Bifurcations in river-dominated deltas are the main actors in the routing of water and sediments throughout the fluvial network. In spite of previous acknowledgments of their importance, we still lack a comprehensive framework on how bifurcation geometry affects the water and sediment partitioning. To investigate this issue, we firstly combine previously calibrated 2D hydrodynamic simulations on the Wax Lake Delta with a Lagrangian particle-tracking model, quantifying the partitioning of water and sediments with different buoyancy at five bifurcations and their correlations with differences in channel width, branching angle and inlet bed elevation between the downstream branches. We compare the sediment partitioning at bifurcations with available field data to validate our methodology. We then employ the same modeling tools on a simplified geometry, whose geometrical and hydraulic features resemble those of the bifurcations in the Wax Lake Delta. Model results show that the branching angle does not affect the partitioning of water and sediments. The combined effect of asymmetries in the channel width and inlet bed elevation is captured by a simple linear formula that accurately estimates the partitioning of water at bifurcations returned by the 2D calibrated hydrodynamic simulations. Our results also highlight the key role played by transverse gradients in the bathymetry of the upstream channel in determining the partitioning of sediments, suggesting that deeper portions of the cross-section of the upstream channel can cause a proportionately larger fraction of sediments with larger Rouse number to be routed towards the corresponding bifurcate.

Remote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), LA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF-RFE) effectively generalizes high and low turbid cases in a single model, achieving a R² of 0.94 ± 0.005. Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non-linear relationships between depth and reflectance. Specifically, the short blue and Near-InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights the attenuation as the key process for deep-depth retrievals. The depth maps of WLD captured by this model distinctly represent the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources.