  Citation: Smit, E.; van Tol, J. Impacts of Soil Information on Process-Based Hydrological Modelling in the Upper Goukou Catchment, South Africa. Water 2022, 14, 407. https://doi.org/ 10.3390/w14030407 Academic Editor: Renato Morbidelli Received: 8 December 2021 Accepted: 24 January 2022 Published: 29 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). water Article Impacts of Soil Information on Process-Based Hydrological Modelling in the Upper Goukou Catchment, South Africa Edward Smit * and Johan van Tol Department of Soil, Crop and Climate Sciences, University of the Free State, Bloemfontein 9300, South Africa; vantoljj@ufs.ac.za * Correspondence: 2015037738@ufs4life.ac.za Abstract: 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 km 2 ), 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. Keywords: hydrological processes; SWAT+ model; hydropedology; land type soil map; hydrological soil map 1. Introduction Soils are a primary control mechanism in determining hydrological processes within a landscape by partitioning precipitation into the different components of the water balance. This is the result of the ability of soils to absorb, store, and transmit water at different spatial and temporal scales [1]. Hydrological processes in the soil determine the volume, variability, and residence times of water resources within a landscape, which in turn affects the functionality and diversity of ecosystems [2]. However, due to the logistical impracticality of measuring hydrological processes at landscape-scale, these processes remain most practically defined using hydrological models to simplify and represent real-world hydrological systems [3,4]. As the need for more sustainable water resources has grown, the need for more ac- curate hydrological models has followed [5,6], particularly in ungauged basins [7]. Soil information is an important input parameter in physically-based hydrological models [8,9], yet the required soil information is often not readily available [10]. Reasons include that ex- isting soil maps were not primarily designed for hydrological modelling purposes [11], and detailed hydrological soil surveys are costly and time-consuming to conduct. Hence, soil information employed in hydrological modelling has remained relatively crude compared Water 2022, 14, 407. https://doi.org/10.3390/w14030407 https://www.mdpi.com/journal/water