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Muddy sediment constitutes a major fraction of the suspended sediment mass carried by the Mississippi River. Thus, adequate knowledge of the transport dynamics of suspended mud in this region is critical in devising efficient management plans for coastal Louisiana. We conducted laboratory tank experiments on the sediment suspended in the lower reaches of the Mississippi River to provide insight into the flocculation behavior of the mud. In particular, we measure how the floc size distribution responds to changing environmental factors of turbulent energy, sediment concentration, and changes in base water composition and salinity during summer and winter. We also compare observations from the tank experiments toin situobservations. Turbulence shear rate, a measure of river hydrodynamic energy, was found to be the most influential factor in determining mud floc size. All flocs produced at a given shear rate could be kept in suspension down to shear rates of approximately 20 s⁻¹. At this shear rate, flocs on the order of 150–200 μm and larger can settle out. Equilibrium floc size was not found to depend on sediment concentration; flocs larger than 100 μm formed in sediment concentrations as low as 20 mgL⁻¹. An increase in salinity generated by adding salts to river water suspensions did not increase the flocculation rate or equilibrium size. However, the addition of water collected from the Gulf of Mexico to river-water suspensions did enhance the flocculation rate and the equilibrium sizes. We speculate that the effects of Gulf of Mexico water originate from its biomatter content rather than its ion composition. Floc sizes in the mixing tanks were comparable to those from the field for similar estimated turbulent energy. Flocs were found to break within minutes under increased turbulence but can take hours to grow under conditions of reduced shear in freshwater settings. Growth was faster with the addition of Gulf of Mexico water. Overall, the experiments provide information on how suspended mud in the lower reaches of the Mississippi might respond to changes in turbulence and salinity moving from the fluvial to marine setting through natural distributary channels or man-made diversions. 

Abstract. Lateral migration of meandering rivers poses erosional risks to human settlements, roads, and infrastructure in alluvial floodplains. While there is a large body of scientific literature on the dominant mechanisms driving river migration, it is still not possible to accurately predict river meander evolution over multiple years. This is in part because we do not fully understand the relative contribution of each mechanism and because deterministic mathematical models are not equipped to account for stochasticity in the system. Besides, uncertainty due to model structure deficits and unknown parameter values remains. For a more reliable assessment of risks, we therefore need probabilistic forecasts. Here, we present a workflow to generate geomorphic risk maps for river migration using probabilistic modeling. We start with a simple geometric model for river migration, where nominal migration rates increase with local and upstream curvature. We then account for model structure deficits using smooth random functions. Probabilistic forecasts for river channel position over time are generated by Monte Carlo runs using a distribution of model parameter values inferred from satellite data. We provide a recipe for parameter inference within the Bayesian framework. We demonstrate that such risk maps are relatively more informative in avoiding false negatives, which can be both detrimental and costly, in the context of assessing erosional hazards due to river migration. Our results show that with longer prediction time horizons, the spatial uncertainty of erosional hazard within the entire channel belt increases – with more geographical area falling within 25 % < probability < 75 %. However, forecasts also become more confident about erosion for regions immediately in the vicinity of the river, especially on its cut-bank side. Probabilistic modeling thus allows us to quantify our degree of confidence – which is spatially and temporally variable – in river migration forecasts. We also note that to increase the reliability of these risk maps, we need to describe the first-order dynamics in our model to a reasonable degree of accuracy, and simple geometric models do not always possess such accuracy.