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

An improved regional flood frequency analysis approach at the global scale

Gang Zhao1, Paul Bates1,2, Jeff Neal1,2, and Bo Pang3
Gang Zhao et al.
  • 1School of Geographical Sciences, University of Bristol, Bristol, UK (;;
  • 2Fathom, Engine Shed, Station Approach, Bristol, UK (;
  • 3College of Water Sciences, Beijing Normal University, Beijing, China (

Design flood estimation in data-poor regions is a fundamental task in hydrology. In this paper, we propose a regional flood frequency analysis approach to estimate design floods anywhere on the global river network. This approach involves two stages: (i) clustering global gauging stations into subareas by a K-means model based on twelve globally available catchment descriptors and (ii) developing a regression model in each subarea for design flood estimation using the same descriptors. Nearly 12,000 discharge stations globally were selected for model development and a benchmark global index-flood method was adopted for comparison. The results showed that: (1) the proposed approach achieved the highest accuracy for design flood estimation when using all catchment descriptors for clustering; and the regression model accuracy improved by considering more descriptors in model development; (2) a support vector machine regression showed the highest accuracy among all regression models tested, with relative root mean squared error of 0.67 for mean flood and 0.83 for 100-year return period flood estimations; (3) 100-year return period flood magnitude in tropical, arid, temperate, continental and polar climate zones could be reliably estimated with relative mean biases of -0.18, -0.23, -0.18, 0.17 and -0.11 respectively by adopting a 5-fold cross-validation procedure; (4) the proposed approach outperformed the benchmark index-flood method for 10, 50 and 100 year return period estimates; We conclude that the proposed RFFA is a valid approach to generate design floods globally, improving our understanding of the flood hazard, especially in ungauged areas.

How to cite: Zhao, G., Bates, P., Neal, J., and Pang, B.: An improved regional flood frequency analysis approach at the global scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11288,, 2020