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.

摘要这一奖项是根据2009年的《美国复苏与再投资法》（公法111-5）资助的。 Susquehanna河流域（SRB）是切萨皮克湾最大的支流。 没有这种流程，河口将无法维持其非凡的多样性和水生生物的生产力。 SRB的管理需要在较淡水资源上的竞争社会和环境需求之间保持平衡。这项研究的长期目标是解决问题：？人类和气候如何影响大型河流盆地中水资源的可持续性？大河在全球气候系统中起什么作用？ 我们试图改变我们检测和/或预测长期变化对SRB水文学的影响的能力。我们认为，我们了解人类气候对SRB环境系统的影响的能力是否需要向分水岭模型的社区层面评估进行范式转变吗？在不确定性下的盆地尺度观测网络的预测能力和适应性设计。这项工作将为一系列模型提供随时间不断发展的敏感性图，这将使我们能够探索在3年的3年气候梯度中的水文流控制如何变化（即2001年的干旱到2003年的潮湿条件）。然后，灵敏度图将用于确定要将关键的不确定性纳入基于集合的数据同化框架中，该框架明确考虑了观测和模型结构中偏差的系统源。数据同化框架将为判断模型的预测技能（即其平均预测错误）以及观察值的价值（即，那些最小化预测时间不断发展的协方差）提供基准。这个拟议的项目将启动网络基础设施（CI）的扩展，以促进不确定性的发布？在用于共享模型输入/输出的集合的水文观测和网络服务中，以鼓励我们的预测实验的科学复制和扩展。总体而言，这项工作的建模和CI组成部分将有助于阐明降水，蒸散量，河流流，土壤水分和地下水水平的不确定测量所提供的信息的价值。