Planning under deep uncertainty, when probabilistic characterizations of the future are unknown, is a major challenge in water resources management. Many planning frameworks advocate for “scenario‐neutral” analyses in which alternative policies are evaluated over plausible future scenarios with no assessment of their likelihoods. Instead, these frameworks use sensitivity analysis to discover which uncertain factors have the greatest influence on performance. This knowledge can be used to design monitoring programs and adaptive policies that respond to changes in the critical uncertainties. However, scenario‐neutral analyses make implicit assumptions about the range and independence of the uncertain factors that may not be consistent with the coupled human‐hydrologic processes influencing the system. These assumptions could influence which factors are found to be most important and which policies are most robust, belying their neutrality; assuming uniformity and independence could have decision‐relevant implications. This study illustrates these implications using a multistakeholder planning problem within the Colorado River Basin, where hundreds of rights holders vie for the river's limited water under the law of prior appropriation. Variance‐based sensitivity analyses are performed to assess users' vulnerabilities to changing hydrologic conditions using four experimental designs: (1) scenario‐neutral samples of hydrologic factors, centered on recent historical conditions, (2) scenarios informed by climate projections, (3) scenarios informed by paleohydrologic reconstructions, and (4) scenario‐neutral samples of hydrologic factors spanning all previous experimental designs. Differences in sensitivities and user robustness rankings across the experiments illustrate the challenges of inferring the most consequential drivers of vulnerabilities to design effective monitoring programs and robust management policies.

中文翻译：

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水资源短缺脆弱性和稳健性的探索性建模是否可以在方案中立起来？

在未知的未来概率特征未知的情况下，进行深度不确定性规划是水资源管理中的主要挑战。许多规划框架都提倡“情景中立”分析，即在可能的未来情景中评估替代政策，而不评估其可能性。相反，这些框架使用敏感性分析来发现哪些不确定因素对性能的影响最大。这些知识可用于设计监视程序和自适应策略，以响应关键不确定性的变化。但是，情景中性分析对不确定因素的范围和独立性做出了隐式假设，这些不确定因素可能与影响系统的人类水文过程耦合不符。这些假设可能会根据中立性影响哪些因素被认为是最重要的，哪些政策最有力。假设统一性和独立性可能具有与决策相关的含义。这项研究使用科罗拉多河流域内的多方利益相关者规划问题来说明这些影响，在该问题中，数百名权利人根据事先拨款的法律争夺河流的有限水量。使用四个实验设计进行基于方差的敏感性分析，以评估用户对变化的水文状况的脆弱性：（1）以近期历史情况为中心的水文因素情景-中性样本;（2）气候预测提供的情景;（3）由古水文重建提供信息的场景，（4）情景中性水文因子样本，涵盖所有先前的实验设计。在整个实验中，敏感性和用户健壮性排名的差异说明了推断最脆弱的漏洞驱动程序来设计有效的监视程序和健壮的管理策略所面临的挑战。