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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... -
Lake Koocanusa Digital Elevation Model (DEM), Lincoln County, Montana
In 2016, the U.S. Army Corps of Engineers (USACE) started collecting high-resolution multibeam echosounder (MBES) data on Lake Koocanusa. The survey originated near the... -
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... -
NHD Major Rivers, Major Rivers and Creeks, Major Lakes and Reservoirs
The USGS National Hydrography Dataset (NHD) Downloadable Data Collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information... -
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... -
GENMOM model: Projected shifts in fish species dominance in Wisconsin lakes...
Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines... -
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... -
California Lakes
NOTE: In 2013, the California Department of Fish and Game (CDFG, DFG) was renamed to California Department of Fish and Widlife (CDFW). This dataset is an update of California... -
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... -
Data release: Process-guided deep learning predictions of lake water temperature
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... -
CM2.0 model: Projected shifts in fish species dominance in Wisconsin lakes...
Temperate lakes may contain both coolwater fish species such as walleye (Sander vitreus) and warmwater species such as largemouth bass (Micropterus salmoides). Recent declines... -
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: 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: 6 Model...
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: 2 Model...
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...