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This section contains the model source code for StreamLight v0.2.0 built as a binary package. Additionally, an R environment is included that contains all of the site parameters, model driver files, and model source code from this data release already loaded. Finally, an example markdown file is included to give a brief summary of the model and a demonstration of running the model.
Tags: biota
This data release contains products from Savoy & Harvey (2021) which modeled photosynthetically active radiation and gross primary productivity (GPP) for 173 streams and rivers across the continental United States. Estimates of photosynthetically active radiation (PAR) were generated using a model that considers the contribution of light attenuation from riparian zones as well as within the water column. PAR estimates at the stream surface and within the water are both provided and each of these two estimates were converted to GPP estimates using a fixed light use efficiency. This data release includes the site parameters, model inputs, model outputs, and model source code use to generate these estimates. Items...
This dataset provides timeseries of model input and output data and is part of a larger data release of inputs and outputs from a model to predict light at the stream surface and within the water column for 173 streams and rivers across the continental United States. The complete release contains model input data, modeled estimates of light at the stream surface and within the water column, and modeled estimates of gross primary productivity.
This child item dataset contains the source of chlorophyll data as well as the approval status of all data aquired from the USGS National Water Information System (NWIS, https://waterdata.usgs.gov/nwis) at the time of data acquisition (10/27/2021). This dataset is part of a larger data release that contains model inputs, observed chlorophyll concentrations, modeled estimates of chlorophyll, and contriubtions of individual variables to modeled predictions.
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),
This dataset provides a common validation set for estimates of gross primary productivity. This data represents a subset of all predictions made in the model inputs and outputs that were converted to GPP based on a light use efficiency. The data was subsetted for only days were all light estimates could be produced. This dataset is part of a larger data release of inputs and outputs from a model to predict light at the stream surface and within the water column for 173 streams and rivers across the continental United States. The complete release contains model input data, modeled estimates of light at the stream surface and within the water column, and modeled estimates of gross primary productivity.
This dataset provides information on site identifiers, location information, and model parameters. The format is a tab-delimted file with 1 row per site. This dataset is part of a larger data release of inputs and outputs from a model to predict light at the stream surface and within the water column for 173 streams and rivers across the continental United States. The complete release contains model input data, modeled estimates of light at the stream surface and within the water column, and modeled estimates of gross primary productivity.
This child item dataset provides site information for 82 streams and rivers from which data was used to train and test a machine learning model of daily chlorophyll concentrations. This dataset is part of a larger data release that contains model inputs, observed chlorophyll concentrations, model estimates of chlorophyll, and contributions of individual variables to modeled predictions.
This child item dataset includes inputs to and outputs from an extreme gradient boosting model (XGBoost) to predict daily chlorophyll concentrations. Included in this dataset are the necessary model inputs, observed chlorophyll concentrations, and SHapley Additive exPlanations (SHAP) values. SHAP values were used to assess the contribution of each variable to estimated chlorophyll.
This data release contains modeled daily chlorophyll concentration for 82 streams and rivers across the conterminous United States. Estimates of daily chlorophyll concentration were generated using an extreme gradient boosting (XGBoost) (Chen & Guestrin, 2016) machine learning model and various measures of water quality, reach characteristics, and meteorology. Child Items in this data release contain: Site information: This child item contains station identifiers, location information, location type, stream order, and associated NHDPlusv2.1 reach common unique identifiers (ComID). The file format is tab-delimted with 1 row per station. Model inputs and outputs: This child item contains XGBoost model inputs, observed...
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