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

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)资助的。萨斯奎哈纳河流域(SRB)是切萨皮克湾最大的支流。如果没有这种流动，河口就不能维持其非凡的水生生物多样性和生产力。对SRB的管理需要在对其淡水资源提出的相互竞争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流域内水资源的可持续性？大江大河在全球气候系统中扮演什么角色？我们寻求改变我们探测和/或预测长期变化对SRB水文学影响的能力。我们假设我们理解人类-气候对SRB？S环境系统的影响的能力将需要向社区层面的流域模型评估的范例转变？不确定条件下流域尺度观测网的预报能力与自适应设计。这项工作将为一套模型提供随时间演变的敏感性图，使我们能够探索水文流量控制如何在强3年气候梯度(即2001年的干旱到2003年的潮湿条件)上发生变化。然后，敏感度图将被用来确定将纳入基于集合的数据同化框架的关键不确定因素，该框架明确考虑了观测和模式结构中的系统性偏差来源。数据同化框架将为判断模式的预报技能(即它们的平均预报误差)以及观测值(即使预报的时间演变协方差最小化的那些观测值)提供基线。这个拟议的项目将启动水域网络基础设施(CI)的扩展，以促进发布？不确定性？在水文观测和网络服务中共享模型输入/产出集合，以鼓励科学复制和推广我们的预测实验。总体而言，这项工作的建模和CI部分将有助于澄清降雨量、蒸散量、河流流量、土壤湿度和地下水位等不确定测量提供的信息的价值。

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.