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Article

Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China

by 1,2,*, 1,3, 4, 4 and 4
1
School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China
2
Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province, Changsha 410114, China
3
Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province, Changsha 410114, China
4
Hydrology and Water Resources Survey Center of Hunan Province, Changsha 410008, China
*
Author to whom correspondence should be addressed.
Academic Editors: Momcilo Markus and Sajjad Ahmad
Water 2022, 14(1), 66; https://doi.org/10.3390/w14010066
Received: 16 October 2021 / Revised: 23 December 2021 / Accepted: 24 December 2021 / Published: 31 December 2021
(This article belongs to the Special Issue Statistics in Hydrology)
The accurate design flood of hydraulic engineering is an important precondition to ensure the safety of residents, and the high precision estimation of flood frequency is a vital perquisite. The Xiangjiang River basin, which is the largest river in Hunan Province of China, is highly inclined to floods. This paper aims to investigate the annual maximum flood peak (AMFP) risk of Xiangjiang River basin under the climate context employing the Bayesian nonstationary time-varying moment models. Two climate covariates, i.e., the average June-July-August Artic Oscillation and sea level pressure in the Northwest Pacific Ocean, are selected and found to exhibit significant positive correlation with AMFP through a rigorous statistical analysis. The proposed models are tested with three cases, namely, stationary, linear-temporal and climate-based conditions. The results both indicate that the climate-informed model demonstrates the best performance as well as sufficiently explain the variability of extreme flood risk. The nonstationary return periods estimated by the expected number of exceedances method are larger than traditional ones built on the stationary assumption. In addition, the design flood could vary with the climate drivers which has great implication when applied in the context of climate change. This study suggests that nonstationary Bayesian modelling with climatic covariates could provide useful information for flood risk management. View Full-Text
Keywords: extreme flood risk; climatic factors; nonstationary frequency analysis; Bayesian modeling; nonstationary return period; Xiangjiang River basin extreme flood risk; climatic factors; nonstationary frequency analysis; Bayesian modeling; nonstationary return period; Xiangjiang River basin
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MDPI and ACS Style

Zeng, H.; Huang, J.; Li, Z.; Yu, W.; Zhou, H. Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China. Water 2022, 14, 66. https://doi.org/10.3390/w14010066

AMA Style

Zeng H, Huang J, Li Z, Yu W, Zhou H. Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China. Water. 2022; 14(1):66. https://doi.org/10.3390/w14010066

Chicago/Turabian Style

Zeng, Hang, Jiaqi Huang, Zhengzui Li, Weihou Yu, and Hui Zhou. 2022. "Nonstationary Bayesian Modeling of Extreme Flood Risk and Return Period Affected by Climate Variables for Xiangjiang River Basin, in South-Central China" Water 14, no. 1: 66. https://doi.org/10.3390/w14010066

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