Collaborative Research: Process-Based Statistical Interpolation Methods for Improved Analysis of WATERS Test-bed Observations and Water Quality Models

0854329 / 0853765 Ball / DiToro切萨皮克湾是一个典型的例子，说明了复杂的流体动力学、地球化学和来自大型流域的不同输入如何导致人类活动对关键环境、经济和社会资源的影响的不确定性。 要更好地科学理解和工程管理这些系统，就需要采取谨慎的综合办法，最大限度地利用所有现有的观测和建模工具，不仅是为了更好地预测未来的影响，而且也是为了更好地理解过去和当前的观测。在这种情况下，以及在规划和设计采样程序的情况下，开发4D（即，空间和时间）对现有观测数据进行插值是环境观测站的一项极为重要的需要。 这项研究将有助于满足这一需要，利用丰富的资源基础，已建立了几十年的切萨皮克湾的研究，最近通过一个原型切萨皮克湾环境观测站（CBEO），已被建立为一个潜在的节点，为国家科学基金会支持的沃茨网络。 目前提出的研究的目标是开发，测试和应用更好的统计模型插值的水质观测，使更有效地利用过程中的理解捕获在目前可用的水动力学和水质模型。 更具体地说，这项工作将通过使用基于过程的“影响度量”（与距离相反）来定义通知插值的相关结构（即，克里格法）。 待测试的影响的替代指标包括旅行时间，水的年龄，和示踪剂的比例，所有通过运行良好的和校准的切萨皮克湾水动力和水质模型。 基于模型的理解还将用于探索在不同时间间隔和历史环境条件下获得的水质参数之间可能的相互关系。 经过开发和全面评估后，新的插值方法将用于探索：（1）在历史数据记录上的缺氧发展，及（2）确定性模式预测与观测到的水质时空趋势之间持续不一致的原因。新开发的过程-基于插值的方法有望克服在流动水体中使用克里金法时经常遇到的许多困难。 用统计模型和基于过程的模型对综合观测数据集进行综合分析，将最大限度地利用每种方法的长处，其中分别包括不确定性估计和预测能力。 将这些方法应用于海湾缺氧的紧迫科学问题将证明它们的优点。 总的来说，这项工作将进一步评估和展示环境观测站的力量，以改变我们对当前和历史数据的使用和理解。产生更好的工具，分析和理解缺氧将产生深远的影响，对管理的切萨皮克湾。 目前，插值工具被用来量化海湾沃茨不符合水质标准的程度，过程模型被用来预测管理活动的影响，如TMDL的发展。改进这两种工具并综合使用这两种工具，将有助于更好地了解和预测水质退化情况，从而有助于确定最有效的管理办法。 该项目的所有人员都与美国环保署的切萨皮克湾计划合作，因此能够将这些改进的工具带给海湾管理人员。 在任何有完善的基于流程的模拟模型的地方，这种概念性方法也同样有价值。 研究结果将通过国家和国际科学会议、同行评审期刊上的出版物以及通过沃茨网络上的CBEO节点（由圣地亚哥超级计算机中心维护）提供新方法进行传播。 这项研究是跨学科的，跨两所大学的合作，包括研究生和本科生。 对K-12教育的影响将通过协助马里兰州大学正在进行的教育计划的合作来实现，该计划使用互动式教育模块向中学生讲授有关表面沃茨中“死区”（缺氧）的问题。

0854329 / 0853765 Ball / DiToro The Chesapeake Bay is a prime example of how complex hydrodynamics, biogeochemistry, and varying inputs from a large watershed can lead to uncertainty about the impacts of human activities on a crucial environmental, economic, and social resource. Better scientific understanding and engineering management of such systems requires carefully integrated approaches that make maximum use of all available observations and modeling tools, not only for better predictions of future impacts, but also for better understanding of past and current observations. In this context, and also in the context of planning and designing sampling programs, the development of new methods for 4D (i.e., space and time) interpolation of existing observational data is a critically important need for environmental observatories. This research will help meet this need by taking advantage of a rich resource base that has been established over many decades of Chesapeake Bay research and most recently through a prototypical Chesapeake Bay Environmental Observatory (CBEO) that has been established as a potential node for the NSF-supported WATERS Network. Objectives of the currently proposed research are to develop, test, and apply better statistical models for the interpolation of water quality observations that make more effective use of the process understanding captured in currently available hydrodynamic and water quality models. More specifically, the work will generate new approaches for statistical interpolation of observations by using process-based "metrics of influence" (as opposed to distance) for defining the correlation structure that informs interpolation (i.e., kriging). The alternative metrics of influence to be tested include travel time, water age, and tracer proportion, all generated through runs of well-established and calibrated Chesapeake Bay hydrodynamic and water quality models. Model-based understanding will also be used to explore possible cross correlations among water quality parameters, as obtained over different time intervals and historical environmental conditions. After their development and thorough evaluation, the new interpolation methods will be applied toward exploring: (1) hypoxia development over a historical data record, and (2) causes for continuing inconsistencies between deterministic model predictions and observed temporal and spatial trends in water quality.The newly developed process-based interpolation methods are expected to overcome many of the difficulties commonly encountered in using kriging in flowing water bodies. The integrated analysis of comprehensive observational data sets with both statistical and process-based models will take maximum advantage of the strengths of each approach, which include uncertainty estimation and predictive ability, respectively. The application of these methods to pressing science questions on Bay hypoxia will demonstrate their merit. Overall, the work will further evaluate and demonstrate the power of environmental observatories to transform our use and understanding of current and historical data.The generation of better tools for analyzing and understanding hypoxia will have far reaching impacts on the management of the Chesapeake Bay. Currently, interpolation tools are used to quantify the extent of Bay waters not meeting water quality criteria, and process models are used to predict impacts of management activities, such as TMDL development. Improvements to both types of tools and integrated use of the two will allow better understanding and prediction of water quality degradation and thus help target the most effective management options. All of the personnel on this project have worked collaboratively with EPA's Chesapeake Bay Program and are thus able to bring these improved tools to Bay managers. The conceptual approach should also prove to be equally valuable at any location where well-developed process-based simulation models are available. The findings will be disseminated through national and international scientific meetings, through publications in peer reviewed journals, and by making the new methods available through the CBEO node on the WATERS network (as maintained through the San Diego Supercomputer Center). This research is interdisciplinary and collaborative across two universities, including both graduate and undergraduate students. Impact on K-12 education will be achieved through collaborations that assist an on-going educational program at the University of Maryland which uses interactive educational modules to teach middle-school students about the issues surrounding "dead zones" (hypoxia) in surface waters.