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A climate change range‐based method for estimating robustness for water resources supply
Author(s) -
Whateley Sarah,
Steinschneider Scott,
Brown Casey
Publication year - 2014
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2014wr015956
Subject(s) - robustness (evolution) , climate change , computer science , environmental science , water supply , climate model , futures contract , environmental resource management , econometrics , environmental economics , mathematics , environmental engineering , business , economics , ecology , biochemistry , chemistry , gene , biology , finance
Many water planning and operation decisions are affected by climate uncertainty. Given concerns about the effects of uncertainty on the outcomes of long‐term decisions, many water planners seek adaptation alternatives that are robust given a wide range of possible climate futures. However, there is no standardized paradigm for quantifying robustness in the water sector. This study uses a new framework for assessing the impact of future climate change and uncertainty on water supply systems and defines and demonstrates a new metric for quantifying climate robustness. The metric is based on the range of climate change space over which an alternative provides acceptable performance. The metric is independent of assumptions regarding future climate; however, GCM‐based (or other) climate projections can be used to create a “climate‐informed” version of the metric. The method is demonstrated for a water supply system in the northeast United States to evaluate the additional robustness that can be attained through optimal operational changes, by comparing optimal reservoir operations with current reservoir operations. Results show the additional robustness gained through adaptation. They also reveal the additional insight regarding robust adaptation gained from the decision‐scaling approach that would not be discerned using a GCM projection‐based analysis.