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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-44
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-44
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  24 Feb 2020

24 Feb 2020

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A revised version of this preprint is currently under review for the journal HESS.

Spatial Dependency in Nonstationary GEV Modelling of Extreme Precipitation over Great Britain

Han Wang and Yunqing Xuan Han Wang and Yunqing Xuan
  • Zienkiewicz Centre for Computational Engineering, College of Engineering, Swansea University Bay Campus, Swansea SA1 8EN, UK

Abstract. This paper presents a study on extreme precipitation using both stationary and non-stationary Generalized Extreme Value (GEV) models over a large number of samples distributed over Great Britain (GB) for the last century, aiming to gain insights in the spatial dependency of the GEV distribution. Not only L-Moments (LM) and Maximum Likelihood (ML) estimation methods but a Bayesian Markov-Chain Monte Carlo (B-MCMC) method are incorporated into the GEV models to characterize the uncertainty in the nonstationary risk-based assessment. The samples are generated using a toolbox of spatial random sampling for grid-based data analysis (SRS-GDA). The results show that a markedly large proportion (70 %) of the samples are favour nonstationary assumption GEV models as far as the annual maximum daily rainfall (AMDR) is concerned. The most frequent AMDR, as represented by the location parameter tend to be increasing over the time for more than half of the samples and in contrast, only 8 % have a downward trend. A spatially clustering pattern is also clearly present. For rarer (with 0.1 probability) AMDR, they are shown to have a tendency of becoming more extreme over time, for more than half of the samples. For the three methods, the LM method with stationary GEV maintain best results but for AMDR values with higher probability (5-year return level); the B-MCMC method with nonstationary GEV, however, outperform other combinations by a large margin for more extreme events (50-year return level). The findings suggest that an overhaul of the current engineering design storm practice may be needed in view of environmental change impact on natural processes.

Han Wang and Yunqing Xuan

Han Wang and Yunqing Xuan

Data sets

Gridded estimates of daily and monthly areal rainfall for the United Kingdom (1890-2015) [CEH-GEAR] M. Tanguy, H. Dixon, I. Prosdocimi, D. G. Morris, and V. D. J. Keller https://doi.org/10.5285/33604ea0-c238-4488-813d-0ad9ab7c51ca

Model code and software

SRS-GDA: A spatial random sampling toolbox for grid-based hydro-climatic data analysis in environmental change studies H. Wang and Y. Xuan https://doi.org/10.1016/j.envsoft.2019.104598

Han Wang and Yunqing Xuan

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Latest update: 28 Sep 2020
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Short summary
The study aims to reveal whether extreme rainfall over Great Britain are gradually changing over time and locations, with respect to the climate change impact. The study makes use of the latest detailed rainfall data at 1 km resolution over the last 100 years looking at the maximum daily rainfall variation. We find that more than 70 % of the area does show variation over time and about half of them has rainfall becomes more extreme over time.
The study aims to reveal whether extreme rainfall over Great Britain are gradually changing over...
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