Performance of Bedload Sediment Transport Formulas Applied to the Lower Minnesota River

Despite limitations in reproducing complex bedload sediment transport processes in rivers, formulas have been preferred over collection and analysis of field data due to the high cost and time-consuming nature of bedload discharge measurements. However, the performance of such formulas depends on the hydraulic and sedimentological conditions they attempt to describe. The availability of field measurements provides a unique opportunity to test bedload transport formulas to better guide formula selection. Hydraulic parameters and bedload discharge data from the Lower Minnesota River and two of its tributaries were used to evaluate nine bedload transport formulas using three different indices. The bedload data for the different sites were collected by the United States Geological Survey (USGS) from 2011 through 2014, with bed material varying from very coarse to medium sand. The formulas calculated higher bedload rates than were measured due to a combination of site-specific physical characteristics, including the presence of bed forms (dunes), and sampling uncertainties. Because of the lack of reproducibility of the tested formulas, five power functions, based on the relation between the specific unit power (independent hydraulic variable) and the USGS measured data (dependent variable), were derived as provisional equations to estimate the bedload discharge on the Lower Minnesota River and tributaries.

Automated summary

Although formulas are preferred over field data collection and analysis for bedload transport due to cost and time constraints, the performance of these formulas is dependent on the hydraulic and sedimentological conditions of the site. In this study, nine bedload transport formulas were evaluated using field measurements from the Lower Minnesota River and two tributaries, resulting in the derivation of provisional equations based on power functions for estimating bedload discharge. However, the formulas calculated higher bedload rates compared to the measured values due to site-specific physical characteristics and sampling uncertainties.