Collaborative: WATERS: Evaluating Community Watershed Models and Observation Networks under Uncertainty within the Susquehanna River Basin

摘要本奖项由2009年美国复苏与再投资法案（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。没有这种水流，河口就无法维持其非凡的多样性和水生生物的生产力。对SRB的管理需要在对其淡水资源的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型流域内水资源的可持续性？大河在全球气候系统中扮演什么角色？我们试图改变我们检测和/或预测SRB水文长期变化影响的能力。我们假设我们理解人类气候对SRB的影响的能力？美国的环境系统将需要向社区层面的流域模型评估范式转变？不确定条件下流域尺度观测网的预测能力与自适应设计。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一个强烈的3年气候梯度（即2001年的干旱到2003年的潮湿条件）中变化。然后，灵敏度图将用于识别关键的不确定性，这些不确定性将被纳入基于集合的数据同化框架，该框架明确考虑了观测和模型结构中系统性偏差的来源。数据同化框架将为判断模型的预测技能（即其平均预测误差）以及观测值（即那些使预测的时间演变协方差最小化的观测值）提供基线。这项建议项目将扩展水域管理系统的网络基础设施，以方便公布“不确定性”报告。在水文观测和共享模型输入/输出集合的网络服务方面，鼓励科学复制和扩展我们的预测实验。总的来说，这项工作的建模和CI组成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等不确定测量所提供的信息的价值。

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.