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Performance of multiple probability distributions in generating daily precipitation for the simulation of hydrological extremes

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Abstract

Stochastic weather generators are statistical models widely used to produce climate time series with similar statistical properties to observed data. They are also used as downscaling tools to generate climate change scenarios for impact studies. Precipitation is one of the main variables simulated by weather generators and is also a key variable for impact studies, especially for hydrology. Precipitation is usually simulated by multiple precipitation models which have been proposed for simulating site-specific precipitation. However, these models’ performance in simulating watershed-averaged extreme precipitation, especially in representing hydrological extremes, has not been well-investigated. Accordingly, this study compares the performance of six probability distributions (exponential, gamma, skewed normal, mixed exponential, hybrid exponential/Pareto, and Weibull distributions) and a polynomial curve-fitting method in generating precipitation for simulating hydrological extremes over three basins using a set of extreme indices. The results show that except for the exponential distribution (EXP), all of the methods produce the distribution of observed precipitation at the daily, monthly and annual scales reasonably well for all three river basins. Although the three-parameter hybrid exponential/Pareto distribution (EXPP) overestimates precipitation extremes, other three-parameter models produce extremes accurately. The three-parameter mixed exponential (MEXP) distribution outperforms other models for simulating precipitation extremes. However, with respect to representing hydrological extremes, the MEXP distribution is not the best model. When simulating extreme streamflows with synthetic weather data, the EXP distribution shows the worst performance, while the curve fitting method (PN) performs the best. The inferiority of the EXPP distribution in generating extreme precipitation does not propagate to extreme flow simulations. Meanwhile, the performance of WEB is moderate in terms of representing hydrological extremes. Overall, finding the model that best reproduces precipitation for simulating hydrological extremes is not as clear-cut, since the performance of each model is extreme-indices dependent. Taking all of the indices into account, the MEXP and the PN appear to be superior in representing extreme precipitation and hydrological extremes.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 51779176, 51539009), the Overseas Expertise Introduction Project for Discipline Innovation (111 Project) funded by the Ministry of Education and State Administration of Foreign Experts Affairs P.R. China (Grant No. B18037), and the Thousand Youth Talents Plan from the Organization Department of the CCP Central Committee (Wuhan University, China). The authors are grateful to the China Meteorological Data Sharing Service System and the Department of Water Resources of Hunan Province for providing meteorological and hydrological data.

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  1. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, People’s Republic of China

    Biying Zhu, Jie Chen & Hua Chen

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  1. Biying Zhu
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  2. Jie Chen
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  3. Hua Chen
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Correspondence to Jie Chen.

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Zhu, B., Chen, J. & Chen, H. Performance of multiple probability distributions in generating daily precipitation for the simulation of hydrological extremes. Stoch Environ Res Risk Assess 33, 1581–1592 (2019). https://doi.org/10.1007/s00477-019-01720-z

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