{"title":"Modeling Riverbed Elevation and Bedload Tracer Transport Resting Times Using Fractional Laplace Motion","authors":"Zi Wu, Arvind Singh","doi":"10.1029/2024JF007771","DOIUrl":null,"url":null,"abstract":"<p>Riverbed 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.</p>","PeriodicalId":15887,"journal":{"name":"Journal of Geophysical Research: Earth Surface","volume":"129 7","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Geophysical Research: Earth Surface","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1029/2024JF007771","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Riverbed 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.
河床高程在泥沙输运和水流阻力方面起着至关重要的作用,因此了解和量化河床高程的影响至关重要。这些知识对河流工程学和溪流生态学等多个领域都至关重要。以往的观测结果表明,河床表面的波动既可以表现为多分形,也可以表现为单分形。具体来说,随着尺度的增大,海拔增量的概率分布函数(PDF)可能从拉普拉斯(双面指数)过渡到高斯分布函数,也可能始终保持高斯分布函数。这些最细时间尺度上的差异会导致不同的床面颗粒交换模式,从而影响颗粒的静止时间和流向传输。在本文中,我们利用分数拉普拉斯运动(FLM)模型来分析河床高程序列,展示了其捕捉单分形和多分形行为的能力。我们的重点是研究下游输运过程中河床颗粒的静止时间分布,FLM 模型的参数主要基于最细时间尺度上增量 PDF 的拉普拉斯分布。通过识别同一海拔高度的相邻沉积和夹带事件对,从河床海拔高度序列中提取静止时间。我们证明,在数据序列长度不足的情况下,FLM 模型能稳健地估计静止时间分布的尾部指数。值得注意的是,在最细尺度上显示拉普拉斯增量PDF的实验测量中,静止时间超标概率分布的尾部要重得多,而之前的研究观察到的是床面高程的高斯PDF。