Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan

doi:10.3390/w11061246

	Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan
	Mehmood, Asif 1,2; Jia, Shaofeng 1,2; Mahmood, Rashid 1,2; Yan, Jiabao 1,2; Ahsan, Moien 3
刊名	WATER
	2019-06-01
卷号	11 期号:6 页码:30
关键词	non-stationary extreme value theory uncertainty flood regime flood management Kabul river basin Pakistan
ISSN号	2073-4441
DOI	10.3390/w11061246
通讯作者	Jia, Shaofeng(jiasf@igsnrr.ac.cn)
英文摘要	Recent evidence of regional climate change associated with the intensification of human activities has led hydrologists to study a flood regime in a non-stationarity context. This study utilized a Bayesian framework with informed priors on shape parameter for a generalized extreme value (GEV) model for the estimation of design flood quantiles for at site analysis in a changing environment, and discussed its implications for flood management in the Kabul River basin (KRB), Pakistan. Initially, 29 study sites in the KRB were used to evaluate the annual maximum flood regime by applying the Mann-Kendall test. Stationary (without trend) and a non-stationary (with trend) Bayesian models for flood frequency estimation were used, and their results were compared using the corresponding flood frequency curves (FFCs), along with their uncertainty bounds. The results of trend analysis revealed significant positive trends for 27.6% of the gauges, and 10% showed significant negative trends at the significance level of 0.05. In addition to these, 6.9% of the gauges also represented significant positive trends at the significance level of 0.1, while the remaining stations displayed insignificant trends. The non-stationary Bayesian model was found to be reliable for study sites possessing a statistically significant trend at the significance level of 0.05, while the stationary Bayesian model overestimated or underestimated the flood hazard for these sites. Therefore, it is vital to consider the presence of non-stationarity for sustainable flood management under a changing environment in the KRB, which has a rich history of flooding. Furthermore, this study also states a regional shape parameter value of 0.26 for the KRB, which can be further used as an informed prior on shape parameter if the study site under consideration possesses the flood type flash. The synchronized appearance of a significant increase and decrease of trends within very close gauge stations is worth paying attention to. The present study, which considers non-stationarity in the flood regime, will provide a reference for hydrologists, water resource managers, planners, and decision makers.
资助项目	CAS-TWAS President Fellowship Program for doctoral students ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20010201]
WOS关键词	FREQUENCY-ANALYSIS ; DIFFERENTIAL EVOLUTION ; EXTREME EVENTS ; TRENDS ; CLIMATE ; RISK ; STATIONARITY ; INFORMATION ; STATISTICS ; STREAMFLOW
WOS研究方向	Water Resources
语种	英语
出版者	MDPI
WOS记录号	WOS:000475346300134
资助机构	CAS-TWAS President Fellowship Program for doctoral students ; Strategic Priority Research Program of the Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/58189]
专题	中国科学院地理科学与资源研究所
通讯作者	Jia, Shaofeng
作者单位	1.Chinese Acad Sci, IGSNRR, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.UET, CEWRE, Lahore 54890, Pakistan
推荐引用方式 GB/T 7714	Mehmood, Asif,Jia, Shaofeng,Mahmood, Rashid,et al. Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan[J]. WATER,2019,11(6):30.
APA	Mehmood, Asif,Jia, Shaofeng,Mahmood, Rashid,Yan, Jiabao,&Ahsan, Moien.(2019).Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan.WATER,11(6),30.
MLA	Mehmood, Asif,et al."Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan".WATER 11.6(2019):30.