Water erosion is identified as the most severe type of soil degradation in the Czech Republic. Systematic protection preventing water erosion is not carried out in large areas of agricultural land. The map of the maximum tolerable CP factor value (the cover-management and the support-practice factors) -CP max was compiled in order to assess erosion hazard on agricultural land. It estimates the requirements of the conservation practices which would not cause soil erosion above the tolerable limit of annual soil loss. The map is based on calculations using an adjusted Universal Soil Loss Equation (USLE) and is easy to apply. It has already been applied in the Czech Republic when creating the map of erosion vulnerability for the purposes of delimitation of Standards of Good Agricultural and Environmental Conditions (GAECs), within Cross Compliance. The map covers the whole territory of the Czech Republic (scale 1:1,000,000).

In the Czech Republic, the Universal Soil Loss Equation provides the basis for defining the soil protection strategy. Field rainfall simulators were used to define the actual cover-management factor values of the most extensively seeded crops in the Czech Republic. The second purpose was to assess rainfall-runoff ratio for different crops and management to contribute to the debate of water retention effectiveness during approaching climate change. The methodology focused on multi-seasonal measurements to cover the most important phenological phases. The rainfall intensity was 60 mm·h−1 for 30 min and a plot size of 16 m2. More than 380 rainfall simulation experiments provided data. Soil conservation techniques proved to have a significant effect on runoff reduction. Conventionally seeded maize can reduce the runoff ratio to around 50%. However, cover crops combined with reduced tillage or direct seeding can reduce the runoff ratio to 10–20% for ‘dry’ conditions and to 12–40% for ‘saturated’ conditions. Conventionally seeded maize on average loses 4.3 Mg·ha−1 per 30 min experiment. However, reduced tillage and direct seeding reduce soil loss to 0.6 and 0.16 Mg·ha−1, respectively. A comparison with the original USDA values for maize showed that it is desirable to redefine the crop cover factor.

Abstract. Soil infiltration is one of the key factors that has an influence on soil erosion caused by rainfall. Therefore, a well-represented infiltration process is a necessary precondition for successful soil erosion modelling. Complex natural conditions do not allow the full mathematical description of the infiltration process and additional calibration parameters are required. The Green-Ampt based infiltration module in the EROSION-2D/3D model is adjusted by calibration of the skinfactor parameter. Previous studies provide skinfactor values for several combinations of soil and vegetation conditions. However, their accuracies are questionable and estimating the skinfactors for other than the measured conditions yields significant uncertainties in the model results. This study presents new empirically based transfer functions for skinfactor estimation that significantly improve the accuracy of the infiltration module and thus the overall EROSION-2D/3D model performance. The transfer functions are based on a statistical analysis of the rainfall-runoff simulation database, which contains 273 experiments compiled by two independent working groups. Linear mixed effects models, with a manual backward elimination approach for the predictor selection, were applied to derive the transfer functions. Soil moisture and bulk density were identified as the most significant predictors explaining 79 % of the skinfactor variability, followed by the soil texture and the impact of previous rainfall events. The mean absolute percentage error of the skinfactor prediction was improved from 192 % using the currently available method, to 66 % using the presented transfer functions. Error propagation of the predicted skinfactors into the surface runoff and soil loss on the hypothetical slope showed significant improvement in the EROSION-2D/3D results. A first validation of real rainfall-runoff events indicates good model performance for events with a higher total precipitation and intensity.

Abstract. Soil infiltration is one of the key factors that has an influence on soil erosion caused by rainfall. Therefore, a well-represented infiltration process is a necessary precondition for successful soil erosion modelling. Complex natural conditions do not allow the full mathematical description of the infiltration process, and additional calibration parameters are required. The Green–Ampt-based infiltration module in the EROSION-2D/3D model introduces a calibration parameter “skinfactor” to adjust saturated hydraulic conductivity. Previous studies provide skinfactor values for several combinations of soil and vegetation conditions. However, their accuracies are questionable, and estimating the skinfactors for other than the measured conditions yields significant uncertainties in the model results. This study brings together an extensive database of rainfall simulation experiments, the state-of-the-art model parametrisation method and linear mixed-effect models to statistically analyse relationships between soil and vegetation conditions and the model calibration parameter skinfactor. New empirically based transfer functions for skinfactor estimation significantly improving the accuracy of the infiltration module and thus the overall EROSION-2D/3D model performance are provided in this study. Soil moisture and bulk density were identified as the most significant predictors explaining 82 % of the skinfactor variability, followed by the soil texture, vegetation cover and impact of previous rainfall events. The median absolute percentage error of the skinfactor prediction was improved from 71 % using the currently available method to 30 %–34 % using the presented transfer functions, which led to significant decrease in error propagation into the model results compared to the present method. The strong logarithmic relationship observed between the calibration parameter and soil moisture however indicates high overestimation of infiltration for dry soils by the algorithms implemented in EROSION-2D/3D and puts the state-of-the-art parametrisation method in question. An alternative parameter optimisation method including calibration of two Green–Ampt parameters' saturated hydraulic conductivity and water potential at the wetting front was tested and compared with the state-of-the-art method, which paves a new direction for future EROSION-2D/3D model parametrisation.

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