Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021

Newcomer, Michelle; Carroll, Rosemary; Williams, Kenneth

doi:10.15485/1868939

Title: Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021

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Abstract

This dataset contains a collection of machine learning assisted gap-filled discharge data created for all discharge stations across the East River Watershed, Colorado. This data was generated by using raw discharge data collected by Rosemary Carroll, and conducting a random forest machine learning analysis to gap-fill discharge data across all years at the hourly time level. Discharge data with gaps creates problems for analysis of measured and modeled fluxes of carbon and nitrogen exported out of each sub-watershed. Gap-filled data is also required as an input to surface water models, which helps to address our main research question related to how snowmelt timing impacts the timing and magnitude of nitrogen exports. Data is provided in one csv file.

Authors:

;

Lawrence Berkeley National Laboratory
Desert Research Institute

Publication Date:: 2022-01-01

Other Number(s):: paf_651_791

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States); Watershed Function SFA

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES

Keywords:: Discharge; Machine learning; gap-fill; River stage/discharge

Geolocation:: 39.034,-106.88|38.88,-106.88|38.88,-107.05|39.034,-107.05|39.034,-106.88

OSTI Identifier:: 1868939

DOI:: https://doi.org/10.15485/1868939

Project Location:

Citation Formats


                    Newcomer, Michelle, Carroll, Rosemary, and Williams, Kenneth. Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021.  United States: N. p., 2022. 
        Web.  doi:10.15485/1868939.

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                    Newcomer, Michelle, Carroll, Rosemary, & Williams, Kenneth. Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021.  United States.  doi:https://doi.org/10.15485/1868939

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                    Newcomer, Michelle, Carroll, Rosemary, and Williams, Kenneth. 2022.  
"Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021".  United States.  doi:https://doi.org/10.15485/1868939.  https://www.osti.gov/servlets/purl/1868939. Pub date:Sat Jan 01 00:00:00 EST 2022

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@article{osti_1868939,

  title        = {Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021},

  author       = {Newcomer, Michelle and Carroll, Rosemary and Williams, Kenneth},

  abstractNote = {This dataset contains a collection of machine learning assisted gap-filled discharge data created for all discharge stations across the East River Watershed, Colorado. This data was generated by using raw discharge data collected by Rosemary Carroll, and conducting a random forest machine learning analysis to gap-fill discharge data across all years at the hourly time level. Discharge data with gaps creates problems for analysis of measured and modeled fluxes of carbon and nitrogen exported out of each sub-watershed. Gap-filled data is also required as an input to surface water models, which helps to address our main research question related to how snowmelt timing impacts the timing and magnitude of nitrogen exports. Data is provided in one csv file.},

  doi          = {10.15485/1868939},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {2022},

  month        = {1}

}

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DOI: https://doi.org/10.15485/1868939

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