Input‐variable sensitivity assessment for sediment transport relations

Abstract

A methodology to assess input‐variable sensitivity for sediment transport relations is presented. The Mean Value First Order Second Moment Method (MVFOSM) is applied to two bed load transport equations showing that it may be used to rank all input variables in terms of how their specific variance affects the overall variance of the sediment transport estimation. In sites where data are scarce or nonexistent, the results obtained may be used to (i) determine what variables would have the largest impact when estimating sediment loads in the absence of field observations and (ii) design field campaigns to specifically measure those variables for which a given transport equation is most sensitive; in sites where data are readily available, the results would allow quantifying the effect that the variance associated with each input variable has on the variance of the sediment transport estimates. An application of the method to two transport relations using data from a tropical mountain river in Costa Rica is implemented to exemplify the potential of the method in places where input data are limited. Results are compared against Monte Carlo simulations to assess the reliability of the method and validate its results. For both of the sediment transport relations used in the sensitivity analysis, accurate knowledge of sediment size was found to have more impact on sediment transport predictions than precise knowledge of other input variables such as channel slope and flow discharge.

Virtual Velocity Approach for Estimating Bed Material Transport in Gravel‐Bed Rivers: Key Factors and Significance

“…The significant variations (between −24% and −94%) obtained introducing geomorphic unit specific D 50 to calculate the τ* active on the streambed lead to consider sediments grain size as the most important factor influencing the approach application. Fernández and Garcia () achieved similar results by a theoretical sensitivity assessment for two sediment transport relations concluding that an accurate knowledge of sediment size has more impact on transport predictions than other input variables. The local grain size represents in fact a fundamental variable in the τ* calculation (equation ).…”

“…It is widely accepted that using formulas for estimating bed material transport can led to calculation errors (e.g., Barry et al, ; Fernández & Garcia, ), and other techniques are currently poorly available for large gravel‐bed rivers (the morphological approach represents the only exception, although its application has some constrains). The virtual velocity approach provides an alternative to trapping techniques (difficult to employ in wide complex rivers) that incorporates some key factors of channel morphology and processes, and it is strongly based on field data.…”

Adaptation of river channels to a wetter or drier climate: Insights from the Lower Pilcomayo River, South America

“…As an example, the range of values of Chézy's coefficient considered in the calibration runs was derived from the results of previous hydrodynamic computations performed using Manning's coefficient (Baldissone et al, 2013;Brea et al, 2014;Testa Tacchino, 2015) concluding that the values to be used for the Pilcomayo River in the study area fall between 0.018 s/m 1/3 and 0.020 s/m 1/3 , which correspond to Chézy's coefficients in the range of 50 to 65 m 1/2 /s, with the highest values for high flow conditions. The sediment particle diameter was optimized by considering only values falling within the measured range to comply with the estimated value of total yearly sediment transport (Fernández and Garcia, 2017). The calibration phase also included the selection of the sediment transport formula.…”

An Attempt to Take Into Account Natural Variability in 1D Bedload Prediction

“…Computational methods have been developed that allow one to account for spatial variability in multidimensional numerical models (Cienciala & Hassan, 2016; Schuurman et al., 2013; Segura & Pitlick, 2015). However, this practice is limited to qualified practitioners, and because the standard of practice still remains on the use of 1D modeling with reach averaged data, the variability must be accounted for in the bedload equations themselves (Fernández & Garcia, 2017). First attempts toward a better consideration of the complexity of natural systems have focused on a better consideration of the grain‐size distribution [GSD] through the development of fractional equations computing bedload not only for a single characteristic grain size but for all sizes representative of each class present at the bed surface (Parker, 1990; Parker & Klingeman, 1982; Wilcock & Crowe, 2003).…”