Simulating particle organic matter dispersal beneath Atlantic salmon fish farms using different resuspension approaches

An accurate representation of the particle organic matter (POM) footprint is necessary in order to effectively predict impacts upon benthic communities and the risk of excessive organic enrichment beneath aquaculture sea-cages. Consequently, bottom-related processes such as particle resuspension must be adequately parametrized and evaluated in the available numerical models. We implemented two approaches to model POM resuspension in a Lagrangian particle tracking model and compared their influence on footprint extension and gradients of depositional flux against a no-resuspension scenario. We performed simulations in both exposed and protected aquaculture locations, and at different stages of the Atlantic Salmon (Salmo salar) production cycle in Norway. Our results indicate that the use of sediment-dependent thresholds for resuspension has the potential to regulate the high levels of erosion produced when selecting a low critical value in constant-threshold approaches, particularly in dynamic environments with mixed sediment types.

Sediment plume model-a comparison between use of measured turbidity data and satellite images for model calibration

In this study, we built a two-dimensional sediment transport model of Lake Diefenbaker, Saskatchewan, Canada. It was calibrated by using measured turbidity data from stations along the reservoir and satellite images based on a flood event in 2013. In June 2013, there was heavy rainfall for two consecutive days on the frozen and snow-covered ground in the higher elevations of western Alberta, Canada. The runoff from the rainfall and the melted snow caused one of the largest recorded inflows to the headwaters of the South Saskatchewan River and Lake Diefenbaker downstream. An estimated discharge peak of over 5200 m³/s arrived at the reservoir inlet with a thick sediment front within a few days. The sediment plume moved quickly through the entire reservoir and remained visible from satellite images for over 2 weeks along most of the reservoir, leading to concerns regarding water quality. The aims of this study are to compare, quantitatively and qualitatively, the efficacy of using turbidity data and satellite images for sediment transport model calibration and to determine how accurately a sediment transport model can simulate sediment transport based on each of them. Both turbidity data and satellite images were very useful for calibrating the sediment transport model quantitatively and qualitatively. Model predictions and turbidity measurements show that the flood water and suspended sediments entered upstream fairly well mixed and moved downstream as overflow with a sharp gradient at the plume front. The model results suggest that the settling and resuspension rates of sediment are directly proportional to flow characteristics and that the use of constant coefficients leads to model underestimation or overestimation unless more data on sediment formation become available. Hence, this study reiterates the significance of the availability of data on sediment distribution and characteristics for building a robust and reliable sediment transport model.