Abstract. Soil erosion models are important tools for soil conservation planning. Although these models are generally well-tested against plot and field data for in-field soil management, challenges arise when scaling up to the landscape level, where sediment trapping along landscape features becomes increasingly critical. At this scale, a separate analysis of model performance for representing erosion, sediment transport, and deposition processes is both challenging and often lacking. Here, we assessed the capacity of the spatially distributed erosion and sediment transport model WaTEM/SEDEM to simulate sediment yields in six highly instrumented micro-scale watersheds ranging from 0.8–7.8 ha, monitored over eight years from 1994–2001, in Southern Germany. The watersheds were composed of two groups: four field-dominated watersheds characterised by arable land with minimal landscape structures, and two structure-dominated watersheds featuring a combination of arable land and linear landscape structures (mainly grassed waterways along thalwegs) that minimise sediment connectivity. Arable fields in both watershed groups were managed for soil conservation, including no-till and optimised crop rotations. A Generalised Likelihood Uncertainty Estimation (GLUE) framework was employed to account for measurement and model uncertainties across multiple spatiotemporal scales. Our results show that while WaTEM/SEDEM captured the magnitude of the very low measured sediment yields in the monitored watersheds, the model did not meet our pre-defined limits of acceptability when operating on annual time steps. Model performance improved substantially when outputs were averaged over the eight-year monitoring period, with mean absolute errors of 0.14 tha-1yr-1 for field-dominated and 0.29 tha-1yr-1 forx structure-dominated watersheds. Our findings demonstrate that WaTEM/SEDEM can represent the influence of soil conservation practices on reducing soil erosion and sediment yield in our study area. However, the model is fit for long-term conservation planning at larger spatial scales and not for precise annual predictions for individual micro-scale watersheds with specific conservation practices even if high-resolution, high-quality input data are available for parameterisation.
Seufferheld et al. (Mon,) studied this question.