Evaluating the sensitivity of robust water resource interventions to climate change scenarios
Evaluating the sensitivity of robust water resource interventions to climate change scenarios
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DOI：
10.1016/j.crm.2022.100442
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发表时间：
2022-06
影响因子：
4.4
通讯作者：
R. Geressu;C. Siderius;Seshagiri Rao Kolusu;J. Kashaigili;M. Todd;D. Conway;J. Harou
中科院分区：
文献类型：
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作者：
R. Geressu;C. Siderius;Seshagiri Rao Kolusu;J. Kashaigili;M. Todd;D. Conway;J. Harou
水资源系统规划因气候变化的幅度和方向的不确定性而变得复杂。因此，新的基础设施或改变的管理规则等发展在未来各种条件下（即，鲁棒的解决方案）是优选的。强大的多目标优化可以帮助确定有利的系统设计，包括现有的基础设施加上选定的新干预措施的子集。该方法使用模拟水资源性能指标进行评估，统计汇总，总结气候情景集合的性能。在大多数情况下，这种“鲁棒性指标”是敏感的情况下，系统表现不佳，因此结果可能会受到少数不利的气候情景。了解特定气候情景对稳健优化决策替代方案的影响有助于更好地解释其结果。我们提出了一个自动化的多标准设计下的不确定性敏感性分析公式，使用多目标进化算法，以揭示强大的和有效的设计下的气候情景合奏不同的样本。该方法被应用到水库管理问题，在Rufiji河流域，坦桑尼亚，其中涉及第二大水坝在非洲。我们发现，在不同的气候情景下，针对鲁棒性优化的解决方案表现出重要的差异。如果分析人员和/或决策者对某些气候情景的相关性有不同的置信水平，这就变得特别与决策相关。所提出的方法激励继续研究气候模型的可信度应如何告知气候情景选择，因为它表明了情景选择对稳健的优化设计过程所产生的建议的影响。
Water resource system planning is complicated by uncertainty on the magnitude and direction of climate change. Therefore, developments such as new infrastructure or changed management rules that would work acceptably well under a diverse set of future conditions (i.e., robust solutions) are preferred. Robust multi-objective optimisation can help identify advantageous system designs which include existing infrastructure plus a selected subset of new interventions. The method evaluates options using simulated water resource performance metrics statistically aggregated to summarise performance over the climate scenario ensemble. In most cases such ‘robustness metrics’ are sensitive to scenarios under which the system performs poorly and so results may be strongly influenced by a minority of unfavorable climate scenarios. Understanding the influence of specific climate scenarios on robust optimised decision alternatives can help better interpret their results. We propose an automated multi-criteria design-under-uncertainty sensitivity analysis formulation that uses multi-objective evolutionary algorithms to reveal robust and efficient designs under different samples of a climate scenario ensemble. The method is applied to a reservoir management problem in the Rufiji River basin, Tanzania, which involves the second largest dam in Africa. We find that solutions optimised for robustness under alternative groups of climate scenarios exhibit important differences. This becomes particularly decision-relevant if analysts and/or decision-makers have differing confidence levels in the relevance of certain climate scenarios. The proposed approach motivates continued research on how climate model credibility should inform climate scenario selection because it demonstrates the influence scenario selection has on recommendations arising from robust optimisation design processes.