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

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 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.

Examining the value of hydropedological information on hydrological modeling at different scales in the Sabie catchment, South Africa

“…Rather, percolation values are haphazardly spatially distributed as a function of the soil mapping units. These results are similar to Smit and Van Tol (2022), where large spatial and temporal differences were created between model simulations with differing soil input information. Figure 9 illustrates absolute difference between the average annual percolation values of the two levels of soil information within the Sabie catchment, where the majority of the catchment differed by average annual values greater than 50 mm, especially in the headwaters of the catchment (X3H001, X3H002, X3H003, and X3H024), becoming less pronounced in the drier eastern savanna segments of the catchment.…”

“…Our assumption remains that detailed hydropedological information, based on modern DSM techniques and infield measured soil physical properties represent a more accurate representation of real‐world percolation rates within the Sabie catchment. The ability of the Land Type model to therefore simulate any form of land use change or climate change scenario should be called into question as it is clear the internal hydrological process simulation as determined by the soil input data creates modeling uncertainty (Smit & Van Tol, 2022; Van Tol et al., 2021). The argument remains that hydropedological information may serve as an effective “soft data” tool to better represent internal hydrological processes within a catchment, leading to improved catchment management practices (Seibert & McDonnell, 2002; Smit & Van Tol, 2022; Van Tol et al., 2021); however, further calibration is required to achieve this goal.…”

“…By combining modern digital soil mapping (DSM) techniques (McBratney et al, 2003) with hydropedological insight , soil scientists can now produce detailed large-scale hydrological soil datasets for modeling purposes (Julich et al, 2012;Van Tol et al, 2015;Van Zijl et al, 2020;Van Tol & Van Zijl, 2022;Wahren et al, 2016). This practice has also been underpinned by several studies, which have indicated that improved soil information does indeed improve hydrological modeling efficiency (Bossa et al, 2012;Diek et al, 2014;Romanowicz et al, 2005;Smit & Van Tol, 2022). Examples of these also include where the Soil Land Inference Model (SoLIM) was used to create a more detailed soil map in a data-scarce catchment in north-central Portugal.…”

“…Detailed hydropedological information has also been applied widely in South Africa which saw hydrological soil information being applied to three different catchment sizes (640 km 2 , 550 km 2 , and 54 km 2 ) in an urbanized catchment in South Africa, where hydropedological soil information improved modeling accuracy at all three catchment sizes when compared to readily available soil information (Van Tol et al., 2021). Smit and Van Tol (2022) illustrated that although long‐term streamflow simulations were similar using hydropedological information compared to readily available soil information, hydropedological information substantially improved the simulation of soil hydrological processes. These results also indicated that accurate streamflow simulations do not necessarily mean accurate internal hydrological processes.…”

Model calibration using hydropedological insights to improve the simulation of internal hydrological processes using SWAT+

“…These results are in accordance with other studies focusing on different calibration procedures in southern Africa Chawanda, George, et al, 2020) as well as using hydrological soil information in hydrological modelling (Harrison et al, 2022;Sierra et al, 2018;Smit & Van Tol, 2022). These findings also emphasize the potential of calibrating hydrological models using hydropedology as an information carrier to inform improved hydrological process representation.…”