90 datasets found
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Federal
Data release: Process-guided deep learning predictions of lake water temperature
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
Process-guided deep learning water temperature predictions: 5 Model prediction data
Department of the Interior —
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.... -
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
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
Daily water column temperature predictions for thousands of Midwest U.S. lakes between 1979-2022 and under future climate scenarios
Department of the Interior —
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are... -
Federal
Data-Driven Drought Prediction Project Model Outputs for Select Spatial Units within the Conterminous United States
Department of the Interior —
This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought... -
Federal
Process-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data
Department of the Interior —
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from... -
Federal
Process-guided deep learning water temperature predictions: 6b Sparkling Lake detailed evaluation data
Department of the Interior —
This dataset includes "test data" compiled water temperature data from an instrumented buoy on Sparkling Lake, WI and discrete (manually sampled) water temperature... -
Federal
Data and model code in support of Stream nitrate dynamics driven primarily by discharge and watershed physical and soil characteristics at intensively monitored sites, Insights from deep learning
Department of the Interior —
We developed a suite of models using deep learning to make hindcast predictions of the 7-day average backward-looking nitrate concentration at 46 predominantly... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 2 Observations
Department of the Interior —
This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to... -
Federal
Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation
Department of the Interior —
Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square... -
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
Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs
Department of the Interior —
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... -
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
Predicting water temperature in the Delaware River Basin: 5 Model prediction data
Department of the Interior —
Several models were used to improve water temperature prediction in the Delaware River Basin. PRMS-SNTemp was used to predict daily temperatures at 456 stream reaches... -
Federal
Process-guided deep learning water temperature predictions: 4c All lakes historical training data
Department of the Interior —
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... -
Federal
Data release: Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes
Department of the Interior —
Climate change and land use change have been shown to influence lake temperatures and water clarity in different ways. To better understand the diversity of lake... -
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-guided deep learning water temperature predictions: 4a Lake Mendota detailed training data
Department of the Interior —
This dataset includes compiled water temperature data from an instrumented buoy on Lake Mendota, WI and discrete (manually sampled) water temperature records from... -
Federal
Model predictions for heterogeneous stream-reservoir graph networks with data assimilation
Department of the Interior —
This data release provides the predictions from stream temperature models described in Chen et al. 2021. Briefly, various deep learning and process-guided deep...
90 datasets found