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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 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).
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: MN,
Minnesota,
SD,
South Dakota,
US,
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...
This data release contains a 17-year record (2005-2022) of discrete chlorophyll data from inland waters, collected from across the nation and territories. These data are from discrete samples (collected in the field and analyzed in the laboratory) from plankton (suspended algae) and periphyton (benthic algae) from lakes, streams, rivers, reservoirs, canals, and other sites. These data are gathered to support process and remote sensing modeling and prediction of Harmful Algal Blooms (HABs). The chlorophyll data were compiled from the Water Quality Portal (WQP) and USGS National Water Quality Lab (NWQL). Data for uncorrected chlorophyll a, corrected chlorophyll a, and pheophytin from EPA Methods 445 and 446 are included...
Categories: Data;
Tags: Aquatic Biology,
Ecology,
USGS Science Data Catalog (SDC),
United States,
Water Quality,
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). In this data release, multiple modeling approaches were used to generate predictions of daily temperature profiles for thousands of lakes in the Midwest. Predictions were generated using two modeling frameworks: a machine learning model (specifically an entity-aware long short-term memory or EA-LSTM model; Kratzert et al., 2019) and a process-based model (specifically the General Lake Model or GLM; Hipsey et al., 2019). Both the EA-LSTM and GLM frameworks were used to generate...
Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood, especially in flowing waters. In order to facilitate future national model development and characterization of HABs, this data release publishes a synthesized and cleaned collection of HABs-related water quality and quantity data for river and stream sites across the United States. It includes nutrients, major ions, sediment,...
Categories: Data;
Tags: Aquatic Biology,
District of Columbia,
Ecology,
Puerto Rico,
USGS Science Data Catalog (SDC),
Lake temperature is an important environmental metric for understanding habitat suitability for many freshwater species and is especially useful when temperatures are predicted throughout the water column (known as temperature profiles). This dataset provides estimates of water temperature at half meter depths for eight reservoirs in Missouri, USA using version 3 of the General Lake Model (Hipsey et al. 2019). The reservoirs are: Bull Shoals Lake, Lake Ozark, Lake Stockton, Mark Twain Lake, Pomme De Terre Lake, Table Rock Lake, Truman Reservoir, and Wapapello Lake. Both calibrated and uncalibrated model configurations (see 'GLM_{cal|uncal}_nml.zip' files), as well as, predicted temperatures (see 'GLM_{cal|uncal}_profile_results.zip'...
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 trained model parameters for process-guided deep learning models (PGDL; Read et al. 2019). 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).
The release of elements of concern (EoC) to surface water can involve both natural and anthropogenic sources. Elevated EoC concentrations can pose a risk to human health, wildlife, and ecosystem health, with the modes of toxicity and extent of risk varying as a function of the specific element, its chemical form and the matrix with which it is associated (for example, dissolved versus particulate). As part of the U.S. Geological Survey (USGS) Water Mission Area (WMA) Water Quality Processes Program, the Proxies (Surrogate) Project was created, in part, to develop models that can be used to estimate the concentration of EoC in riverine surface water at spatial scales ranging from (sub)basin to multi-basin. Three...
Categories: Data;
Tags: Delaware,
Illinois,
Integrated Water Science,
USGS Science Data Catalog (SDC),
Upper Colorado,
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