Doctoral Dissertation Research: Climate Change Impacts and Adaptation in California's Central Valley: Implications of Risk and Risk Aversion for Integrated Assessment

本论文的研究将提高区域适应气候变化，越来越多的科学家和政策制定者认识到至关重要的问题，目前的理解。它将整合气候学，水文学和决策分析，以解决目前关于风险和风险规避在气候变化评估中的重要性的辩论。风险厌恶（管理事件以避免风险的愿望）在人类决策者中很常见，但迄今为止，水资源和气候变化的建模都隐含着风险中性（对风险漠不关心）。拟议的研究将把风险和风险规避纳入对加州中央谷水资源的气候变化影响和适应战略的综合评估。分析将分四个步骤进行。首先，一个综合水文和水运作模拟模型将描述三个盆地在加州的中央谷。第二，重新采样合成的历史天气时间序列（和扰动他们的基础上缩小尺度的GCM数据）在许多模型运行将产生供水可靠性的概率分布在模型中的每个需求节点。第三，经济技术的效用函数引出将被用来确定风险偏好的研究流域的水组织。将这些效用函数与水文模型的概率输出相结合，将允许估计预期效用。最后，将在不同的排放轨迹假设下分析适应预测变化的管理方案，以便比较每个模拟方案下每个水组织的预期效用。其结果将是比现有研究更清楚地了解影响和适应。该项目将有助于自然地理和风险分析领域的水资源研究。首先，它将从理论上挑战水资源规划者的普遍假设，即未来的气候将与有记录的历史非常相似。它将产生一种方法，更清楚地阐明水资源管理者所面临的水文风险。其次，在风险分析领域，本研究将挑战水资源和气候变化文献中隐含的风险中性假设。鉴于气候变化可能会导致极端事件增加和供水不确定性增加，如果明确承认风险规避，其预计影响可能会大得多。通过将风险和风险规避应用于区域尺度气候影响评估，本研究将产生一组大于单个部分之和的产品。这项研究所产生的工具将直接应用于这个和其他水资源系统。水文/水运作模型将是一个更透明，更容易使用的工具，比那些目前存在的建模水运作在加州。在短期内，该模型将有助于目前无法获得此类规划工具的利益攸关方进行敏感性分析。从长远来看，该模型将与其他模型连接，形成一个全州范围的代表性，整合新的功能，包括适合气候变化评估的物理水文动态表示。此外，这一通用工具可应用于从环境评估到供水可靠性分析的各种主题。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立的研究生涯。

This dissertation research will improve current understandings of regional adaptation to climate change, a problem increasingly recognized as critically important by scientists and policymakers. It will integrate climatology, hydrology, and decision analysis to address a current debate about the importance of risk and risk aversion in climate change assessments. Risk aversion (the desire to manage events so as to avoid risk), is common in human decision-makers, but to date modeling of water resources and climate change have implicitly assumed risk neutrality (indifference to risk). The proposed research will incorporate risk and risk aversion into an integrated assessment of climate change impacts and adaptation strategies in water resources in California's Central Valley. The analysis will proceed in four steps. First, an integrated hydrology and water operations simulation model will describe three basins in California's Central Valley. Second, re-sampling synthetic historical weather time series (and perturbing them based on downscaled GCM data) over many model runs will generate probability distributions for water supply reliability at each demand node in the model. Third, economic techniques for utility function elicitation will be used to determine the risk preferences of water organizations in the study basins. Combining these utility functions with probabilistic output from the hydrology model will allow estimation of expected utility. Finally, scenarios of management options for adapting to projected changes will be analyzed under different assumptions of emissions trajectories, allowing for comparison of the expected utility to each water organization under each modeled scenario. The result will be a clearer understanding of impacts and adaptation than existing studies. The project will make contributions to the study of water resources in the fields of physical geography and risk analysis. First, it will take up theoretical challenges to the widespread assumptions by water planners that future climate will closely resemble that in recorded history. It will produce a method to more clearly articulate the hydrologic risks faced by water managers. Second, in the field of risk analysis, the research will challenge the implicit assumption of risk neutrality in the water resources and climate change literatures. Given that climate change will likely involve increased extreme events and uncertainty about water supply, its projected impacts will likely be significantly greater when risk aversion is explicitly acknowledged. By applying both risk and risk aversion to regional scale climate impacts assessment, this research will produce a set of products that are greater than the sum of the individual parts. The tools produced by this research will have direct application in this and other water resource systems. The hydrology/water operations model will be a more transparent, accessible tool than those that currently exist for modeling water operations in California. In the short term, the model will be useful for sensitivity analysis by stakeholders who currently lack access to such planning tools. In the longer term, the model will be connected with others to form a statewide representation that integrates new functionality, including a dynamic representation of physical hydrology appropriate for climate change assessment. Additionally, this general tool can be applied to topics ranging from environmental assessments to water supply reliability analysis. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.