DOE Data Explorer title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Dataset for 'Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning', Water 2022

Abstract

This data package presents forcing data, model code, and model output for classical machine learning models that predict monthly stream water temperature as presented in the manuscript ‘Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning’, Water (Weierbach et al., 2022). Specifically, for input forcing datasets we include two files each generated using the BASIN-3D data integration tool (Varadharajan et al., 2022) for stations in the Pacific Northwest and Mid Atlantic Hydrologic regions. Model code (written in python with the use of jupyter notebooks) includes codes for data preprocessing, training Multiple Linear Regression, Support Vector Regression, and Extreme Gradient Boosted Tree models, and additional notebooks for analysis of model output. We include specific model output files which represent modeling configurations presented in the manuscript also presented in an hdf5 format. Together, these data make up the workflow for predictions across three scenarios (single station, regional, and predictions in unmonitored basins) presented in the manuscript and allow for reproducibility of modeling procedures.

Authors:
ORCiD logo ; ORCiD logo ; ORCiD logo ; ORCiD logo ; ORCiD logo ; ORCiD logo ; ORCiD logo
  1. Lawrence Berkeley National Laboratory
  2. Aquatic Informatics
  3. University of California Berkeley
Publication Date:
Research Org.:
Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States); Investigating the Impacts of Streamflow Disturbances on Water Quality Using a Data-Driven Framework
Sponsoring Org.:
U.S. DOE > Office of Science > Biological and Environmental Research (BER)
Subject:
54 ENVIRONMENTAL SCIENCES
Keywords:
EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY; Machine Learning; XGBoost; Support Vector Regression; Stream Water Temperature; Catchments; Modeling; Predictions in Unmonitored Basins ; ESS-DIVE File Level Metadata Reporting Format; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > WATER QUALITY/WATER CHEMISTRY; EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > SURFACE WATER PROCESSES/MEASUREMENTS > DISCHARGE/FLOW; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION > SOLAR RADIATION; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > MAXIMUM/MINIMUM TEMPERATURE; EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT; EARTH SCIENCE > OCEANS > OCEAN TEMPERATURE > WATER TEMPERATURE; EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > SURFACE WATER PROCESSES/MEASUREMENTS > DISCHARGE/FLOW; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION > SOLAR RADIATION; EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE > MAXIMUM/MINIMUM TEMPERATURE; EARTH SCIENCE > ATMOSPHERE > PRECIPITATION > PRECIPITATION AMOUNT
Geolocation:
49.0,-112.79|42.06,-112.79|42.06,-124.06|49.0,-124.06|49.0,-112.7942.11,-74.27|38.04,-74.27|38.04,-79.9|42.11,-79.9|42.11,-74.27
OSTI Identifier:
1854257
DOI:
https://doi.org/10.15485/1854257
Project Location:

Project Location:


Citation Formats

Weierbach, Helen, Lima, Aranildo R., Willard, Jared D., Hendrix, Valerie C., Christianson, Danielle S., Lubich, Misha, and Varadharajan, Charuleka. Dataset for 'Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning', Water 2022. United States: N. p., 2022. Web. doi:10.15485/1854257.
Weierbach, Helen, Lima, Aranildo R., Willard, Jared D., Hendrix, Valerie C., Christianson, Danielle S., Lubich, Misha, & Varadharajan, Charuleka. Dataset for 'Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning', Water 2022. United States. doi:https://doi.org/10.15485/1854257
Weierbach, Helen, Lima, Aranildo R., Willard, Jared D., Hendrix, Valerie C., Christianson, Danielle S., Lubich, Misha, and Varadharajan, Charuleka. 2022. "Dataset for 'Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning', Water 2022". United States. doi:https://doi.org/10.15485/1854257. https://www.osti.gov/servlets/purl/1854257. Pub date:Sat Jan 01 00:00:00 EST 2022
@article{osti_1854257,
title = {Dataset for 'Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning', Water 2022},
author = {Weierbach, Helen and Lima, Aranildo R. and Willard, Jared D. and Hendrix, Valerie C. and Christianson, Danielle S. and Lubich, Misha and Varadharajan, Charuleka},
abstractNote = {This data package presents forcing data, model code, and model output for classical machine learning models that predict monthly stream water temperature as presented in the manuscript ‘Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning’, Water (Weierbach et al., 2022). Specifically, for input forcing datasets we include two files each generated using the BASIN-3D data integration tool (Varadharajan et al., 2022) for stations in the Pacific Northwest and Mid Atlantic Hydrologic regions. Model code (written in python with the use of jupyter notebooks) includes codes for data preprocessing, training Multiple Linear Regression, Support Vector Regression, and Extreme Gradient Boosted Tree models, and additional notebooks for analysis of model output. We include specific model output files which represent modeling configurations presented in the manuscript also presented in an hdf5 format. Together, these data make up the workflow for predictions across three scenarios (single station, regional, and predictions in unmonitored basins) presented in the manuscript and allow for reproducibility of modeling procedures.},
doi = {10.15485/1854257},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Jan 01 00:00:00 EST 2022},
month = {Sat Jan 01 00:00:00 EST 2022}
}