U.S. Department of EnergyOffice of Scientific and Technical Information
Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021
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; Lawrence Berkeley National Laboratory
- Desert Research Institute
- Lawrence Berkeley National Laboratory
- Publication Date:
- Other Number(s):
- paf_651_791
- Research Org.:
- Environmental System Science Data Infrastructure for a Virtual Ecosystem; Watershed Function SFA
- Sponsoring Org.:
- U.S. DOE > Office of Science > Biological and Environmental Research (BER)
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Discharge; Machine learning; River stage/discharge; gap-fill
- OSTI Identifier:
- 1868939
- DOI:
- https://doi.org/10.15485/1868939
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.
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
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 04:00:00 UTC 2022
@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 = {Sat Jan 01 04:00:00 UTC 2022},
month = {Sat Jan 01 04:00:00 UTC 2022}
}