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5 Model Predictions: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

Metadata Updated: July 6, 2024

<p>This data release item contains water temperature predictions for 455 river sites across the U.S. Predictions are from the models described by Rahmani et al. (2021b).</p>

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date June 1, 2023
Metadata Updated Date July 6, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date July 6, 2024
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/ee9bebd831ce8f990a3c547f0c015d54
Identifier USGS:6084cb2ed34eadd49d31aeaf
Data Last Modified 20210927
Category geospatial
Public Access Level public
Bureau Code 010:12
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://datainventory.doi.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id a4d8b1b0-a3bf-4fb0-881e-b6979d062209
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -124.138658984335,29.1524975232233,-67.8714112090545,49.0018341836332
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 431c268f4565871eeece31003b582c9c93d46f3107e5e504fdb783bd060343ad
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -124.138658984335, 29.1524975232233, -124.138658984335, 49.0018341836332, -67.8714112090545, 49.0018341836332, -67.8714112090545, 29.1524975232233, -124.138658984335, 29.1524975232233}

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