A series of linear regression models were developed and calibrated for two Lower Minnesota River sites. The linear regression models were either calibrated using acoustic or streamflow data to estimate suspended-sediment or sand concentration data. Data were collected during calendar years 2012 through 2019. The estimates of suspended-sediment and concentrations from the linear regression were used to calculate loads. The calibrated models were used to improve understanding of sediment and sand transport processes and increase accuracy of estimating sediment and sand concentrations and loads for the Lower Minnesota River, as part of the associated report, U.S. Geological Survey Open File Report 2021–1005 (https://doi.org/10.3133/ofr20211005).

A simple linear regression model was developed and calibrated for the Minnesota River at Mankato, Minnesota (Site Number: 05325000). The linear regression model was calibrated using acoustic and suspended-sediment concentration data collected from 2016 through 2019.The calibrated model will be used to improve understanding of sediment transport processes and increase accuracy of estimating sediment concentrations and loads for the Minnesota River at Mankato, Minnesota.

A simple linear regression model was developed and calibrated for the Minnesota River at Mankato, Minnesota (Site Number: 05325000). The linear regression model was calibrated using acoustic and suspended-sediment concentration data collected from 2016 through 2019.The calibrated model will be used to improve understanding of sediment transport processes and increase accuracy of estimating sediment concentrations and loads for the Minnesota River at Mankato, Minnesota.

A series of linear regression models were developed and calibrated for the Minnesota and Mississippi Rivers. The linear regression models were calibrated using acoustic and suspended-sediment concentration data collected from March through November 2016 and 2017. The estimates of suspended-sediment concentrations from the simple linear regression were used to calculate loads. The calibrated models were used to improve understanding of sediment transport processes and increase accuracy of estimating sediment concentrations and loads for the Lower Minnesota River, the Mississippi River, and Lake Pepin.

A series of linear regression models were developed and calibrated for the Minnesota and Mississippi Rivers. The linear regression models were calibrated using acoustic and suspended-sediment concentration data collected from March through November 2016 and 2017. The estimates of suspended-sediment concentrations from the simple linear regression were used to calculate loads. The calibrated models were used to improve understanding of sediment transport processes and increase accuracy of estimating sediment concentrations and loads for the Lower Minnesota River, the Mississippi River, and Lake Pepin.

This model archive summary documents the suspended-sediment concentration (SSC) model developed to estimate 15-minute SSC at Muddy Creek above Paonia Reservoir, U.S. Geological Survey (USGS) site number 385903107210800. The methods used follow USGS guidance as referenced in relevant Office of Surface Water Technical Memorandum (TM) 2016.07 and Office of Water Quality TM 2016.10, and USGS Techniques and Methods, book 3, chap. C5 (Landers and others, 2016). A total of 438 suspended-sediment samples were collected during the calibration period. Forty-one of these samples (22 equal-width-interval [EWI] samples and 19 single-point pump samples) were used in the model calibration dataset. These 41 samples were collected over the range of observed streamflow, Sediment Corrected Backscatter (SCB), and Sediment Attenuation Coefficient (SAC) conditions. Samples used in calibration were plotted on duration curve plots for streamflow from March 2005 to November 2016 (Colorado Division of Water Resources data from 2005 to 2014, and USGS data for 2015–16), and SAC for the period of record. 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 SAC. Streamflow, SCB, dummy variables, and seasonality were also examined as potential variables. An ordinary least squares linear regression model was developed using the ‘stats’ and ‘smwrStats’ packages in R (R Core Team, 2018). Streamflow, SCB, SAC, dummy variable, and seasonality were examined as potential explanatory variables for estimating SSC. A square root transformed SAC was selected as the explanatory variable.

This model archive summary documents the suspended-sediment concentration (SSC) model developed to estimate 15-minute SSC at Muddy Creek above Paonia Reservoir, U.S. Geological Survey (USGS) site number 385903107210800. The methods used follow USGS guidance as referenced in relevant Office of Surface Water Technical Memorandum (TM) 2016.07 and Office of Water Quality TM 2016.10, and USGS Techniques and Methods, book 3, chap. C5 (Landers and others, 2016). A total of 438 suspended-sediment samples were collected during the calibration period. Forty-one of these samples (22 equal-width-interval [EWI] samples and 19 single-point pump samples) were used in the model calibration dataset. These 41 samples were collected over the range of observed streamflow, Sediment Corrected Backscatter (SCB), and Sediment Attenuation Coefficient (SAC) conditions. Samples used in calibration were plotted on duration curve plots for streamflow from March 2005 to November 2016 (Colorado Division of Water Resources data from 2005 to 2014, and USGS data for 2015–16), and SAC for the period of record. 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 SAC. Streamflow, SCB, dummy variables, and seasonality were also examined as potential variables. An ordinary least squares linear regression model was developed using the ‘stats’ and ‘smwrStats’ packages in R (R Core Team, 2018). Streamflow, SCB, SAC, dummy variable, and seasonality were examined as potential explanatory variables for estimating SSC. A square root transformed SAC was selected as the explanatory variable.

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.

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.

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.