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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 i...
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: 6c All lakes hist...
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 u...
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 predictio...
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 d...
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 Mo...
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 mo...
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 hist...
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 d...
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 un...
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 d...
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 d...
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 evaluatio...
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 configura...
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... -
Process-guided deep learning water temperature predictions: 3b Sparkling Lake...
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... -
ECHAM5.0 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: 4b Sparkling Lake...
This dataset includes compiled water temperature data from an instrumented buoy on Sparkling Lake, WI and discrete (manually sampled) water temperature records from North...