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Prediction of harmful water quality parameters combining weather, air quality and ecosystem models with in situ measurement
Citation:
Nowakowski, C., M. Astitha, V. Garcia, P. Vlahos, E. Cooter, C. Tang, AND B. Hinckley. Prediction of harmful water quality parameters combining weather, air quality and ecosystem models with in situ measurement. 15th Annual CMAS Conf. October 24-26, 2016, UNC, Chapel Hill, NC, Chapel Hill, NC, October 24 - 26, 2016.
Impact/Purpose:
In this study we demonstrate how modeled and observed variables can be used to identify algal blooms using chlorophyll-a concentrations as proxies. The area of focus is Lake Erie because of its history of excessive algal blooms and the abundance of available data for the period 2002-2012.
Description:
The ability to predict water quality in lakes is important since lakes are sources of water for agriculture, drinking, and recreational uses. Lakes are also home to a dynamic ecosystem of lacustrine wetlands and deep waters. They are sensitive to pH changes and are dependent on dissolved oxygen and nutrient levels. Even small changes in these variables can have drastic impacts on a lake?s biota, physiochemical state, and hydrology. To date, numerical prediction models do not dynamically describe the entirety of air-water-soil interactions. Nevertheless, there is abundance of data from observations and weather, air quality, agroecosystem and hydrological models. In this study we demonstrate how modeled and observed variables can be used to identify algal blooms using chlorophyll-a concentrations as proxies. The area of focus is Lake Erie because of its history of excessive algal blooms and the abundance of available data for the period 2002-2012.? We are using weather variables from the WRF model, hydrological variables from the VIC model and variables from the Community Multiscale Air Quality Bidirectional (CMAQ-Bidi) modeling system which includes agricultural practices. Both VIC and CMAQ-Bidi were run with the same WRF meteorology, land use and emissions files to retain consistency among the models.? We built various regression models and examine the relationships between factors such as meteorology, land use, hydrology, applied and deposited nutrients (nitrogen and phosphorus) and chlorophyll-a concentrations taken by the Lake Erie Forage Task Group.? Through a multivariate analysis we determine the significance of these predictors (simulated variables) on chlorophyll-a concentrations, an indicator of productivity, within Lake Erie.??
