Parsimonious sediment transport equations based on Bagnold's stream power approach

Abstract

It is increasingly recognized that effective river management requires a catchment scale approach. Sediment transport processes are relevant to a number of river functions but quantifying sediment fluxes at network scales is hampered by the difficulty of measuring the variables required for most sediment transport equations (e.g. shear stress, velocity, and flow depth). We develop new bedload and total load sediment transport equations based on specific stream power. These equations use data that are relatively easy to collect or estimate throughout stream networks using remote sensing and other available data: slope, discharge, channel width, and grain size. The new equations are parsimonious yet have similar accuracy to other, more established, alternatives. We further confirm previous findings that the dimensionless critical specific stream power for incipient particle motion is generally consistent across datasets, and that the uncertainty in this parameter has only a minor impact on calculated sediment transport rates. Finally, we test the new bedload transport equation by applying it in a simple channel incision model. Our model results are in close agreement to flume observations and can predict incision rates more accurately than a more complicated morphodynamic model. These new sediment transport equations are well suited for use at stream network scales, allowing quantification of this important process for river management applications. Copyright © 2017 John Wiley & Sons, Ltd.

Flood‐duration‐integrated stream power and frequency magnitude of >50‐year‐long sediment discharge out of a hyperarid watershed

“…To eliminate the subjective and sometimes arbitrarily derived “effective discharge” (e.g., Sichingabula, 1999), we propose the effective FISP (or EFISP) as a parameter for expressing the effectiveness of an event in performing “geomorphic work.” This parameter can be applied to all environments where flow data are available. Sediment yield from a watershed is a needed parameter in practical research and usually is represented by the mean annual sediment yield or by a range of sediment yields based on measurements during a variable number of years (e.g., Griffiths et al, 2006) or based on empirical equations (e.g., Lammers & Bledsoe, 2018). In hyperarid to semiarid environments, characterized by years with a minimum flow or no flow at all, the length of the measured data is crucial if a representative value of sediment yield is required.…”

Sediment Routing and Floodplain Exchange (SeRFE): A Spatially Explicit Model of Sediment Balance and Connectivity Through River Networks

“…SeRFE uses an empirical stream-power sediment transport equation (Lammers & Bledsoe, 2018b) to calculate sediment flux (q s ):…”

“…SeRFE uses an empirical stream‐power sediment transport equation (Lammers & Bledsoe, 2018b) to calculate sediment flux ( q s ): where q s is in ppm (multiplied by discharge and density to obtain a flux), D is a representative bed grain diameter (m), and q is unit discharge ( Q/w ).…”

“…To calculate ω c bed , we use the parsimonious approach developed by C. Parker et al (2011) and used by Lammers and Bledsoe (2018b). Nondimensional stream power ( ω c* ) is calculated as follows (after Einstein, 1941): where ρ s is sediment density.…”

Inter‐model Comparison of Turbidity‐Discharge Rating Curves and the Implications for Reservoir Operations Management

“…These limitations have motivated the development of physics‐based and statistical models that can estimate fluvial SS and T n . Physics‐based models use the governing equations of fate and transport to provide a detailed description of the space‐time distribution of SS and T n in the fluvial system (Lammers and Bledsoe 2018a, 2018b), but they often require significant amounts of data and calibration that can be difficult to satisfy (Öztürk et al 2001). Statistical models for predicting SS and T n are often more parsimonious, require less data, and are more easily calibrated to available data (Jain 2001).…”