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-guided deep learning predictions of lake water temperature

Metadata Updated: June 15, 2024

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 68 lakes in Minnesota and Wisconsin during 1980-2018. The data are organized into these items: Spatial data - One shapefile of polygons for all 68 lakes in this study (.shp, .shx, .dbf, and .prj files) Model configurations - Model parameters and metadata used to configure models (1 JSON file, with metadata for each of 68 lakes, indexed by "site_id") Model inputs - Data formatted as model inputs for predicting temperature a. Lake Mendota model inputs - Tables with 1 row per timestep for weather data and ice flags (2 comma-delimited files) b. Sparkling Lake model inputs - Tables with 1 row per timestep for weather data and ice flags (2 comma-delimited files) c. Historical model inputs for 68 lakes - Tables with 1 row per timestep for weather data and ice flags, with two files for each lake (138 comma-delimited files, compressed into 2 zip files) Training data - Data used to train or calibrate predictive models a. Lake Mendota training data - Tables with 1 row per date and depth, with the corresponding observed water temperature (3 comma-delimited files) b. Sparkling Lake training data - Tables with 1 row per date and depth, with the corresponding observed water temperature (3 comma-delimited files) c. Historical training data for 68 lakes - Tables with 1 row per date, depth, and site_id, with the corresponding observed water temperature (1 comma-delimited file) Prediction data - Predictions from PGDL, DL, and PB models a. Lake Mendota predictions - Tables with 1 row per date, a column for predicted temperature at each depth, and experiment metadata (10 comma-delimited files) b. Sparkling Lake predictions - Tables with 1 row per date, a column for predicted temperature at each depth, and experiment metadata (10 comma-delimited files) c. Historical predictions for 68 lakes - Tables with 1 row per date and depth, with the corresponding observed water temperature (4 comma-delimited files for each lake compressed into 68 zip files) Model evaluation - test data and overall assessment of model performance a. Lake Mendota evaluation - Tables with 1 row per date, a column for predicted temperature at each depth, and experiment metadata (3 comma-delimited files) b. Sparkling Lake evaluation - Tables with 1 row per date, a column for predicted temperature at each depth, and experiment metadata (2 comma-delimited files) c. Historical evaluation for 68 lakes - Tables with 1 row per date and depth, with the corresponding observed water temperature (4 comma-delimited files for each lake compressed into 68 zip files) This research was funded by the Department of the Interior Northeast and North Central Climate Adaptation Science Centers, a Midwest Glacial Lakes Fish Habitat Partnership grant through F&WS, an NSF Expedition in Computing Grant 1029711 to the University of Minnesota, a postdoctoral fellowship awarded to J Zwart under NSF EAR-PF-1725386, as well as a seed grant from the Digital Technology Center at the University of Minnesota. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute and USGS Advanced Research Computing, USGS Yeti Supercomputer (https://doi.org/10.5066/F7D798MJ). We thank North Temperate Lakes Long-Term Ecological Research (NSF DEB-1440297) and Global Lake Ecological Observatory Network (NSF #1702991).

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 June 15, 2024

Metadata Source

Harvested from DOI EDI

Additional Metadata

Resource Type Dataset
Metadata Created Date June 1, 2023
Metadata Updated Date June 15, 2024
Publisher Climate Adaptation Science Centers
Maintainer
@Id http://datainventory.doi.gov/id/dataset/c7e69426f376472d420cab81170f9dfc
Identifier 30767744-d080-4dc5-85c0-f4c4a4f249c8
Data Last Modified 2020-08-20
Category geospatial
Public Access Level public
Bureau Code 010:00
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 2b210b68-cb2f-4c27-a985-4111389a9920
Harvest Source Id 52bfcc16-6e15-478f-809a-b1bc76f1aeda
Harvest Source Title DOI EDI
Metadata Type geospatial
Old Spatial -94.2609062308,42.5692312673,-87.9475441739,48.6427837912
Source Datajson Identifier True
Source Hash acec77fb48feae13b90c06642b359ce29965cfbfa0e7ab98afaba18762b4a3dd
Source Schema Version 1.1
Spatial {"type": "Polygon", "coordinates": -94.2609062308, 42.5692312673, -94.2609062308, 48.6427837912, -87.9475441739, 48.6427837912, -87.9475441739, 42.5692312673, -94.2609062308, 42.5692312673}

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