Data

This model archive summary documents the sediment acoustic index model for suspended-sediment concentration developed to compute 15-minute suspended sediment concentration for the U.S. Geological Survey (USGS) station, San Juan River near Bluff, UT (USGS ID: 09379500) from May 2, 2018, onward. This is the first model developed for the site to compute continuous suspended sediment...

This model archive summary (MAS) documents the suspended-sediment concentration (SSC) model developed to compute 15-minute SSC from turbidity readings. This is the first model developed for the Blue Earth River at Highway 169 at Mankato, Minnesota (Station ID 05321195).

This model archive summary details the suspended-sediment concentration (SSC) model developed to compute 15-minute SSC values from 15-minute turbidity data for the period October 01, 2017 through September 30, 2020. The methods used follow U.S. Geological Survey (USGS) guidance as referenced in Office of Water Quality Technical Memorandum 2016.10 (USGS 2016) and USGS Techniques and...

This model archive summary details the suspended-sediment concentration (SSC) model developed to compute 15-minute SSC values from 15-minute turbidity data for the period October 01, 2017 through September 30, 2020. The methods used follow U.S. Geological Survey (USGS) guidance as referenced in Office of Water Quality Technical Memorandum 2016.10 (USGS 2016) and USGS Techniques and...

This model archive summary (MAS) documents the revision of suspended sediment concentration (SSC) unit and daily values computed from WY 2008-2017, and the additional computation of SSC unit values from WY 2018-2020 using a new log-transformed regression model with SSC samples from the period of record at this site. The new, revised model computes SSC unit values for WY 2008-2020. This...

This model archive summary (MAS) is a model update to an existing log-transformed turbidity-suspended sediment concentration (SSC) regression model computing SSC for water year (WY) 2008-2017. This model supersedes all models prior to Oct 1, 2017 and includes a new model for WY 2018-2020. The methods used follow USGS guidance as referenced in Office of Surface Water (OSW)/Office of Water...

Data from an optical turbidity sensor deployed at the stream station were recorded at 15-minute intervals by a data logger and uploaded every hour to the U.S. Geological Survey (USGS) database (Anderson, 2005; Wagner, 2006). Suspended-sediment samples were collected using a suction pump autosampler, equal width increments or grab sampling techniques (Edwards, 1999). The use of an optical...

Continuous 15-minute time-series suspended-sediment concentration (SSC) data computed from USGS instream turbidity data using a YSI 6-series multi-parameter water quality sonde for the South Mokelumne River at New Hope Bridge near Walnut Grove, California station (SMR; USGS #11336680). A model archive summary describes the development of a continuous 15-minute SSC time-series regression...

Continuous 15-minute time-series suspended-sediment concentration (SSC) data computed from U.S. Geological Survey (USGS) instream turbidity data using a YSI 6-series multi-parameter water quality sonde for the North Mokelumne River near Walnut Grove, California, USGS station #11336685. A model archive summary describes the development of a continuous 15-minute SSC time-series regression...

The Cape Sable Seaside Sparrow (CSSS; Ammospiza maritima mirabilis) is an endangered species that has experienced population declines of more than 60% since the 1990s. The CSSS is restricted in geographic extent to a relatively small area of remaining marl prairie in the southern Florida Everglades and remains in six subpopulation areas (named A-F). There has been over two decades of...

This data release contains model code and input data for, and predictions from, both a dynamic stream dissolved solids and a static baseflow dissolved solids SPARROW (SPAtially Referenced Regression On Watershed attributes) model of the Upper Colorado River Basin for water years 1986-2017. Input data includes information on dissolved solids sources, landscape transport characteristics...

This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four...