Process-based water temperature predictions in the Midwest US: 5 Model prediction data

Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. General Lake Model verion 2 process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error for 449 lakes (PBALL). Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations for 7,150 lakes.

Data and Resources

Field Value
accessLevel public
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identifier USGS:5db819a8e4b0b0c58b5a4c45
metadata_type geospatial
modified 20210727
old-spatial -104.016631682319, 37.1076610090681, -83.0716153148296, 49.3749961973185
publisher U.S. Geological Survey
publisher_hierarchy Department of the Interior > U.S. Geological Survey
resource-type Dataset
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spatial {"type": "Polygon", "coordinates": [[[-104.016631682319, 37.1076610090681], [-104.016631682319, 49.3749961973185], [ -83.0716153148296, 49.3749961973185], [ -83.0716153148296, 37.1076610090681], [-104.016631682319, 37.1076610090681]]]}
theme {geospatial}
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • ckan
  • climate-change
  • environment
  • geo
  • geoss
  • hybrid-modeling
  • ia
  • il
  • illinois
  • in
  • indiana
  • inlandwaters
  • iowa
  • mi
  • michigan
  • minnesota
  • missouri
  • mn
  • mo
  • modeling
  • national
  • nd
  • ne
  • nebraska
  • north-america
  • north-dakota
  • oh
  • ohio
  • reservoirs
  • sd
  • south-dakota
  • temperate-lakes
  • temperature
  • thermal-profiles
  • united-states
  • us
  • usgs-5db819a8e4b0b0c58b5a4c45
  • water
  • water-resources
  • wi
  • wisconsin
isopen False
license_id notspecified
license_title License not specified
maintainer Jordan S. Read
maintainer_email jread@usgs.gov
metadata_created 2025-11-21T11:19:25.069406
metadata_modified 2025-11-21T11:19:25.069410
notes Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of Minnesota and Wisconsin. General Lake Model verion 2 process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error for 449 lakes (PBALL). Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations for 7,150 lakes.
num_resources 2
num_tags 43
title Process-based water temperature predictions in the Midwest US: 5 Model prediction data