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Phoebe Hänsel
Soil and Water Conservation Unit, Technical University Bergakademie Freiberg, Agricolastraße 22, 09599 Freiberg, Germany

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Journal article
Published: 16 September 2019 in Geosciences
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The monitoring, modeling, and prediction of storm events and accompanying heavy rain is crucial for intensively used agricultural landscapes and its settlements and transport infrastructure. In Saxony, Germany, repeated and numerous storm events triggered muddy floods from arable fields in May 2016. They caused severe devastation to settlements and transport infrastructure. This interdisciplinary approach investigates three muddy floods, which developed on silty soils of loess origin tending to soil surface sealing. To achieve this, the study focuses on the test of a historical forecast modeling of three muddy floods in ungauged agricultural landscapes. Therefore, this approach firstly illustrates the reconstruction of the muddy floods, which was performed by high-resolution radar precipitation data, physically-based erosion modeling, and the qualitative validation by unmanned aerial vehicle-based orthophotos. Subsequently, historical radar precipitation forecasts served as input data for the physically-based erosion model to test the forecast modeling retrospectively. The model results indicate a possible warning for two of the three muddy floods. This method of a historical forecast modeling of muddy floods seems particularly promising. Naturally, the data series of three muddy floods should be extended to more reliable data and statistical statements. Finally, this approach assesses the feasibility of a real-time muddy flood early warning system in ungauged agricultural landscapes by high-resolution radar precipitation forecasts and physically-based erosion modeling.

ACS Style

Phoebe Hänsel; Stefan Langel; Marcus Schindewolf; Andreas Kaiser; Arno Buchholz; Falk Böttcher; Jürgen Schmidt. Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes. Geosciences 2019, 9, 401 .

AMA Style

Phoebe Hänsel, Stefan Langel, Marcus Schindewolf, Andreas Kaiser, Arno Buchholz, Falk Böttcher, Jürgen Schmidt. Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes. Geosciences. 2019; 9 (9):401.

Chicago/Turabian Style

Phoebe Hänsel; Stefan Langel; Marcus Schindewolf; Andreas Kaiser; Arno Buchholz; Falk Böttcher; Jürgen Schmidt. 2019. "Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes." Geosciences 9, no. 9: 401.

Journal article
Published: 21 November 2018 in Geosciences
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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.

ACS Style

Phoebe Hänsel; Andreas Kaiser; Arno Buchholz; Falk Böttcher; Stefan Langel; Jürgen Schmidt; Marcus Schindewolf. Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring. Geosciences 2018, 8, 427 .

AMA Style

Phoebe Hänsel, Andreas Kaiser, Arno Buchholz, Falk Böttcher, Stefan Langel, Jürgen Schmidt, Marcus Schindewolf. Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring. Geosciences. 2018; 8 (11):427.

Chicago/Turabian Style

Phoebe Hänsel; Andreas Kaiser; Arno Buchholz; Falk Böttcher; Stefan Langel; Jürgen Schmidt; Marcus Schindewolf. 2018. "Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring." Geosciences 8, no. 11: 427.

Journal article
Published: 17 November 2016 in Hydrology
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The silty soils of the intensively used agricultural landscape of the Saxon loess province, eastern Germany, are very prone to soil erosion, mainly caused by water erosion. Rainfall simulations, and also increasingly structure-from-motion (SfM) photogrammetry, are used as methods in soil erosion research not only to assess soil erosion by water, but also to quantify soil loss. This study aims to validate SfM photogrammetry determined soil loss estimations with rainfall simulations measurements. Rainfall simulations were performed at three agricultural sites in central Saxony. Besides the measured data runoff and soil loss by sampling (in mm), terrestrial images were taken from the plots with digital cameras before and after the rainfall simulation. Subsequently, SfM photogrammetry was used to reconstruct soil surface changes due to soil erosion in terms of high resolution digital elevation models (DEMs) for the pre- and post-event (resolution 1 × 1 mm). By multi-temporal change detection, the digital elevation model of difference (DoD) and an averaged soil loss (in mm) is received, which was compared to the soil loss by sampling. Soil loss by DoD was higher than soil loss by sampling. The method of SfM photogrammetry-determined soil loss estimations also include a comparison of three different ground control point (GCP) approaches, revealing that the most complex one delivers the most reliable soil loss by DoD. Additionally, soil bulk density changes and splash erosion beyond the plot were measured during the rainfall simulation experiments in order to separate these processes and associated surface changes from the soil loss by DoD. Furthermore, splash was negligibly small, whereas higher soil densities after the rainfall simulations indicated soil compaction. By means of calculated soil surface changes due to soil compaction, the soil loss by DoD achieved approximately the same value as the soil loss by rainfall simulation.

ACS Style

Phoebe Hänsel; Marcus Schindewolf; Anette Eltner; Andreas Kaiser; Jürgen Schmidt. Feasibility of High-Resolution Soil Erosion Measurements by Means of Rainfall Simulations and SfM Photogrammetry. Hydrology 2016, 3, 38 .

AMA Style

Phoebe Hänsel, Marcus Schindewolf, Anette Eltner, Andreas Kaiser, Jürgen Schmidt. Feasibility of High-Resolution Soil Erosion Measurements by Means of Rainfall Simulations and SfM Photogrammetry. Hydrology. 2016; 3 (4):38.

Chicago/Turabian Style

Phoebe Hänsel; Marcus Schindewolf; Anette Eltner; Andreas Kaiser; Jürgen Schmidt. 2016. "Feasibility of High-Resolution Soil Erosion Measurements by Means of Rainfall Simulations and SfM Photogrammetry." Hydrology 3, no. 4: 38.