The soil moisture retention curve (MRC) is time consuming and expensive to measure directly. Several attempts have been made to establish a relation between readily available soil properties, like particle‐size distribution, organic matter content, and bulk density, and the soil moisture retention curve. Those relationships are referred to as pedotransfer functions (PTFs). The objective of this study was to evaluate some PTFs with respect to their accuracy in predicting the soil moisture retention curve. Five widely used and four more recently developed PTFs were selected for evaluation. Seven of the selected PTFs predict moisture retention function parameters, whereas the other two predict the moisture content at certain matric potentials. In order to quantify the prediction accuracy, the mean of the absolute value of mean differences (MAMD), the mean and the standard deviation of the root of mean squared differences (MRMSD and SDRMSD, respectively), and the mean of the Pearson correlation coefficient (Mr) were used. The evaluated PTFs were finally ranked based on these validation indices. The PTFs showed good to poor prediction accuracy with MAMD values ranging from 0.0312 to 0.0603 m3 m−3 and with MRMSDs between 0.0412 and 0.0774 m3 m−3 The SDRMSDs and Mrs ranged from 0.0212 to 0.0349 m3 m−3, and from 0.9468 to 0.9980, respectively. The validation indices computed by the PTF of Vereecken and coworkers gave the best results. Moreover, it predicts moisture retention function parameters, and therefore, this PTF is recommended most to predict the moisture retention curve from readily available soil properties.

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Erosion and loss of organic carbon (OC) result in degradation of the soil surface. Rill and interrill erosion processes on a silt loam soil were examined in laboratory rainfall and flume experiments. These experiments showed that rill and interrill erosion processes have contrasting impacts on enrichment of OC in transported sediment. Rill erosion was found to be nonselective, while for interrill erosion the enrichment ratio of OC, EROC, varied between 0.9 and 2.6 and was inversely related to the unit sediment discharge. At unit sediment discharge values >0.0017 kg s−1 m−1, the EROC remained equal to 1. The enrichment process was not influenced by raindrop impact. Enrichment of OC by “aggregate stripping” was found to be unimportant in our study. This was attributed to the low aggregate stability of the soil and the equal distribution of OC within the different soil aggregate classes.

This study was conducted to evaluate ten closed‐form unimodal analytical expressions to describe the soil‐water retention curve, in terms of their accuracy, linearity, Akaike Information Criterion (AIC), and prediction potential. The latter was evaluated by correlating the model parameters to basic soil properties. Soil samples were taken in duplicate from 48 horizons of 24 soil series in Flanders, Belgium. All sample locations were under forest and hence the samples had, besides their difference in texture, a high variety in bulk density (ρb) and organic matter content (OM). The van Genuchten model with m as a free parameter showed the highest overall performance in terms of goodness‐of‐fit. It had the highest accuracy, the highest degree of linearity, and the lowest AIC value. However, it had a low prediction potential. Imposing the constraint m = 1 − 1/n and hence reducing the number of model parameters by one, increased the prediction potential of the model significantly, without loosing much of the model's accuracy and linearity. A high degree of accuracy and linearity was also observed for the two Kosugi models tested. Restricting the bubbling pressure to be equal to zero resulted in a rather high prediction potential, which was not the case when keeping the bubbling pressure as a free parameter. A major drawback of van Genuchten and Kosugi type models is that they do not define the soil‐water retention curve beyond the residual water content. We further demonstrated that the performance of all but one model in terms of their match to the data increased with increasing clay content and decreasing sand content, which is contradictory to the deterministic character of these models. Bulk density and OM did not have a significant effect on the accuracy of most models.