WRF: Collaborative Research: Extended-range forecasts of atmospheric rivers for adaptive management of flood risk, water supply, and environmental flows in California

WRF：合作研究：大气河流的长期预测，用于加利福尼亚州洪水风险、供水和环境流量的适应性管理

• 批准号: 1803563
• 负责人: Scott Steinschneider
• 金额: $ 15.2万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1803563&HistoricalAwards=false
• 关键词: WRF; Collaborative; Research; Extended; range

This research will develop ways to improve the robustness of human and environmental water supplies in California and similar regions with highly variable climates. Adaptive control policies that explicitly account for uncertainty in forecasts of extreme storm events will be designed. This work will explicitly tailor water system operations for regional storm track patterns and associated forecast errors for those storm types. Expected outcomes include three major scientific advancements: 1) characterization and modeling of spatial and temporal uncertainty in extended-range forecasts of cold-season precipitation, temperature, and floods at different locations and lead times 2) this improved knowledge of forecast error structure will be coupled with computational approaches for water resources control policy design to develop adaptive policies that are robust to forecast uncertainty; 3) determination of how forecast-informed control policies should be designed for long-term climate uncertainty represented by decadal-scale droughts and floods. Through these outcomes, this work will support a shift toward integrating state-of-the-art climate information with decision-making in the water sector of California, and findings from this work will be transferable to other semi-arid regions with similar flood regimes. This work will enable extensive interactions and technology transfer with key stakeholders in water management agencies in California throughout the project to promote the translation of research findings and methods into practice. All software and data analysis performed for this project will be open source and hosted on GitHub, enabling researchers around the world to reproduce and extend the project's findings. These software tools will be designed to support analyses on desktop computers as well as high-performance computing clusters, including NSF XSEDE resources, to support a range of decision-making processes. The team will advance graduate and undergraduate education in the key areas of data science and software design, identified in recent National Academy reports as critical issues for scientific reproducibility and workforce preparedness. These education expansions include of the PIs' statistics courses at Cornell and water resources engineering courses at UC Davis, incorporating simplified data analysis and modeling tasks from this project. Educational developments will be shared broadly through the ASCE Task Committee on Education (ECSTATIC).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.

这项研究将开发方法来提高加利福尼亚州和气候变化剧烈的类似地区人类和环境供水的稳健性。将设计明确考虑极端风暴事件预测不确定性的自适应控制政策。这项工作将明确针对区域风暴路径模式和这些风暴类型的相关预报误差定制水系统运行。预期成果包括三项重大科学进展：1) 不同地点和提前期的冷季降水、温度和洪水长期预报中时空不确定性的表征和建模 2) 对预报误差结构的了解将得到改善结合水资源控制政策设计的计算方法，制定对预测不确定性具有鲁棒性的适应性政策； 3）确定如何针对以十年尺度干旱和洪水为代表的长期气候不确定性设计基于预测的控制政策。通过这些成果，这项工作将支持将最先进的气候信息与加利福尼亚州水务部门的决策相结合，并且这项工作的结果将可转移到具有类似洪水状况的其他半干旱地区。这项工作将在整个项目过程中与加州水管理机构的主要利益相关者进行广泛的互动和技术转让，以促进研究成果和方法转化为实践。该项目执行的所有软件和数据分析都将开源并托管在 GitHub 上，使世界各地的研究人员能够重现和扩展该项目的研究结果。这些软件工具旨在支持台式计算机和高性能计算集群（包括 NSF XSEDE 资源）上的分析，以支持一系列决策过程。该团队将推进数据科学和软件设计关键领域的研究生和本科生教育，美国国家科学院最近的报告将这些领域确定为科学再现性和劳动力准备的关键问题。这些教育扩展包括 PI 在康奈尔大学的统计课程和加州大学戴维斯分校的水资源工程课程，并纳入了该项目的简化数据分析和建模任务。教育发展将通过 ASCE 教育任务委员会 (ECSTATIC) 广泛分享。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Climate Adaptation as a Control Problem: Review and Perspectives on Dynamic Water Resources Planning Under Uncertainty

气候适应作为控制问题：不确定性下动态水资源规划的回顾与展望

• DOI: 10.1029/2019wr025502
• 发表时间: 2020-02-01
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: J. Herman;J. Quinn;S. Steinschneider;M. Giuliani;S. Fletcher
• 通讯作者: S. Fletcher

Balancing Flood Risk and Water Supply in California: Policy Search Integrating Short‐Term Forecast Ensembles With Conjunctive Use

平衡加利福尼亚州的洪水风险和供水：将短期预报集合与联合使用相结合的政策搜索

• DOI: 10.1029/2018wr023177
• 发表时间: 2018-10-01
• 期刊: Water Resources Research
• 影响因子: 5.4
• 作者: M. Nayak;J. Herman;S. Steinschneider
• 通讯作者: S. Steinschneider

Spatial bias in medium-range forecasts of heavy precipitation in the Sacramento River basin: Implications for water management

萨克拉门托河流域强降水中期预报的空间偏差：对水管理的影响

• DOI: 10.1175/jhm-d-19-0226.1
• 发表时间: 2020-06
• 期刊: Journal of hydrometeorology
• 影响因子: 3.8
• 作者: Brodeur, Zachary;Steinschneider, Scott
• 通讯作者: Steinschneider, Scott

A Multivariate Approach to Generate Synthetic Short-To-Medium Range Hydro-Meteorological Forecasts Across Locations, Variables, and Lead Times

生成跨地点、变量和提前期的综合中短程水文气象预报的多变量方法

• 发表时间: 2021-05
• 期刊: Water resources research
• 影响因子: 5.4
• 作者: Brodeur, Z;Steinschneider, S
• 通讯作者: Steinschneider, S