Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change

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• 作者：Julie Shortridge ; Seth Guikema ; Ben Zaitchik
• 刊名：Climatic Change
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：140
• 期：2
• 页码：323-337
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Atmospheric Sciences; Climate Change/Climate Change Impacts;
• 出版者：Springer Netherlands
• ISSN：1573-1480
• 卷排序：140

文摘

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.