Flocculation of Riverine Sediment Draining to the Great Barrier Reef, Implications for Monitoring and Modeling of Sediment Dispersal Across Continental Shelves

Sediment transport models, utilized to guide land management in the Great Barrier Reef (GBR), assume settling velocities for individual silt and clay particles on the order of 0.01 mm/s; however, silts and clays once flocculated exhibit settling velocities on the order of 1 mm/s. In this study, in-situ (n = 144,912) and laboratory-dispersed (n = 64) particle size measurements collected using laser diffractometry were compared from nine rivers discharging along 800 km of GBR coastline to determine the extent of in-situ flocculation. Environmental controls on in-situ particle size were investigated using decision tree algorithms trained on coeval measurements of salinity, shear rate, and turbidity. Comparison of in-situ and dispersed particle size measurements demonstrate that suspended-sediment across all catchments flocculated into larger aggregates with an order-of-magnitude difference in median particle size between in-situ (D-50v = 132 mu m, sigma = 60 mu m for all data) and dispersed (D-50v = 15 mu m, sigma = 11 mu m for all data) particles. Machine learning algorithms showed excellent promise predicting various measures of in-situ particle size. Model validation R-2 ranged from 0.72 to 0.99, inclusion of catchment as a categorical variable only marginally (<1%) increased R-2. These results demonstrate that flocculation is prevalent across all rivers surveyed and that hydrodynamics are more important than inter-catchment differences (e.g., differences in climate, geology, or land-use). Implications of widespread flocculation on the determination of end-of-catchment sediments loads and subsequent dispersal patterns across continental shelves are discussed to inform the refinement and monitoring of GBR sediment targets.

Automated summary

This study found that flocculation occurs in all rivers along the Great Barrier Reef coastline, resulting in an order-of-magnitude difference in particle size between in-situ and dispersed sediment. Machine learning algorithms showed promise in predicting in-situ particle size. Hydrodynamics were found to be more important than inter-catchment differences in controlling particle size.