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Process-guided deep learning water temperature predictions: 5c All lakes hist...
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: 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-... -
Data release: Process-based predictions of lake water temperature in the Midw...
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... -
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... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 4 Mo...
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... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 5 Mo...
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... -
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... -
Process-guided deep learning water temperature predictions: 1 Spatial data (G...
This dataset provides shapefile of outlines of the 68 lakes where temperature was modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx,... -
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: 6a Lake Mendota d...
This dataset includes "test data" compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from... -
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... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 4 Mo...
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... -
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 th...
Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal... -
Process-guided deep learning water temperature predictions: 1 Spatial data (G...
This dataset provides shapefile of outlines of the 68 lakes where temperature was 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 data...
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: 5 Model predic...
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... -
Thermal metrics: A large-scale database of modeled contemporary and future wa...
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... -
Process-based water temperature predictions in the Midwest US: 2 Model config...
This dataset provides model specifications used to estimate water temperature from the process-based model, General Lake Model verion 2 (Hipsey et al. 2019) using calibrated... -
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...