Constraining the Distribution of 3D Fractal Structures in Mud Flocs

Mud builds coastal landscapes and carries pollutants and carbon, yet predicting its transport is difficult because mud aggregates into flocs that control settling rates. Flocs are typically characterized using fractal theory with a constant fractal dimension , inferred from aggregated size–settling velocity data, despite diverse fractal structures. We conducted experiments on freshwater flocs under varied shear stress and organic concentration to characterize this diversity. Rather than the conventional approach of aggregating settling data to infer , we grouped measurements by the two‐dimensional (2D) box‐counting dimension from particle tracking, partially resolving structural variability. This stratified method reveals that increases with shear velocity and decreases with organic matter (OM) content, trends obscured by aggregation. The stratified analysis resolves variations of 0.2–0.35 units across experimental conditions, whereas conventional aggregation reduces this range roughly fivefold. The inferred primary particle diameter co‐varies with these conditions, and the concurrent variation of , , and floc size introduces competing effects on settling velocity. Our approach yields lower values (1.6–2.1) than the canonical range, which propagates nonlinearly into settling rates, implying that mud flocs are more porous and settle more slowly than expected. We also find that the 2D‐to‐3D relationship can be described by a nonlinear model, consistent with empirical conversions for synthetic aggregates. Overall, our results indicate that mud retention in floodplains and deltas is less efficient than current floc models assume, at least in the freshwater conditions we analyzed, with implications for sediment budgets, contaminant dispersal, and coastal landscape evolution.