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Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models

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Abstract

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate.

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Funding

This work was partly supported by the Natural Science Foundation of Jiangsu Province of China (Grant No. BK20150922).

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Authors and Affiliations

  1. School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Peng Deng

  2. Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY, 82071, USA

    Jianting Zhu

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  1. Peng Deng
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Contributions

Peng Deng designed and directed the project, wrote most of the article, proceeded the computation, and drew the figures; Jianting Zhu participated in writing and editing the manuscript.

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Correspondence to Peng Deng.

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Deng, P., Zhu, J. Forecast and uncertainty analysis of extreme precipitation in China from ensemble of multiple climate models. Theor Appl Climatol 145, 787–805 (2021). https://doi.org/10.1007/s00704-021-03660-7

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