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: 1539605
• 负责人: Hamid Moradkhani
• 金额: $ 39.96万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1539605&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.

人们越来越关注有证据表明，由于人口增长和其他因素推动的持续土地开发，旱灾加剧。这相应地加剧了水资源短缺，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。为了缓解干旱的脆弱性，一个有效的干旱监测和预报系统以及干旱恢复时间的评估对决策者来说至关重要。该项目将开发一个基于网络的集合数据同化和陆地模拟系统，以表征地表条件，不仅用于评估农业干旱，而且为概率干旱预报提供初始条件。这些估算将为旱灾恢复估算提供依据。该项目将作为建设大规模干旱研究和应用能力的原型，并将直接提高国家水资源管理者在干旱造成的缺水时期采取适当和及时措施的能力。这项研究依赖于各种与先进的动力学和贝叶斯模型有关的大量地球和环境观测数据，以考虑不确定性并提供可靠的干旱估计，目的是进一步促进淡水资源的可持续性。通过数据同化来量化地球系统科学中的不确定性的集合建模和概率估计一直是业务运作中的一个突出瓶颈。由于水文过程的多尺度特性和大多数水文系统的不确定性性，以及认知性和随机性的存在，基于物理和随机的动态模型要在季节尺度上对干旱发生进行概率描述和预测，需要对系统进行适当的参数设置。这项研究提出了一个实时运行的现代集合数据同化系统来表征用于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，并在计算框架中实现了一种以计算可伸缩性、能力和可靠性为目标的方法。