Abstract
A soil erosion and sediment transport model (WRF-Hydro-Sed) is introduced to WRF-Hydro. As a process-based, fully distributed soil erosion model, WRF-Hydro-Sed accounts for both overland and channel processes. Model performance is evaluated using observed rain gauge, streamflow, and sediment concentration data during rainfall events in the Goodwin Creek Experimental Watershed in Mississippi, USA. Both streamflow and sediment yield can be calibrated and validated successfully at a watershed scale during rainfall events. Further discussion reveals the model’s uncertainty and the applicability of calibrated hydro- and sediment parameters to different events. While an intensive calibration over multiple events can improve the model’s performance to a certain degree compared with single event-based calibration, it might not be an optimal strategy to carry out considering the tremendous computational resources needed.
Highlights
Eroded by different forcing agents, soil is being lost at a rate that is orders-of-magnitude greater than its replenishment [1]
As Goodwin Creek Experimental Watershed (GCEW) is self-drained watershed, we assume that large river discharge corresponds to large overland runoff flux
The total volume from the automated calibration is closer to observation than the manually calibrated one, we chose to use the manually calibrated hydro-parameters to drive the sediment model since the high
Summary
Eroded by different forcing agents, soil is being lost at a rate that is orders-of-magnitude greater than its replenishment [1]. Due to the current climate change, the frequency, and the intensity of extreme rainfall events are projected to increase, which will lead to more intensive erosion [2]. With this in mind, a lot of soil erosion models have been developed to mainly simulate water-induced erosion. Based on the numerical algorithms applied, these models can be classified as conceptual, empirical, and process-based models [3] The latter, with detailed representation of physical processes, is becoming the mainstream in both academia and industry [4]
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