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Process-based water temperature predictions in the Midwest US: 4 Model inputs (meteorological inputs and ice flags)

Metadata Updated: October 29, 2023

This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 7,150 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9CA6XP8).

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.

Downloads & Resources

Dates

Metadata Created Date June 1, 2023
Metadata Updated Date October 29, 2023

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date October 29, 2023
Publisher U.S. Geological Survey
Maintainer
@Id http://datainventory.doi.gov/id/dataset/24881a745e6db456bbf5decb3c89aa0b
Identifier USGS:5db81996e4b0b0c58b5a4c43
Data Last Modified 20210727
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 7b035e02-e089-4836-8796-484c0d0d41e4
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -104.016631682319,37.1076610090681,-83.0716153148296,49.3749961973185
Publisher Hierarchy White House > U.S. Department of the Interior > U.S. Geological Survey
Source Datajson Identifier True
Source Hash 5e761497f293c4cb9bb27bab1d7a59236f94d06db2ddc6e9356f8629f5ac698b
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -104.016631682319, 37.1076610090681, -104.016631682319, 49.3749961973185, -83.0716153148296, 49.3749961973185, -83.0716153148296, 37.1076610090681, -104.016631682319, 37.1076610090681}

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