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

合作研究：基于过程的统计插值方法，用于改进 WATERS 试验台观测和水质模型的分析

• 批准号: 0854329
• 负责人: William Ball
• 金额: $ 25.22万
• 依托单位: Johns Hopkins 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: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0854329&HistoricalAwards=false
• 关键词: Collaborative; Research; Process; Based; Statistical

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.

0854329 /0853765球 / Chesapeake湾是一个很好的例子，说明了从大流域中复杂的流体动力学，生物地球化学和不同的投入如何导致人们对人类活动对人类活动对人类活动对环境，经济，经济，社会资源的影响的不确定性。 对此类系统的更好的科学理解和工程管理需要精心整合的方法，以最大程度地利用所有可用的观察和建模工具，这不仅是为了更好地预测未来影响，而且还可以更好地理解过去和当前的观察结果。在这种情况下，以及在计划和设计采样程序的背景下，开发4D（即时空和时间）插值的新方法是对环境观察者的至关重要的重要需求。 这项研究将通过利用数十年来切萨皮克湾研究建立的丰富资源基础来帮助满足这一需求，以及最近通过典型的切萨皮克湾环境天文台（CBEO）建立的，该研究已被确定为NSF支持的水域网络的潜在节点。 当前提出的研究的目标是开发，测试和应用更好的统计模型，以插值水质观测值，从而更有效地利用在当前可用的流体动力和水质模型中捕获的过程理解。 更具体地说，这项工作将通过使用基于过程的“影响的指标”（而不是距离）来定义有关插值的相关结构（即Kriging）来生成新的观测统计插值方法。 要测试的影响力的替代指标包括旅行时间，水年龄和示踪剂比例，这些指标都是通过建立良好和校准的切萨皮克湾水力学和水质模型而产生的。 基于模型的理解也将用于探索水质参数之间可能的跨相关性，如不同的时间间隔和历史环境条件所获得的。 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. 对具有统计和基于过程模型的全面观察数据集的综合分析将最大程度地利用每种方法的优势，这些方法分别包括不确定性估计和预测能力。 这些方法在向科学问题施加海湾缺氧方面的应用将证明其优点。 总体而言，这项工作将进一步评估并证明环境观测值的力量改变了我们对当前和历史数据的使用和理解。生成更好的分析和理解缺氧的工具将对切萨皮克湾的管理产生巨大影响。 当前，插值工具用于量化不符合水质标准的海湾水域的程度，并且过程模型用于预测管理活动的影响，例如TMDL开发。改进两种类型的工具和两者的集成使用将可以更好地理解和预测水质降解，从而有助于针对最有效的管理选择。 该项目的所有人员都与EPA的Chesapeake Bay计划合作，因此能够将这些改进的工具带给Bay Manager。 概念方法还应被证明在可用的基于过程的仿真模型的任何位置都具有同等价值。 这些发现将通过国家和国际科学会议，通过同行评审期刊的出版物以及通过Waters Network上的CBEO节点提供的新方法（通过圣地亚哥超级计算机中心维护）来传播。 这项研究是两所大学的跨学科和合作，包括研究生和本科生。 将通过合作来实现对K-12教育的影响，该合作有助于马里兰州大学持续的教育计划，该计划使用互动教育模块向中学学生讲述了地表水域中“死区”（缺氧）围绕的问题。