Abstract

AbstractBedload transport is a key process in fluvial morphodynamics, but difficult to measure. The advent of seismic monitoring techniques has provided an alternative to in‐stream monitoring, which is often costly and cannot be utilized during large floods. Seismic monitoring is a method requiring several steps to convert seismic data into bedload flux data. State‐of‐the‐art conversion approaches exploit physical models predicting the seismic signal generated by bedload transport. However, due to a lack of well‐constrained validation data, the accuracy of the resulting inversions is unknown. We use field experiments to constrain a seismic bedload model and compare the results to high‐quality independent bedload measurements. Constraining the Green's function (i.e., seismic ground properties) with an active seismic survey resulted in an average absolute difference between modeled and empirically measured seismic bedload power of 11 dB in the relevant frequency band. Using generically estimated Green's function parameters resulted in a difference of 20 dB, thus highlighting the importance of using actual field parameters. Water turbulence and grain hiding are unlikely to be the cause of differences between field observations and our analysis. Rather, they may be either due to the inverted model being particularly sensitive to the coarse tail of the grain‐size distribution, which is least well constrained from field observations, or due to the seismic model underestimating effects of the largest grains.

Highlights

  • Rivers are key features of ecosystems, transferring water, dissolved, and particulate matter across the Earth's surface

  • The method used in Phase 3 steadily recorded higher bedload fluxes in comparison to the measurements in the other two phases, by an average of 79% compared with method two during Phase 2

  • Inverting seismic data using a physical model of bedload transport (Tsai et al, 2012) to estimate bedload flux provides a promising approach in river monitoring with several advantages over in-stream monitoring concepts, including cost, accessibility and small demands on infrastructure and staff

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Summary

Introduction

Rivers are key features of ecosystems, transferring water, dissolved, and particulate matter across the Earth's surface. Driven by the power of moving water, sediment helps rivers to shape landscapes and contribute to the evolution of river morphology (Leopold et al, 1964). Sediment in rivers is either carried as suspended load or as bedload (rolling, sliding, or saltating on the bed). Bedload contributes to channel changes, such as creating micro- and macroforms, narrowing, widening, shifting, aggrading, and degrading. It affects riverbed and bank stability (Little & Mayer, 1976). Predictive models are needed to correctly constrain and understand the evolution of river morphology

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