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Using predicted lake temperatures from uncalibrated, process-based models (PB0) and process-guided deep learning models (PGDL), this dataset summarized a collection of thermal metrics to characterize lake temperature impacts on fish habitat for 881 lakes. Included in the metrics are daily thermal optical habitat areas and a set of over 172 annual thermal metrics.
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, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 7,150 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9CA6XP8).
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Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term memory deep learning model. In-situ measurements used for model training and evaluation are from 12,227 lakes and are included as well as daily meteorological conditions and lake properties. Median per-lake estimated error found through cross validation on lakes with in-situ surface temperature observations was 1.24 °C. The generated dataset will be beneficial for a wide range of applications including estimations of thermal habitats and the impacts of climate change on inland lakes.
Categories: Data; Tags: AL, AR, AZ, Alabama, Aquatic Biology, All tags...
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This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section contains observations related to the amount and quality of water in the Delaware River Basin. Data from a subset of reservoirs in the basin include observed daily depth-resolved water temperature, water levels, diversions, and releases. Data from streams in the basin include daily flow and temperature observations. Observations were compiled from a variety of sources, including the National Water Inventory System, Water Quality Portal, EcoSHEDS stream...
This dataset summarized a collection of annual thermal metrics to characterize lake temperature impacts on fish habitat for 7,150 lakes from uncalibrated models (PB0) and 449 from calibrated models (PBALL). The dataset includes over 172 annual thermal metrics.
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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 lake based on the "site_id". This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).
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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 estimates (all other time periods). There are two comma-delimited files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of...
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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-Based (PB) models were configured and calibrated with training data to reduce root-mean squared error. Uncalibrated models used default configurations (PB0; see Winslow et al. 2016 for details) and no parameters were adjusted according to model fit with observations. Deep Learning (DL) models were Long Short-Term Memory artificial recurrent neural network models which used training data to adjust model structure and weights for temperature predictions (Jia et al. 2019). Process-Guided Deep Learning (PGDL) models were DL models with an added...
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This dataset includes model inputs that describe weather conditions for the 68 lakes included in this study. Weather data comes from gridded estimates (Mitchell et al. 2004). There are two comma-separated files, one for weather data (one row per model timestep) and one for ice-flags, which are used by the process-guided deep learning model to determine whether to apply the energy conservation constraint (the constraint is not applied when the lake is presumed to be ice-covered). The ice-cover flag is a modeled output and therefore not a true measurement (see "Predictions" and "pb0" model type for the source of this prediction). This dataset is part of a larger data release of lake temperature model inputs and outputs...
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In 2004, about 90 migrating elk drowned after attempting to cross thin ice on the Mores Creek arm of Lucky Peak Lake upstream of the Highway 21 bridge. To better understand the depths over a range of reservoir pool elevations in the Mores Creek Arm, the U.S. Geological Survey, in cooperation with the Lucky Peak Power Plant Project, conducted high-resolution multibeam echosounder (MBES) bathymetric surveys on the Mores Creek arm on Lucky Peak Lake. The MBES data will assist reservoir managers and wildlife biologists with regulating reservoir water surface elevations (WSE) to support successful big game migration across Mores Creek on Lucky Peak Lake. Data collection provided nearly 100 percent coverage of bed elevations...
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As part of the Triangle Area Water Supply Monitoring Project, the U.S. Geological Survey conducted a study to evaluate the occurrence and distribution of phytoplankton, taste-and-odor compounds, and cyanotoxin occurrence in four water-supply reservoirs in the Triangle area of North Carolina. This data release contains the associated data described in the Scientific Investigations Report, "Phytoplankton, Taste-and-Odor Compounds, and Cyanotoxin Occurrence in Drinking Water Supply Reservoirs in the Triangle Area of North Carolina". Surface-water samples were collected four times between April and October 2014 at five study sites for laboratory analysis of nutrients, chlorophyll a, major ions, metals, taste and odor...
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This dataset provides shapefile outlines of the 2,332 lakes that had temperature modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). A csv file of lake metadata is included, which includes lake metadata and all features that were considered for the meta transfer model (not all meta features were used). This dataset is part of a larger data release of lake temperature model inputs and outputs for 2,332 lakes in the U.S. (https://doi.org/10.5066/P9I00WFR).
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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, .shx, .dbf, and .prj files). A csv file of lake metadata is also included. This dataset is part of a larger data release of lake temperature model inputs and outputs for 881 lakes in the U.S. state of Minnesota (https://doi.org/10.5066/P9PPHJE2).
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Water temperature estimates from multiple models were evaluated by comparing predictions to observed water temperatures. The performance metric of root-mean square error (in degrees C) is calculated for each lake and each model type, and matched values for predicted and observed temperatures are also included to support more specific error estimation methods (for example, calculating error in a particular month). Errors for the process-based model are compared to predictions as shared in Model Predictions data since these models were not calibrated. Errors for the process-guided deep learning models were calculated from validation folds and therefore differ from the comparisons to Model Predictions because those...
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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 International Boundary (River Mile (RM) 271.0) and extended down the reservoir, hereinafter referred to as downstream, about 1.4 miles downstream of the Montana 37 Highway Bridge near Boulder Creek (about RM 253). USACE continued the survey in 2017, completing a reach that extended from about RM 253 downstream to near Tweed Creek (RM 244.5). In 2018, the U.S. Geological Survey (USGS) Idaho Water Science Center completed the remaining portion of the reservoir from RM 244.5 downstream to Libby Dam (RM 219.9). The MBES data collected in 2016 and 2017...
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In 2004, about 90 migrating elk drowned after attempting to cross thin ice on the Mores Creek arm of Lucky Peak Lake upstream of the Highway 21 bridge. To better understand the depths over a range of reservoir pool elevations in the Mores Creek Arm, the U.S. Geological Survey, in cooperation with the Lucky Peak Power Plant Project, conducted high-resolution multibeam echosounder (MBES) bathymetric surveys on the Mores Creek arm on Lucky Peak Lake. The MBES data will assist reservoir managers and wildlife biologists with regulating reservoir water surface elevations (WSE) to support successful big game migration across Mores Creek on Lucky Peak Lake. Data collection provided nearly 100 percent coverage of bed elevations...
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The importance of riparian ecosystems in semiarid and arid regions has generated interest in understanding processes that drive the distribution and abundance of dominant riparian plants. Changes in streamflow patterns downstream of dams have profoundly affected riparian vegetation composition and structure. For example, in the southwestern United States, flow regulation has contributed to the replacement of many riparian forests historically dominated by the native Populus fremontii (Fremont Cottonwood) and Salix gooddingii (Goodding’s Willow) by the exotic species Tamarix spp. (Salt Cedar). The proposed project will help guide reservoir release decision making to enhance downstream recruitment of native cottonwood...
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Executive Summary: Evolution of policies that guide operation of individual reservoir systems begins with a relative flurry of activity associated with building of dams. Over perhaps a ten year period, operations are proposed in anticipation of construction, implemented when a dam is complete, and then modified as the effects, capabilities, and limitations of the project become better understood. After these initial adjustments, the policy process slowly begins to simmer. Operational changes are the driven by short-term influences that are largely episodic (e.g. droughts and floods) and long-term influences (e.g. social and economic factors) that affect operations more gradually.
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This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section contains forcing data for water temperature forecasting models reported in Zwart et al. (2023), including a process-based pre-trainer, gridded weather and forecasted weather data, and flow and temperature for reservoir inlets and outlets.
map background search result map search result map Managing water and riparian habitats on the Bill Williams River with scientific benefit for other desert river systems Resources: Managing water and riparian habitats on the Bill Williams River with scientific benefit for other desert river systems Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes) Process-based water temperature predictions in the Midwest US: 6 Habitat metrics Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes Lake Koocanusa Maximum and Minimum Pool Elevation Contours, Lincoln County, Montana Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020) Mores Creek Arm Bathymetric Survey - Depth Contours, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Mores Creek Arm Bathymetric Survey - Depth DEM, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Associated Data for the Phytoplankton, Taste-and-Odor Compounds, and Cyanotoxin Occurrence in Drinking Water Supply Reservoirs in the Triangle Area of North Carolina Data to support water quality modeling efforts in the Delaware River Basin: 2) River and Reservoir Observations Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data Process-guided deep learning water temperature predictions: 3b Sparkling Lake inputs Mores Creek Arm Bathymetric Survey - Depth Contours, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Mores Creek Arm Bathymetric Survey - Depth DEM, Lucky Peak Lake, Boise County, Idaho, May 11 - 13, 2021 Lake Koocanusa Maximum and Minimum Pool Elevation Contours, Lincoln County, Montana Associated Data for the Phytoplankton, Taste-and-Odor Compounds, and Cyanotoxin Occurrence in Drinking Water Supply Reservoirs in the Triangle Area of North Carolina Managing water and riparian habitats on the Bill Williams River with scientific benefit for other desert river systems Resources: Managing water and riparian habitats on the Bill Williams River with scientific benefit for other desert river systems Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data Data to support water quality modeling efforts in the Delaware River Basin: 2) River and Reservoir Observations Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs Process-guided deep learning water temperature predictions: 2 Model configurations (lake metadata and parameter values) Process-guided deep learning water temperature predictions: 5c All lakes historical prediction data Process-guided deep learning water temperature predictions: 3c All lakes historical inputs Process-based water temperature predictions in the Midwest US: 6 Habitat metrics Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 7 thermal and optical habitat estimates Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 1 Lake information for 881 lakes Walleye Thermal Optical Habitat Area (TOHA) of selected Minnesota lakes: 6 model evaluation Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning: 1 Lake information for 2,332 lakes Process-based water temperature predictions in the Midwest US: 1 Spatial data (GIS polygons for 7,150 lakes) Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020)