geosciences

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

Phoebe Hänsel 1,*, Andreas Kaiser 2, Arno Buchholz 1, Falk Böttcher 3, Stefan Langel 1, Jürgen Schmidt 1 and Marcus Schindewolf 4

1 Soil and Water Conservation Unit, Technical University Bergakademie Freiberg, Agricolastraße 22, 09599 Freiberg, Germany; [email protected] (A.B.); [email protected] (S.L.); [email protected] (J.S.) 2 Geocoptix, Max-Planck-Straße 6, 54296 Trier, Germany; [email protected] 3 Deutscher Wetterdienst, Abteilung Agrarmeteorologie, Kärrnerstraße 68, 04288 Leipzig, Germany; [email protected] 4 Thüringer Landesanstalt für Landwirtschaft, Referat 420 Acker- und Pflanzenbau, Naumburger Straße 98, 07743 Jena, Germany; [email protected] * Correspondence: [email protected]; Tel.: +49-371-39-2679



Received: 31 July 2018; Accepted: 14 November 2018; Published: 21 November 2018


Abstract: 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.

Keywords: storm event; heavy rain; mud flow; soil erosion; radar-based precipitation data; physical erosion modeling; UAV monitoring; SfM Photogrammetry; agricultural landscapes

1. Introduction Storm events and accompanying heavy rain endanger the fertile and fragile landscape of the Saxon loess province in the European loess belt and other loess deposits worldwide. During cultivation periods of bare soil surfaces, the silty soils can easily be eroded by water and wind due to their tendency towards soil surface sealing. Beside the primary impact of soil loss and decreasing soil fertility in the

Geosciences 2018, 8, 427; doi:10.3390/geosciences8110427 www.mdpi.com/journal/geosciences Geosciences 2018, 8, 427 2 of 21

field, erosion can also cause significant off-site effects. Eroded sediments from agricultural land can enter downhill located settlements and infrastructure as mud flows and cause damage there. In late spring 2016, the macro-atmospheric condition called “low-pressure system middle Europe” dominated weather conditions in Germany [1]. This persistent weather condition was characterized by repeated and numerous storm events and accompanying heavy rain. They triggered flash floods, landslides, and mud flows, and caused severe devastation to infrastructure and settlements throughout Germany [1,2]. In Saxony, a federal state in Germany, the rail service between Germany and the Czech Republic was disrupted twice due to two mud flows within eight days. On the evening on 23 May and in the afternoon on 31 May 2016, storm events occurred over the surrounding areas of the long-distance stretch between Dresden and Prague. As a result of the evening storm event and the triggered mud flow, a freight train ran into the dirt covering the tracks near the railway station Schmilka-Hirschmühle and derailed. Fortunately, nobody was injured, but material damage was reported [3]. Eight days later, the same location was affected by another mud flow due to the afternoon storm and accompanying heavy rain. The line had to be closed once again [4]. The German Meteorological Service (DWD) defines heavy rain as large amounts of precipitation per time unit. By definition, it mostly falls from convective clouds, and can lead to rapidly rising water levels, flooding, and often, to soil erosion [5]. With radar rainfall data, the spatial distribution of precipitation becomes possible for the first time. These radar-based precipitation data in high spatial and temporal resolution also help to detect individual storm cells and accompanying heavy rain, as well as their intensity, size, and location [6]. The effects of heavy rain have been modeled by means of radar-based precipitation data. Yu et al. [7] and Yu et al. [8] utilized a hydrologic model for runoff generation and flow routing, as well as for a real-time flood forecasting system for urban catchments. Bronstert et al. [2] investigated the flash flood of Braunsbach, Baden-Wuerttemberg, which happened during the same persistent weather condition as the aforementioned mud flows. With the help of radar-based precipitation and rain gauge data of the DWD, the flash flood was meteorologically and hydrologically analyzed. This study aims to utilize the radar-based precipitation data of the DWD for soil erosion modeling. In soil erosion studies, the soil erosion model EROSION 3D (E 3D) is used to predict soil erosion by water in case of extreme precipitation events. The process- and physically- based model simulates the sub-processes of soil erosion by water in catchment areas. As a computer-aided and grid-based model, it is also compatible with Geographic Information Systems (GIS) [9]. Moreover, E 3D has been applied in numerous catchment areas [10–22]. Recent publications also deal with the generation of erosion risk maps for Saxony [20,21,23]. Depending on the resolution of the input Digital Terrain Model (DTM), these maps can be of high resolution. The overall objective of this interdisciplinary study was to examine the feasibility of mud flow reconstruction by means of high-resolution radar-based precipitation data, high-resolution physical erosion modeling, and Structure from Motion (SfM) Photogrammetry. High-resolution, radar-based precipitation data of the DWD were used to assess the precipitation situation of the two storm events, which triggered the mud flows. Two different 5-min radar-based precipitation datasets were assessed, which is more suitable in cases of storm events and accompanying heavy rain. These radar-based precipitation data were also used as input data for the soil erosion model E 3D to reconstruct and predict mud flows. Using orthophotos generated from images taken by an Unmanned Aerial Vehicle (UAV) and by SfM Photogrammetry, the mud flows were qualitatively validated.

2. Materials and Methods

2.1. Study Area The study area is situated near Reinhardtsdorf-Schöna within Saxony, Germany, in the immediate vicinity of the Czech border (Figure1a). It covers an area of almost 1 km 2, and is located in the Elbe Geosciences 2018, 8, 427 3 of 21 Geosciences 2018, 8, x FOR PEER REVIEW 3 of 21

Sandstonein the Elbe Mountains. Sandstone The Mountains. mean annual The mean rainfall annual is around rainfall 700 is around mm, and 700 the mm mean, and the annual mean temperature annual liestemperature between 7 and lies 8 ◦ betweenC. Furthermore, 7 and 8 it°C. is characterizedFurthermore, it by is different characterized land uses: by different agriculture, land meadows, uses: forests,agriculture, settlements, meadows, infrastructure, forests, settlements, and the infrastructure river Elbe (Figure, and the1b). river Belonging Elbe (Figure to the1b). soilBelonging region of mountainousto the soil region and hilly of mountainous regions with and high hilly sandstone regions with amounts, high sandstone the soil amounts, textures the of thesoil studytextures area of are the study area are comprised of particular clayey silt (maize field) and pure sand (forest). The soil comprised of particular clayey silt (maize field) and pure sand (forest). The soil type of the fields is a type of the fields is a loess Luvisol, while that of the forest is classified as a Podzol [24]. Additionally, loess Luvisol, while that of the forest is classified as a Podzol [24]. Additionally, the forest of the study the forest of the study area was affected by clear cutting and associated skid trails at that time (Figure area1 wasb). affected by clear cutting and associated skid trails at that time (Figure1b).

FigureFigure 1. The1. The study study area area in in ( a(a)) Germany Germany (data(data basis: basis: [25 [25])]),, (b (b) its) its land land use use and and (c) (itsc) relief. its relief.

2.2.2.2 High-Resolution. High-Resolution Radar-Based Radar-Based Precipitation Precipitation DataData

2.2.1.2.2.1 Data. Data Description Description TheThe term term RADOLAN RADOLAN is is abbreviated abbreviated from from the the German RADar RADar-OnLine-ANeichung,-OnLine-ANeichung, meaning meaning RadarRadar Online Online Adjustment. Adjustment. With With the the RADOLAN RADOLAN procedure procedure,, the the DWD DWD offers offers a wide a wide range range of real of realtime time,, high-resolution,high-resolution radar-based, radar-based precipitation precipitation productsproducts in in different different temporal temporal resolution resolutionss and and processing processing stepssteps [26 ].[2 The6]. The procedure procedure combines combines the the advantagesadvantages of of quantitative quantitative radar radar data data by bythe the radar radar network network of the DWD, and terrestrial rain gauge data by the common rain gauge network of the DWD and the of the DWD, and terrestrial rain gauge data by the common rain gauge network of the DWD and federal states of Germany. Every five minutes, the weather radars record the reflected signals of rain the federal states of Germany. Every five minutes, the weather radars record the reflected signals of and snow from higher layers of the atmosphere [27]. With these reflected and recorded signals of the rainso and-called snow hydrometeors from higher, layersthe radar of the reflectivity atmosphere factor [27 Z]. With can be these determined. reflected To and determin recordede signalsthe of theprecipitation so-called hydrometeors, height, Z is converted the radar into reflectivity the precipitation factor Z can intensity be determined. R via the so To-called determine Z-R- the precipitationrelationship. height, Thus, Z is radar converted measures into th thee precipitation precipitation remotely intensity and R via indirectly the so-called [6]. Though Z-R-relationship. these Thus,quantitative radar measures radar data the precipitation provide the spatial remotely distribution and indirectly of precipitation, [6]. Though their these quality quantitative for water radar datamanagement provide the tasks spatial is unsatisfactory. distribution Therefore, of precipitation, the measurements their quality of fallen for waterprecipitation management measured tasks at is unsatisfactory.rain gauge stations Therefore, are used the measurementsto adjust the quantitative of fallen precipitationradar data. During measured RADOLAN at rain, this gauge adjustment stations are usedhappens to adjust operationally the quantitative every radarhour [ data.27]. During RADOLAN, this adjustment happens operationally every hourThe [ 27RADOLAN]. products comprise high-resolution radar-based precipitation amounts from 5- 2 minThe time RADOLAN steps ranging products over one comprise hour to 30 high-resolution days and having radar-based a spatial resolution precipitation of 1 km amounts. Before the from hourly adjustment due to rain gauge data, the quantitative 5-min radar data pass through different 5-min time steps ranging over one hour to 30 days and having a spatial resolution of 1 km2. Before the processing steps due to influences of the radar signal. The different processing steps are the correction hourly adjustment due to rain gauge data, the quantitative 5-min radar data pass through different of orographic attenuation, the application of a variable Z-R-relationship, quantitative compositing of processingthe 17 weather steps due radar to influences sites in Germany of the radar, and signal. additional The radardifferent sites processing of the neighboring steps are the countries, correction of orographicstatistical clutter attenuation, suppression, the applicationsmoothing, and of a pre variable-adjustment Z-R-relationship, [27]. quantitative compositing of the 17By weather means of radar the two sites quantitative in Germany,, high and-resolution additional, radar radar-based sites precipitation of the neighboring products, RY countries, and statisticalRZ, the clutter overall suppression, objective of the smoothing, present study and will pre-adjustment be fulfilled. Although [27]. these products are unadjusted By means of the two quantitative, high-resolution, radar-based precipitation products, RY and RZ, the overall objective of the present study will be fulfilled. Although these products are unadjusted Geosciences 2018, 8, 427 4 of 21 by rain gauge data, these products pass the processing steps correction of orographic attenuation and application of a variable Z-R-relationship. Furthermore, these products contain quantitative data on precipitation in mm/5 min, and are available after two minutes [26]. Additionally, RY is quality-tested by the correction of further radar errors [28]. Both radar-based precipitation products can be applied for quantitative monitoring and visualization of precipitation and climatologic analysis of rainfall [6]. Thereby, small-scale and short-lived storm events and their quantitative and spatial distribution in time intervals of five minutes can be observed.

2.2.2. Data Preparation and Analysis RZ- and RY-products were provided for this study by the DWD. These two and the other radar-based precipitation products of the DWD are stored in the RADOLAN Binary Data Format. These data format cannot be easily read by external users without the appropriate software application. One of these applications is Wradlib. Wradlib is an open source library for weather radar data processing. It is written in the free programming language, Python. Wradlib assists the user in the steps of processing radar data. In particular, these can be the reading of common data formats, converting reflectivity to precipitation intensity, georeferencing, and visualizing the radar data [29]. By means of Wradlib, the two 5-min radar products RZ and RY could be read, prepared, converted into another format, and finally, exported for subsequent work steps. One of the next steps was the visualization of the two storm events for the defined area of Saxony, and not for the whole of Germany. Therefore, a Python script was written which fulfilled this requirement. After converting the radar-based precipitation data into ASCII format, they could be visualized in the open source GIS QGIS [30]. Using another Python script, which included the reading of precipitation intensities for a defined area, these intensities were automatically written to a csv-file. The latter serves not only as input data for E 3D, but also provides the actual precipitation intensities for analytical purposes. Therefore, it is possible to assess the intensity of the evening and afternoon storm events in five- and ten-minute, and one-hour increments (Table1). Thus, heavy rain is characterized by a precipitation amount greater than or equal to 10 mm in one hour and 1.7 mm in ten minutes, respectively [5].

Table 1. The classification of the intensity of precipitation per time unit [5].

Intensity mm/5 min 1 mm/10 min mm/h Light <0.2 <0.5 <2.5 Moderate ≥0.2–<0.8 ≥0.5–<1.7 ≥2.5–<10.0 Heavy ≥0.8–<4.2 ≥1.7–<8.3 ≥10.0–<50.0 Very heavy ≥4.2–<6.7 ≥8.3–<13.3 ≥50.0–<80.0 Extreme ≥6.7 ≥13.3 ≥80.0 1 5-min intensity levels are calculated by adapting the calculation of the DWD [5].

2.2.3. Data Comparison with Rain Gauge Data RZ and RY were not adjusted with rain gauge data, but were compared with each other. In addition, radar-based precipitation amounts are quantitative precipitation estimation products, as they are measured remotely and indirectly. The nearest rain gauge station to the study area is Lichtenhain-Mittelndorf, and was used for the comparison. It is a precipitation monitoring station of the DWD and situated in Mittelndorf, a place near Reinhardtsdorf-Schöna, but on the eastern side of the Elbe river. Data of the rain gauge station are freely available via the Open Data Server of the DWD [31]. Geosciences 2018, 8, 427 5 of 21

2.3. High-Resolution Physical Erosion Modeling Using EROSION 3D

2.3.1. Modeling Approach and Components

SchmidtGeosciences [32 2018],developed 8, x FOR PEER REVIEW the theoretical principles of E 3D. Initially, these principles5 of 21 were implemented in the model EROSION 2D [10,33], the slope profile version for the prediction of soil erosion. In theSchmidt following [32] developed years, von the Werner theoretical developed principles the of catchment E 3D. Initially, area versionthese principles EROSION were 3D [34]. implemented in the model EROSION 2D [10,33], the slope profile version for the prediction of soil Infiltrationerosion. In was the modeledfollowing years, using von the Werner Green-Ampt developed approach the catchment [35]. Thearea infiltrationversion EROSION rate was 3D [3 calculated4]. with the DarcyInfiltration equation was [10 modeled]. A flow using path the Green model-Ampt derived approach from [3 a5]. DTM The wasinfiltration used rate for the was spatial distributioncalculated of the with surface the Darcy runoff equation [36]. [10]. Particle A flow detachmentpath model derived and transportfrom a DTM were was simulatedused for the by the momentumspatial flux distribution approach of bythe Schmidtsurface runoff [10,32 [3,633]. ].Particle E 3D detachment is well validated and transport [37–41 were] and simulated documented by [9]. E 3Dthe consistsmomentum of flux two approach main components:by Schmidt [10,3 the2,33]. pre-processing E 3D is well validated and [3 the7–41 processing] and documented component. [9]. The pre-processing component performs the digital relief analysis with its GIS module in order to E 3D consists of two main components: the pre-processing and the processing component. The calculatepre the-processing runoff and component its movement performs in the the digital terrain. relief The analysis processing with its component GIS module executes in order the to actual simulationcalculate with the runoff sub-processes and its movement of soil erosion.in the terrain. The The latter processing are as follows: compone infiltrationnt executes the of precipitationactual (Green-Amptsimulation approach), with the runoffsub-processes generation, of soil erosion. detachment The latter of soilare as particles follows: infiltration from the groundof precipitation (momentum flux approach),(Green-Ampt particle approach), and associated runoff generation, pollutant detachmenttransport, as of well soil as particles deposition, from depending the ground on the transport(momentum capacity of flux the approach), surface runoff particle and and the associated enrichment pollutant of soil transport particles, as along well as the deposition transport, path depending on the transport capacity of the surface runoff and the enrichment of soil particles along (Figure2)[9]. the transport path (Figure 2) [9].

FigureFigure 2. Flow 2. Flow chart chart of EROSIONof EROSION 3D 3D programprogram sequences sequences (changed (changed after after [42]). [42]).

2.3.2. Input2.3.2 and. Input Output and Output Data Data Figure 2 demonstrates the individual program sequences of E 3D and the input and output data Figure2 demonstrates the individual program sequences of E 3D and the input and output data requirements. The input data sets include a DTM for the relief parameter, a digital soil and a digital requirements.land use The map input for the data soil and sets land include use parameter a DTM, foras well the as relief precipitation parameter, data afor digital the meteorological soil and a digital land useparameter. map for theThe soilgeometric and landbasis usefor the parameter, grid-based asE 3D well is a assquare precipitation grid having data a maximum for the resolution meteorological parameter.of 1 Them. Thus, geometric depending basis on forthe theinput grid-based DTM, high- Eresolution 3D is a squareerosion gridrisk and having deposition a maximum maps can resolution be of 1 m. Thus,created depending [9]. on the input DTM, high-resolution erosion risk and deposition maps can be created [9]. The precipitation data input file requires a resolution of ten minutes or higher. E 3D is not only able to simulate discrete precipitation events, but also a number of successive events by means of the The precipitation data input file requires a resolution of ten minutes or higher. E 3D is not only long-term simulation module [9]. able to simulateThe soil discrete and land precipitation use data set requires events, additional but also adata number and processing of successive steps including events bya land means use of the long-termgrid, simulation the physical module soil properties [9]. in form of a table with the corresponding land use IDs of the land Theuse soil grid and, as landwell as use a lookup data set table. requires The lookup additional table consists data of and the processing land use ID and steps the including corresponding a land use grid, thesoil physical parameter soil data properties set key of in the form table of [9]. a table with the corresponding land use IDs of the land use E 3D requires seven soil parameters for the table of the soil and land use data set: (1) bulk density grid, as well as a lookup table. The lookup table consists of the land use ID and the corresponding soil [kg/m3]; (2) organic carbon [% by weight]; (3) initial soil moisture [% by volume]; (4) hydraulic parameterroughness data set [s/m key1/3] of; (5) the resistance table [9 to]. erosion [N/m2]; (6) cover [%], and (7) soil texture [% by weight] E 3D[9]. requiresFurthermore, seven E 3D soil offers parameters an additional for factor, the the table so-called of the skinfactor soil and [–], landto correct use the data infiltration set: (1) bulk 3 density [kg/m ]; (2) organic carbon [% by weight]; (3) initial soil moisture [% by volume]; (4) hydraulic Geosciences 2018, 8, 427 6 of 21 roughness [s/m1/3]; (5) resistance to erosion [N/m2]; (6) cover [%], and (7) soil texture [% by weight] [9]. Furthermore, E 3D offers an additional factor, the so-called skinfactor [–], to correct the infiltration ability [10]. Cultivation related effects on infiltration, surface runoff, and particle detachment are considered by the parameters skinfactor, resistance to erosion, and hydraulic roughness. An extensive data basis for the parameterization of the soil parameter table was created by 116 rainfall simulations on erosion-prone slopes during the “soil erosion measurement program for Saxony” [37–41], which is documented in the parameter catalogue of Saxony [43]. Here, the corresponding soil parameter can be determined depending on the land use, type of use, cultivation, and soil texture. Additionally, this data basis is included in the data base of 726 rainfall simulation experiments for surface runoff from arable land, which is freely available [44]. Furthermore, a software for the automated parameterization of the soil parameters exists, which is called DPROC, and uses the data of the parameter catalogue. With current Digital Landscape Models (DLMs), it is possible to automatically assign the soil parameters of a given landscape extract via the DPROC [45,46].

2.3.3. Model Parameterization This study utilizes a DTM with a resolution of 2 m [47] for the generation of the relief data set. The radar-based precipitation intensities of the RZ- and RY-products in mm/5 min and with a precipitation duration of 3 h serve as meteorological input data for E 3D. Thereby, one RADOLAN-grid cell with an area of 1 km2 represents the precipitation intensity on the study area. The land use grid of this study was generated by the union and raster converting of the digital soil map 1:50,000 [24], and the digitalized land use by means of the WMS digital orthophotos RGB [48] in ArcGIS [49]. The DPROC was not used due to the missing current DLM. The relevant soil parameters were determined according to the parameter catalogue. Thereby, soil parameters of missing anthropogenic land uses like roads and settlements were adapted following Arévalo [50]. At the time in which the mud flows happened, the forest was affected by clear cutting and associated skid trails (Figure1b). Usually, the soil parameters of a forest on a pure, sandy soil would be parameterized as follows (Table2):

Table 2. The parameterization of an undisturbed forest.

Land Bulk Organic Initial Hydraulic Erosion Soil Skinfactor Cover Use Density Carbon Moisture Roughness Resistance Texture Pure Forest 1000 0.9 8 METVER 1 0.9 0.1 100 sand 1 see Section 2.3.5.

By the inspection of the study area, it became obvious that the pure sandy soil was riddled with pebbles and stones. Therefore, the bulk density was increased to 1400 kg/m3 (Table3). The parameter catalogue did not comprise soil parameters for clear cuttings and skid trails. Following Zemke [51], who investigated runoff and soil erosion on forest roads, the bulk densities of these areas were increased, and their hydraulic roughnesses decreased (Table3). The complete parameterization of the physical soil properties of the study area is available in the Supplementary Materials.

Table 3. The parameterization of a disturbed forest.

Bulk Organic Initial Hydraulic Erosion Soil Land Use Skinfactor Cover Density Carbon Moisture Roughness Resistance Texture Forest 1400 0.9 8 METVER 1 0.9 0.1 100 Pure sand Skid trail 1800 0.9 0.01 METVER 1 0.02 0.001 0 Pure sand Clear cutting 1700 0.9 0.1 METVER 1 0.1 0.02 20 Pure sand 1 see Section 2.3.5. Geosciences 2018, 8, 427 7 of 21

2.3.4. Derivation of Land Cover from NDVI Products of Land Viewer Geosciences 2018, 8, x FOR PEER REVIEW 7 of 21 The NDVI product of the Land Viewer [52] was used to derive the land cover for the E 3D soil parameterThe ‘cover’. NDVI Itproduct was used of the to Land identify Viewer sparsely-vegetated [52] was used to derive fields the with land bare cover soil for surfaces, the E 3D which soil are proneparameter to soil erosion. ‘cover’. It was used to identify sparsely-vegetated fields with bare soil surfaces, which are proneNDVI to is soil the erosion. Normalized Difference Vegetation Index, and is calculated using the Red band (with its informationNDVI is about the Normalized chlorophyll Difference absorption) Vegetation and Index the Near, and is Infrared calculated band using (with the Red its band relatively (with high its information about chlorophyll absorption) and the Near Infrared band (with its relatively high reflectance of vegetation) of a satellite scene. As a result, an image is created revealing the relative reflectance of vegetation) of a satellite scene. As a result, an image is created revealing the relative biomass. The NDVI is not only used to map desert encroachment and to monitor drought, but also to biomass. The NDVI is not only used to map desert encroachment and to monitor drought, but also monitorto monitor agricultural agricultural production production [52]. [52]. LandLand Viewer Viewer is a is web a web interface interface for for satellite satellite imagery of of the the company company EOS. EOS. The The user user has free has access free access to remoteto remote sensing sensing imagery imagery of the of satellitesthe satellites Landsat Landsat 7, Landsat 7, Landsat 8, Sentinel-2, 8, Sentinel Sentinel-1,-2, Sentinel- and1, and other other remote sensingremote data. sensing The images data. The are images readily are available readily available and processed and processed on-the-fly, on-the as- wellfly, as as well in real-time as in real [-52]. timeFigure [523]. contains the NDVI for the study area on 29 May 2016 that was acquired from Sentinel-2. With a brightFigure green 3 contains color the indicating NDVI for a the NDVI study of area 0.25, on it 29 reveals May 201 a6 sparsely that was vegetatedacquired from maize Sentinel field.- The light2. green With anda bright cauliflower-like green color indicating structure a NDVI over of the 0.25, maize it reveals field, a sparsely the dark vegetated green forest, maize andfield. the The white light green and cauliflower-like structure over the maize field, the dark green forest, and the white Elbe are clouds. Consequently, the E 3D soil parameter ‘cover’ can be estimated at 15%. Elbe are clouds. Consequently, the E 3D soil parameter ‘cover’ can be estimated at 15%.

FigureFigure 3. 3.The The NDVINDVI of of the the study study area area [52 [52]. ].

2.3.5.2.3.5 Soil. Soil Moisture Moisture Modeling Modeling Using Using METVER METVER ModelingModeling soil soil moisture moisture is is required required since since it is subject subject to to temporal temporal and and spatial spatial fluctuations fluctuations due to due to weatherweather and soiland conditionssoil conditions [53]. [5 This3]. This study study utilizes utilizes METVER METVER to simulateto simulate the the E 3DE 3D soil soil parameter parameter ‘initial ‘initial soil moisture’ using the mean value of the soil moisture of the nearest three weather stations soil moisture’ using the mean value of the soil moisture of the nearest three weather stations around Reinhardtsdorf-Schöna (Lichtenhain-Mittelndorf, Dresden-Hosterwitz and Dippoldiswalde-Reinberg).

Geosciences 2018, 8, 427 8 of 21

METVER is a water balance model for use in agricultural landscapes, and predicts daily soil moisture. It has been developed by Müller and Müller [54–56], and is used and updated by the DWD. METVER is based on the determination of real and potential evapotranspiration by the method of Turc [57] and its modification after Wendling et al. [58]. As the soil moisture depends on land use, soil texture, and type of use, it requires several parameters. The meteorological data for sunshine duration, precipitation, and daily mean temperature, and the soil data for soil texture, soil capacity, and permanent wilting point, as well as land use are used to model the soil moisture [59,60].

2.3.6. Comparison of Model Results with Undisturbed Forest Model Results As the parameterization of anthropogenic land use, such as clear cutting and skid trails, requires a comprehensible adaption of all soil parameters, and taking into account the fact that forest was disturbed at that time, it is useful to compare the model results including clear cutting with undisturbed forest model results.

2.4. UAV Monitoring For this study, E 3D was validated qualitatively by comparing UAV orthophotos to high-resolution, simulated erosion and deposition patterns and source areas. The UAV flight was performed on 1 June 2016 with the two mud flows in advance. Thereby, the last mud flow happens one day behind.

2.4.1. UAV and Digital Camera The study area was mapped with the UAV ‘Phantom 20 (quadrocopter) from DJI Innovations (Shenzhen, China). The UAV is equipped with a ‘PowerShot S1000 from Canon (Tokyo, Japan) with image stabilization, allowing it to maintain the stability and quality while acquiring photos. About 700 overlapping images were utilized to guarantee the cover of the sediment source and deposition areas, as well as the erosion patterns.

2.4.2. SfM Photogrammetry The resulting overlapping images were processed with SfM Photogrammetry and multi-view dense matching methods in Agisoft PhotoScan [61], resulting in a dense point cloud. In a further work step, an orthophoto was generated from the dense point cloud, representing eroded parts of the study area. Small data gaps in the orthophoto are due to missing images which occur because of the rapidly changing relief in the slope area and associated challenge of flying over the trees.

3. Results

3.1. High-Resolution Radar Precipitation Data RZ and RY products were analyzed concerning their precipitation intensity and spatial distribution for the two storm events on 23 May 2016 and on 31 May 2016. Each product will be described and then compared and discussed later. The obviously lower amount of precipitation and intensity of the RY-product will be discussed later.

3.1.1. The RZ-Product The selected storm events were categorized according to their precipitation intensity (Table1). Figure4 illustrates the distribution of selected storm events for the RZ-product in 5-min time steps. A moderate precipitation intensity began on 23 May 2016 at 6:20 p.m. and was characterized by heavy intensity at 6:40 p.m., culminating in an extreme value of 12.6 mm/5 min at 7:20 p.m. (Figure4a). Subsequently, precipitation declined to heavy and moderate intensities before another heavy event at 8:00 p.m. Within one hour, 33 mm of rain had fallen, from which 16.6 mm fell within ten minutes. Consequently, this storm event was categorized as a heavy rain event, which was characterized by a Geosciences 2018, 8, x FOR PEER REVIEW 9 of 21 Geosciences 2018, 8, 427 9 of 21 Geosciences 2018, 8, x FOR PEER REVIEW 9 of 21 event at 8:00 p.m. Within one hour, 33 mm of rain had fallen, from which 16.6 mm fell within ten minutes. Consequently, this storm event was categorized as a heavy rain event, which was precipitationevent at 8:00 amount p.m. Within of more one than hour, 10 mm33 mm in oneof rain hour. had Regarding fallen, from its which intensity 16.6 ofmm 16.6 fell mm within within ten ten minutes.characterized Consequently, by a precipitation this storm amount event of more was than categorized 10 mm in as one a heavyhour. Regarding rain event, its whichintensity was of minutes, the storm event contained extreme amounts of precipitation. characterized16.6 mm within by ten a pre minutes,cipitation the amount storm event of more contained than 10 extreme mm in oneamounts hour. of Regarding precipitation. its intensity of 16.6 mm within ten minutes, the storm event contained extreme amounts of precipitation.

FigureFigure 4. The 4. The storm storm events events and and its its intensities intensities for the RZ RZ-product-product on on (a ()a 23) 23 and and (b) ( b31) 31May May 2016. 2016. Figure 4. The storm events and its intensities for the RZ-product on (a) 23 and (b) 31 May 2016. FigureFigure4b illustrates 4b illustrates a different a different rainfall rainfall situation situation for for the the RZ-product RZ-product on on 31 31 May May 2016. 2016. Between Between 1:30 and1:30 2:00 Figure andp.m. 2:00 a 4 smallb pillustrates.m. precipitation a small a different precipitation cell rainfall passed cell situation Reinhardtsdorf-Schöna passed for Reinhardtsdorf the RZ-product-Schöna with on a31 precipitation withMay a2016. precipitation Between amount of 11.71:30amount mm andresulting of 2:00 11.7 p inmm.m a. heavy aresulting small intensity. precipitation in a heavy Then, intensity. cell 15 min passed Then later, Reinhardtsdorf, another15 min later, storm -anotherSchöna cell passed storm with the acell precipitation same passed area the with same area with higher amounts of precipitation and intensity, where 54.2 mm of rain fell within one higheramount amounts of 11.7 of mm precipitation resulting in and a heavy intensity, intensity. where Then 54.2, mm15 min of rainlater, fell another within storm one hourcell passed and 14.7 the mm hour and 14.7 mm within ten minutes. This corresponded to a high intensity event over one hour, withinsame ten area minutes. with higher This correspondedamounts of precipitation to a high and intensity intensity event, where over 54.2 one mm hour, of rain and fell an within extreme one event and an extreme event over ten minutes. overhour ten minutes.and 14.7 mm within ten minutes. This corresponded to a high intensity event over one hour, and anFigure extreme 5 contains event overa part ten of minutes.the storm event on 23 May 2016 from 7:10 p.m. to 7:25 p.m. One of the Figure5 contains a part of the storm event on 23 May 2016 from 7:10 p.m. to 7:25 p.m. One of precipitationFigure 5 contains cells of thea part northwestward of the storm event-moving on 23 thunderstorm May 2016 from passed 7:10 p the.m. to study 7:25 p area..m. One The of most the the precipitation cells of the northwestward-moving thunderstorm passed the study area. The most precipitationintense 15 min cells of the of thunderstorm the northwestward are m-appedmoving showing thunderstorm, very heavy passed and theextreme study intensity. area. The most intense 15 min of the thunderstorm are mapped showing, very heavy and extreme intensity. intense 15 min of the thunderstorm are mapped showing, very heavy and extreme intensity.

Figure 5. The selected storm event in 5-min time steps on 23 May 2016 from (a) 7:10 p.m., (b) 7:15 p.m., (c) 7:20 p.m. to (d) 7:25 p.m. Geosciences 2018, 8, x FOR PEER REVIEW 10 of 21 Geosciences 2018, 8, x FOR PEER REVIEW 10 of 21 Geosciences 2018, 8, 427 10 of 21 FigureFigure 5 .5 .The The selected selected storm storm eventevent inin 55--minmin timetime steps onon 2323 MayMay 20162016 fromfrom ( a(a) )7:10 7:10 p p.m.m., .(, b()b )7:15 7:15 p.pm.m., .,(c ()c 7:20) 7:20 p p.m.m. .to to ( d(d) )7:25 7:25 p p.m..m. The heavy and very heavy storm events of 31 May 2016 developed over the Elbe Sandstone The heavy and very heavy storm events of 31 May 2016 developed over the Elbe Sandstone MountainsThe and heavy moved and very westward heavy storm in the events early afternoonof 31 May 2016 [1]. Figuredeveloped6 demonstrates over the Elbe the Sandstone route and Mountains and moved westward in the early afternoon [1]. Figure 6 demonstrates the route and the the mostMountains intensive and moved 15 min westward of the selected in the early storm afternoon event. The [1]. northwestward-movingFigure 6 demonstrates the stormroute and event thewas mostmost intensive intensive 15 15 min min of of the the selected selected storm storm event. The northwestward--movmovinging storm storm event event was was much much much smaller than the thunderstorm on 23 May 2016, but with longer lasting, very heavy to extreme smallersmaller than than the the thunderstorm thunderstorm on on 23 23 May May 2016, but with longer longer lasting lasting, , veryvery heavy heavy to to extreme extreme precipitation intensities. precipitationprecipitation intensities. intensities.

FigureFigure 6. The 6. The selected selected storm storm event event in 5-minin 5-min time time steps steps on on 31 31 May May 2016 2016 from from (a )(a 2:20) 2:20 p.m., p.m. (,b ()b 2:25) 2:25 p.m., Figure 6. The selected storm event in 5-min time steps on 31 May 2016 from (a) 2:20 p.m., (b) 2:25 (c) 2:30p.m p.m.., (c) 2:30 to ( dp).m 2:35. to p.m.(d) 2:35 p.m. p.m., (c) 2:30 p.m. to (d) 2:35 p.m. 3.1.2.3.1.2 The. The RY-Product RY-Product 3.1.2. The RY-Product TheThe RY-product RY-product on on 23 23 May May 2016 2016 (Figure (Figure7 a)7a) yielded yielded precipitationprecipitation amounts amounts ofof 13.9 13.9 mm mm over over one one The RY-product on 23 May 2016 (Figure 7a) yielded precipitation amounts of 13.9 mm over one hour,hour, from from which which 9.2 9.2 mm mm fell fell within within ten ten minutes minutes and was was classified classified as as a heavy a heavy rain rain event. event. Moreover, Moreover, hour,this stormfrom which event 9.2exceeded mm fell the within very heavyten minutes threshold and ofwas 8.3 classified mm/10 min. as a In heavy addition, rain event.the value Moreover, of 7.8 this storm event exceeded the very heavy threshold of 8.3 mm/10 min. In addition, the value of thismm/5 storm min even at 7:20t exceeded p.m. exceeded the very the heavy limit for threshold classification of 8.3 as mm/10 extreme min. precipitation In addition, intensity. the value of 7.8 7.8 mm/5 min at 7:20 p.m. exceeded the limit for classification as extreme precipitation intensity. mm/5 min at 7:20 p.m. exceeded the limit for classification as extreme precipitation intensity.

Figure 7. The storm event and its intensities for the RY-product on (a) 23 and (b) 31 May 2016. Geosciences 2018, 8, x FOR PEER REVIEW 11 of 21

Geosciences 2018, 8, 427 11 of 21 Figure 7. The storm event and its intensities for the RY-product on (a) 23 and (b) 31 May 2016.

TheThe pre-event pre-event on on 31 May31 May 2016 2016 lasted lasted from from 1:35 1:35 to 1:55 to p.m.1:55 p and.m. resultedand resulted in a precipitation in a precipitation amount ofamount 1.6 mm of (Figure 1.6 mm7b). (Figure Another 7b). precipitationAnother precipitation cell passed cell thepassed same the area same with area higher with higher amounts amounts 20 min 20 min later. In this second event, 23.8 mm fell within one hour and 9.0 mm within ten minutes, later. In this second event, 23.8 mm fell within one hour and 9.0 mm within ten minutes, respectively. respectively. Considering the amount of precipitation falling within one hour, the event was Considering the amount of precipitation falling within one hour, the event was characterized as heavy characterized as heavy rain. In its most intensive ten minutes, it achieved the intensity of a very heavy rain. In its most intensive ten minutes, it achieved the intensity of a very heavy rain event. rain event. As shown in Figure8, the northwestward-moving thunderstorm cell of the 23 May 2016 evening As shown in Figure 8, the northwestward-moving thunderstorm cell of the 23 May 2016 evening stormstorm was was small-scale small-scale and and short-lived, short-lived,and and heavyheavy to extreme in in its its intensity. intensity.

FigureFigure 8. 8The. The selected selected storm storm event event in in 5-min 5-min time time steps steps on on 23 23 May May 2016 2016 from from (a) ( 7:10a) 7:10 p.m., p.m (b., )( 7:15b) 7:15 p.m., (c)p 7:20.m., p.m.(c) 7:20 to p (d.m) 7:25. to (d p.m.) 7:25 p.m.

FigureFigure9 contains 9 contains the the most most intense intense 15 15 min min of of thethe afternoonafternoon storm on on 31 31 May May 2016. 2016. The The second second eventevent started started with with heavy heavy intensities, intensities, beforebefore thethe storm event event culminated culminated in in a avery very heavy heavy intensity intensity at at 2:302:30 p.m p.m (5.4 (5.4 mm/5 mm/5 min). min). Then,Then, thethe precipitation intensity intensity declined declined to to heavy heavy rain. rain.

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FigureFigure 9. The 9. selectedThe selected storm storm event event in 5-minin 5-min time time steps steps on on 31 31 May May 2016 2016 from from ( a()a) 2:20 2:20 p.m., p.m., ((bb)) 2:252:25 p.m., p.m., (c) 2:30 p.m. to (d) 2:35 p.m. (c) 2:30Figure p.m. 9. toThe (d )selected 2:35 p.m. storm event in 5-min time steps on 31 May 2016 from (a) 2:20 p.m., (b) 2:25 p.m., (c) 2:30 p.m. to (d) 2:35 p.m. 3.1.3.3.1.3 Data. Data Comparison Comparison with with Rain Rain Gauge Gauge Data Data By comparing radar precipitation data (Figures 4 and 7) with rain gauge data, it became obvious 3.1.3By. comparing Data Comparison radar with precipitation Rain Gauge data Data (Figures4 and7) with rain gauge data, it became that the storm events began later at the rain gauge station (Figure 10). On the one hand, the obviousBy that comparing the storm radar events precipitation began later data at(Figures the rain 4 and gauge 7) with station rain gauge (Figure data, 10 it). became On the obvious one hand, thunderstorm cells approached Lichtenhain-Mittelndorf later on both days. On the other hand, the that the storm events began later at the rain gauge station (Figure 10). On the one hand, the the thunderstormmaximum value cells of theapproached storm event Lichtenhain-Mittelndorf at Lichtenhain-Mittelndorf later was on indicated both days. earlier On in the the other radar hand, thunderstorm cells approached Lichtenhain-Mittelndorf later on both days. On the other hand, the the maximumprecipitation value data of than the storm at the event rain gaugeat Lichtenhain-Mittelndorf station. The RZ- and was RY-products indicated demonstrate earlier in the the radar precipitationmaximummaximum data valuevalue than ofat Lichtenhain atthe the storm rain event- gaugeMitte atlndorf station. Lichtenhain on 23 The May- RZ-Mittelndorf 2016 and at RY-products 7:30 was and indicated on 31 demonstrate May earlier 2016 at in the 2:45 the maximum p radar.m. valueprecipitationThere at Lichtenhain-Mittelndorf was a time data difference than at of the 10 on rainto 23 20 May gaugemin between 2016 station. at 7:30radar The and and RZ on rain- and 31 gauge May RY -data 2016products (Figure at 2:45 demonstrates p.m.4 and There7). This the was a maximum value at Lichtenhain-Mittelndorf on 23 May 2016 at 7:30 and on 31 May 2016 at 2:45 p.m. time differencetime shifting of will 10 to be 20 discussed min between later. In radar addition, and rain the pre gauge-event data on (Figures31 May 42016 and was7). Thismissing time in shifting the There was a time difference of 10 to 20 min between radar and rain gauge data (Figures 4 and 7). This will berain discussed gauge data later. because In addition, this little theheavy pre-event thunderstorm on 31 cell May did 2016 not reach was missing Lichtenhain in the-Mittelndorf. rain gauge data time shifting will be discussed later. In addition, the pre-event on 31 May 2016 was missing in the becauserain gauge this little data heavy because thunderstorm this little heavy cell thunderstorm did not reach cell Lichtenhain-Mittelndorf. did not reach Lichtenhain-Mittelndorf.

Figure 10. The storm events and its intensities for the rain gauge data on (a) 23 and (b) 31 May 2016.

FigureFigureBy 10.comparing 10The. The storm storm the events precipitation events and and its its intensitiesintensities intensities forforof the the rainfallrain rain gauge gauge data datadata (Table on on ( a4 ()), a23 )rain 23and and gauge (b) ( 31b) dataMay 31 May are2016. 2016.more similar to the RZ-product on 23 May 2016. The second storm event on 31 May 2016 contained nearly By comparingBy comparing the the precipitation precipitation intensities intensities ofof the rainfall data data (Table (Table 4),4), rain rain gauge gauge data data are are more more similarsimilar to the to the RZ-product RZ-product on on 23 23 May May 2016. 2016. TheThe secondsecond storm storm event event on on 31 31 May May 2016 2016 contained contained nearly nearly the same values between the RZ-product and rain gauge data for the 10-min precipitation intensities, Geosciences 2018, 8, 427 13 of 21 but not for the hourly intensity. By looking at the precipitation situation, the difference between the hourly intensities becomes comprehensible. The thunderstorm passed Lichtenhain-Mittelndorf faster than it did the study area. This, in turn, led to lower precipitation amounts in one hour at the rain gauge station. But the precipitation intensities of the most intensive ten minutes remained the same for both locations. As a result, the RZ-product should provide more reliable precipitation data in case of a storm event.

Table 4. Comparison of the radar-based precipitation with rain gauge data.

Intensity mm/h mm/10 min RZ 23 May 2016 33.3 16.6 RY 23 May 2016 13.8 9.2 Rain gauge 23 May 2016 35.5 20.2 RZ 31 May 2016 54.2 14.7 RY 31 May 2016 23.8 9.0 Rain gauge 31 May 2016 30.7 15.6

3.2. High-Resolution Physical Erosion Modeling Using EROSION 3D

3.2.1. Soil Erosion Modeling for 23 May 2016 Figure 11 illustrates the erosion risk map as sediment budget maps of the study area created by the soil erosion model E 3D for RY- and RZ-products on 23 May 2016. On the underlying land use, yellow to red colors indicate loss, and light green to dark green colors indicate deposition of the soil particles. Most of the soil erosion for the RY-product occurred on skid trails and the clear cutting area. Only a very small part reaches the railway line in the valley. The modeled soil deposition amounts to 65.6 kg/m2. Besides the areas south of the maize field and the little erosion alongside the road in the middle of the picture, no further erosion occurred (Figure 11a). Geosciences 2018, 8, x FOR PEER REVIEW 14 of 21

FigureFigure 11. 11The. The erosion erosion risk risk map map withwith (a) RY RY-- and ( (bb)) RZ RZ-product-product for for 23 23May May 2016. 2016.

3.2.2Results. Soil areErosion different Modeling using for the 31 May RZ-product 2016 (Figure 11b). Following the bottom contour lines of the reliefSoil onerosion the maizeoccurred field to a and much forming greater erosion extent for rills the (Figure RY-Product1c), soil on 31 particles May 2016 were (Figure eroded 12a) and transportedthan on 23 due May to the2016. higher Almost precipitation the complete amount maize field of the was RZ-product. affected by erosion. They passed Eroded the soil small particles forest in the middlewere deposited of the study as a linear area andfeature the on narrow the meadow meadow in front strip of in the front forest of theand largein the forest. clear cutting Downgradient area. eroded63.8 sedimentskg/m2 soil were entered deposited the forest on the and railway followed line, the approximately skid trails and the same the surrounding amount as on clear 23 May cutting areas.2016. Besides the southeast skid trail, the particle loaded water sought its own way by passing the bottom contour line of the slope and eroding particles and gravel from the shallow soils of the forest.

Figure 12. The erosion risk map with (a) RY- and (b) RZ-product for 31 May 2016.

The RZ-product for 31 May 2016 shows the heaviest soil erosion and deposition patterns and amounts due to more than two times the precipitation amount (54.2 mm/h) of the RY-product (23.8 mm/h) (Figure 12b). Additionally, the RZ precipitation amount was more than one and a half times higher than that of 23 May 2016 (33.3 mm/h). Furthermore, the RZ model results demonstrated the same reproduction of the erosion rills on the maize field and in the clear cutting areas as for the other simulated cases. The deposition on the railway tracks amounted to 757.4 kg/m2. On the railway tracks themselves, another soil erosion event occurred in the model results. Due to the fact that with higher resolution of the DTM, the whole runoff is only routed over one grid cell, the excess sediment laden water can erode again. Afterward, it deposited on the meadows in the direction of the Elbe.

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These evolved erosion rills are visible on the erosion risk map. Part of the sediment load was deposited at the margin of the bottom contour line. As the remaining load reached the railway line, it partly deposited over a distance of 160 m between the two skid trails, amounting to 386.0 kg/m2. Figure 11b reveals another soil particle deposit on the railway line, at the end of the northeast skid trail to the northwest. At this point, a culvert drains excessive water. However, E 3D cannot model culverts, and consequently, the model incorrectly resulted in deposition on the railway line. Finally, E 3D models another soil deposit on the meadows behind the railway line in the direction of the Elbe. This fact will beFigure described 11. The later. erosion risk map with (a) RY- and (b) RZ-product for 23 May 2016.

3.2.2.3.2.2 Soil. Soil Erosion Erosion Modeling Modeling for for 31 31 May May2016 2016 SoilSoil erosion erosion occurred occurred to to a a much much greatergreater extent for for the the RY RY-Product-Product on on 31 31May May 2016 2016 (Figure (Figure 12a) 12a) thanthan on 23on May23 May 2016. 2016. Almost Almost the the complete complete maizemaize field field was was affected affected by by erosion. erosion. Eroded Eroded soil soilparticles particles were deposited as a linear feature on the meadow in front of the forest and in the clear cutting area. were deposited as a linear feature on the meadow in front of the forest and in the clear cutting area. 63.8 kg/m2 soil were deposited on the railway line, approximately the same amount as on 23 May 63.8 kg/m2 soil were deposited on the railway line, approximately the same amount as on 23 May 2016. 2016.

FigureFigure 12. 12The. The erosion erosion risk risk mapmap with ( a)) RY RY-- and and (b (b) )RZ RZ-product-product for for 31 31May May 2016. 2016.

TheThe RZ-product RZ-product for for 31 31 May May 2016 2016 showsshows thethe heaviest soil soil erosion erosion and and deposition deposition patterns patterns and and amounts due to more than two times the precipitation amount (54.2 mm/h) of the RY-product (23.8 amounts due to more than two times the precipitation amount (54.2 mm/h) of the RY-product mm/h) (Figure 12b). Additionally, the RZ precipitation amount was more than one and a half times (23.8 mm/h) (Figure 12b). Additionally, the RZ precipitation amount was more than one and a half higher than that of 23 May 2016 (33.3 mm/h). Furthermore, the RZ model results demonstrated the times higher than that of 23 May 2016 (33.3 mm/h). Furthermore, the RZ model results demonstrated same reproduction of the erosion rills on the maize field and in the clear cutting areas as for the other the samesimulated reproduction cases. of the erosion rills on the maize field and in the clear cutting areas as for the other simulatedThe deposition cases. on the railway tracks amounted to 757.4 kg/m2. On the railway tracks themselves, 2 anotherThe deposition soil erosion on event the railway occurred tracks in the amounted model results. to 757.4 Due kg/m to the. fact On that the railwaywith higher tracks resolution themselves, anotherof the soil DTM erosion, the whole event runoff occurred is only in routed the model over results.one grid Duecell, tothe the excess fact sediment that with laden higher water resolution can of theerode DTM, again. the Afterward, whole runoff it deposited is only routedon the meadows over one in grid the cell,direction the excess of the Elbe. sediment laden water can erode again. Afterward, it deposited on the meadows in the direction of the Elbe.

3.2.3. Comparison of Model Results with Undisturbed Forest Model Results Figure 13 illustrates the model results for all storm events and radar-based precipitation data for an undisturbed forest. Almost all erosion maps show the known soil erosion and deposition patterns for the maize field, but no notable erosion in the forest. Eroded soil particles were deposited on the meadow and in the forest. However, Figure 13d revealed another situation. A portion of the runoff passed the bottom contour line of the slope and deposited 381.0 kg/m2 on the railway line. That is nearly half the amount Geosciences 2018, 8, x FOR PEER REVIEW 15 of 21

3.2.3. Comparison of Model Results with Undisturbed Forest Model Results Figure 13 illustrates the model results for all storm events and radar-based precipitation data for an undisturbed forest. Almost all erosion maps show the known soil erosion and deposition patterns for the maize field, but no notable erosion in the forest. Eroded soil particles were deposited on the Geosciencesmeadow2018 and, 8, 427 in the forest. 15 of 21 However, Figure 13d revealed another situation. A portion of the runoff passed the bottom contour line of the slope and deposited 381.0 kg/m2 on the railway line. That is nearly half the amount of the disturbed forest for the same precipitation amount on the same day. As a result, this model of the disturbed forest for the same precipitation amount on the same day. As a result, this model result indicated that the mud flow would have happened even in case of an undisturbed forest. result indicated that the mud flow would have happened even in case of an undisturbed forest.

FigureFigure 13. 13The. The erosion erosion risk risk map map with with ((aa)) RY-RY- and (b)) RZ RZ-product-product for for 23 23 May May 2016 2016 and and for for31 May 31 May 2016 2016 (c,d),(c, respectively,d), respectively, for for an an undisturbed undisturbed forest. forest.

3.3.3.3 UAV. UAV Monitoring Monitoring FigureFigure 14 demonstrates14 demonstrates the the high-resolution high-resolution simulated simulated erosion erosion and and deposition deposition patterns patterns and and source areassource for the areas RZ-product for the RZ- onproduct 31 May on 201631 May in 2016 comparison in comparison to the t correspondingo the corresponding orthophoto. orthophoto. The SfM generated orthophoto matches the main flow routes of sediment laden water on the maize field, through the small forest, the meadow, and in the forest. Moreover, the sediment source areas are visible and identifiable. Consequently, E 3D results could be satisfactorily validated in their spatial context.

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FigureFigure 14. 14(a). ( Thea) The erosion erosion risk risk map map with with thethe RZ-productRZ-product for for 31 31 May May 2016 2016 and and (b) ( bthe) the UAV UAV acquired acquired orthophoto of the study site after the mud flows had occurred. orthophoto of the study site after the mud flows had occurred.

4. DiscussionThe SfM generated orthophoto matches the main flow routes of sediment laden water on the maize field, through the small forest, the meadow, and in the forest. Moreover, the sediment source areasThe are analysis visible and and visualizationidentifiable. Consequently of the radar-based, E 3D results precipitation could be sa datatisfactorily for the validated evening in storm their on 23 Mayspatial 2016 context. and the afternoon storm on 31 May 2016 revealed lower amounts of precipitation and intensities for the RY-product. Differences with the RZ-product were due to the quality test of the4. Discussion RY-product. However, by comparing radar-based precipitation data with rain gauge data, the RZ-productThe analysis appeared and visualization to provide of more the radar reliable-based data precipitation in cases of data storm for events. the evening As discussed storm on 23 in [6], the RZ-productMay 2016 and provides the afternoon plausible storm data, on especially 31 May 2016 in cases revealed of high lower amounts amounts of precipitation.of precipitation However, and littleinte tonsities moderate for the precipitation RY-product. are Differences overestimated with the by RZ RZ.-product Therefore, were due it is to particularly the quality suitedtest of the for the analysisRY-product. of extreme However, precipitation by comparing events. radar In contrast,-based precipitation RY is of high data quality with in rain cases gauge of little-to-moderate data, the RZ- precipitation.product appeared According to provide to [62], more who reliable compared data rain in case gauges of storm with bothevents. 5-min As discussed radar-based in [6], precipitation the RZ- data,product the quality provides tested plausible RY underestimated data, especially heavyin cases rain of high events amounts to a greaterof precipitation. extent than However RZ. The, little same publicationto moderate and precipitation Einfalt and are Frerk overestimated [63], as well by RZ. as PetersTherefore, [64], it emphasizedis particularly thatsuited there for the is aanalysis time shift of extreme precipitation events. In contrast, RY is of high quality in cases of little-to-moderate between radar and rain gauge data. Radar detects the precipitation significantly earlier than the rain precipitation. According to [62], who compared rain gauge with both 5-min radar-based precipitation gauge station. Namely, this time shift is a result of different measurement heights, with the rain data, the quality tested RY underestimated heavy rain events to a greater extent than RZ. The same gaugepublication stations beingand Einfalt on the and ground Frerk and[63], radaras well aloft as Peters above [6 ground.4], emphasized Therefore, that thethere heavy is a time rain shift with its consequentbetween mudradar flowsand rain must gauge have data. happened Radar detects on 23 the May precipitation around 8 p.m.significantly and on earlier 31 May than 2016 the between rain 2:30gauge and 3 station. p.m., as Namely, was reported this time for shift 23 is May a result 2016 of [different65]. measurement heights, with the rain gauge stationsThe lower being precipitation on the ground amounts and radar of the aloft RY-product above ground. led to soilTherefore, erosion the almost heavy solely rain with in the its clear cuttingconsequent area on mud 23 May flows 2016. must The have modeled happened soil on erosion 23 May andaround deposition 8 p.m. and patterns on 31 May and 2 amounts016 between for the RZ-2:30 (23 andand 313 p May).m., as and wasRY-products reported for 23 (31 May May) 2016 result [65]. from higher precipitation amounts and modeled soil moisture,The lower but differprecipitation in their amounts intensity. of After the RY exceeding-product led a threshold to soil erosion value, almost with increasingsolely in the initial clear soil moisture,cutting the area soil on loss23 May increases 2016. The [10 ].modeled soil erosion and deposition patterns and amounts for the RZThe- (23 erosion and patterns31 May) on and the RY maize-products field, (31 through May) the result small from forest, higher the precipitation meadow, and amounts in the disturbed and modeled soil moisture, but differ in their intensity. After exceeding a threshold value, with increasing forest were reconstructed with a DTM of 2 m. However, only after adapting the soil parameters of the initial soil moisture, the soil loss increases [10]. railway line, the clear cutting area, and the skid trails, the soil erosion in the forest and its deposition The erosion patterns on the maize field, through the small forest, the meadow, and in the on the railway line could be achieved. Here, their bulk densities were increased and their hydraulic disturbed forest were reconstructed with a DTM of 2 m. However, only after adapting the soil roughnessesparameters were of the decreased. railway line, Model the clear results cutting indicate area, soiland depositionthe skid trails on, the the soil railway erosion between in the forest the two skidand trails its overdeposition a length on the of 160railway m. line In reality, could be the achieved. deposition Here, was their longer, bulk densities up to 260 were m, increased between the railway station and the culvert [66]. Model results with the RZ-product on 31 May 2016 indicate that a Geosciences 2018, 8, 427 17 of 21

mud flow would have happened even without the parameterized clear cutting. Finally, soil erosion model results were qualitatively validated by SfM-generated orthophotos.

5. Conclusions The modeled RZ erosion risk maps for 23 and 31 May 2016 appear to be the most representative, especially as is evidenced in the orthophoto. The distribution of the sediment deposition on the railway line was less than actual measurements, but mostly fit with reality. The erosion risk map for 31 May 2016 indicated that a mud flow would have happened even without clear cutting. Consequently, the reconstruction of mud flows by means of high-resolution real-time radar-based precipitation data, high-resolution physical erosion modeling, and SfM Photogrammetry is possible. Therefore, the simulated erosion risk maps can aid in making predictions of soil erosion. Hence, the development of an early warning system for soil erosion in agricultural lands by means of E 3D and high-resolution, radar-based precipitation forecasting data seems possible. As these radar-based precipitation forecasting data could be available up to two hours in advance, a warning is possible. As early warning systems need an automated process chain with current data, future work should first focus on the acquisition of an actual data base like DTMs or DLMs. Furthermore, the DPROC for the automated assignment of the soil parameters has to be updated. Further research should also tackle the automated derivation of land use with the NDVI to determine the E 3D soil parameter ‘cover’. Finally, before the development of interfaces, other possible soil moisture models should be tested for the determination of the sensitive soil parameter ‘initial soil moisture’.

Supplementary Materials: The following are available online at http://www.mdpi.com/2076-3263/8/11/427/s1, Table S1: Parameterization of the study area. Author Contributions: Conceptualization, A.K., M.S., J.S.; Methodology, A.K., M.S., P.H., F.B.; Software, P.H., A.K., M.S., S.L.; Validation, P.H., A.K., M.S., S.L.; Formal Analysis, P.H., A.K., M.S.; Investigation, P.H., A.K., M.S.; Resources, X.X.; Data Curation, X.X.; Writing—Original Draft Preparation, P.H.; Writing—Review & Editing, P.H., J.S., M.S.; Visualization, P.H.; Supervision, M.S., J.S.; Project Administration, M.S., J.S.; Funding Acquisition, A.K., J.S., M.S. Funding: The present study was only possible due to the financial support of the European Social Fund in the Free State of Saxony (project: doctorate). Acknowledgments: This study was only possible due to the provision of the RZ- and RY-products by Tanja Winterrath of the DWD. The authors wish to thank her and Mario Hafer of the DWD for his GIS-expertise of the RADOLAN products. Furthermore, the authors would like to thank the team of Wradlib for their instructive help with Wradlib. We want to thank the reviewers for their valuable comments on the manuscript. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Abbreviations

DLM Digital Landscape Model DTM Digital Terrain Model german term, abbreviated from Deutscher Wetterdienst meaning German Meteorological DWD Service E 3D EROSION 3D Soil Erosion Model GIS Geographic Information System german term, abbreviated from METeorologisches VERdunstungsmodell, meaning METVER meteorological evaporation model NDVI Normalized Difference Vegetation Index german term, abbreviated from RADar-OnLine-ANeichung meaning Radar Online RADOLAN Adjustment SfM Structure from Motion UAV Unmanned Aerial Vehicle Geosciences 2018, 8, 427 18 of 21

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