CAREER: CAS-Climate: Forecast-informed Flexible Reservoir System Modeling Enabled by Artificial Intelligence Algorithms Using Subseasonal-to-Seasonal Hydroclimatological Forecasts

职业：CAS-气候：利用次季节到季节水文气候预测的人工智能算法实现基于预测的灵活水库系统建模

• 批准号: 2236926
• 负责人: Tiantian Yang
• 金额: $ 51万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236926&HistoricalAwards=false
• 关键词: CAREER; CAS; Climate; Forecast; informed

Abrupt weather extremes, changing climate, and frequent natural hazards, such as floods and droughts, have created new challenges for the effective, sustainable, and flexible operation of our nation’s reservoir systems. To avoid reservoir failures due to insufficient operational flexibility and unpredictable water fluxes during extreme events, dam operators need two essential items: (1) accurate and reliable hydrological forecasts at extended lead times (ranging from days to months in the near future); and (2) powerful and adaptive decision support tools, which not only could assist real-time decision making about how much water to release at a certain time, but also allow reservoir operators to nimbly incorporate engineering constraints and hydroclimatological forecast scenarios into flexible release planning. Over the past four decades, significant scientific advancements have been made in deterministic forecasts, linear programming, optimization algorithms, and rule-based simulation models to guide reservoir operations. However, these approaches are unable to address future operational challenges due to current limitations in understanding the variabilities of Subseasonal-to-Seasonal (S2S) hydroclimatological forecasts and a lack of modeling capabilities that utilize ensemble forecasts for more effective water release decision-making. Therefore, the goals of this CAREER project are twofold: 1) to develop an integrated solution that can account for the spatial and temporal variability of precipitation and its uncertainty; and 2) to develop a novel Artificial Intelligence & Data Mining (AI&DM) decision support tool that allows reservoir operators to use improved ensemble forecasts to develop adaptive release strategies. This research targets enabling better response to, and mitigation of the impacts of, extreme weather events and climate uncertainty in reservoir operation and planning.The project will (1) leverage the advantages of state-of-the-art deep learning models to discover and correct the spatial and temporal errors associated with S2S precipitation forecasts from multiple forecasting models in the North American Multi-Model Ensemble dataset; and (2) develop an adaptive Ensemble Boosting Tree-based Predictive Control Model, which can effectively incorporate improved ensemble forecasts into scenario-based reservoir release simulations for planning purposes. Hydrological modeling and uncertainty analysis will be performed to help understand how meteorological uncertainty propagates from atmospheric conditions into water resources planning and infrastructure management. Large-scale hydrological validation experiments (over 671 watersheds) and reservoir simulations (over 316 dams) across the U.S. will be conducted. The results will be used to validate the improved forecasts, quantify the ensemble hydrological forecast uncertainty, and evaluate the forecast-informed reservoir decision support tool. The AI&DM models will be comprehensively tested in collaboration with the U.S. Bureau of Reclamation (USBR) and the U.S. Army Corps of Engineers (USACE), which are two major reservoir agencies in the USA. The expected outcomes are aimed to allow reservoir operators to develop suitable reservoir storage and release strategies that address sudden fluxes of incoming water or a lack of water supply, while simultaneously meeting various demands and constraints. Educational tasks are tightly coupled with research. Active learning activities will help graduate students develop the ability to tackle complex research problems. Undergraduate students will obtain skills in programming. Outreach includes hosting an annual “Water Festival” exhibit at the National Weather Museum and Science Center (NWMSC) in Norman, Oklahoma. During and beyond this CAREER project, museum visitors and children will witness the importance of hydrology, meteorology, water resources management, and the impacts of extreme weather and climate. The NSF-funded CUAHSI organization will also collaborate with the project to maximize the broader impacts of developed data, models, and algorithms via various educational and outreach activities.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.

突然的天气极端，气候变化以及经常的自然危害（例如地板和干旱）为我们国家储层系统的有效，可持续性和灵活的运营带来了新的挑战。为了避免由于运营灵活性不足而导致的储层失败，在极端事件中，水坝运营商需要两个基本项目：（1）在延长的交货时间（从不久的将来的天数到几个月）的准确可靠的水文预测； （2）强大而自适应的决策支持工具，不仅可以帮助实时决策在一定时间释放多少水，而且还可以使水库运营商细致地将工程限制和氢化气候预测场景纳入灵活的发布计划。在过去的四十年中，确定性的外国人，线性编程，优化算法和基于规则的仿真模型已取得了重大的科学进步，以指导参与者操作。但是，由于当前在理解亚季节到季节至季节（S2S）氢化气候森林的变化方面的局限性以及缺乏利用合奏森林以更有效的水释放决策制定的建模能力，因此这些方法无法应对未来的运营挑战。因此，该职业项目的目标是双重的：1）开发一个可以解释降水的空间和临时变化及其不确定性的集成解决方案； 2）开发一种新颖的人工智能和数据挖掘（AI＆DM）决策工具，该工具使水库运营商可以使用改进的集合森林来制定自适应释放策略。这项研究的目标是对储层运营和计划中极端天气事件以及气候不确定性的更好反应和缓解影响。项目将（1）利用最先进的深度学习模型的优势发现并纠正与北美北美多个数据中的多个预测模型相关的空间和临时误差，与S2S的降水模型相关。 （2）开发一种自适应集合增强基于树的预测控制模型，该模型可以有效地将改进的集合森林纳入基于方案的储量中，以释放模拟。将进行水文建模和不确定性分析，以帮助了解气象不确定性如何从大气条件传播到水资源规划和基础设施管理。将进行大规模的水文验证实验（超过671个流域）和储层模拟（超过316个大坝）。结果将用于验证改良的森林，量化集成氢病预测的不确定性，并评估预测信息的储层决策支持工具。 AI＆DM模型将与美国填海局（USBR）和美国陆军工程兵团（USACE）合作进行全面测试，它们是美国两个主要的储层机构。预期的结果旨在允许储层运营商制定合适的储层存储和释放策略，以解决突然解决接入水或缺乏供水的通量，同时满足各种需求和约束。教育任务与研究紧密相结合。积极的学习活动将有助于研究生发展解决复杂研究问题的能力。本科生将获得编程技能。外展活动包括在俄克拉荷马州诺曼的国家气象博物馆和科学中心（NWMSC）举办年度“水节”展览。在这个职业项目期间，博物馆游客和儿童将见证水文学，气象，水资源管理以及极端天气和气候的影响。由NSF资助的Cuahsi组织还将与该项目合作，以通过各种教育和外展活动最大化开发数据，模型和算法的更广泛影响。该奖项反映了NSF的法定使命，并认为通过基金会的知识分子和更广泛的影响审查Criteria，通过评估进行评估。