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.

中文翻译：

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使用降雨径流实验数据库改进了EROSION-2D / 3D模型中绿色安培入渗的校准

摘要。土壤入渗是影响降雨引起土壤侵蚀的关键因素之一。因此，良好的渗透过程是成功进行土壤侵蚀建模的必要前提。复杂的自然条件无法对渗透过程进行完整的数学描述，因此需要其他校准参数。EROSION-2D / 3D模型中基于Green-Ampt的渗透模块可通过校准skinfactor参数进行调整。先前的研究为土壤和植被条件的几种组合提供了皮肤因子值。但是，它们的准确性是有问题的，估计除所测条件外的趋肤因子会在模型结果中产生明显的不确定性。这项研究提出了新的基于经验的，用于换肤因子估计的传递函数，这些函数显着提高了渗透模块的准确性，从而提高了EROSION-2D / 3D模型的整体性能。传递函数基于对降雨径流模拟数据库的统计分析，该数据库包含由两个独立的工作组编制的273个实验。采用线性混合效应模型，并使用手动后向消除方法进行预测变量选择，以得出传递函数。土壤水分和容重被认为是最重要的预测因子，可以解释79％的表皮因子变异性，其次是土壤质地和先前降雨事件的影响。趋肤因子预测的平均绝对百分比误差从使用当前可用方法的192％提高到使用提出的传递函数的66％。在假设的坡度上，预测的表皮因子向地表径流和土壤流失的误差传播表明，EROSION-2D / 3D结果得到了显着改善。实际降雨径流事件的首次验证表明，对于总降水量和强度更高的事件，模型表现良好。