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

基本信息

  • 批准号:
    0854329
  • 负责人:
  • 金额:
    $ 25.22万
  • 依托单位:
  • 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

项目摘要

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球球/迪托罗切萨皮克湾是一个最好的例子,说明复杂的水动力学、生物地球化学和来自大流域的不同输入可能导致对人类活动对关键的环境、经济和社会资源的影响的不确定性。要更好地对这类系统进行科学理解和工程管理,就需要精心综合的办法,最大限度地利用所有可用的观测和建模工具,不仅是为了更好地预测未来的影响,也是为了更好地了解过去和现在的观测。在这一背景下,以及在规划和设计采样方案的背景下,开发现有观测数据的4D(即空间和时间)内插的新方法是环境观测站极其重要的需要。这项研究将利用切萨皮克湾几十年来建立的丰富资源基础,以及最近建立的切萨皮克湾环境观测站(CBEO)原型,帮助满足这一需求。切萨皮克湾环境观测站已被建立为国家科学基金会支持的水域网络的潜在节点。目前拟议研究的目标是开发、测试和应用更好的统计模型,以便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,对水质观测值进行内插。更具体地说,这项工作将产生新的方法,通过使用基于过程的“影响度量”(而不是距离)来定义通知内插的相关结构(即,克里格法),从而产生对观测进行统计内插的新方法。待测试的其他影响指标包括行程时间、水龄和示踪剂比例,所有这些都是通过切萨皮克湾建立和校准的水动力和水质模型生成的。基于模型的理解还将用于探索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。经过发展和深入的评估,新的内插方法将被应用于探索:(1)历史数据记录上的缺氧发展,以及(2)确定性模型预测与观测到的水质时空趋势之间持续不一致的原因。新开发的基于过程的内插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。综合分析综合观测数据集与统计模型和基于过程的模型将最大限度地利用每种方法的长处,其中分别包括不确定度估计和预测能力。这些方法在海湾缺氧的紧迫科学问题上的应用将证明它们的优点。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能力。产生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深远的影响。目前,使用内插工具来量化不符合水质标准的海湾水域的范围,并使用过程模型来预测管理活动的影响,例如TMDL的开发。改进这两类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,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环境保护局的切萨皮克湾项目合作,因此能够将这些改进的工具带给海湾经理。概念性方法在任何有完善的基于流程的模拟模型的地方也应被证明同样有价值。这些成果将通过国家和国际科学会议、同行评议期刊上的出版物以及通过水网络上的CBEO节点(通过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,在两所大学之间进行合作,包括研究生和本科生。对K-12教育的影响将通过合作来实现,以帮助马里兰大学正在进行的一个教育项目,该项目使用互动教育模块向中学生传授关于地表水中“死区”(缺氧)的问题。

项目成果

期刊论文数量(0)
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William Ball其他文献

Correlation between provisional and actual diagnosis in emergency surgical patients
  • DOI:
    10.1016/j.ijsu.2011.07.065
  • 发表时间:
    2011-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    William Ball;Christina Lam;Mark Dilworth
  • 通讯作者:
    Mark Dilworth
Orion: A 1–5 Micron Focal Plane for the 21st Century
  • DOI:
    10.1023/a:1026154825645
  • 发表时间:
    2014-02-15
  • 期刊:
  • 影响因子:
    2.200
  • 作者:
    Albert M. Fowler;K. Michael Merrill;William Ball;Arne Henden;Fred Vrba;Craig McCreight
  • 通讯作者:
    Craig McCreight
P9. Are we over treating axillae following positive axillary lymph node biopsy?
  • DOI:
    10.1016/j.ejso.2015.08.114
  • 发表时间:
    2015-11-01
  • 期刊:
  • 影响因子:
  • 作者:
    William Ball;Megha Tandon;Soni Soumian;Robert Kirby;Vallipuram Gopalan;Sankaran Narayanan
  • 通讯作者:
    Sankaran Narayanan
A Version Space Approach to Learning Context-free Grammars
一种学习上下文无关文法的版本空间方法
  • DOI:
    10.1023/a:1022812926936
  • 发表时间:
    1987-03-01
  • 期刊:
  • 影响因子:
    2.900
  • 作者:
    Kurt Vanlehn;William Ball
  • 通讯作者:
    William Ball
Domains of Convergence for Polyhedral Packings
多面体填料的收敛域
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Noor Ahmed;William Ball;Ellis Buckminster;Emilie Rivkin;Dylan Torrance;Jake Viscusi;Runze Wang;Ian Whitehead;S. Yang
  • 通讯作者:
    S. Yang

William Ball的其他文献

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{{ truncateString('William Ball', 18)}}的其他基金

Workshop: Chesapeake Modeling Symposium 2016 and Proactive Visioning Workshops
研讨会:2016 年切萨皮克建模研讨会和前瞻性愿景研讨会
  • 批准号:
    1639835
  • 财政年份:
    2016
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant
WSC Category 3 Collaborative: Impacts of Climate Change on the Phenology of Linked Agriculture-Water Systems
WSC 第 3 类协作:气候变化对相关农业-水系统物候的影响
  • 批准号:
    1360415
  • 财政年份:
    2014
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant
2008 Gordon Research Conference on Environmental Sciences: Water
2008 年戈登环境科学研究会议:水
  • 批准号:
    0829354
  • 财政年份:
    2008
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant
Effect of Surface Oxidation on the Colloidal Stability and Sorption Properties of Carbon Nanotubes
表面氧化对碳纳米管胶体稳定性和吸附性能的影响
  • 批准号:
    0731147
  • 财政年份:
    2007
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Continuing Grant
Collaborative Research: CUAHSI/CLEANER Project for Demonstration and Development of a Test-Bed Digital Observatory for the Susquehanna River Basin and Chesapeake Bay
合作研究:CUAHSI/CLEANER 项目,用于示范和开发萨斯奎哈纳河流域和切萨皮克湾试验台数字观测站
  • 批准号:
    0609813
  • 财政年份:
    2006
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant
CEO:P--A Prototype System for Multi-Disciplinary Shared Cyberinfrastructure: Chesapeake Bay Environmental Observatory (CBEO)
CEO:P--多学科共享网络基础设施原型系统:切萨皮克湾环境观测站(CBEO)
  • 批准号:
    0618986
  • 财政年份:
    2006
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Continuing Grant
CLEANER: Collaborative Research: Concept Development Toward a Collaborative Large-Scale Engineering Analysis Network for Environmental Research with Focus on the Chesapeake Bay
CLEANER:协作研究:以切萨皮克湾为重点的环境研究协作大型工程分析网络的概念开发
  • 批准号:
    0414372
  • 财政年份:
    2004
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant
Exploring the Role of Surface Characteristics in Determining Sorption Properties of Chars and Soots
探索表面特性在确定炭和烟灰吸附特性中的作用
  • 批准号:
    0332160
  • 财政年份:
    2003
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Continuing Grant
Sorption of Organic Contaminants from Water by Environmental Solids: Additivity of Contributions In Heterogeneous Systems
环境固体对水中有机污染物的吸附:异质系统中贡献的可加性
  • 批准号:
    9910174
  • 财政年份:
    2000
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant
Characterization of the Digitalis Receptor and Digitalis Mimics
洋地黄受体和洋地黄模拟物的表征
  • 批准号:
    9422022
  • 财政年份:
    1995
  • 资助金额:
    $ 25.22万
  • 项目类别:
    Standard Grant

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