Applied Sciences (Jul 2023)
Improving Grain Size Analysis to Characterize Sedimentary Processes in a Low-Energy River: A Case Study of the Charente River (Southwest France)
Abstract
The recognition and quantification of fluvial transport and depositional processes has widely been studied. However, few works have focused on the interpretation and quantification of sedimentary processes in low-energy fluvial environments. This paper features and compares the results of five methods of grain size data processing (statistic moments, textural analysis, multivariate statistics combining Principal Component Analysis and hierarchical cluster analysis, and CM image and end-member modeling analysis) and discusses their efficiencies in characterizing low-energy alluvial plain deposits. These environments are characterized by fine grain size, high-homogeneity deposits at the macroscopic scale, and low grain size variability, hence presenting a difficulty in identifying and splitting an apparently homogeneous sedimentary record into sedimentary sequences. These statistical methods are applied on a ~9 m long core extracted from the fluvial island of la Baine located in the downstream section of the Charente River (Chaniers, Charente-Maritime, France). In the light of these results, elementary statistical parameters (statistical moments, modes, and sorting index) have limited interest in the sedimentary description and interpretation of fine fluvial deposits. Textural analyses are more informative but highly dependent on the classification scheme. Only the multivariate statistics approach and end-member modeling analysis present interesting results and allow the robust identification of sub-units. However, multivariate statistics results are dependent on the choice of input variables and do not support non-zero values, while the second method, the most recent and complex one, needs further developments to clearly connect end-member classes to sedimentary processes.
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