Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM

Bonney, Kirk; Gunda, Thushara; Ferencz, Stephen; Jackson, Nicole

doi:10.57931/2441443

Title: Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM

Dataset
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Abstract

This archive is the data companion to the bonney_et-al_2024_jgrml metarepo which generates synthetic data, trains an LSTM model, and generates performance metrics on the trained model. While the generation of the synthetic data is fully reprodicible, it is a computationally expensive process. This data archive contains the synthetic datasets needed for training and testing an LSTM model and reproduction of figures and tables. In addition, supplemenatary data products generating and visualizing results is also included, such as geospatial data for the basin. Contents There are two high level directories: `WRAP_archive/` and `repo_data/`. The `WRAP_archive` directory contains compressed intermediate dataproducts from the dataset generation workflow (marked as "I_Dataset_Generation" in the metarepo). These data products are not required by any scripts in the metarepo, but they are archived as they are expensive to generate and may have useful information for other analyses. The `repo_data` directory contains the necessary data for reproducing the workflow in the metarepo and should be decompressed and moved into the top level of the metarepo. Additional details are provided in README.md.

Authors:

;

Sandia National Laboratories; Pacific Northwest National Laboratory
Sandia National Laboratories
Pacific Northwest National Laboratory

Publication Date:: Tue Sep 17 00:00:00 EDT 2024

Research Org.:: MultiSector Dynamics - Living, Intuitive, Value-adding, Environment

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Subject:: Machine Learning; Water; drought; streamflow; synthetic; watersheds

OSTI Identifier:: 2441443

DOI:: https://doi.org/10.57931/2441443

Citation Formats


                    Bonney, Kirk, Gunda, Thushara, Ferencz, Stephen, and Jackson, Nicole. Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM.  United States: N. p., 2024. 
        Web.  doi:10.57931/2441443.

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                    Bonney, Kirk, Gunda, Thushara, Ferencz, Stephen, & Jackson, Nicole. Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM.  United States.  doi:https://doi.org/10.57931/2441443

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                    Bonney, Kirk, Gunda, Thushara, Ferencz, Stephen, and Jackson, Nicole. 2024.  
"Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM".  United States.  doi:https://doi.org/10.57931/2441443.  https://www.osti.gov/servlets/purl/2441443. Pub date:Tue Sep 17 00:00:00 EDT 2024

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@article{osti_2441443,

  title        = {Synthetic Streamflow Datasets to Support Emulation of Water Allocations via LSTM},

  author       = {Bonney, Kirk and Gunda, Thushara and Ferencz, Stephen and Jackson, Nicole},

  abstractNote = {This archive is the data companion to the bonney_et-al_2024_jgrml metarepo which generates synthetic data, trains an LSTM model, and generates performance metrics on the trained model. While the generation of the synthetic data is fully reprodicible, it is a computationally expensive process. This data archive contains the synthetic datasets needed for training and testing an LSTM model and reproduction of figures and tables. In addition, supplemenatary data products generating and visualizing results is also included, such as geospatial data for the basin. Contents There are two high level directories: `WRAP_archive/` and `repo_data/`. The `WRAP_archive` directory contains compressed intermediate dataproducts from the dataset generation workflow (marked as "I_Dataset_Generation" in the metarepo). These data products are not required by any scripts in the metarepo, but they are archived as they are expensive to generate and may have useful information for other analyses. The `repo_data` directory contains the necessary data for reproducing the workflow in the metarepo and should be decompressed and moved into the top level of the metarepo. Additional details are provided in README.md.},

  doi          = {10.57931/2441443},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Sep 17 00:00:00 EDT 2024},

  month        = {Tue Sep 17 00:00:00 EDT 2024}

}

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DOI: https://doi.org/10.57931/2441443

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