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 Ball / DiToro 切萨皮克湾是一个典型的例子，说明复杂的流体动力学、生物地球化学和来自大流域的不同输入如何导致人类活动对关键环境、经济和社会资源影响的不确定性。 对此类系统更好的科学理解和工程管理需要仔细综合的方法，最大限度地利用所有可用的观测和建模工具，不仅可以更好地预测未来的影响，而且可以更好地理解过去和当前的观测。在此背景下，以及在规划和设计采样方案的背景下，开发现有观测数据 4D（即空间和时间）插值的新方法对于环境观测站来说至关重要。 这项研究将利用切萨皮克湾数十年研究建立的丰富资源基础，以及最近通过原型切萨皮克湾环境观测站（CBEO）（已被建立为 NSF 的潜在节点）来帮助满足这一需求。 -支持WATERS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来插值水质观测值，从而更有效地利用当前可用的水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“影响度量”（而不是距离）来定义用于插值的相关结构（即克里金法），从而产生新的观测统计插值方法。 要测试的替代影响指标包括行程时间、水龄和示踪剂比例，所有这些指标都是通过运行完善且经过校准的切萨皮克湾水动力和水质模型而生成的。 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。 经过开发和彻底评估后，新的插值方法将用于探索：（1）历史数据记录的缺氧发展，以及（2）确定性模型预测与观察到的水质时间和空间趋势之间持续不一致的原因。新开发的基于过程的插值方法有望克服在流动水体中使用克里格法时经常遇到的许多困难。 对综合观测数据集与统计和基于过程的模型的综合分析将最大限度地利用每种方法的优势，其中分别包括不确定性估计和预测能力。 将这些方法应用于解决海湾缺氧的紧迫科学问题将证明它们的优点。 总体而言，这项工作将进一步评估和展示环境观测站改变我们对当前和历史数据的使用和理解的力量。用于分析和理解缺氧的更好工具的产生将对切萨皮克湾的管理产生深远的影响。 目前，插值工具用于量化海湾水域不符合水质标准的范围，过程模型用于预测管理活动（例如 TMDL 开发）的影响。对这两种工具的改进以及两者的综合使用将有助于更好地了解和预测水质退化，从而有助于制定最有效的管理方案。 该项目的所有人员都与 EPA 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者。 概念方法还应该被证明在任何有成熟的基于过程的仿真模型可用的地方都具有同等的价值。 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 WATERS 网络上的 CBEO 节点（由圣地亚哥超级计算机中心维护）提供新方法来传播。 这项研究是跨学科的，由两所大学合作进行，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大学正在进行的教育项目的合作来实现，该项目使用交互式教育模块向中学生传授有关地表水中“死区”（缺氧）的问题。