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Although soils form an integral part of landscape hydrological processes, the importance of soil information in hydrological modelling is often neglected. This study investigated the impact of soil information on streamflow modelling accuracy and hydrological process representation. Two different levels of soil information were compared to long-term streamflow in the upper Goukou catchment (230 km2), South Africa, over a period of 23 years using the Soil Water Assessment Tool (SWAT+). The land-type soil map (LTSM) dataset was less detailed and derived from the best, readily available soil dataset for South Africa currently. The hydrological soil map (HSM) dataset was more detailed and was created using infield hydropedological soil observations combined with digital soil-mapping techniques. Monthly streamflow simulation was similar for both soil datasets, with Nash–Sutcliffe efficiency and Kling–Gupta efficiency values of 0.57 and 0.59 (HSM) and 0.56 and 0.60 (LTSM), respectively. It is, however, important to assess through which hydrological processes were these streamflow values generated as well as their spatial distribution within the catchment. Upon further assessment, the representation of hydrological processes within the catchment differed greatly between the two datasets, with the HSM more accurately representing the internal hydrological processes, as it was based on infield observations. It was concluded that hydropedological information could be of great value in effective catchment management strategies since it improves representation of internal catchment processes.

Impacts of Soil Information on Process-Based Hydrological Modelling in the Upper Goukou Catchment, South Africa