An empirical geomorphology-based approach to the spatial prediction of soil thickness at catchment scale

Catchment modeling in areas dominated by active geomorphologic processes, such as soil erosion and landsliding, is often hampered by the lack of reliable methods for the spatial estimation of soil depth. In a catchment, soil thickness h can vary as a function of many different and interplaying factors, such as underlying lithology, climate, gradient, hillslope curvature, upslope contributing area, and vegetation cover, making the distributed estimation of h challenging and often unreliable. In this work we present an alternative methodology which links soil thickness to gradient, horizontal and vertical slope curvature, and relative position within the hillslope profile. While the relationship with gradient and curvature should reflect the kinematic stability of the regolith cover, allowing greater soil thicknesses over planar and concave areas, the distance from the hill crest (or from the valley bottom) accounts for the position within the soil toposequence. This last parameter is fundamental; points having equal gradient and curvature can have significantly different soil thickness due to their dissimilar position along the hillslope profile. The proposed model has been implemented in a geographic information system environment and tested in the Terzona Creek basin in central Italy. Results are in good agreement with field data (mean absolute error is 11 cm with 8.5 cm standard deviation) and average errors are lower than those obtained with other topography-based methods, where mean absolute error ranges from 47 cm for a model based on curvature, position, and slope gradient to 94 cm for a model based solely on slope gradient. As a further test, the predicted soil thickness was used to determine derived quantities, such as the factor of safety for landsliding potential. Our model, when compared to other empirical methods, returns the best results and, therefore, can improve the prediction of soil losses and sediment production when utilized in conjunction with hydrological and landsliding models.