Collaborative Proposal: WATERS: EVALUATING COMMUNITY MODELS AND OBSERVATION NETWORKS UNDER UNCERTAINTY WITHIN THE SUSQUEHANNA RIVER BASIN

合作提案：WATERS：评估萨斯奎哈纳河流域不确定性下的社区模型和观测网络

• 批准号: 0838307
• 负责人: Franco Montalto
• 金额: $ 20.03万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0838307&HistoricalAwards=false
• 关键词: Collaborative; Proposal; WATERS; EVALUATING; COMMUNITY

AbstractThis award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The Susquehanna River Basin (SRB) is the largest tributary to the Chesapeake Bay. Without this flow the estuary could not sustain its extraordinary diversity and productivity of aquatic life. Management of the SRB requires a balance between the competing societal and environmental demands placed on its freshwater resources. A long term goal of this research is to address the questions: ?How do humans and climate impact the sustainability of the water resources within large river basins? What role do large rivers play in the global climate system??. We seek to transform our ability to detect and/or predict the impacts of long-term changes on the hydrology of the SRB. We posit that our ability to understand human-climate impacts on the SRB?s environmental systems will require a paradigmatic shift towards community level evaluations of watershed models? predictive power and adaptive design of the basin-scale observation networks under uncertainty. This work will contribute time-evolving sensitivity maps for a suite of models that will allow us to explore how hydrologic flow controls change across a strong 3 year climatic gradient (i.e., drought in 2001 to wet conditions in 2003). The sensitivity maps will then be used to identify the key uncertainties to be incorporated into an ensemble-based data assimilation framework that explicitly considers systematic sources of bias in observations and model structures. The data assimilation framework will provide baselines for judging the forecasting skills of models (i.e., their mean prediction errors) as well as the value of observations (i.e., those observables that minimize the time evolving covariance of predictions). This proposed project will initiate the extension of WATERS cyberinfrastructure (CI) to facilitate the publication of the ?uncertainty? in hydrologic observations and web-services for sharing ensembles of model inputs/outputs to encourage scientific replication and extension of our prediction experiments. Overall the modeling and CI components of this work will help clarify the value of information provided by uncertain measurements of precipitation, evapotranspiration, river flow, soil moisture, and groundwater levels.

摘要该奖项由 2009 年美国复苏和再投资法案（公法 111-5）资助。萨斯奎哈纳河流域（SRB）是切萨皮克湾最大的支流。 如果没有这种水流，河口就无法维持水生生物非凡的多样性和生产力。 SRB 的管理需要在对其淡水资源的竞争性社会和环境需求之间取得平衡。这项研究的长期目标是解决以下问题：人类和气候如何影响大河流域内水资源的可持续性？大河流在全球气候系统中发挥什么作用？ 我们寻求转变我们检测和/或预测长期变化对SRB水文影响的能力。我们假设，我们要了解人类气候对 SRB 环境系统影响的能力，就需要向流域模型的社区层面评估进行范式转变？不确定性下流域尺度观测网络的预测能力和自适应设计。这项工作将为一套模型提供随时间变化的敏感性图，使我们能够探索水文流量控制如何在三年的强气候梯度（即 2001 年干旱到 2003 年潮湿条件）中变化。然后，灵敏度图将用于识别要纳入基于集合的数据同化框架的关键不确定性，该框架明确考虑观测和模型结构中系统偏差的来源。数据同化框架将为判断模型的预测技能（即它们的平均预测误差）以及观测值（即那些最小化预测的时间演化协方差的可观测值）提供基线。该拟议项目将启动 WATERS 网络基础设施 (CI) 的扩展，以促进“不确定性”的发布。水文观测和网络服务，用于共享模型输入/输出集合，以鼓励科学复制和扩展我们的预测实验。总体而言，这项工作的建模和 CI 部分将有助于阐明降水、蒸散、河流流量、土壤湿度和地下水位的不确定测量所提供的信息的价值。