Modeling Riverbed Elevation and Bedload Tracer Transport Resting Times Using Fractional Laplace Motion

Author:

Wu Zi1234ORCID,Singh Arvind4ORCID

Affiliation:

1. State Key Laboratory of Hydroscience and Engineering Tsinghua University Beijing China

2. Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources Tsinghua University Beijing China

3. Department of Hydraulic Engineering Tsinghua University Beijing China

4. Department of Civil, Environmental, and Construction Engineering University of Central Florida Orlando FL USA

Abstract

AbstractRiverbed elevations play a crucial role in sediment transport and flow resistance, making it essential to understand and quantify their effects. This knowledge is vital for various fields, including river engineering and stream ecology. Previous observations have revealed that fluctuations in the bed surface can exhibit both multifractal and monofractal behaviors. Specifically, the probability distribution function (PDF) of elevation increments may transition from Laplace (two‐sided exponential) to Gaussian with increasing scales or consistently remain Gaussian, respectively. These differences at the finest timescale lead to distinct patterns of bedload particle exchange with the bed surface, thereby influencing particle resting times and streamwise transport. In this paper, we utilize the fractional Laplace motion (FLM) model to analyze riverbed elevation series, demonstrating its capability to capture both mono‐ and multi‐fractal behaviors. Our focus is on studying the resting time distribution of bedload particles during downstream transport, with the FLM model primarily parameterized based on the Laplace distribution of increments PDF at the finest timescale. Resting times are extracted from the bed elevation series by identifying pairs of adjacent deposition and entrainment events at the same elevation. We demonstrate that in cases of insufficient data series length, the FLM model robustly estimates the tail exponent of the resting time distribution. Notably, the tail of the exceedance probability distribution of resting times is much heavier for experimental measurements displaying Laplace increments PDF at the finest scale, compared to previous studies observing Gaussian PDF for bed elevation.

Funder

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

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