Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed 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.

Data and Resources

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Groups
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  • National Provider
  • North America
Tags
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  • amerigeoss
  • ckan
  • climate-change
  • deep-learning
  • geo
  • geoss
  • hybrid-modeling
  • machine-learning
  • modeling
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  • north-america
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maintainer U.S. Geological Survey (Point of Contact)
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metadata_created 2025-11-22T20:39:06.026887
metadata_modified 2025-11-22T20:39:06.026892
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.
num_resources 8
num_tags 20
title Process-guided deep learning water temperature predictions: 5a Lake Mendota detailed prediction data