Muddy sediments are abundant across aquatic ecosystems, consisting of mineral grains and biological material. Erosional characteristics of these cohesive sediments are impacted by micro-organisms providing bio-stabilisation. Deposition may be impacted by chemical and biological composition, along with turbulence properties, which in turn influence flocculation of suspended particulate matter. Flocculation processes affect settling velocity, porosity and density characteristics. Cohesive sediments absorb contaminants, as well as influencing interactive processes between sedimentary dynamics and hydrodynamics, through their bio-physical attributes. It is therefore beneficial to predict muddy sediment transport processes via numerical modelling. Accurate modelling relies on quantitative erosional and depositional data for calibration. Through collation and analysis of field- and laboratory-derived data sets, this study examined aspects of erodibility and deposition across several aquatic environments (including estuarine, intertidal and lake sediments). A range of case studies examined floc properties, sediment composition, erosion thresholds, turbulent shear stress and suspended particulate matter concentration. Investigation of floc dynamics in estuarine sediments revealed larger, faster settling flocs in muddy sediment (mean settling velocity, Wsmean = 4.1-5.2 mm.s⁻¹; mean floc effective density, ρe,mean = 317-352 kg.m⁻³). In mixed sediment, flocs were smaller and settled more slowly (Wsmean = 3.8-4.0 mm.s⁻¹; ρe,mean = 288-508 kg.m⁻³). Comparison of oil-contaminated sediments revealed the importance of floc size class and mineral type. On the addition of oil, larger, faster settling flocs were produced in pure bentonite cases, while smaller, slower settling flocs were observed in kaolinite cases. In highly organic lake sediments (organic content = 62%), settling velocity varied over increasing suspended sediment concentration and between floc size classes (macroflocs faster than microflocs by 0.95 mm.s⁻¹). Such findings may be utilised to increase the understanding of complex sedimentary and hydrodynamic interactions within aquatic environments. This study provides quantitative data, applicable to the improvement of predictive numerical model reliability.