are estimated using a probabilistic multi-variable vulnerability model. Effects of adaptation actions are exemplified by scenarios of private precaution. Changes in risk are illustrated with exceedance probability curves for different event sets representing current and future climate on different spatial aggregation levels which are of interest for adaptation planning.

How to cite: Schröter, K., Wortmann, M., Lüdtke, S., Hayes, B., Drews, M., and Kreibich, H.: Current and future flood risk assessment in the Danube region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8410,, 2020


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  • CC1: Comment on EGU2020-8410, Punit Bhola, 06 May 2020

    Dear Authors,

    Thanks for a really nice study, I think it is benefical to observe future trend. I have only one question regarding the framework

    What is the rationale behind using two separate hydraulic models and do they have same model structure ? Introducing another model requires seperate calibration/validation and brings incosistency in the modelling results, especially for events that are both pluvial and fluvial. As far as I know MIKE FLOOD can model both fluvial-pluvial and has no restriction with run-time (provided GPU licence) so why not use only one.

    Best regards,


    • AC1: Reply to CC1, Kai Schröter, 08 May 2020

      Dear Punit,

      that's a good point. The focus of the model chain is, however, to look at large-scale flood risk. Even with GPU license, I doubt that the runtime of a combined MIKE-FLOOD model would be doable for 280,000 years of synthetic data. The idea of the pluvial model is to analyse selected hotspots also for pluvial flood hazard and risk. The combined occurrence of fluvial and pluvial flooding is given by using the same weather input data. For the risk assessment, the fluvial and pluvial inundations are combined, which is a simplification. The implication of this assumption is currently addressed in further analysis.  More details about the model chain are available here DOI:10.1016/j.cliser.2018.07.001.

      Best wishes,