Abstract
A new method for granulometric-parameter-based reconstruction of sediment-transport pathways is proposed and is termed P-GSTA (grain-size trend analysis using principal component analysis) herein. The main advantage of this method is its applicability to depositional environments involving mixed transport processes, for instance, fluvial, tidal, and wave-influenced environments. In the P-GSTA method, a linear function with all significant granulometric parameters that are summed with different weighting factors was used to infer sediment-transport direction (sediment flux pattern); the previous grain-size trend analysis (GSTA) methods considered only three parameters (mean grain size, sorting, and skewness) with equal weighting. This study chose six parameters (namely, median grain size, coefficient of variation, skewness, kurtosis, and mud and gravel log-ratios) for calculation. First, the zero values of mud and gravel fractions are replaced, and their log-ratios are defined. Then, all values are standardized. Thereafter, principal component analysis (PCA) is conducted to determine the weighting factor of each granulometric parameter. Each principal component is then interpreted, and the function best representing a sediment flux pattern is chosen from these components. Trend vectors are calculated, solely on the basis of a map interpolated from the scores of the chosen principal component, as the two-dimensional gradient of this value. The P-GSTA method proposed in this study was applied to a modern microtidal coast (tidal sand flat along the Kushida River Delta, central Japan). Sediment-transport pathways reconstructed by this method were consistent with observed sediment-transport patterns determined by field experiments using tracer sediments and geomorphologic observation; the results of the previous GSTA method were inconsistent with the observations. The proposed method also revealed additional minor depositional processes on the sand flat, namely, the deposition of fluvial-channel lags and muddy particles. Thus, this study demonstrates that the proposed P-GSTA method is a potentially powerful tool to reconstruct sediment-transport patterns even under mixed transport processes, where the estimation of the sediment-transport function is difficult.
Highlights
Depositional systems are consequences of sediment dispersal from their provenances
In contrast with the results obtained using the previous grain-size trend analysis (GSTA) method, the grain-size trend analyzed using the Grain-size trend analysis using principal component analysis (P-GSTA) method closely matches the actual sedimenttransport estimated from the tracer experiments and geomorphological features observed soon after fluvial flooding events (Gao and Collins 1992; Asselman 1999; Fig. 12)
The previous GSTA method performs quite poorly, in that no trend vector is defined around the braided channels, despite direct observations of the sediment transport by a fluvial flooding event in July 2011 (Figs. 3, 4, 5, and 12)
Summary
Depositional systems are consequences of sediment dispersal from their provenances. Progressive and selective transport of grains differentiates lithofacies deposited in different sedimentary environments (Swift et al 2003). Many researchers have attempted to identify spatial variations of granulometric parameters, which have been referred to as ‘grain-size trends,’ associated with net sedimenttransport pathways (McCave 1978; McLaren 1981; Le Roux and Rojas 2007) This is because methods to reconstruct sediment-transport pathways from granulometric patterns are inexpensive and available compared to direct measurements of sediment movement. In order to address this disparity, McLaren and Bowles (1985) discussed the combination of three parameters (mean grain size, sorting, and skewness) on the basis of flume experiments and statistical calculations They concluded that sediments always become better sorted during transport, whereas the mean grain size and skewness vary with specific combination patterns (FB−: finer mean grain size, better sorting value, and greater negative skewness; or CB+: coarser mean grain size, better sorting value, and greater positive skewness)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
