Study on evolution characteristics of dry season runoff using a non-stationary GEV model in Source Region of the Yellow River
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(1.State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;2.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China;3.Jiangning District Water Affairs Bureau of Nanjing City, Nanjing 211112, China;4.College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

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P333

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    Abstract:

    The non-stationary characteristics of the average runoff in driest days are tested in this paper based on the measured runoff data of Tangnaihai Hydrological Station from 1957 to 2018 using the 5-year moving average and Mann-Kendall abrupt change test to analyze the effect of climate change on the non-stationary of hydrological extreme value. According to the Kendall rank correlation analysis method, the climate index was selected, and the non-stationary generalized extreme value distribution model (generalized extreme value, GEV) was constructed with time and climate index as covariates. Parameter estimation and model selection were carried out, and application effects of the stationary and non-stationary GEV models in the runoff simulation of dry season were compared. The results indicated that the dry season runoff shows obvious non-stationary characteristics. Meanwhile, the simulated value of the stationary GEV model is higher than the measured value, and the GEV model with the western Pacific index as covariate produces better fit for the extreme value, which better explained the volatility of extreme dry events.

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江善虎,任明明,章二子,等.基于非平稳GEV模型的黄河源区枯季径流演变特征分析[J].河海大学学报(自然科学版),2023,51(2):1-7, 63.(JIANG Shanhu, REN Mingming, ZHANG Erzi, et al. Study on evolution characteristics of dry season runoff using a non-stationary GEV model in Source Region of the Yellow River[J]. Journal of Hohai University (Natural Sciences),2023,51(2):1-7, 63.(in Chinese))

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  • Received:April 18,2022
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  • Online: April 14,2023
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