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

Finding homogeneous regions of extreme rainfall in reanalysis data

Marjanne Zander1,2, Frederiek Sperna Weiland1, and Albrecht Weerts1,2
Marjanne Zander et al.
  • 1Deltares, Inland Water Systems - Catchment and Urban Hydrology, Delft, Netherlands (
  • 2Wageningen University and Research- Environmental Sciences Group, Hydrology and Quantitative Watermanagement

In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.

Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.

Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017).  For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).

Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009).  To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.

Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.

This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (, which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.


Gabriele, S., & Chiaravalloti, F. (2013). “Searching regional rainfall homogeneity using atmospheric fields”. Advances in Water Resources, 53, 163–174.

Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., …, Zuo, H. (2018). “Operational global reanalysis: progress, future directions and synergies with NWP”, ECMWF.

Hosking, J. R. M., & Wallis, J. R. (2009). “Regional Frequency Analysis: An Approach Based on L-Moments”. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.

Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., … Wilkinson, J. M. (2017). “Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?” BAMS, 98(1), 79–93.

 Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). “A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.” Journal of Hydrology, 547(Supplement C), 413–427.

How to cite: Zander, M., Sperna Weiland, F., and Weerts, A.: Finding homogeneous regions of extreme rainfall in reanalysis data , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8985,, 2020

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