Watershed erosion modeling using the probability of sediment connectivity in a gently rolling system

Sediment connectivity has been shown in recent years to explain how the watershed configuration controls sediment transport. However, we find no studies develop a watershed erosion modeling framework based on sediment connectivity, and few, if any, studies have quantified sediment connectivity for gently rolling systems. We develop a new predictive sediment connectivity model that relies on the intersecting probabilities for sediment supply, detachment, transport, and buffers to sediment transport, which is integrated in a watershed erosion model framework. The model predicts sediment flux temporally and spatially across a watershed using field reconnaissance results, a high-resolution digital elevation models, a hydrologic model, and shear-based erosion formulae. Model results validate the capability of the model to predict erosion pathways causing sediment connectivity. More notably, disconnectivity dominates the gently rolling watershed across all morphologic levels of the uplands, including, microtopography from low energy undulating surfaces across the landscape, swales and gullies only active in the highest events, karst sinkholes that disconnect drainage areas, and floodplains that de-couple the hillslopes from the stream corridor. Results show that sediment connectivity is predicted for about 2% or more the watershed's area 37 days of the year, with the remaining days showing very little or no connectivity. Only 12.8 ± 0.7% of the gently rolling watershed shows sediment connectivity on the wettest day of the study year. Results also highlight the importance of urban/suburban sediment pathways in gently rolling watersheds, and dynamic and longitudinal distributions of sediment connectivity might be further investigated in future work. We suggest the method herein provides the modeler with an added tool to account for sediment transport criteria and has the potential to reduce computational costs in watershed erosion modeling.