EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Introducing a dynamic spatiotemporal rainfall generator for flood risk analysis 

Shahin Khosh Bin Ghomash1,2, Daniel Bachmann1, Daniel Caviedes-Voullième3,4,5, and Christoph Hinz2
Shahin Khosh Bin Ghomash et al.
  • 1Reaseach Group Flood Risk Management, Magdeburg-Stendal University of Applied Sciences, Magdeburg, Germany (
  • 2Chair of Hydrology, Brandenburg University of Technology Cottbus-Senftenberg, Germany
  • 3SimDataLab Terrestrial Systems, Jülich Supercomputing Centre, Forschungszentrum Jülich (Germany)
  • 4Institute for Bio- Geosciences: Agrosphere IBG-3, Forschungszentrum Jülich (Germany)
  • 5Centre for High Performance Scientific Computing for Terrestrial Systems (HPSC-TerrSys), Geoverbund ABC/J (Germany)

Precipitation scenario analysis is a crucial step in flood risk assessment, in which storm events with different probabilities are defined and used as input for the hydrological/hydrodynamic calculations. Rainfall generators may serve as a basis for the precipitation analysis. With the increase in the use of high resolution spatially-explicit hydrological/hydrodynamic models in flood risk calculations, demand for synthetic gridded precipitation input is increasing. In this work, we present a dynamic spatiotemporal rainfall generator. The model is capable of generating catchment-scale rainfields containing moving storms, which enable physically-plausible and spatiotemporally coherent precipitation events. This is achieved by the tools event-based approach, where dynamic storms are identified as clusters of related data that occur at different locations in space and time, and are then used as basis for event regeneration. The implemented methodology, mainly inspired by Dierden et al. (2019), provides an improvement in the spatial coherence of precipitation extremes, which can in turn be beneficial in flood risk calculations.

The model has been validated under different databases such as the radar-based RADALON dataset or spatially-interpolated historical raingauge timeseries of different catchments in Germany, which is also presented in this work. The validation indicates the models ability to adequately preserve observed storm statistics in the generated timeseries. The generator is developed as an extension to the state-of-the-science flood risk modelling tool ProMaIDes (Promaides 2023). The model also puts great focus on user accessibility with offering features such as an easy installation process, support for most operating systems, a user interface and an online user manual.


Diederen, D., Liu, Y., 2020. Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes. Stoch Environ Res Risk Assess 34, 1369–1383.

ProMaIDes (2023): Protection Measures against Inundation Decision support.

How to cite: Khosh Bin Ghomash, S., Bachmann, D., Caviedes-Voullième, D., and Hinz, C.: Introducing a dynamic spatiotemporal rainfall generator for flood risk analysis , EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2599,, 2023.