EGU21-2985, updated on 30 Nov 2023
https://doi.org/10.5194/egusphere-egu21-2985
EGU General Assembly 2021
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

RIMurban – A generalized GPU-based model for urban pluvial flood risk modelling and forecasting

Heiko Apel1, Sergiy Vorogushyn1, Mostafa Farrag1, Nguyen Viet Dung1, Melanie Karremann2, Heidi Kreibich1, and Bruno Merz1
Heiko Apel et al.
  • 1GFZ German Research Center for Geoscience, Section 5.4 Hydrology, Potsdam, Germany (hapel@gfz-potsdam.de)
  • 2Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research

Urban flash floods caused by heavy convective precipitation pose an increasing threat to communes world-wide due to the increasing intensity and frequency of convective precipitation caused by a warming atmosphere. Thus, flood risk management plans adapted to the current flood risk but also capable of managing future risks are of high importance. These plans necessarily need model based pluvial flood risk simulations. In an urban environment these simulations have to have a high spatial and temporal resolution in order to site-specific management solutions. Moreover, the effect of the sewer systems needs to be included to achieve realistic inundation simulations, but also to assess the effectiveness of the sewer system and its fitness to future changes in the pluvial hazard. The setup of these models, however, typically requires a large amount of input data, a high degree of modelling expertise, a long time for setting up the model setup and to finally run the simulations. Therefor most communes cannot perform this task.

 In order to provide model-based pluvial urban flood hazard and finally risk assessments for a large number of communes, the model system RIMurban was developed. The core of the system consists of a simplified raster-based 2D hydraulic model simulating the urban surface inundation in high spatial resolution. The model is implemented on GPUs for massive parallelization. The specific urban hydrology is considered by a capacity-based simulation of the sewer system and infiltration on non-sealed surfaces, and flow routing around buildings. The model thus considers the specific urban hydrological features, but with simplified approaches. Due to these simplifications the model setup can be performed with comparatively low data requirements, which can be covered with open data in most cases. The core data required are a high-resolution DEM, a layer of showing the buildings, and a land use map.

The spatially distributed rainfall input can be derived local precipitation records, or from an analysis of weather radar records of heavy precipitation events. A catalogue of heavy rain storms all over Germany is derived based on radar observations of the past 19 years. This catalogue serves as input for pluvial risk simulations for individual communes in Germany, as well as a catalogue of possible extreme events for the current climate. Future changes in these extreme events will be estimated based on regional climate simulations of a ΔT (1.5°C, 2°C) warmer world.

RIMurban simulates the urban inundation caused by these events, as well as the stress on the sewer system. Based on the inundation maps the damage to residential buildings will be estimated and further developed to a pluvial urban flood risk assessment. Because of the comparatively simple model structure and low data demand, the model setup can be easily automatized and transferred to most small to medium sized communes in Europe and even beyond, if the damage estimation is modified. RIMurban is thus seen as a generally appölicable screening tool for urban pluvial flood risk and a starting point for adapted risk management plans.

How to cite: Apel, H., Vorogushyn, S., Farrag, M., Dung, N. V., Karremann, M., Kreibich, H., and Merz, B.: RIMurban – A generalized GPU-based model for urban pluvial flood risk modelling and forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2985, https://doi.org/10.5194/egusphere-egu21-2985, 2021.

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