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首页> 外文期刊>Geosciences >Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring
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Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring

机译：通过物理侵蚀模型，高分辨率雷达降水数据和无人机监测来重建泥浆流

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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.
机译：暴风雨和随之而来的大雨危及欧洲黄土带撒克逊黄土省肥沃且被广泛使用的农业景观中的粉质土壤。 2016年春末，持续不断的天气条件和多次暴风雨事件引发了山洪，泥石流和泥石流，并对整个德国的基础设施和居民区造成了严重破坏。在萨克森州，德国和捷克共和国之间的铁路服务两次中断，因为八天之内有两次泥浆流。这项跨学科研究旨在通过高分辨率物理侵蚀模型，高分辨率，基于雷达的降水数据以及无人飞行器监测来重建两种泥浆流。因此，高分辨率的，基于雷达的降水数据产品用于评估两个风暴事件，这两个风暴事件触发了该不受监控区域的泥浆流。随后，这些数据被用作土壤侵蚀模型EROSION 3D的气象输入，以侵蚀风险图的形式重建和预测泥浆流。最后，通过从无人飞行器监视的图像和运动摄影测量的结构生成的正射照片定性验证模型结果。基于雷达的高分辨率降水数据揭示了这两天的重度到极端风暴事件。侵蚀风险图根据实际情况显示了侵蚀和沉积模式以及源区，具体取决于基于雷达的降水产物。因此，通过这些跨学科的方法重建泥浆流是可能的。因此，通过E 3D和高分辨率，基于雷达的降水预报数据开发农业景观土壤侵蚀预警系统无疑是可以想象的。

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