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Data release: Process-guided deep learning predictions of lake water temperature
<p>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... -
Process-guided deep learning water temperature predictions: 3a Lake Mendota inputs
This dataset includes model inputs that describe local weather conditions for Lake Mendota, WI. Weather data comes from two sources: locally measured (2009-2017) and gridded... -
Process-guided deep learning water temperature predictions: 5 Model prediction data
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-... -
Process-guided deep learning water temperature predictions: 4 Training data
This dataset includes compiled water temperature data from a variety of sources, including the Water Quality Portal (Read et al. 2017), the North Temperate Lakes Long-TERM... -
Process-guided deep learning water temperature predictions: 5a Lake Mendota...
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-... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 3...
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... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6...
Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in... -
Process-guided deep learning water temperature predictions: 4c All lakes...
Observed water temperatures from 1980-2018 were compiled for 68 lakes in Minnesota and Wisconsin (USA). These data were used as training data for process-guided deep learning... -
Process-guided deep learning water temperature predictions: 4a Lake Mendota...
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North... -
Process-guided deep learning water temperature predictions: 5a Lake Mendota...
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-... -
Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
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... -
Process-guided deep learning water temperature predictions: 4b Sparkling...
This dataset includes compiled water temperature data from an instrumented buoy on Sparkling Lake, WI and discrete (manually sampled) water temperature records from North... -
Process-guided deep learning water temperature predictions: 4a Lake Mendota...
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from North... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2...
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... -
Predicting water temperature in the Delaware River Basin: 1 Waterbody...
This dataset provides one shapefile of polylines for the 456 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs. -
Process-based water temperature predictions in the Midwest US: 1 Spatial...
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,... -
Process-based water temperature predictions in the Midwest US: 1 Spatial...
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,... -
Model predictions for heterogeneous stream-reservoir graph networks with...
<p>This data release provides the predictions from stream temperature models described in Chen et al. 2021. Briefly, various deep learning and process-guided deep learning... -
Process-guided deep learning water temperature predictions: 6c All lakes...
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB) models were... -
Process-guided deep learning water temperature predictions: 3 Model inputs...
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...