Heavy rain events can cause soil material to be widely transported by surface runoff in arable soil landscapes. While soils of the eroded hilltop and backslope areas are affected by a loss of material, soil properties in deposition areas are affected by sedimentation of particulate and dissolved mineral and organic substances. During the event, the composition of mineral and organic matter (OM) fractions changes dynamically with runoff velocity. However, changes in OM composition during runoff have mostly been neglected. The objective was to identify changes of OM composition of runoff fractions with time and flow velocity during a single rainfall-runoff event. By the comparison of runoff from a conventionally-tilled (CT) plot and a no-till (NT) plot, we aimed at discussing process-based explanations for dynamic OM composition in runoff. Surface runoff was collected from CT and NT plots at the toe-slope of an experimental hillslope. Time-dependent runoff samples were compared with cumulative bulk samples obtained by Coshocton wheels and separated in a coarser sediment (sed>63 mu m) and a finer fraction of < 63 m-sized particles plus dissolved OM (<63 m&diss). The OM composition was determined with Fourier transform infrared (FTIR) spectroscopy. It was found to change with runoff volume due to contact with litter (NT) or soil minerals (CT) mainly for finer fractions from both CT and NT plots. Even during a single runoff event, the OM mass and composition in runoff changes characteristically. The OM composition dynamics was dominated by OMcat/C-O-C ratios in runoff from CT and by C-H/CO ratios in runoff from NT due to contact with soil minerals (CT) and litter (NT). The time-dependent collection schemes suggested that OM composition in runoff reflected the surface properties and could help to further improve explanations for spatial distribution of OM-related soil properties such as the potential wettability in arable soil landscapes.

The agricultural production of maize (Zea mays L.) increases the risk of water erosion. Perennial crops like cup plant (Silphium perfoliatum L.) offer a sustainable alternative to produce biomass for biogas plants. The assessment of soil conservation measures requires calibrated soil erosion models that spatially identify soil erosion processes. These support decision-making by farmers and policymakers. Input parameters for the physically based soil erosion model EROSION 3D for cup plant cultivation were established in a field study. Rainfall simulation experiments were conducted to determine the model input parameter’s skinfactor and surface roughness. The results showed a reduction of soil erosion and higher infiltration rates for cup plant resulting in higher skinfactors of 11.5 in June and 0.75 post-harvest (cup plant) compared to 1.2 in June and 0.21 post-harvest (maize). With the extended parameter catalogue of EROSION 3D for cup plant cultivation model simulations were conducted for a rainfall event in June (64 mm). The sediment budget would have been reduced by 92.6% through the growth of cup plant in comparison to conventionally grown maize. Perennial cup plant can, therefore, contribute to achieving the targets outlined in the European Green Deal by reducing soil erosion and enhancing soil health.

Agricultural production is facing a challenging transition through changing political framework conditions and climate change. Innovative field use and land management through temporal and spatial diversification measures support the political efforts to achieve the European Green Deal. However, increasing precipitation intensities through climate change are leading to an increased risk of soil erosion by water. To mitigate such risk, soil erosion should be taken into account when redesigning fields and landscapes. This paper aims to assess the present erosion risk situation in the innovative on-farm field experiment "patchCROP " with several implemented spatio-temporal crop diversification measures (field size, flower strips, crop rotation), using the physically-based Erosion 3D simulation model at the field scale. The modelling results showed that field reshaping from one large field into smaller field segments had the potential to reduce soil erosion. Flower strips reduced the sediment discharge to approximately half of that of small field segments without flower strips. However, as model results indicated, heterogeneous landscapes showed complex erosion and deposition patterns. To identify these, making use of physically based soil erosion models in new field arrangements is a critical future task.

The paper presents the development, adaptive improvement and use of the method to estimate the wind erosion risk in Germany for Cross Compliance (CC) regulations, based on the German standard DIN19706. It is illustrated by the example of the Federal State of Brandenburg. A landscape structure model was developed which calculates the sheltering effects of landscape elements. Basic inputs are the heights of all landscape elements and the frequencies and directions of erosive winds. In combination with the soil map of erodibility the wind erosion risk is derived in a high spatial resolution according to the CC requirements. In addition to improving the input data in terms of its spatial resolution by using air-borne laser scanning data, an innovative approach is presented which derives the sheltered areas behind landscape elements from the transport capacities of wind speeds above a threshold. Thus, our analysis represents one of the most comprehensive wind erosion assessment of cropland that can be used for landscape structure assessment well beyond CC use. The derivation of effective protection zones from the frequencies of erosive winds when critical thresholds are adjusted represents an innovative approach that provides an objective and transferable assessment of wind protection of landscape features in different wind regimes.

Soil erosion is a major threat to soil fertility, food security and water resources. Besides a quantitative assessment of soil loss, the dynamics of erosion-affected arable soil surfaces still poses challenges regarding field methods and predictions because of scale-dependent and soil management-related complex soil-crop-atmosphere processes. The objective was to test a photogrammetric Structure-from-Motion (SfM) technique for the mm-scale mapping of the soil surface micro-topography that allows the monitoring without special equipment and with widely available cameras. The test was carried out in May 2018 on three plots of 1.5 m 2 (upper-, middle-, and footslope) covering surface structural features (tractor wheel lane, seed rows) along a Maize-cultivated hillslope with a coarse-textured topsoil and a runoff monitoring station. The changes in mm-scaled surface micro-topography were derived from repeatedly photographed images of the same surface area during a 2-weeks period with two rain events. A freely available SfM-program (VisualSfM) and the QGIS software were used to generate 3D-models of the surface topography. Soil cores (100 cm 3 ) were sampled to gravimetrically determine the topsoil bulk density. The micro-topographical changes resulting from rainfall–induced soil mass redistribution within the plots were determined from the differences in SfM maps before and after rain. The largest decrease in mean soil surface elevation and roughness was observed after rain for the middle slope plot and primarily in initially less compacted regions. The spatially-distributed intra-plot changes in soil mass at the mm-scale derived from the digital micro-topography models indicated that local depressions were filled with sediments from surrounding knolls during rainfall. The estimated mass loss determined with the SfM technique decreased, if core sample-based soil settlement was considered. The effect of changes in the soil bulk density could be described after calibration also with an empirical model suggested in the Root-Zone-Water-Quality-Model. Uncertainties in the presented plot-scale SfM-technique were due to geo-referencing and the numerical limitations in the freely available SfM-software. The photogrammetric technique provided valuable information on soil surface structure parameters such as surface roughness. The successful application of SfM with widely available cameras and freely available software might stimulate the monitoring of erosion in regions with limited accessibility.

Soil erosion can present a major threat to agriculture due to loss of soil, nutrients, and organic carbon. Therefore, soil erosion modelling is one of the steps used to plan suitable soil protection measures and detect erosion hotspots. A bibliometric analysis of this topic can reveal research patterns and soil erosion modelling characteristics that can help identify steps needed to enhance the research conducted in this field. Therefore, a detailed bibliometric analysis, including investigation of collaboration networks and citation patterns, should be conducted. The updated version of the Global Applications of Soil Erosion Modelling Tracker (GASEMT) database contains information about citation characteristics and publication type. Here, we investigated the impact of the number of authors, the publication type and the selected journal on the number of citations. Generalized boosted regression tree (BRT) modelling was used to evaluate the most relevant variables related to soil erosion modelling. Additionally, bibliometric networks were analysed and visualized. This study revealed that the selection of the soil erosion model has the largest impact on the number of publication citations, followed by the modelling scale and the publication's CiteScore. Some of the other GASEMT database attributes such as model calibration and validation have negligible influence on the number of citations according to the BRT model. Although it is true that studies that conduct calibration, on average, received around 30% more citations, than studies where calibration was not performed. Moreover, the bibliographic coupling and citation networks show a clear continental pattern, although the co-authorship network does not show the same characteristics. Therefore, soil erosion modellers should conduct even more comprehensive review of past studies and focus not just on the research conducted in the same country or continent. Moreover, when evaluating soil erosion models, an additional focus should be given to field measurements, model calibration, performance assessment and uncertainty of modelling results. The results of this study indicate that these GASEMT database attributes had smaller impact on the number of citations, according to the BRT model, than anticipated, which could suggest that these attributes should be given additional attention by the soil erosion modelling community. This study provides a kind of bibliographic benchmark for soil erosion modelling research papers as modellers can estimate the influence of their paper.

In arable land management, different crop rotation patterns and sequences, such as changing agricultural land use to erosion prone crops, or crops providing less ground cover, can greatly influence soil loss rate through their impact on soil cover status (C factor value). The influence of crop rotation on C value and on erosion rate is often determined on an experimental plot scale, so the results are often erroneous when extrapolated to large heterogeneous landscapes, where they fail to capture the spatiotemporal variability beyond the experimental sites. In the present study we have endeavored to investigate the impact of various crop rotation patterns on C value and on subsequent soil erosion rate, at a landscape level, by combining 28 time-series satellite images (from 2013 to 2016) along with annually updated land-use data, via the integrated administration and control system (IACS), from the Uckermark district of north eastern Germany. In total, 21 different crop sequences were investigated. Winter wheat (WW), winter rape (WR), and maize (Mz) were found to be the predominant arable crops grown in the study area. The highest average annual C values were estimated from crop sequences involving Mz and sugar beet (SB), both as pre-crops and succeeding crops. The highest value of 0.39 was computed from SB/Mz rotation. On the other hand, crop rotation involving WR gave significantly lower annual C values in all the years considered, with the lowest average annual C value of 0.07 calculated on WR parcels preceded by winter cereals. It was also apparent that crop rotation patterns influenced C value in a temporally variable manner. Among the self-sequencing patterns, WR/WR reduced the C value significantly compared with Mz/Mz and to a lesser extent compared with WW/WW. Continuous cultivation of Mz increased the potential soil loss rate by as much as 72 % compared to WR/WR and by 51 % compared to WW/WW. It was also possible to determine the spatial distribution of the impact of crop rotation on soil erosion risk within the study area. The results obtained agreed with the results of other international and regional studies. Overall, the output from this research could contribute towards further efficient investigation of the impact of agronomic practices on the environment in a large agricultural landscape, without the need to set up multi-location experimental plots.

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling published between 1994 and 2017. We aimed to identify (i) the processes and models most frequently addressed in the literature, (ii) the regions within which models are primarily applied, (iii) the regions which remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we combined the collective knowledge of 67 soil-erosion scientists from 25 countries. The resulting database, named ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’, includes 3030 individual modelling records from 126 countries, encompassing all continents (except Antarctica). Out of the 8471 articles identified as potentially relevant, we reviewed 1697 appropriate articles and systematically evaluated and transferred 42 relevant attributes into the database. This GASEMT database provides comprehensive insights into the state-of-the-art of soil- erosion models and model applications worldwide. This database intends to support the upcoming country-based United Nations global soil-erosion assessment in addition to helping to inform soil erosion research priorities by building a foundation for future targeted, in-depth analyses. GASEMT is an open-source database available to the entire user-community to develop research, rectify errors, and make future expansions.

The topography is one of the determining site characteristics, of which the slope inclination is significant for natural science aspects, including the estimation of water erosion risk and as a criterion for agricultural subsidies. The slopes within an area vary greatly and occupy very different proportions of the area. Algorithms that take this heterogeneity into account were developed in the 1970s with the medium-scale agricultural site mapping (MMK). It also contains the slope association types (SAT, in German: “Hangneigungsflächentyp”), which classifies different slopes and summarizes them as one value per reference area. The SAT can be used across various scales and different targets. Applicability is given to soil and water conservation tasks, administrative tasks as field selection or agricultural subsidies, and over a wide range of scales from small catchments areas to whole landscape analyses. Thus, one value on an area basis characterizes an important topographic factor.

The Normalized Difference Vegetation Index (NDVI), has been increasingly used to capture spatiotemporal variations in cover factor (C) determination for erosion prediction on a larger landscape scale. However, NDVI-based C factor (Cndvi) estimation per se is sensitive to various biophysical variables, such as soil condition, topographic features, and vegetation phenology. As a result, Cndvi often results in incorrect values that affect the quality of soil erosion prediction. The aim of this study is to multi-temporally estimate Cndvi values and compare the values with those of literature values (Clit) in order to quantify discrepancies between C values obtained via NDVI and empirical-based methods. A further aim is to quantify the effect of biophysical variables such as slope shape, erodibility, and crop growth stage variation on Cndvi and soil erosion prediction on an agricultural landscape scale. Multi-temporal Landsat 7, Landsat 8, and Sentinel 2 data, from 2013 to 2016, were used in combination with high resolution agricultural land use data of the Integrated Administrative and Control System, from the Uckermark district of north-eastern Germany. Correlations between Cndvi and Clit improved in data from spring and summer seasons (up to r = 0.93); nonetheless, the Cndvi values were generally higher compared with Clit values. Consequently, modelling erosion using Cndvi resulted in two times higher rates than modelling with Clit. The Cndvi values were found to be sensitive to soil erodibility condition and slope shape of the landscape. Higher erodibility condition was associated with higher Cndvi values. Spring and summer taken images showed significant sensitivity to heterogeneous soil condition. The Cndvi estimation also showed varying sensitivity to slope shape variation; values on convex-shaped slopes were higher compared with flat slopes. Quantifying the sensitivity of Cndvi values to biophysical variables may help improve capturing spatiotemporal variability of C factor values in similar landscapes and conditions.

Climate change is expected to affect the occurrence of heavy rainfall. We analyzed trends of heavy rainfall days for the last decades in Germany. For all available stations with daily data, days exceeding daily thresholds (10, 20, 30 mm) were counted annually. The Mann–Kendall trend test was applied to overlapping periods of 30 years (1951–2019). This period was extended to 1901 for 111 stations. The stations were aggregated by natural regions to assess regional patterns. Impacts of data inconsistencies on the calculated trends were evaluated with the metadata and recent hourly data. Although the trend variability depended on the chosen exceedance threshold, a general long-term trend for the whole of Germany was consistently not evident. After 1951, stable positive trends occurred in the mountainous south and partly in the northern coastal region, while parts of Central Germany experienced negative trends. The frequent location shifts and the recent change in the time interval for daily rainfall could affect individual trends but were statistically insignificant for regional analyses. A case study supported that heavy rains became more erosive during the last 20 years. The results showed the merit of historical data for a better understanding of recent changes in heavy rainfall.

The monitoring of extreme weather events is crucial to adapt measures for farmers, support decision making and refining soil policies especially in the context of climate change. A precondition for an effective monitoring is the availability of indices representing the spatiotemporal dynamic of influencing factors like precipitation and soil coverage. Against this background, we introduce a core algorithms of the „Extreme weather Monitoring and Risk Assessment“ tool (EMRA), which enables a dynamic geodata integration as well as the spatial and temporal identification of extreme weather events in Germany. On the example of the county Uckermark, which is situated in north-eastern Germany, and the crop type winter wheat, we present a process chain for the derivation of a dynamic precipitation and soil cover index for specific phenological phases and parcels. The algorithm couples Germany-wide phenological information, MODIS satellite imagery as well as daily data sets of precipitation (Gerstmann et al. 2016, Möller et al. 2017). The resulting database allows the localization of historical and current hot spot parcels, which show a potentially high risk of soil erosion during sensitive crops’ growing periods. I doing so, more complex soil erosion models can be parameterized in an efficient and focused manner (Volk et al. 2010) or more detailed assessments can be made by using high resolution data. This includes the detection of soil surface’s changes by remote sensed imagery or the comparison of terrain surfaces before and after rainfall events using digital elevation data.

In hummocky landscapes, soil erosion is forming truncated profiles at steep slope positions and colluvial soils in topographic depressions thereby affecting soil organic carbon (SOC) storage. However, the knowledge on the spatial distribution and composition of differently stable organic matter (OM) fractions in arable landscapes is still limited. Here, amount and composition of OM from top- and subsoil horizons at eroded, colluvic, and non-eroded slope positions were compared. The horizons were from a Luvisol at plateau (LV), an eroded Luvisol (eLV) at mid slope (6% slope gradient), a calcaric Regosol (caRG) at steep slope (13%), and a colluvic Regosol (coRG) at hollow position. Water soluble (OM-W) and pyrophosphate soluble (OM-PY) fractions were extracted sequentially. Soil samples, OM fractions, and extraction residues were analyzed with transmission Fourier transform infrared (FIR) spectroscopy. The soluble fractions were 3% of SOC for OM-W and 15% of SOC for OM-PY. For topsoil samples, extraction rates were independent of slope position. The highest intensities of both, C-H (alkyl groups) and C=O (carboxyl groups) absorption band, were found in FTIR spectra of OM-PY from top and subsoil horizons at the steep slope position (caRG). The C-H/C=O ratio in OM-PY decreased with increasing contents of oxalate soluble Fe and Al oxides from steep slope (0.25 for caRG-Ap) towards plateau, and hollow position (0.09 for coRG-Ap) except for the Bt-horizons. This relation is reflecting that the downslope-deposited Ap material, which is higher in poorly crystalline Fe and Al oxides, consists of relatively stable OM. This OM is enriched in C=O groups that are known for their interaction with soil minerals. These OM-mineral interactions may help explaining C storage in arable soil landscapes. (C) 2015 Elsevier B.V. All rights reserved.

The universal soil loss equation (USLE) is widely used to identify areas of erosion risk at regional scales. In Brandenburg, USLE R factors are usually estimated from summer rainfall, based on a relationship from the 1990s. We compared estimated and calculated factors of 22 stations with 10-min rainfall data. To obtain more realistic estimations, we regressed the latter to three rainfall indices (total and heavy-rainfall sums). These models were applied to estimate future R factors of 188 climate stations. To assess uncertainties, we derived eight scenarios from 15 climate models and two representative concentration pathways (RCP), and compared the effects of index choice to the choices of climate model, RCP, and bias correction. The existing regression model underestimated the calculated R factors by 40%. Moreover, using heavy-rainfall sums instead of total sums explained the variability of current R factors better, increased their future changes, and reduced the model uncertainty. The impact of index choice on future R factors was similar to the other choices. Despite all uncertainties, the results indicate that average R factors will remain above past values. Instead, the extent of arable land experiencing excessive soil loss might double until the mid-century with RCP 8.5 and unchanged land management.

For bare soil conditions, the most important process driving and initiating splash and interrill erosion is the detachment of soil particles via raindrop impact. The kinetic energy of a rainfall event is controlled by the drop size and fall velocity distribution, which is often directly or indirectly implemented in erosion models. Therefore, numerous theoretical functions have been developed for the estimation of rainfall kinetic energy from available rainfall intensity measurements. The aim of this study is to assess differences inherent in a wide number of kinetic energy-rainfall intensity (KE-I) relations and their role in soil erosion modelling. Therefore, 32 KE-I relations are compared against measured rainfall energies based on optical distrometer measurements carried out at five stations of two substantially different rainfall regimes. These allow for continuous high-resolution (1-min) direct measurements of rainfall kinetic energies from a detailed spectrum of measured drop sizes and corresponding fall velocities. To quantify the effect of different KE-I relations on sediment delivery, we apply the erosion model WATEM/SEDEM in an experimental setup to four catchments of NE-Germany. The distrometer data shows substantial differences between measured and theoretical models of drop size and fall velocity distributions. For low intensities the number of small drops is overestimated by the Marshall and Palmer (1948; MP) drop size distribution, while for high intensities the proportion of large drops is overestimated by the MP distribution. The distrometer measurements show a considerable proportion of large drops falling at slower velocities than predicted by the Gunn and Kinzer (1949) terminal velocity model. For almost all rainfall events at all stations, the KE-I relations predicted higher cumulative kinetic energy sums compared to the direct measurements of the optical distrometers. The different KE-I relations show individual characteristics over the course of rainfall intensity levels. Our results indicate a high sensitivity (up to a range from 10 to 27 t ha(-1)) of the simulated sediment delivery related to different KE-I relations. Hence, the uncertainty associated with KE-I relations for soil erosion modelling is of critical importance.

Tillage erosion on arable land is a very important process leading to a net downslope movement of soil and soil constitutes. Tillage erosion rates are commonly in the same order of magnitude as water erosion rates and can be even higher, especially under highly mechanized agricultural soil management. Despite its prevalence and magnitude, tillage erosion is still understudied compared to water erosion. The goal of this study was to bring together experts using different techniques to determine tillage erosion and use the different results to discuss and quantify uncertainties associated with tillage erosion measurements. The study was performed in northeastern Germany on a 10m by 50m plot with a mean slope of 8%. Tillage erosion was determined after two sequences of seven tillage operations. Two different micro-tracers (magnetic iron oxide mixed with soil and fluorescent sand) and one macro-tracer (passive radio-frequency identification transponders (RFIDs), size: 4×22mm) were used to directly determine soil fluxes. Moreover, tillage induced changes in topography were measured for the entire plot with two different terrestrial laser scanners and an unmanned aerial system for structure from motion topography analysis. Based on these elevation differences, corresponding soil fluxes were calculated. The mean translocation distance of all techniques was 0.57m per tillage pass, with a relatively wide range of mean soil translocation distances ranging from 0.39 to 0.72m per pass. A benchmark technique could not be identified as all used techniques have individual error sources, which could not be quantified. However, the translocation distances of the macro-tracers used were consistently smaller than the translocation distances of the micro-tracers (mean difference=−26±12%), which questions the widely used assumption of non-selective soil transport via tillage operations. This study points out that tillage erosion measurements, carried out under almost optimal conditions, are subject to major uncertainties that are far from negligible.

The paper introduces different approaches to parallelization based on OpenMPI and OpenMP applied to the Universal Soil Loss Equation (USLE). The USLE was used as a proxy for similar models from the “impact assessment toolbox”. The simulation of impact assessment takes into account climate change and changes in management. Even such a simple model as the USLE can lead to a time-consuming simulation when applied to a large region and when including stochastic data. The paper discusses the pros and cons of the implemented parallelization techniques. The key technology is to divide the simulation into two parts: a binary part implemented in C++ and an interpreter part which controls the parallel simulation written in Python. Python and its modules were also used to preand post-process the simulation.

In this paper the storage potential of soils within a heterogeneous structured hummocky young moraine region for organic carbon is discussed with respect to climate change and erosion. Erosion is discussed to be either a global terrestrial CO2 sink or a source. In hummocky young moraine regions of North East Germany water and tillage erosion are steadily changing factors since the beginning of arable landuse in ancient times. For such topographically complex landscapes the knowledge on soil organic carbon (SOC) dynamics and the limits of carbon storage are still limited. Our objective is to combine data collected during former soil erosion studies with recent findings on (i) soil property and (ii) estimated “optimal” SOC data to predict the SOC storage related to tillage and crop rotation, among others. Classified catenae were analysed for texture, SOC, CO3-C, nutrient contents, and depth of weathering. Optimal SOC contents were estimated on the fine sized particle content. Arable soil at convex slope positions of steep catenae show 4 time smaller SOC stocks as compared to respective forest soils and to arable soils at concave position. Our findings suggest changes in SOC stocks to be almost exclusively related to decomposable carbon pools. Comparison of estimated optimal with measured SOC contents in soils at such positions indicated that such soils could potentially store a surplus of 0.6 to 0.8gkg−1. SOC protection at convex positions is limited by soil texture, and frequent truncation of the respective soil profiles. Whereas truncation followed by downhill transfer may bury SOC at sedimentation/concave positions resulting in long-term SOC storage as far as decomposition is prevented by site conditions.