Prediction of Muddy Floods Using High-Resolution Radar Precipitation Forecasts and Physically-Based Erosion Modeling in Agricultural Landscapes

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.

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Mud Flow Reconstruction by Means of Physical Erosion Modeling, High-Resolution Radar-Based Precipitation Data, and UAV Monitoring

Geosciences ◽

10.3390/geosciences8110427 ◽

2018 ◽

Vol 8 (11) ◽

pp. 427 ◽

Cited By ~ 5

Author(s):

Phoebe Hänsel ◽

Andreas Kaiser ◽

Arno Buchholz ◽

Falk Böttcher ◽

Stefan Langel ◽

...

Keyword(s):

High Resolution ◽

Soil Erosion ◽

Precipitation Data ◽

Storm Events ◽

Erosion Modeling ◽

Erosion Risk ◽

Vehicle Monitoring ◽

High Resolution Radar ◽

Aerial Vehicle ◽

Physical Erosion

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.

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Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas

Remote Sensing ◽

10.3390/rs13152890 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2890

Author(s):

Dawit T. Ghebreyesus ◽

Hatim O. Sharif

Keyword(s):

High Resolution ◽

Return Period ◽

Extreme Values ◽

Stage Iv ◽

Radar Data ◽

The State ◽

Storm Events ◽

High Resolution Radar ◽

Idf Curves ◽

Spatio Temporal

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values.

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