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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...
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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.
This dataset provides shapefile outlines of the 7,150 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 7,150 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9CA6XP8).
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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 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
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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 Minnesota and Wisconsin. 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. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
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This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
This dataset summarized a collection of annual thermal metrics to characterize lake temperature impacts on fish habitat for 7,150 lakes from uncalibrated models (PB0) and 449 from calibrated models (PBALL). The dataset includes over 172 annual thermal metrics.
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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...
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This dataset includes model inputs (specifically, weather and flags for predicted ice-cover) and is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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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...
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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).
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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).
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Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines in walleye and increases in largemouth bass populations have raised questions regarding the future trajectories and appropriate management actions for these important species. We developed a thermodynamic model of water temperatures driven by downscaled climate data and lake specific characteristics to estimate daily water temperature profiles for 2148 lakes in Wisconsin, USA under contemporary (1989-2014) and future (2040-2064 and 2065-2089) conditions. We correlated contemporary walleye recruitment success and largemouth bass relative abundance...
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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...
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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...
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This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North Temperate Lakes Long-TERM Ecological Research Program (NTL-LTER; https://lter.limnology.wisc.edu/). The buoy is supported by both the Global Lake Ecological Observatory Network (gleon.org) and the NTL-LTER. This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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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...
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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 Minnesota and Wisconsin. General Lake Model verion 2 process-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error for 449 lakes (PBALL). Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations for 7,150 lakes.


map background search result map search result map CM2.0 model: Projected shifts in fish species dominance in Wisconsin lakes under climate change Model configuration: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes) Process-based water temperature predictions in the Midwest US: 5 Model prediction data Process-based water temperature predictions in the Midwest US: 6 Habitat metrics Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2 Water temperature observations Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 6 model evaluation Daily water column temperature predictions for thousands of Midwest U.S. lakes between 1979-2022 and under future climate scenarios Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data CM2.0 model: Projected shifts in fish species dominance in Wisconsin lakes under climate change Process-guided deep learning predictions of lake water temperature Process-guided deep learning water temperature predictions: 3 Model inputs (meteorological inputs and ice flags) Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-based water temperature predictions in the Midwest US: 6 Habitat metrics Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2 Water temperature observations Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Model configuration: A large-scale database of modeled contemporary and future water temperature data for 10,774 Michigan, Minnesota and Wisconsin Lakes Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 2 Water temperature observations Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 6 model evaluation Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes) Process-based water temperature predictions in the Midwest US: 5 Model prediction data Daily water column temperature predictions for thousands of Midwest U.S. lakes between 1979-2022 and under future climate scenarios