In 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 for making 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.
The ability to predict water quality in lakes, rivers, and coastal areas is essential to long-term sustainable water resource management. As one example, we need to be able to understand how the amount of oxygen in water is controlled by the amount of nutrients entering the water, by weather patterns, and by a variety of other factors. This is because a lack of oxygen, referred to hypoxia, can disrupt the aquatic food chain and even lead to fish kills, which have not only ecological but economic consequences. In order to predict water quality, we must first understand what controls it. The challenge is that the limited data that we have available give us only a partial answer to key questions needed to develop better water management strategies. This project aimed to address this problem by developing a variety of statistical modeling tools that, in various ways, make it possible to make optimal use of the limited data that we do have available. Specifically, we proposed to develop tools (1) for incorporating known physical bounds (e.g. solubility limits) into statistical models, (2) for integrating data that are available at different scales, (3) for integrating data taken at different times and locations, and (4) for merging data of different types and from different sources. These methodological developments were applied to specific questions and problems, and then also culminated in a series of studies that jointly took advantages of several of these tools to further our understanding of hypoxia in Lake Erie, the Gulf of Mexico, and the Chesapeake Bay. In doing so, we created the first fully consistent multi-decadal records of hypoxic conditions in each of these three systems. We were able to demonstrate that both nutrient inputs from human activity and meteorological variability play key roles in controlling how hypoxia develops and how severe it becomes. This is especially important because meteorological conditions are changing as climate changes, such that future changes in meteorology (wind, rain, etc.) must be considered when developing targets for reductions in nutrient loading. In the final study coming out of this project, we showed that the widespread North American drought in 2012 led to a record-breaking dead zone in Lake Erie, providing a cautionary tale about how climate extremes can directly lead to extreme impacts on water quality. The path forward will require creative solutions that build on the scientific understanding developed as part of this and other projects to develop strategies to manage our water resources in a sustainable way.
