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Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal metrics to characterize lake temperature impacts on fish habitat for 881 lakes. Included in the metrics are daily thermal optical habitat areas and a set of over 172 annual thermal metrics.
Observed water temperatures from 1980-2018 were compiled for 877 lakes in Minnesota (USA). There were four lakes included in this data release that did not have temperature observations available at the time of compilation or these data existed elsewhere and were unknown to the compilation team. These data were used as training, test, and error-estimation data for process-guided deep learning models and the evaluation of process-based models. The data are formatted as a single csv (comma separated values) file with attributes corresponding to the unique combination of lake identifier, time, and depth. Data came from a variety of sources, including the Water Quality Portal, the North Temperate Lakes Long-Term Ecological...
This dataset provides shapefile outlines of the 881 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota (https://doi.org/10.5066/P9PPHJE2).
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: MN,
Minnesota,
SD,
South Dakota,
US,
Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in degrees C) is calculated for each lake and each model type, and matched values for predicted and observed temperatures are also included to support more specific error estimation methods (for example, calculating error in a particular month). Errors for the process-based model are compared to predictions as shared in Model Predictions data since these models were not calibrated. Errors for the process-guided deep learning models were calculated from validation folds and therefore differ from the comparisons to Model Predictions because those...
Provide a space for datasets used in continuous integration testing for R packages or datasets used in training materials to live publicly.
Climate change and land use change have been shown to influence lake temperatures and water clarity 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 and optical habitat in 881 lakes in Minnesota during 1980-2018. The data are organized into these items: 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 Access to computing facilities was provided by USGS Advanced Research Computing, USGS Yeti Supercomputer...
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. state of Minnesota. Uncalibrated models used default configurations (PB0; see Read et al. 2019 for details) of the General Lake Model version 3.1 (Hipsey et al. 2019) and no parameters were further adjusted according to model fit with observations. Process-Guided Deep Learning (PGDL; see Read et al. 2019 and Jia et al. 2019) models were deep learning models pre-trained PB0 outputs and a physical constraint for energy conservation as a loss term. After pre-training, these PGDL models were training on actual temperature observations.
This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release includes data that characterize both rivers and a subset of reservoirs in the basin. This release provides an update to many of the files provided in a previous data release (Oliver et al., 2021). The data are stored in 3 child folders: 1) spatial information, 2) observations, and 3) model driver data. 1) Spatial Information...
This dataset includes model inputs (specifically, weather, water clarity, and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota (https://doi.org/10.5066/P9PPHJE2).
This dataset provides model parameters used to estimate water temperature from a process-based model (Hipsey et al. 2019) using uncalibrated model configurations (PB0) and the trained model parameters for process-guided deep learning models (PGDL; Read et al. 2019). This dataset is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota(https://doi.org/10.5066/P9PPHJE2).
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