Soils affect the distribution of hydrological processes by partitioning precipitation into different components of the water balance. Therefore, understanding soil-water dynamics at a catchment scale remains imperative to future water resource management. In this study the value of hydropedological insights to calibrate a processes based model. Soil morphology was used as soft data to assist in the calibration of the SWAT+ model at five different catchment sizes (48 km 2, 56 km 2, 174 km 2, 674 km 2 and 2421 km 2) in the Sabie River catchment, South Africa. The aim of this study was to calibrate the SWAT+ model to accurately simulate long-term monthly streamflow predictions as well as to reflect internal soil hydrological processes using a procedure focusing on hydropedology as a calibration tool in a multigauge system. Results indicated that calibration improved streamflow predictions where R 2 and Nash-Sutcliffe Efficiency (NSE) improved substantially, R 2 improved by 2 to 8% and NSE from negative correlations to values exceeding 0.5 at four of the five catchment scales compared to the uncalibrated model. Results confirm that soil mapping units can be calibrated individually within SWAT+ to improve the representation of hydrological processes. Particularly, the spatial linkage between hydropedology and hydrological processes, which is captured within the soil map of the catchment, can be adequately reflected within the model structure after calibration. This research should lead to an improved understanding of hydropedology as soft data to improve hydrological modelling accuracy.

Protected areas are often regarded as pristine land, but in reality, they require rehabilitation and effective management to prevent increased land degradation. Soil management requires soil maps to make informed decisions, which is difficult to create in protected areas due to the large size of land, limited accessibility, little available soil data and limited budgets of such projects. In this paper a hybrid expert knowledge and machine learning digital soil mapping (DSM) method is used to create such maps for Benfontein, a 9900 ha protected area in the semi-arid region of South Africa. The hybrid method uses soil landscape rules to determine virtual soil observations which is added to the training observations used in a machine learning algorithm to create a soil associations map. Soil properties were assigned to each soil class at the 0.1, 0.5 and 0.9 percentile level, to indicate the range of properties at an 80% certainty. The soil maps were interpreted to indicate soil carbon sequestration potential, soil erodibility and off-road driving potential. The soil association map was acceptable as it achieved a kappa value of 0.69. Additionally, it was determined that the site has a large potential for carbon sequestration, the soils are relatively stable against water erosion, and off-road driving should be prohibited on approximately half of the area. The results indicate that the hybrid DSM method is viable to create useful soil maps to inform management decisions in the unique settings of protected areas.