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The Lake-Catchment (LakeCat) Dataset for characterizing hydrologically-relevant landscape features for lakes across the conterminous US
Citation:
Hill, R., M. Weber, R. Debbout, S. Leibowitz, AND Tony Olsen. The Lake-Catchment (LakeCat) Dataset for characterizing hydrologically-relevant landscape features for lakes across the conterminous US. Society for Freshwater Science Annual Meeting, Raleigh, North Carolina, June 04 - 08, 2017.
Impact/Purpose:
R.A. Hill, M.H. Weber, R.M. Debbout, S.G. Leibowitz, and A.R. Olsen. The Lake-Catchment (LakeCat) Dataset for characterizing hydrologically-relevant landscape features for lakes across the conterminous US. Annual meeting of the Society for Freshwater Science, Raleigh, NC. June 4-8, 2017. To effectively study and manage lake ecosystems, it is critical to understand the distribution of both natural and human-related landscape features within contributing catchments. We developed the Lake-Catchment (LakeCat) Dataset to improve the accessibility of such information for lakes within the conterminous USA (CONUS) to researchers, managers, and the public. LakeCat summarizes several hundred natural (e.g., soils, geology, climate, and land cover) and anthropogenic (e.g., urbanization, agriculture, mining, and forest management) landscape features for ca. 376,000 lakes across the CONUS. The purpose of this presentation is to introduce participants at the annual meeting of the Society for Freshwater Science (SFS) to this new and unique dataset. SFS is a premier international society of scholars and managers that work in freshwater ecosystems. LakeCat supports the development of a robust national map of lake conditions, which is of interest to the Monitoring Branch within the Office of Water. It also contributes to an FY17 deliverable under SSWR 3.01B.
Description:
Lake conditions, including their biota, respond to both natural and human-related landscape features. Characterizing these features within the contributing areas (i.e., delineated watersheds) of lakes could improve the analysis and the sustainable use and management of these important aquatic resources. However, the specialized geospatial techniques required to define and characterize lake watersheds has limited their widespread use in both scientific and management efforts. We developed the LakeCat Dataset to improve the accessibility of such information for lakes within the conterminous US (CONUS). LakeCat parallels another recent USEPA dataset developed for streams (i.e., StreamCat). LakeCat contains watershed-level characterizations of several hundred natural (e.g., soils, geology, climate, and land cover) and anthropogenic (e.g., urbanization, agriculture, mining, and forest management) landscape features for ca. 376,000 lakes across the CONUS. LakeCat can be quickly paired with lake samples to provide independent variables for modeling and other analyses. We will present the LakeCat framework, main features of the dataset, and examples of linking field data to LakeCat to model and predict the condition of all lakes nationally rather than only those that have been sampled.