Adaptive Management of Water Supply Infrastructure for Persistent Anomalies versus Climate Trends

针对持续异常与气候趋势的供水基础设施的适应性管理

• 批准号: 2207036
• 负责人: Sarah Fletcher
• 金额: $ 49.8万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207036&HistoricalAwards=false
• 关键词: Adaptive; Management; Water; Supply; Infrastructure

This project addresses climate challenges faced by critical infrastructure sectors by establishing a new adaptive management approach to water supply infrastructure planning that incorporate the impacts of multi-year climate oscillations on water availability. Large scale infrastructure investments in the water sector will be needed to maintain reliable water supply globally in a changing climate. To enable resilience at lower cost, adaptive management approaches have been developed to monitor gradual changes in precipitation patterns to guide adaptations. However, existing adaptive management approaches are confounded by the presence of multi-year climate oscillations, which make it difficult to infer long-term trends from near-term observations. This grant supports research that seeks to address this gap by integrating climate science on multi-year oscillations with decision-support modeling for water resources management. The outcome of this research is expected to help water planners identify low-cost, adaptive approaches to maintain water supply reliability. The research team will partner with water planners to make sure the research is useful for decision-making. Additionally, the grant will support new interactive educational materials in water resources management.This project designs new adaptive management strategies for water supply planning that can address uncertainty in climate trends, multi-year large-scale climate oscillations, and annual variability. It will develop an approach for decomposing precipitation uncertainty into these three timescales and quantifying future opportunities to reduce uncertainty in each timescale. This uncertainty characterization will be integrated into stochastic dynamic optimization models of water resource systems. This approach will be used to assess the potential for different combinations of adaptive strategies including flexibility in the timing, sizing, modularity, and operations of water supply infrastructure to address the different timescales of uncertainty in future precipitation. By applying this approach to river basins in sub-Saharan Africa with contrasting patterns of precipitation variability, the project will build theory on how different temporal patterns of precipitation variability affect adaptive approaches to water infrastructure planning, design, and operations. This new knowledge can help water planners target water supply infrastructure investment to support low-cost water supply reliability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目通过建立一种新的自适应管理方法来解决供水基础设施计划，以解决关键基础设施部门所面临的气候挑战，该方法纳入了多年气候振荡对水的影响的影响。在不断变化的气候下，将需要在水部门进行大规模的基础设施投资，以维持全球可靠的供水。为了使以较低的成本启用韧性，已经开发了自适应管理方法来监测降水模式的逐渐变化以指导适应。但是，现有的自适应管理方法被多年气候振荡的存在混淆，这使得很难从近期观察中推断长期趋势。该赠款支持旨在通过将气候科学与多年振荡与水资源管理的决策支持建模相结合，旨在解决这一差距的研究。这项研究的结果有望帮助水计划者确定低成本的自适应方法，以维持供水可靠性。研究团队将与水计划者合作，以确保研究对决策有用。此外，该赠款将支持水资源管理中的新互动式教育材料。该项目设计的新型自适应管理策略可以解决气候趋势，多年大规模气候振荡和年度变化的不确定性。它将开发一种将降水不确定性分解为这三个时间尺度的方法，并量化未来的机会，以减少每个时间尺度的不确定性。这种不确定性表征将集成到水资源系统的随机动态优化模型中。这种方法将用于评估自适应策略不同组合的潜力，包括在时间，尺寸，模块化和供水基础设施的操作中的灵活性，以解决未来降水中不确定性的不同时间尺度。通过将这种方法应用于撒哈拉以南非洲的河流盆地，并具有对比的降水量变异性模式，该项目将建立理论，以降水可变性的不同时间模式如何影响对水基础设施计划，设计和操作的适应性方法。这些新知识可以帮助水供应商针对水供应基础设施投资，以支持低成本的供水可靠性。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。

项目成果

Valuing Combinations of Flexible Planning, Design, and Operations in Water Supply Infrastructure

重视供水基础设施灵活规划、设计和运营的组合

• DOI: 10.1029/2023wr036048
• 发表时间: 2024
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Willebrand, Keani;Zaniolo, Marta;Skerker, Jennifer;Fletcher, Sarah
• 通讯作者: Fletcher, Sarah

Bayesian Estimation of Advanced Warning Time of Precipitation Emergence

降水出现提前预警时间的贝叶斯估计

• DOI: 10.1029/2023ef004079
• 发表时间: 2024
• 期刊: Earth's Future
• 作者: Lickley, Megan;Fletcher, Sarah
• 通讯作者: Fletcher, Sarah

Quantifying the Value of Learning for Flexible Water Infrastructure Planning

量化灵活水利基础设施规划学习的价值

• DOI: 10.1029/2022wr034412
• 发表时间: 2023
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: Skerker, J. B.;Zaniolo, M.;Willebrand, K.;Lickley, M.;Fletcher, S. M.
• 通讯作者: Fletcher, S. M.