Title: Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

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Abstract

This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.

Authors:

;

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Archive
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Publication Date:: Tue Sep 02 00:00:00 EDT 2025

Other Number(s):: V1

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office

OSTI Identifier:: 2587036

DOI:: https://doi.org/10.21951/RiTHyMs_V1/2587036

Citation Formats


                    Gomez-Velez, Jesus, Siddik, Md Abu Bakar, Turner, Sean, and Kao, Shih-Chieh. Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States.  United States: N. p., 2025. 
        Web.  doi:10.21951/RiTHyMs_V1/2587036.

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                    Gomez-Velez, Jesus, Siddik, Md Abu Bakar, Turner, Sean, & Kao, Shih-Chieh. Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States.  United States.  doi:https://doi.org/10.21951/RiTHyMs_V1/2587036

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                    Gomez-Velez, Jesus, Siddik, Md Abu Bakar, Turner, Sean, and Kao, Shih-Chieh. 2025.  
"Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States".  United States.  doi:https://doi.org/10.21951/RiTHyMs_V1/2587036.  https://www.osti.gov/servlets/purl/2587036. Pub date:Tue Sep 02 00:00:00 EDT 2025

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@article{osti_2587036,

  title        = {Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States},

  author       = {Gomez-Velez, Jesus and Siddik, Md Abu Bakar and Turner, Sean and Kao, Shih-Chieh},

  abstractNote = {This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.},

  doi          = {10.21951/RiTHyMs_V1/2587036},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Sep 02 00:00:00 EDT 2025},

  month        = {Tue Sep 02 00:00:00 EDT 2025}

}

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DOI: https://doi.org/10.21951/RiTHyMs_V1/2587036

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