An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity

USLE-type models are widely used to estimate average annual soil loss at large scales, with the erodibility factor (K) being the sole component that accounts for soil's susceptibility to erosion. The factor includes the information on permeability in the equation, however, most definitions of the K factor consider the soil hydrological influence only very crudely and indirectly. Thus, the direct impact of surface runoff infiltration and drainage on soil erosion is largely neglected. The objective of this study is to incorporate soil hydraulic properties in the K factor map by merging available global-scale measured saturated hydraulic conductivity (Ksat) data with soil texture and organic carbon information into a modified K factor. To achieve this, the Wischmeier and Smith (1978) soil texture-and permeability-based equation (KWischmeier factor) was modified to include Ksat, called Kksat factor. Using the Random Forest machine learning algorithm, the KWischmeier factor and the Kksat factor were each correlated with soil and remote sensing covariates for spatial extrapolation of two independent K factor maps at 1 km spatial resolution. We noted a clear decrease in the mean value of the Kksat factor (0.023 t ha h ha-1 MJ-1 mm-1) compared to the mean value of the KWischmeier factor (0.027 t ha h ha-1 MJ-1 mm-1). The reduction in Kksat factor values was most pronounced in tropical regions reflecting the difference in soil properties (e.g., clay and iron), whereas other climate regions showed relatively minor changes in comparison to the KWischmeier factor as well as to the recent global modeling of Borrelli et al. (2017) (KGloSEM factor maps). As many studies discussed an overall overestimation of (R)USLE based erosion rates compared to measurements, this reduction in the K factor might improve modeled erosion rates in the right direction. The Kksat marks an important initial step in integrating hydraulic properties into the K factor of USLE-type models and can prove their significance in future studies.

Automated summary

The objective of this study is to incorporate soil hydraulic properties into the erodibility factor (K) of USLE-type models. By modifying and improving the existing equations for soil texture and permeability, the study successfully included information on saturated hydraulic conductivity (Ksat) into the calculation of K factor. Using the Random Forest machine learning algorithm, two independent K factor maps with different spatial resolutions were generated. The results show that the decrease in K factor values has a positive impact on the modeling of soil erosion rates.