The team collected surface sediment samples from the inner shelf before and after the monsoon and analyzed their sand, silt, and clay fractions. Inverse Distance Weighting interpolation in ArcGIS, run with a power parameter of two and a twelve-point fixed-radius neighborhood, transformed these point measurements into continuous maps of seabed texture. The results are striking: sand concentrations reach 99 percent in high-energy offshore zones during the pre-monsoon period and 98 percent after the monsoon, while silt climbs to 69 percent pre-monsoon and 67 percent post-monsoon in sheltered nearshore and estuarine settings. Clay, the finest fraction, peaks at 11 percent before the monsoon and 8 percent after it. These patterns trace the boundary between energetic open waters that winnow away fines and calm, protected embayments where fine particles settle out.

Grain-size statistics, computed with the Folk and Ward logarithmic method using GRADISTAT v9.1, add texture to the picture. Mean grain sizes ranged from 1.533 to 3.767 phi before the monsoon and from 1.267 to 4.233 phi afterward, spanning medium to very fine sands. Most samples were poorly sorted, about 70 percent pre-monsoon and 76.6 percent post-monsoon, a signature of deposition under fluctuating currents and river influence. Skewness values, ranging from minus 0.620 to 0.614 phi pre-monsoon and minus 0.425 to 0.496 phi post-monsoon, were predominantly positive, indicating the accumulation of fine sediments under low-energy conditions. Localized negative skewness at a handful of nearshore and offshore stations betrayed pockets of active winnowing, where waves strip away fines and leave coarser grains behind.

Correlation analysis quantified the hydraulic sorting that governs the system. Sand and silt displayed a strong inverse relationship, with coefficients near minus 0.97 after reprocessing, and sand and clay correlated negatively at roughly minus 0.84 to minus 0.88. Silt and clay, by contrast, co-occurred strongly, with positive correlations of about 0.79 to 0.85, reflecting their shared fate in low-energy depositional environments such as estuarine mouths and tidal flats. Notably, the authors report that an initially tabulated perfect inverse correlation of minus 1.00 between sand and silt turned out to be an artifact of output rounding, and the corrected values did not change the study’s central interpretation. Most telling of all, inter-seasonal correlations were exceptionally high, with pre- and post-monsoon sand fractions matching at roughly 0.95 and silt fractions at 0.96, evidence that the monsoon redistributes sediment without dismantling the underlying spatial order.

To disentangle the sediment populations driving these patterns, the team applied End-Member Modelling Analysis using the EMMAgeo package in R. Three statistically robust end members emerged. The first, peaking near 1.0 phi, represents coarse to medium sand winnowed and transported by high-energy offshore wave action; its contribution fell from 43 percent pre-monsoon to 31 percent post-monsoon as hydrodynamic energy waned. The second, peaking near 2.5 phi, is fine sand redistributed by littoral drift and mid-shelf reworking, and it held nearly steady at 38 to 34 percent across seasons, marking it as a year-round player. The third end member, peaking near 5.0 phi, captures silt-clay mixtures deposited in quiet estuarine and nearshore waters, and its share nearly doubled, rising from 19 percent to 35 percent after the monsoon, consistent with monsoon-fed river discharge delivering suspended fines while wave reworking diminished.

Canonical Correlation Analysis sharpened the seasonal contrast. Pre-monsoon samples clustered tightly within narrow canonical score ranges, indicating a uniform sand-silt-clay distribution under steady wave energy and limited river input. Post-monsoon samples, however, spread widely across the score plot, a statistical fingerprint of the turbulence unleashed by monsoon waves, increased river discharge, and runoff that resuspend seabed sediments and sweep finer particles shoreward. Complementary Empirical Orthogonal Function analysis of the interpolated sand grids identified two dominant modes explaining about 65 percent of total variance: an offshore-onshore gradient accounting for roughly 45 percent, and a nearshore fine-sediment accumulation zone adjacent to the estuarine mouth contributing about 20 percent, with stronger expression after the monsoon.

Principal Component Analysis and Hierarchical Cluster Analysis then distilled the dataset into its essential geography. The first two principal components explained 48.0 and 26.6 percent of variance respectively, a cumulative 74.6 percent, and the analysis passed standard suitability checks with a Kaiser-Meyer-Olkin value of 0.81 and a significant Bartlett’s test of sphericity. High positive scores on the first component marked sand-dominated, high-energy shelf environments, while the second component separated sites by finer distinctions between clay and silt. Cluster analysis, using Ward’s linkage and Euclidean distance, grouped samples into three recurring settings: sand-rich high-energy zones exposed to wave action, transitional sand-silt environments shaped by littoral drift and estuarine mixing, and fine-rich, low-energy mud deposits near estuarine mouths. With a cophenetic correlation coefficient of 0.89 and 86.7 percent of samples retaining consistent cluster membership across seasons, these three depositional provinces proved to be the enduring architecture of the Nagapattinam inner shelf.

The study also sounds a note of caution about human fingerprints on the seabed. Stations near jetties, seawalls, and harbor structures consistently fell within transitional clusters, showing elevated silt and clay contents and poorer sorting, hallmarks of sediment trapped on the updrift side of structures and starved supply downdrift. Dredging near fishing harbors resuspends fine sediments that later settle in sheltered nearshore and estuarine zones, contributing to the fine-fraction anomalies visible in the interpolation maps. The region’s new jetty for transporting offshore oil and gas adds another engineered element to a coastline already juggling natural and anthropogenic pressures. Model performance metrics confirmed that predictions were most reliable for sand, with an R-squared of 0.91 pre-monsoon, while clay showed the largest relative errors, at 48.3 percent pre-monsoon and 55.8 percent post-monsoon, reflecting the challenge of tracking suspended fines.

The authors acknowledge that a two-season sampling design cannot capture disturbances from cyclones, storm surges, or extreme river discharge events, and they call for continued monitoring, sediment-transport modeling, and wave-climate analysis using wave-rose diagrams. Still, the takeaway is compelling for coastal managers and ecologists alike: the Nagapattinam inner shelf is not a chaotic system at the mercy of the monsoon but a structured one, in which seasonal pulses of energy and river discharge redistribute sediment within a stable, three-part granulometric framework. That stability, maintained by persistent wave regimes, littoral drift, and the steady delivery of Cauvery sediments, provides a baseline against which future erosion, habitat change, and infrastructure impacts can be measured, in a monsoon-dominated coastal segment that has, until now, lacked such a detailed assessment.

Subject of Research: Seasonal sediment dynamics and grain-size distribution on the Nagapattinam inner shelf of the Cauvery basin, southern India

Article Title: Seasonal variations of inner shelf sediment dynamics and granulometric characteristics along the Nagapattinam coast of the Cauvery basin, Southern India

Article References: Pitchaimani, V. S., Abishek, S. R., Joe, R. J. J., & Promilton, A. A. A. (2026). Seasonal variations of inner shelf sediment dynamics and granulometric characteristics along the Nagapattinam coast of the Cauvery basin, Southern India. Discover Oceans, 3(1), Article 4. https://doi.org/10.1007/s44289-025-00109-x

Image Credits: AI Generated

DOI: 10.1007/s44289-025-00109-x

Keywords: sediment dynamics, granulometric analysis, Nagapattinam coast, Cauvery basin, monsoon, inner shelf, grain size analysis, IDW interpolation, end-member modelling, PCA, coastal geomorphology, Bay of Bengal