中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
Non-Stationary Bayesian Modeling of Annual Maximum Floods in a Changing Environment and Implications for Flood Management in the Kabul River Basin, Pakistan

文献类型：期刊论文

作者Mehmood, Asif1,2; Jia, Shaofeng1,2; Mahmood, Rashid1,2; Yan, Jiabao1,2; Ahsan, Moien3
刊名WATER
出版日期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
DOI10.3390/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.
WOS关键词FREQUENCY-ANALYSIS ; DIFFERENTIAL EVOLUTION ; EXTREME EVENTS ; TRENDS ; CLIMATE ; RISK ; STATIONARITY ; INFORMATION ; STATISTICS ; STREAMFLOW
资助项目CAS-TWAS President Fellowship Program for doctoral students ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA20010201]
WOS研究方向Water Resources
语种英语
WOS记录号WOS:000475346300134
出版者MDPI
资助机构CAS-TWAS President Fellowship Program for doctoral students ; Strategic Priority Research Program of the Chinese Academy of Sciences
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.

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。