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Federal
Process-guided deep learning water temperature predictions: 6c All lakes historical evaluation data
Department of the Interior —
This dataset includes evaluation data ("test" data) and performance metrics for water temperature predictions from multiple modeling frameworks. Process-Based (PB)... -
Federal
Predicting water temperature in the Delaware River Basin: 2 Water temperature and flow observations
Department of the Interior —
Observations related to water and thermal budgets in the Delaware River Basin. Data from reservoirs in the basin include reservoir characteristics (e.g., bathymetry),... -
Federal
Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs
Department of the Interior —
This dataset includes model inputs that describe local weather conditions for Sparkling Lake, WI. Weather data comes from two sources: locally measured (2009-2017)... -
Federal
Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes)
Department of the Interior —
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... -
Federal
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin
Department of the Interior —
Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal... -
Federal
A deep learning model and associated data to support understanding and simulation of salinity dynamics in Delaware Bay
Department of the Interior —
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water... -
Federal
Examining the influence of deep learning architecture on generalizability for predicting stream temperature in the Delaware River Basin
Department of the Interior —
This data release and model archive provides all data, code, and modelling results used in Topp et al. (2023) to examine the influence of deep learning architecture... -
Federal
Process-based water temperature predictions in the Midwest US: 3 Temperature observations
Department of the Interior —
Observed water temperatures from 1980-2019 were compiled for 5,584 lakes in Minnesota and Wisconsin (USA). A subset of these data were used as calibration for... -
Federal
Process-based water temperature predictions in the Midwest US: 2 Model configurations (lake metadata and parameter values)
Department of the Interior —
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... -
Federal
Predicting water temperature in the Delaware River Basin
Department of the Interior —
Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for... -
Federal
Process-guided deep learning water temperature predictions: 1 Spatial data (GIS polygons for 68 lakes)
Department of the Interior —
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... -
Federal
Data release: Process-based predictions of lake water temperature in the Midwest US
Department of the Interior —
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... -
Federal
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 2 Water temperature observations
Department of the Interior —
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... -
Federal
Data and model code used to evaluate a process-guided deep learning approach for in-stream dissolved oxygen prediction
Department of the Interior —
This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen... -
Federal
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes
Department of the Interior —
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,... -
Federal
Multi-task Deep Learning for Water Temperature and Streamflow Prediction (ver. 1.1, June 2022)
Department of the Interior —
This item contains data and code used in experiments that produced the results for Sadler et. al (2022) (see below for full reference). We ran five experiments for... -
Federal
Process-guided deep learning water temperature predictions: 3a Lake Mendota inputs
Department of the Interior —
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... -
Federal
Process-guided deep learning water temperature predictions: 4b Sparkling Lake detailed training data
Department of the Interior —
This dataset includes compiled water temperature data from an instrumented buoy on Sparkling Lake, WI and discrete (manually sampled) water temperature records from... -
Federal
Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values)
Department of the Interior —
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... -
Federal
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates
Department of the Interior —
Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection...