An experimental laboratory setup was developed and evaluated in order to investigate detachment of soil particles by raindrop splash impact. The soil under investigation was a silty loam Cambisol, which is typical for agricultural fields in Central Europe. The setup consisted of a rainfall simulator and soil samples packed into splash cups (a plastic cylinder with a surface area of 78.5 cm²) positioned in the center of sediment collectors with an outer diameter of 45 cm. A laboratory rainfall simulator was used to simulate rainfall with a prescribed intensity and kinetic energy. Photographs of the soil’s surface before and after the experiments were taken to create digital models of relief and to calculate changes in surface roughness and the rate of soil compaction. The corresponding amount of splashed soil ranged between 10 and 1500 g m⁻² h⁻¹. We observed a linear relationship between the rainfall kinetic energy and the amount of the detached soil particles. The threshold kinetic energy necessary to initiate the detachment process was 354 J m⁻² h⁻¹. No significant relationship between rainfall kinetic energy and splashed sediment particle-size distribution was observed. The splash erosion process exhibited high variability within each repetition, suggesting a sensitivity of the process to the actual soil surface microtopography.

In this study, the particle size distribution (PSD) of the soil sediment from topsoil obtained from soil erosion experiments under different conditions was measured. Rainfall simulators were used for rain generation on the soil erosion plots with slopes 22°, 30°, 34° and length 4.25 m. The influence of the external factors (slope and initial state) on the particle and aggregate size distribution were evaluated by laser diffractometer (LD). The aggregate representation percentage in the eroded sediment was also investigated. It has been found that when the erosion processes are intensive (steep slope or long duration of the raining), the eroded sediment contains coarser particles and lower amounts of aggregates. Three methods for the soil particle analyses were tested: (i) conventional–sieving and hydrometer method; (ii) PARIO Soil Particle Analyzer combined with sieving; and (iii) laser diffraction (LD) using Mastersizer 3000. These methods were evaluated in terms of reproducibility of the results, time demands, and usability. It was verified that the LD has significant advantages compared to other two methods, especially the short measurement time for one sample (only 15 min per sample for LD) and the possibility to destroy soil aggregates using ultrasound which is much easier than using hexametaphosphate.

Field observations and consecutive modelling of soil erosion events proved to be essential for understanding and predicting erosion and sediment transport. An experimental approach often utilizes a large variety of rainfall simulators. In this technical note a complex methodology is introduced, using a mobile rainfall simulator developed at the Czech Technical University in Prague. An experimental setup with two watered plots (16 + 1 m2) was established, which enables simultaneous measurements in two scales and monitoring of surface runoff, flow velocity, infiltration, sediment subsurface flow, vegetation cover effect suspended solids and phosphorus transport, surface roughness and surface evolution under rainfall and other variables. The simulator is built on a trailer transportable by car with folding arm carrying four FullJet WSQ nozzles operating independently. The configuration and water pressure 0.7 bar leads to the total watered area 2.4 x 9.6 m. Average drop size (d50) reaches 1.75 mm for 0.7 bar pressure. Christiansen uniformity index CU reaches 85%. A selection of experimental results highlights both the advantages and the weaknesses of the presented experimental setup.

Future global change is likely to give rise to novel combinations of the factors which enhance or inhibit soil erosion by water. Thus, there is a need for erosion models, necessarily process-focused ones, which are able to reliably represent the rates and extents of soil erosion under unprecedented circumstances. The process-focused cellular automaton erosion model RillGrow is, given initial soil surface microtopography for a plot-sized area, able to predict the emergent patterns produced by runoff and erosion. This study explores the use of structure-from-motion photogrammetry as a means to calibrate and evaluate this model by capturing detailed, time-lapsed data for soil surface height changes during erosion events. Temporally high-resolution monitoring capabilities (i.e. 3D models of elevation change at 0.1 Hz frequency) permit the evaluation of erosion models in terms of the sequence of the formation of erosional features. Here, multiple objective functions using three different spatio-temporal averaging approaches are assessed for their suitability in calibrating and evaluating the model's output. We used two sets of data from field- and laboratory-based rainfall simulation experiments lasting 90 and 30 min, respectively. By integrating 10 different calibration metrics, the outputs of 2000 and 2400 RillGrow runs for, respectively, the field and laboratory experiments were analysed. No single model run was able to adequately replicate all aspects of either the field or the laboratory experiments. The multiple objective function approaches highlight different aspects of model performance, indicating that no single objective function can capture the full complexity of erosion processes. They also highlight different strengths and weaknesses of the model. Depending on the focus of the evaluation, an ensemble of objective functions may not always be necessary. These results underscore the need for more nuanced evaluation of erosion models, e.g. by incorporating spatial-pattern comparison techniques to provide a deeper understanding of the model's capabilities. Such calibrations are an essential complement to the development of erosion models which are able to forecast the impacts of future global change. For the first time, we use data with a very high spatio-temporal resolution to calibrate a soil erosion model.

Water erosion is the main cause of soil degradation in agricultural areas. Rill erosion can contribute vastly to the overall erosion rate. It is therefore crucial to identify areas prone to rill erosion in order to protect soil quality. Research on rainfall-runoff and subsequent sediment transport processes is often based on observing these processes at several scales, followed by a mathematical description of the observations. This paper presents the use of a combination of data obtained by different approaches at multiple scales to validate the SMODERP2D episodic hydrological-erosion model. This model describes infiltration, surface retention, surface runoff, and rill flow processes. In the model, the surface runoff generation is based on a water balance equation and is described by two separate processes: (a) for sheet flow, the model uses the kinematic wave approximation, which has been parameterized for individual soil textural classes using laboratory rainfall simulations, and (b) for rill flow, the Manning formula is used. Rill flow occurs if the critical water level of sheet flow is exceeded. The concept of model validation presented here uses datasets at different scales to study the surface runoff and erosion processes on the Býkovice agricultural catchment. The first dataset consisted of runoff generated by simulated rainfall on plots with dimensions of 2 × 8 m. The second dataset consisted of the runoff response to natural rainfall events obtained from long-term monitoring of 50 m² plots. These two datasets were used to validate and calibrate the sheet flow and infiltration parameters. The third dataset consisted of occurrence maps of rills formed during heavy rainfalls obtained using remote sensing methods on a field plot with an area of 36.6 ha. This last dataset was used to validate the threshold critical water level that is responsible in the model for rill flow initiation in the SMODERP2D model. The validation and the calibration of the surface runoff are performed well according to the Nash–Sutcliffe efficiency coefficient. The scale effect was evident in the 50 m² plots where parameters lower than the mean best fit the measured data. At the field plot scale, pixels with measured rills covered 5% of the total area. The best model solution achieved a similar rill cover for a vegetated soil surface. The model tended to overestimate the occurrence of rills in the case of simulations with bare soil. Although rills occurred both in the model and in the monitored data in many model runs, a spatial mismatch was often observed. This mismatch was caused by flow routing algorithm displacement of the runoff path. The suitability of the validation and calibration process at various spatial scales has been demonstrated. In a future study, data will be obtained from various localities with various land uses and meteorological conditions to confirm the transferability of the procedure.