CAREER: Development of Geostatistical Data Assimilation Tools for Water Quality Monitoring

Michalak，安娜M.密歇根大学安娜堡分校提案编号：0644648职业生涯：开发地质统计数据同化工具用于水质监测在任何人都可以在线查看天气预报以了解他们是否应该在没有下雨风险的情况下为即将到来的周末计划野餐的时候，为何不可以登入互联网，看看本地海滩的海水在当天是否预期没有大肠杆菌？水质预报系统的发展对于长期可持续的水资源管理至关重要。为了实现这一目标，需要新的工具，以严格的统计方式合并水质数据，同时最佳利用现有测量提供的信息。与天气监测和预报不同，由于数据收集的困难和费用，水质评估将始终受到数据相对稀少的影响。因此，概率框架对于任何水质预测框架的成功至关重要，因为与采样水相关的不确定性的影响-在整个分析过程中，需要考虑与水质相关的参数。阻碍概率水质预测框架实施的一个重要知识缺口是缺乏同化水质中不同类型数据的方法。监控网络如果能够获得与水质有关的参数分布的数据驱动的统计描述，那么这种信息一旦与水流、运输以及化学和生物相互作用的数值模型相结合，就可以构成水质预测系统的基础。将空间数据同化为数值模型带来了许多自然属于地质统计学领域的统计问题。该项目的主要研究目标是开发所需的统计和数值工具，以最佳利用稀疏和不完善的水质监测数据，通过克服与其分析相关的基本限制，如物理约束，支持和缩放问题，不确定性评估和计算问题。这些研究目标与该项目的教育计划和更广泛的影响密切相关，该项目的核心是向多学科受众广泛传播研究成果，开发创新的教育材料，并大力强调在科学和工程领域招募和留住妇女。该项目的研究目标集中在新的统计严格的工具，通过创新地使用辅助信息，优化利用有限的水质监测数据。我们会介绍水质数据的特点。(1)将开发地质统计马尔可夫链蒙特卡罗地质统计工具，以纳入已知的物理限制因素，并评估其对水质参数分布的影响。(2)可用的数据通常具有不同的物理尺度，使得数据集彼此不兼容，即使它们测量的是相同的数量。该项目将开发适用于水质和相关数据的地质统计缩小尺度工具。(3)为了处理大量、类型和来源的水质数据，将建立一个卡尔曼滤波和平滑统计框架，用于连续更新水质参数分布的估计。(4)将在第二和第三个目标成果的基础上，开发用于合并多个数据流的工具。实地数据将用于测试和验证作为前四个目标的一部分而开发的各个工具。(5)在该项目的最后阶段，开发的统计工具将同时应用于试点实地研究。

Michalak, Anna M.University of Michigan Ann ArborProposal Number: 0644648CAREER: Development of Geostatistical Data Assimilation Tools for Water Quality MonitoringIn a time when anyone can check weather forecasts online to know whether they should plan a picnic for the upcoming weekend without the risk of rain, why is it not possible to log on to check whether the water at the local beach is expected to be free from e-coli on that same day? The development of water quality forecasting systems is essential to long-term sustainable water resource management. In anticipation of this goal, new tools are needed to merge water quality data in statistically rigorous manner while making optimal use of the information provided by the available measurements. Unlike weather monitoring and forecasting, water quality assessment will always suffer from a relative sparsity of data due to the difficulty and expense associated with data collection. As a result, a probabilistic framework is essential to the success of any water quality prediction framework, because the impact of the uncertainty associated with sampled water-quality related parameters needs to be taken into account throughout the analysis.A significant gap in knowledge preventing the implementation of a probabilistic water quality forecasting framework is the lack of methods for assimilating the disparate types of data in a water quality monitoring network. If a data-driven statistical description of the distribution of water-quality-related parameters could be obtained, then this information, once coupled to numerical models of water flow, transport, and chemical and biological interactions, could form the basis of a water quality forecasting system. The assimilation of spatial data into numerical models brings about a number of statistical problems that fall naturally into the realm of geostatistics. The main research goal of this project is to develop the statistical and numerical tools needed to make optimal use of sparse and imperfect water quality monitoring data, by overcoming basic limitations associated with their analysis, such as physical constraints, support and scaling issues, uncertainty assessment, and computational issues. These research goals are closely connected with the educational plan and broader impacts of this project, which center on the broad dissemination of research results to a multidisciplinary audience, the development of innovative educational materials, and the strong emphasis on the recruitment and retention of women in science and engineering.Intellectual merit: The research objectives of this project center on novel statistically rigorous tools formaking optimal use of limited water quality monitoring data, through innovative use of auxiliary information. our specific features typical of water quality data will be addressed. (1) Geostatistical Markov chain Monte Carlo geostatistical tools will be developed for incorporating known physical constraints and assessing their impact on water quality parameter distributions. (2) Available data often have different physical scales, making datasets incompatible with one another even if they are measuring the same quantity. This project will develop tools for geostatistical downscaling applicable to water quality and related data. (3) To deal with large volumes, types and sources of water quality data, a Kalman filtering and smoothing statistical framework will be built for sequentially updating estimates of water quality parameter distributions. (4) Tools for merging multiple data streams will be developed, building on results from the second and third objectives. Field data will be used to test and validate the individual tools developed as part of these first four objectives. (5) In the last phase of the project, the developed statistical tools will be applied concurrently to a pilot field study.