Storm events and accompanying heavy rain endanger the silty soils of the fertile and intensively-used agricultural landscape of the Saxon loess province in the European loess belt. In late spring 2016, persistent weather conditions with repeated and numerous storm events triggered flash floods, landslides, and mud flows, and caused severe devastation to infrastructure and settlements throughout Germany. In Saxony, the rail service between Germany and the Czech Republic was disrupted twice because of two mud flows within eight days. This interdisciplinary study aims to reconstruct the two mud flows by means of high-resolution physical erosion modeling, high-resolution, radar-based precipitation data, and Unmanned Aerial Vehicle monitoring. Therefore, high-resolution, radar-based precipitation data products are used to assess the two storm events which triggered the mud flows in this unmonitored area. Subsequently, these data are used as meteorological input for the soil erosion model EROSION 3D to reconstruct and predict mud flows in the form of erosion risk maps. Finally, the model results are qualitatively validated by orthophotos generated from images from Unmanned Aerial Vehicle monitoring and Structure from Motion Photogrammetry. High-resolution, radar-based precipitation data reveal heavy to extreme storm events for both days. Erosion risk maps show erosion und deposition patterns and source areas as in reality, depending on the radar-based precipitation product. Consequently, reconstruction of the mud flows by these interdisciplinary methods is possible. Therefore, the development of an early warning system for soil erosion in agricultural landscapes by means of E 3D and high-resolution, radar-based precipitation forecasting data is certainly conceivable.

The Shivalik foothills of northwestern India are very prone to soil erosion by water due to undulating slopes, highly erodible soils and high intensity rainstorm events during monsoon season. Physically based soil erosion modeling is seen as viable method for planning of measurements to reduce damages done by soil erosion. Nevertheless, parametrization of such models is a major challenge for large inaccessible areas. Several methods do exist for the estimation of the input parameters skin factor, surface roughness and resistance to erosion for the physically based soil erosion model EROSION-3D. Four rainfall experiments, each including dry and wet run, were conducted on different land use conditions on a research farm of the Regional Research Station Ballowal Saunkhri to test estimation methods. Modeling parameters were determined from these experiments. Parameter estimation by two methods for experimental conditions produced values in close range to experimentally determined values for resistance to erosion and surface roughness. Therefore, existing estimation methods are considered to be applicable for the conditions of the Shivalik mountains, except for skin factor. A first modeling with EROSION-3D using preliminary data of a small example catchment shows uncertainties resulting from range of determined and estimated soil parameters.