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. The data are organized into these items: Spatial data - A lake metadata file, and one shapefile of polygons for all 7,150 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 7,150 lakes, and one zip file with each lake's glm2.nml file) Temperature observations - Data formatted as model inputs for training, calibrating, or evaluating temperature models Model inputs - Data used to drive predictive models (35 zip files with ice-flags; 35 zip files with daily meteorological data) Prediction data - Predictions calibrated and uncalibrated PB models (35 zip files) Predicted habitat - Data formatted for ecological use 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).
|Title||Data release: Process-based predictions of lake water temperature in the Midwest US|
|Authors||Jordan S Read, Jacob A. Zwart, Holly Kundel, Hayley R Corson-Dosch, Gretchen J.A. Hansen, Kelsey Vitense, Alison P. Appling, Samantha K. Oliver, Lindsay Platt|
|Product Type||Data Release|
|Record Source||USGS Digital Object Identifier Catalog|
|USGS Organization||Integrated Information Dissemination Division|