Sediment fate and transport: Influence of sediment source and rainfall

The capability of hydrologic models to spatially simulate the changes in hydrologic processes, like precipitation, is an important consideration in capturing the impacts of these processes on sediment prediction across the domain. Radar-derived precipitation (RDP) provides an enhanced detail of rainfall characteristics in time and space compared to estimates from rain-gauge precipitation (RGP) commonly used in hydrologic modeling. However, the impacts of these datasets on sediment fate and transport depend on how sediment sources were conceptualized in the model. This paper developed a modeling framework to simulate sediment transport from upland to the stream and to the outlet of the watershed based on a gridded conceptualization and to examine the impacts of RGP and RDP with different types of sediment sources on sediment prediction. The Water Erosion Prediction Project (WEPP) model was used to estimate daily sediment sources in a semi-distributed and fully distributed manner using the hydrologic model, MIKE SHE and MIKE 11. Model comparison was performed in a watershed in Illinois characterized by a dominant agricultural landscape. The results indicated that the use of RDP only ensured better model performance for sediment yield with the fully distributed sediment source. That is, combining both the ability of the RDP to capture the spatial variability of rainfall across the watershed and assessing sediment production at higher resolution improved the accuracy of predictions in sediment yield while decreasing uncertainties associated with sediment simulations. Advancing modeling capabilities will require the development of new modeling platforms that aim to seamlessly integrate large-scale distributed simulations and environmental input data at finer spatial resolutions.

Automated summary

This study examined the impact of radar-derived precipitation (RDP) and rain-gauge precipitation (RGP) on sediment prediction, finding that combining RDP led to better model performance by improving the accuracy of sediment yield predictions at higher resolution. This highlights the importance of considering the spatial variability of rainfall and assessing sediment production in order to improve predictions and reduce uncertainties associated with sediment simulations.