Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

Willard, Jared; Ciulla, Fabio; Weierbach, Helen; Kumar, Vipin; Varadharajan, Charuleka

doi:10.15485/2448016

Title: Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

Dataset
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Abstract

This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwatermore »« less

Authors:

;

Lawrence Berkeley National Laboratory; ESS-DIVE
Lawrence Berkeley National Laboratory
University of Minnesota Department of Computer Science

Publication Date:: Mon Jan 01 04:00:00 UTC 2024

DOE Contract Number:: AC02-05CH11231

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem; iNAIADS

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER); U.S. DOE > Office of Science > Advanced Scientific Computing Research (ASCR); U.S. DOE > Office of Science > Workforce Development for Teachers and Scientists

Subject:: 54 ENVIRONMENTAL SCIENCES; DEEP LEARNING; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY > WATER CHARACTERISTICS > WATER TEMPERATURE; ESS-DIVE File Level Metadata Reporting Format; LSTM; MACHINE LEARNING; META TRANSFER LEARNING; PREDICTION IN UNGAUGED BASINS; RNN; STREAM TEMPERATURE

OSTI Identifier:: 2448016

DOI:: https://doi.org/10.15485/2448016

Citation Formats


                    Willard, Jared, Ciulla, Fabio, Weierbach, Helen, Kumar, Vipin, and Varadharajan, Charuleka. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024).  United States: N. p., 2024. 
        Web.  doi:10.15485/2448016.

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                    Willard, Jared, Ciulla, Fabio, Weierbach, Helen, Kumar, Vipin, & Varadharajan, Charuleka. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024).  United States.  doi:https://doi.org/10.15485/2448016

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                    Willard, Jared, Ciulla, Fabio, Weierbach, Helen, Kumar, Vipin, and Varadharajan, Charuleka. 2024.  
"Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".  United States.  doi:https://doi.org/10.15485/2448016.  https://www.osti.gov/servlets/purl/2448016. Pub date:Mon Jan 01 04:00:00 UTC 2024

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@article{osti_2448016,

  title        = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)},

  author       = {Willard, Jared and Ciulla, Fabio and Weierbach, Helen and Kumar, Vipin and Varadharajan, Charuleka},

  abstractNote = {This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwater influenceData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2024streamdata, author = {Jared Willard and Fabio Ciulla and Helen Weierbach and Vipin Kumar and Charuleka Varadharajan}, title = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models"}, year = {2024}, doi = {10.15485/2448016}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016}}MLA: Willard, Jared, et al. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models". 2024. ESS-DIVE Repository, doi:10.15485/2448016.},

  doi          = {10.15485/2448016},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Jan 01 04:00:00 UTC 2024},

  month        = {Mon Jan 01 04:00:00 UTC 2024}

}

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DOI: https://doi.org/10.15485/2448016

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