Process-guided deep learning water temperature predictions: 5c All lakes historical 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. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Zip files for each lake contain four files, one for each of PB, PB0, DL, and PGDL.

Data and Resources

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Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
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  • amerigeoss
  • ckan
  • climate-change
  • deep-learning
  • geo
  • geoss
  • hybrid-modeling
  • machine-learning
  • modeling
  • national
  • north-america
  • reservoirs
  • temperate-lakes
  • temperature
  • thermal-profiles
  • united-states
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  • water
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license_title License not specified
maintainer U.S. Geological Survey (Point of Contact)
maintainer_email jread@usgs.gov
metadata_created 2025-11-21T01:26:23.657661
metadata_modified 2025-11-21T01:26:23.657664
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. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training on actual temperature observations. Zip files for each lake contain four files, one for each of PB, PB0, DL, and PGDL.
num_resources 8
num_tags 20
title Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data