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Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan
Mehmood, Asif1,2; Jia, Shaofeng1,2; Mahmood, Rashid1,2; Yan, Jiabao1,2; Ahsan, Moien3
2019-06-01
Source PublicationWATER
ISSN2073-4441
Volume11Issue:6Pages:30
Corresponding AuthorJia, Shaofeng(jiasf@igsnrr.ac.cn)
AbstractRecent 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.
Keywordnon-stationary extreme value theory uncertainty flood regime flood management Kabul river basin Pakistan
DOI10.3390/w11061246
WOS KeywordFREQUENCY-ANALYSIS ; DIFFERENTIAL EVOLUTION ; EXTREME EVENTS ; TRENDS ; CLIMATE ; RISK ; STATIONARITY ; INFORMATION ; STATISTICS ; STREAMFLOW
Indexed BySCI
Language英语
Funding ProjectCAS-TWAS President Fellowship Program for doctoral students ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20010201]
Funding OrganizationCAS-TWAS President Fellowship Program for doctoral students ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS Research AreaWater Resources
WOS SubjectWater Resources
WOS IDWOS:000475346300134
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/58190
Collection中国科学院地理科学与资源研究所
Corresponding AuthorJia, Shaofeng
Affiliation1.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
Recommended Citation
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.
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