DOI: 10.1007/s10584-016-1845-4
Scopus记录号: 2-s2.0-84994275147
论文题名: Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change
作者: Shortridge J. ; Guikema S. ; Zaitchik B.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2017
卷: 140, 期: 2 起始页码: 323
结束页码: 337
语种: 英语
Scopus关键词: Climate models
; Decision making
; Decision support systems
; Developing countries
; Uncertainty analysis
; Different time scale
; Infrastructure development
; Infrastructure planning
; Integrated model framework
; Irrigation efficiency
; Streamflow modeling
; Transient climate change
; Water infrastructure
; Climate change
; climate change
; climate modeling
; data processing
; decision making
; decision support system
; developing world
; hydrological modeling
; infrastructure planning
; integrated approach
; streamflow
; vulnerability
; water planning
; Ethiopia
; Lake Tana Basin
英文摘要: In the face of deeply uncertain climate change projections, robust decision frameworks are becoming a popular tool for incorporating climate change uncertainty into water infrastructure planning. These methodologies have the potential to be particularly valuable in developing countries where extensive infrastructure development is still needed and uncertainties can be large. However, many applications of these methodologies have relied on a sophisticated process of climate model downscaling and impact modeling that may be unreliable in data-scarce contexts. In this study, we demonstrate a modified application of the robust decision making (RDM) methodology that is specifically tailored for application in data-scarce situations. This modification includes a novel method for generating transient climate change sequences that account for potential variable dependence but do not rely on detailed GCM projections, and an emphasis on identifying the relative importance of data limitations and uncertainty within an integrated modeling framework. We demonstrate this methodology in the Lake Tana basin in Ethiopia, showing how the approach can highlight the vulnerability of alternative plans across different time scales and identify priorities for research and model refinement. We find that infrastructure performance is particularly sensitive to uncertainty in streamflow model accuracy, irrigation efficiency, and evaporation rates, suggesting that additional research in these areas could provide valuable insights for long-term infrastructure planning. This work demonstrates how tailored application of robust decision frameworks using simple modeling approaches can provide decision support in data-scarce regions where more complex modeling and analysis may be impractical. © 2016, Springer Science+Business Media Dordrecht. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84119
Appears in Collections: 气候减缓与适应 气候变化事实与影响
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作者单位: Department of Biological Systems Engineering, Virginia Tech, 155 Ag Quad Lane, Blacksburg, VA, United States; Department of Industrial and Operations Engineering, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, United States; Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD, United States
Recommended Citation:
Shortridge J.,Guikema S.,Zaitchik B.. Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change[J]. Climatic Change,2017-01-01,140(2)