CyberSEES: Type 1: Cyber-Enabled Ensemble Data Assimilation for Drought Monitoring, Forecasting and Recovery

Cyber​​SEES：类型 1：用于干旱监测、预报和恢复的网络集成数据同化

• 批准号: 1830955
• 负责人: Hamid Moradkhani
• 金额: $ 13.23万
• 依托单位: University of Alabama Tuscaloosa
• 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: 2018-01-09 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830955&HistoricalAwards=false
• 关键词: CyberSEES; Type; Cyber; Enabled; Ensemble

There is growing concern with evidence that droughts have been intensified due to ongoing land development driven by population growth and other factors. This has correspondingly aggravated water scarcity, which threatens the long-term sustainability of water resources. Since the US is one of the largest in terms of water footprint, the country is very vulnerable to moderate and severe drought. To mitigate the drought vulnerability, an effective drought monitoring and forecasting system with assessment of drought recovery time is critical for decision makers. This project will develop a cyber-enabled ensemble data assimilation and terrestrial modeling system to characterize the land surface condition for not only assessing the agricultural drought but also providing the initial condition for probabilistic drought forecasting. These estimates will provide the basis for drought recovery estimation. The project will serve as a prototype to build capacity for large-scale drought studies and applications, and will directly enhance the ability of state water managers to take appropriate and timely measures during periods of water scarcity as a result of drought.The study relies on a variety of massive earth and environmental observational data in connection with advanced dynamical and Bayesian modeling, to account for uncertainties and provide reliable drought estimation with the goal to further freshwater resource sustainability. Ensemble modeling and probabilistic estimation to quantify the uncertainties in Earth system science by means of data assimilation has been a salient bottleneck in operationalization. Due to the multi-scale nature of hydrologic processes and under-determinedness of most hydrologic systems as well as the presence of epistemic and random uncertainties, dynamic physical-based and stochastic modeling to probabilistically characterize drought onset and predict it at seasonal scale requires proper parameterization of the system. This research proposes a modern ensemble data assimilation system operating in real time to characterize land surface conditions for monitoring drought, develops a computational framework for effective data assimilation, and implements an approach that targets computational scalability, power, and reliability in the computational framework.

越来越令人关切的是，有证据表明，由于人口增长和其他因素推动的土地开发，干旱加剧了。这相应地加剧了缺水问题，威胁到水资源的长期可持续性。由于美国是水足迹最大的国家之一，该国非常容易受到中度和严重干旱的影响。为了减轻干旱脆弱性，一个有效的干旱监测和预测系统，评估干旱恢复时间，对决策者至关重要。该项目将开发一个网络化的集合数据同化和陆地建模系统，以描述陆地表面状况，不仅用于评估农业干旱，而且还为概率干旱预报提供初始条件。这些估计数将为干旱恢复估计提供依据。该项目将作为建立大规模干旱研究和应用能力的原型，并将直接提高各州水资源管理人员在干旱造成的缺水期间采取适当和及时措施的能力。考虑到不确定性，并提供可靠的干旱估计，以促进淡水资源的可持续性。利用数据同化技术对地球系统科学中的不确定性进行量化的模拟和概率估计一直是业务化的一个突出瓶颈。由于水文过程的多尺度性质和大多数水文系统的不确定性，以及存在认知和随机不确定性，基于动态物理和随机建模，以概率特征干旱发生和预测它在季节尺度上需要适当的系统参数化。本研究提出了一个现代集合数据同化系统运行在真实的时间来表征监测干旱的地表条件，开发了一个有效的数据同化的计算框架，并实现了一种方法，目标计算的可扩展性，功率和可靠性的计算框架。