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.

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 Green-Ampt 入渗模块的校准

土壤入渗是影响降雨引起的土壤侵蚀的关键因素之一。因此，具有良好代表性的入渗过程是成功进行土壤侵蚀建模的必要先决条件。复杂的自然条件不允许对渗透过程进行完整的数学描述，需要额外的校准参数。EROSION-2D/3D 模型中基于 Green-Ampt 的渗透模块引入了校准参数“皮肤因子”来调整饱和导水率。以前的研究为土壤和植被条件的几种组合提供了表皮因子值。然而，它们的准确性是有问题的，并且估计测量条件以外的皮肤因子会在模型结果中产生很大的不确定性。这项研究汇集了大量降雨模拟实验数据库、最先进的模型参数化方法和线性混合效应模型，以统计分析土壤和植被条件与模型校准参数皮肤因子之间的关系。新的基于经验的皮肤因子估计传递函数显着提高了渗透模块的准确性，从而在本研究中提供了整体 EROSION-2D/3D 模型性能。土壤水分和容重被确定为最重要的预测因子，解释了 82% 的表皮因子变异性，其次是土壤质地、植被覆盖和先前降雨事件的影响。皮肤因子预测的中值绝对百分比误差从使用当前可用方法的 71% 提高到使用提供的传递函数的 30%–34%，与本方法相比，这导致误差传播到模型结果中显着减少。然而，在校准参数和土壤水分之间观察到的强对数关系表明，在 EROSION-2D/3D 中实施的算法对干燥土壤的入渗高度高估，并使最先进的参数化方法受到质疑。测试了另一种参数优化方法，包括校准两个 Green-Ampt 参数的饱和导水率和润湿前沿的水势，并与最先进的方法进行比较，