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USING THE LAKE-CATCHMENT (LAKECAT) DATASET TO CHARACTERIZE, MODEL, AND PREDICT LAKE CONDITIONS ACROSS THE CONTERMINOUS US
Citation:
Hill, R., S. Leibowitz, M. Weber, J. Iiames, B. Schaeffer, AND W. Salls. USING THE LAKE-CATCHMENT (LAKECAT) DATASET TO CHARACTERIZE, MODEL, AND PREDICT LAKE CONDITIONS ACROSS THE CONTERMINOUS US. ASLO 2019 Aquatic Sciences Meeting, San Juan, Puerto Rico, February 23 - March 02, 2019.
Impact/Purpose:
Scientists at the US EPA’s Western Ecology Division have worked to develop data and methods to improve our ability to model, map, and understand the condition of the Nation’s water resources, including lakes. These data allow scientists to easily obtain watershed information for lakes across the continental US and are called the LakeCat Dataset. This dataset includes information on the percent of the watershed composed of land uses (e.g., urbanization or agriculture) and natural features (e.g., precipitation and soils). These types of information can be important for understanding human-related effects on the biological, physical, and chemical conditions of water resources. LakeCat data are available for ca. 378,000 lakes across the US. This presentation provides and overview of this efforts. It will introduce the algorithms that were critical for developing LakeCat to an audience of potential users of the data; limnologists. LakeCat supports the development of robust national maps of lake conditions, which is of interest to the Monitoring Branch within the Office of Water. The data produced by this work have been used to model the chlorophyll a concentrations of lakes across the CONUS.
Description:
Lake conditions are influenced by both natural and human-related landscape features. Understanding how these features vary within contributing areas (i.e., watersheds) can advance our understanding of how lake conditions vary spatially and improve the use, management, and restoration of these ecosystems. The specialized geospatial techniques required to define and characterize lake watersheds has limited the use of such data at large spatial scales. We developed the LakeCat dataset to model, predict, and map the water quality conditions of lakes across the conterminous US. LakeCat contains watershed-level metrics of >100 natural (e.g., soils and land cover) and anthropogenic (e.g., urbanization and agriculture) landscape features for 378,088 lakes. This dataset can be paired with field samples or satellite data to provide independent variables for modeling. For example, we paired 1,073 samples from the USEPA National Lakes Assessment with LakeCat and used random forest to model lake eutrophication. The model correctly predicted eutrophication at 73% of sites. Combining the model and LakeCat data allowed us to map the probability of eutrophication at 297,071 lakes. Other applications include: pairing lakes based on watershed features for regional studies, identifying reference-condition lakes, and modeling and mapping harmful algal blooms. In this presentation, we will describe the development and main features of LakeCat and provide examples of how the data can be used in these types of applications and to advance our understanding and management of lentic ecosystems.