4.2 Article

Spatiotemporal Lake Skin Summer Temperature Trends in the Northeast United States

Journal

EARTH INTERACTIONS
Volume 20, Issue -, Pages 1-21

Publisher

AMER GEOPHYSICAL UNION
DOI: 10.1175/EI-D-16-0015.1

Keywords

Watersheds; Remote sensing; Statistics

Funding

  1. National Institutes of Health (NIH) National Institute of Environmental Health Sciences (NIEHS) Grant [R44 ES022103-03]
  2. National Science Foundation (NSF) Geography and Spatial Sciences (GSS) Grant [1433756]
  3. Division Of Behavioral and Cognitive Sci
  4. Direct For Social, Behav & Economic Scie [1433756] Funding Source: National Science Foundation

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Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8 degrees C decade(-1), with smaller lakes warming at a faster rate. Complementing regression and space-time analyses showed similar results (R-2 = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximum or minimum air temperature. No major hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers.

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