Filters: Tags: temperate lakes (X) > partyWithName: Jordan S Read (X)
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Observed water temperatures from 1980-2019 were compiled for 2,332 lakes in the US. 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 Research Project, and digitized temperature records from the MN Department of Natural Resources. This dataset is part of a larger data release of lake temperature model inputs and outputs for these same lakes...
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.
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.
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 2,332 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 included, which includes lake metadata and all features that were considered for the meta transfer model (not all meta features were used). This dataset is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. (https://doi.org/10.5066/P9I00WFR).
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: 007,
012,
IA,
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Illinois,
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...
Predictions are evaluated against observed temperatures.
Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum...
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...
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). In this data release, multiple modeling approaches were used to generate predictions of daily temperature profiles for thousands of lakes in the Midwest. Predictions were generated using two modeling frameworks: a machine learning model (specifically an entity-aware long short-term memory or EA-LSTM model; Kratzert et al., 2019) and a process-based model (specifically the General Lake Model or GLM; Hipsey et al., 2019). Both the EA-LSTM and GLM frameworks were used to generate...
This dataset includes model inputs (specifically, meteorological inputs to the predictive models and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. states of North Dakota, South Dakota, Minnesota, Wisconsin, and Michigan (https://doi.org/10.5066/P9PPHJE2).
Multiple modeling frameworks were used to predict daily temperatures at 0.5m depth intervals for a set of diverse lakes in the U.S. states of South Dakota, North Dakota, Minnesota, Wisconsin, and Michigan. Process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Process-Guided Deep Learning (PGDL) models were deep learning models with an added physical constraint for energy conservation as a loss term. These models were pre-trained with uncalibrated Process-Based model outputs (PB0) before training...
Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum...
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...
This dataset provides model specifications used to estimate water temperature from a process-based model (Hipsey et al. 2019). The format is a single JSON file indexed for each lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. (https://doi.org/10.5066/P9I00WFR).
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.
Climate change has been shown to influence lake temperatures globally. To better understand the diversity of lake responses to climate change and give managers tools to manage individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota and Wisconsin for contemporary (1979-2015) and future (2020-2040 and 2080-2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. From simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We included all available supporting metadata including satellite and in-situ observations of water clarity, maximum...
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). This dataset provides estimates of water temperature at half meter depths for eight reservoirs in Missouri, USA using version 3 of the General Lake Model (Hipsey et al. 2019). The reservoirs are: Bull Shoals Lake, Lake Ozark, Lake Stockton, Mark Twain Lake, Pomme De Terre Lake, Table Rock Lake, Truman Reservoir, and Wapapello Lake. Both calibrated and uncalibrated model configurations (see 'GLM_{cal|uncal}_nml.zip' files), as well as, predicted temperatures (see 'GLM_{cal|uncal}_profile_results.zip'...
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).
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