Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Skip to content

Data release: Process-based predictions of lake water temperature in the Midwest US

Metadata Updated: July 6, 2024

<p>Climate change has been shown to influence lake temperatures in different ways. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we focused on improving prediction accuracy for daily water temperature profiles in 7,150 lakes in Minnesota and Wisconsin during 1980-2019. <br/> <br/>The data are organized into these items:</p> <ol> <li><a href="https://www.sciencebase.gov/catalog/item/5db8194be4b0b0c58b5a4c3c">Spatial data</a> - A lake metadata file, and one shapefile of polygons for all 7,150 lakes in this study (.shp, .shx, .dbf, and .prj files)</li> <li><a href="https://www.sciencebase.gov/catalog/item/5db81967e4b0b0c58b5a4c3f">Model configurations</a> - Model parameters and metadata used to configure models (1 JSON file, with metadata for each of 7,150 lakes, and one zip file with each lake's glm2.nml file)</li> <li><a href="https://www.sciencebase.gov/catalog/item/5db81985e4b0b0c58b5a4c41">Temperature observations</a> - Data formatted as model inputs for training, calibrating, or evaluating temperature models</li> <li><a href="https://www.sciencebase.gov/catalog/item/5db81996e4b0b0c58b5a4c43">Model inputs</a> - Data used to drive predictive models (35 zip files with ice-flags; 35 zip files with daily meteorological data)</li> <li><a href="https://www.sciencebase.gov/catalog/item/5db819a8e4b0b0c58b5a4c45">Prediction data</a> - Predictions calibrated and uncalibrated PB models (35 zip files)</li> <li><a href="https://www.sciencebase.gov/catalog/item/5db819bbe4b0b0c58b5a4c47">Predicted habitat</a> - Data formatted for ecological use</li> <br/> <p>This study was funded by the Department of the Interior Northeast and North Central Climate Adaptation Science Centers. Access to computing facilities was provided by USGS Core Science Analytics and Synthesis Advanced Research Computing, USGS Yeti Supercomputer (https://doi.org/10.5066/F7D798MJ).</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.

Downloads & Resources

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/e814d3971f43efea4b99f068d136606d
Identifier USGS:5db761e7e4b0b0c58b5a4978
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 b8df3e67-b297-4ad0-8403-6825d2676d5f
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 f5adeac512d79b57beb20d6488150f3fed6c27f7605088f79688180d2173b164
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}

Didn't find what you're looking for? Suggest a dataset here.