Model Archive Summary for Suspended-Sediment Concentration at U.S. Geological Survey Site 385553107243301; North Fork Gunnison below Raven Gulch near Somerset, Colorado

This model archive summary documents the suspended-sediment concentration (SSC) model developed to estimate 15-minute SSC at North Fork Gunnison River below Raven Gulch, U.S. Geological Survey (USGS) site number 385553107243301. The methods used follow USGS guidance as referenced in relevant Office of Surface Water/Office of Water Quality Technical Memoranda and USGS Techniques and Methods, book 3, chap. C5 (Landers and others, 2016), and USGS Techniques and Methods, book 3, chap. C4 (Rasmussen and others, 2009). A total of 456 suspended-sediment samples were collected during the calibration period (45 cross-section and 411 single-station automatic pump samples). Thirty-nine samples (18 pump samples and 21 equal-width-interval (EWI) samples) with associated streamflow and turbidity were used in the model calibration dataset. These 39 samples were collected over the range of observed streamflow and turbidity conditions. Samples used in calibration were plotted on duration curve plots for streamflow from April 24, 2015 to October 7, 2017 and turbidity from April 24, 2015 to October 7, 2017. The plots indicate that samples were collected for the observed range of conditions at the site. Suspended-sediment concentrations at this site were computed from a calibrated regression model between SSC and turbidity. Streamflow, 2 frequencies of sediment corrected backscatter (SCB) (1.5 and 3.0 megahertz (MHz)), and 2 frequencies of sediment attenuation coefficient (SAC) (1.5 and 3.0 MHz) were also examined as potential variables but did not significantly improve the model. An ordinary least squares linear regression model was developed using the ‘stats’ and ‘smwrStats’ packages in R (R Core Team, 2018). Streamflow, SCB, SAC, and turbidity were examined as potential explanatory variables for estimating SSC. A natural log transformed turbidity was selected as the best explanatory variable.

                                            [
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                                            {
  "fn": "Rodney Richards",
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                                            [
  "Colorado",
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