Abandoned channels are essential in the Quaternary floodplains, and their infill contains different paleoenvironment recorders. Grain-size distribution (GSD) is one proxy that helps characterize the alluviation and associated sedimentological processes of the abandoned channels. The classic statistical methods of the grain-size analysis provide insufficient information on the whole distribution; this necessitates a more comprehensive approach. Grain-size endmember modeling (EMM) is one approach beyond the traditional procedures that helps unmix the GSDs. This study describes the changes in the depositional process by unmixing the GSDs of a Holocene abandoned channel through parameterized EMM integrated with lithofacies, age–depth model, loss-on-ignition (LOI), and magnetic susceptibility (MS). This approach effectively enabled the quantification and characterization of up to four endmembers (EM1-4); the characteristics of grain-size endmembers imply changes in sedimentary environments since 8000 BP. EM1 is mainly clay and very fine silt, representing the fine component of the distribution corresponding to the background of quiet water sedimentation of the lacustrine phase. EM2 and EM3 are the intermediate components representing the distal overbank deposits of the flood. EM4 is dominated by coarse silt and very fine sand, representing deposition of overbank flow during the flood periods. This paper demonstrates that the parametrized grain-size EMM is reasonable in characterizing abandoned channel infill sedimentary depositional and sedimentation history.

Traditional approaches to fluvial sedimentary analysis often face challenges in deciphering complex, multivariate datasets. This study combines compositional data analysis (CoDA) with principal component analysis (PCA) to enhance the characterization of depositional processes and sub-environments within the Shendi Formation. The PCA applied to centered log ratio (clr)-transformed grain size distributions, reveals three principal components with ~ 91.86% of the data variance explained, representing distinct processes: bedload-dominated channel-bar dynamics, overbank deposition, and high-energy flood events. Specific lithofacies associations strongly correlate to each principal component. This integrated approach enables the identification of subtle yet significant patterns within the complex sedimentological record. The Shendi Formation exhibits characteristics of a dynamic fluvial setting with variations in flow energy, channel migration, and periodic flooding. Our findings demonstrate the power of CoDA-PCA in refining the understanding of fluvial depositional systems and highlight its potential for broader applications.

Grain size distribution is one of the paleoenvironmental proxies that provide insight statistical distribution of size fractions within the sediments. Multivariate statistics have been used to investigate the depositional process from the grain size distribution. Still, the direct application of the standard multivariate methods is not straightforward and can yield misleading interpretations due to the compositional nature of the raw grain size data. This paper is a methodological framework for grain size data characterization through the centered log ratio transformation and euclidean data, coupled with principal component analysis, cluster analysis, and linear discriminant analysis to examine Quaternary sediments from Tövises bed in the southeast Great Hungarian Plain. These approaches provide statistically significant and sedimentologically interpretable results for both datasets. However, the details by which they supplemented the conceptual model were significantly different, and this discrepancy resulted in a different temporal model of the depositional history.

To obtain the multiproxy paleoenvironmental dataset from southeast Great Hungarian Plain (GHP), 345 sediment samples were collected at one cm intervals from the cores retrieved from Tovisies bed paleochannel, and six samples were analyzed for 14C dates. The obtained radiocarbon dates were calibrated to calendar ages using the IntCal20 calibration curve. Bayesian statistics within the R bacon 2.5.8 age-depth modeling package were used to establish the age-depth model and it represented the time frame for the entire dataset. The obtained polymodal grain size distribution (GSD) data was unmixed into four EMs using the AnalySize v.1.2.0 algorithm, employing the built-in General Weibull function which helped explain the dynamicity of the endmembers' sedimentation process. To understand the alluviation history, the endmember abundances were correlated with LOI55, LOI950, and magnetic susceptibility. The dataset presented in this article could be of potential reuse for studying the spatial-temporal environmental changes and in geoarchaeological research, providing insights into how human societies adapted to environmental shifts across the southeast GHP.