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

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.

Data e Risorse

Campo Valore
accessLevel public
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identifier USGS:5d8a47bce4b0c4f70d0ae61f
metadata_type geospatial
modified 20200820
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publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
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theme {geospatial}
Gruppi
  • AmeriGEOSS
  • National Provider
  • North America
Tag
  • amerigeo
  • amerigeoss
  • biota
  • ckan
  • climate-change
  • deep-learning
  • environment
  • geo
  • geoss
  • hybrid-modeling
  • inlandwaters
  • machine-learning
  • minnesota
  • mn
  • modeling
  • national
  • north-america
  • reservoirs
  • temperate-lakes
  • temperature
  • thermal-profiles
  • united-states
  • us
  • usgs-5d8a47bce4b0c4f70d0ae61f
  • water
  • wi
  • wisconsin
isopen False
license_id notspecified
license_title License not specified
maintainer Jordan S. Read
maintainer_email jread@usgs.gov
metadata_created 2025-11-22T20:21:08.011090
metadata_modified 2025-11-22T20:21:08.011094
notes 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.
num_resources 2
num_tags 27
title Process-guided deep learning water temperature predictions: 4c All lakes historical training data