Process-guided deep learning water temperature predictions: 4c All lakes historical training data

Description

Observed water temperatures from 1980-2018 were compiled for 68 lakes in Minnesota and Wisconsin (USA). These data were used as training data for process-guided deep learning models and deep learning models, and calibration data for process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological Research Project, and digitized temperature records from the MN Department of Natural Resources.

Resources

Name Format Description Link
0 http://dx.doi.org/10.1029/2019WR024922
0 CSDGM IMPORT ERROR: No digtinfo/formcont http://dx.doi.org/10.5066/P9AQPIVD
55 The metadata original source https://data.doi.gov/harvest/object/33f5fcda-68a7-4fe9-a3ca-623dda18a0f0
0 sdata.2017.53 http://dx.doi.org/10.1038/sdata.2017.53

Tags

  • reservoirs
  • hybrid-modeling
  • usgs-5d8a47bce4b0c4f70d0ae61f
  • modeling
  • temperate-lakes
  • deep-learning
  • united-states
  • thermal-profiles
  • us
  • climate-change
  • water
  • temperature
  • machine-learning

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Categories