中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Many-objective robust decision making for water allocation under climate change

文献类型:期刊论文

作者Yan, Dan1,2; Ludwig, Fulco1; Huang, He Qing2; Werners, Saskia E.1
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2017-12-31
卷号607页码:294-303
ISSN号0048-9697
关键词Multi-objective evolutionary algorithms Climate uncertainties Robust decision making Pearl River basin
DOI10.1016/j.scitotenv.2017.06.265
通讯作者Yan, Dan(dan.yan@wur.nl)
英文摘要Water allocation is facing profound challenges due to climate change uncertainties. To identify adaptive water allocation strategies that are robust to climate change uncertainties, a model framework combining many-objective robust decision making and biophysical modeling is developed for large rivers. The framework was applied to the Pearl River basin (PRB), China where sufficient flow to the delta is required to reduce saltwater intrusion in the dry season. Before identifying and assessing robust water allocation plans for the future, the performance of ten state-of-the-art MOEAs (multi-objective evolutionary algorithms) is evaluated for the water allocation problem in the PRB. The Borg multi-objective evolutionary algorithm (Borg MOEA), which is a self-adaptive optimization algorithm, has the best performance during the historical periods. Therefore it is selected to generate new water allocation plans for the future (2079-2099). This study shows that robust decision making using carefully selected MOEAs can help limit saltwater intrusion in the Pearl River Delta. However, the framework could perform poorly due to larger than expected climate change impacts on water availability. Results also show that subjective design choices from the researchers and/or water managers could potentially affect the ability of the model framework, and cause the most robust water allocation plans to fail under future climate change. Developing robust allocation plans in a river basin suffering from increasing water shortage requires the researchers and water managers to well characterize future climate change of the study regions and vulnerabilities of their tools. (C) 2017 Elsevier B.V. All rights reserved.
WOS关键词EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION ; CHANGE UNCERTAINTIES ; RESOURCES ; MODEL ; RIVER ; ALGORITHMS ; CHINA ; SIMULATION ; FRAMEWORK ; DROUGHT
资助项目Joint Scientific Thematic Research Programme (JSTP) Working with Water: adaptive land use and water management in the Pearl River Delta under climate change and sea level rise[842.00.002] ; International Science & Technology Cooperation Program of China[2013DFA91700] ; National Natural Science Foundation of China[41330751] ; External Cooperation Program of Chinese Academy of Sciences[GJHZ1019] ; China Postdoctoral Science Foundation[2013M540135]
WOS研究方向Environmental Sciences & Ecology
语种英语
出版者ELSEVIER SCIENCE BV
WOS记录号WOS:000408755300031
资助机构Joint Scientific Thematic Research Programme (JSTP) Working with Water: adaptive land use and water management in the Pearl River Delta under climate change and sea level rise ; International Science & Technology Cooperation Program of China ; National Natural Science Foundation of China ; External Cooperation Program of Chinese Academy of Sciences ; China Postdoctoral Science Foundation
源URL[http://ir.igsnrr.ac.cn/handle/311030/61694]  
专题中国科学院地理科学与资源研究所
通讯作者Yan, Dan
作者单位1.Wageningen Univ & Res, Water Syst & Global Change, POB 47, NL-6700 AA Wageningen, Netherlands
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, 11A Datun Rd, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714
Yan, Dan,Ludwig, Fulco,Huang, He Qing,et al. Many-objective robust decision making for water allocation under climate change[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2017,607:294-303.
APA Yan, Dan,Ludwig, Fulco,Huang, He Qing,&Werners, Saskia E..(2017).Many-objective robust decision making for water allocation under climate change.SCIENCE OF THE TOTAL ENVIRONMENT,607,294-303.
MLA Yan, Dan,et al."Many-objective robust decision making for water allocation under climate change".SCIENCE OF THE TOTAL ENVIRONMENT 607(2017):294-303.

入库方式: OAI收割

来源:地理科学与资源研究所

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