Suspended sediment and bedload data, simple linear regression models, loads, elevation data, and FaSTMECH models for Rice Creek, Minnesota, 2010-2019
Dates
Publication Date
2022-03-02
Start Date
2010
End Date
2019
Citation
Groten, J.T., Livdahl, C.T., and DeLong, S.B., 2022, Suspended sediment and bedload data, simple linear regression models, loads, elevation data, and FaSTMECH models for Rice Creek, Minnesota, 2010-2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9SJIY32.
Summary
A series of simple linear regression models were developed for the U.S. Geological Survey (USGS) streamgage at Rice Creek below Highway 8 in Mounds View, Minnesota (USGS station number 05288580). The simple linear regression models were calibrated using streamflow data to estimate suspended-sediment (total, fines, and sands) and bedload. Data were collected during water years 2010, 2011, 2014, 2018, and 2019. The estimates from the simple linear regressions were used to calculate loads for water years 2010 through 2019. The calibrated simple linear regression models were used to improve understanding of sediment transport processes and increase accuracy of estimating sediment and loads for Rice Creek. Two multidimensional flow and [...]
Summary
A series of simple linear regression models were developed for the U.S. Geological Survey (USGS) streamgage at Rice Creek below Highway 8 in Mounds View, Minnesota (USGS station number 05288580). The simple linear regression models were calibrated using streamflow data to estimate suspended-sediment (total, fines, and sands) and bedload. Data were collected during water years 2010, 2011, 2014, 2018, and 2019. The estimates from the simple linear regressions were used to calculate loads for water years 2010 through 2019. The calibrated simple linear regression models were used to improve understanding of sediment transport processes and increase accuracy of estimating sediment and loads for Rice Creek. Two multidimensional flow and models were developed with the International River Interface Cooperative (iRIC) software and Flow and Sediment Transport with Morphological Evolution of Channels (FaSTMECH) solver. These models were developed with elevation data from terrestrial laser scanning, airborne light detection and ranging, acoustic Doppler current profiler, total station, and real-time kinematic global navigation satellite system. The models were calibrated, validated, and run for multiple streamflow scenarios to compare an original section to a restored section of Rice Creek. All contents of this data release are part of the associated report, U.S. Geological Survey Scientific Investigations Report 2022–5004 (https://doi.org/10.3133/sir20225004).
Click on title to download individual files attached to this item.
usgsdatarelease.xml Original FGDC Metadata
View
10.13 KB
application/fgdc+xml
data_SLR_models_loads.zip
622.74 KB
application/zip
data_FaSTMECH_models.zip
262.39 MB
application/zip
Related External Resources
Type: Related Primary Publication
Groten, J.T., Livdahl, C.T., DeLong, S.B., Lund, J.W., Nelson, J.M., Coenen, E.N., Ziegeweid, J.R., and Kocian, M.J., 2022, Sediment monitoring and streamflow modeling before and after a stream restoration in Rice Creek, Minnesota, 2010–2019: U.S. Geological Survey Scientific Investigations Report 2022–5004, 40 p., https://doi.org/10.3133/sir20225004.
These simple linear regression models were created to increase the accuracy of estimating suspended sediment, bedload, and total loads at Rice Creek, Minnesota. The multidimensional flow models were developed to compare an original section to a restored section of Rice Creek.
