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Julie A Padilla

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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...
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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,...
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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'...
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Data to support water quality modeling efforts in the Delaware River Basin
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. Reservoirs in the DRB serve an important role as a source of drinking water, but also affect downstream water quality. Therefore, this data release includes data that characterize both rivers and a subset of reservoirs in the basin. This release provides an update to many of the files provided in a previous data release (Oliver et al., 2021). The data are stored in 3 child folders: 1) spatial information, 2) observations, and 3) model driver data. 1) Spatial Information...
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