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Detailed soil information is increasingly sought after for watershed‐scale hydrological modeling to better understand the soil–water interactions at a landscape level. In South Africa, 8% of the surface area is responsible for 50% of the mean annual runoff. Thus, understanding the soil–water dynamics in these catchments remains imperative to future water resource management. In this study, the value of hydropedological information is tested by comparing a detailed hydropedological map based on infield soil information to the best readily available soil information at five different catchment sizes (48, 56, 174, 674, and 2421 km2) using the soil and water assessment tool (SWAT)+ model in the Sabie catchment, South Africa. The aim was to determine the value of hydropedological information at different scales as well as illustrate the value of hydropedology as soft data to improve hydrological process representation. Improved hydropedological information significantly improved long‐term streamflow simulations at all catchment sizes, except for the largest catchment (2421 km2). It is assumed that the resulting improved streamflow simulations are a direct result of the improved hydrological process representation achieved by the hydropedological information. Here, we argue that hydropedological information should form an important soft data tool to better understand and simulate different hydrological processes. Core Ideas: Improved soil information affects hydrological modeling accuracy.Catchment size affects the importance of soil information in modeling accuracy.Soil input resolution affects soil and water assessment tool (SWAT)+ hydrological response unit (HRU) structure and functionality.Hydropedology improves soil hydrological process representation. [ABSTRACT FROM AUTHOR]

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 was examined to calibrate a processes‐based model. Soil morphology was used as soft data to assist in the calibration of the Soil Water Assessment Tool (SWAT+) model at five different catchment scales (48, 56, 174, 674, and 2421 km2) 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 R2 improved by 2%–8%. Nash‐Sutcliffe Efficiency (NSE) improved 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 simulations after calibration. This research will lead to an improved understanding of hydropedology as soft data to improve hydrological modelling accuracy. [ABSTRACT FROM AUTHOR]

Savannas are characterized by the co-occurrence of two different plant life forms: grasses and trees. Herbivory plays a major role in the balance between grasses and trees in savanna ecosystems. The present study aimed to investigate the impact and interactions between long-term (i.e. 20 years) herbivory and/or its exclusion and plant life form on the soil biochemistry of a protected semi-arid savanna ecosystem in the Kruger National Park (KNP), South Africa. To study the effects of herbivory on soil properties, herbivore exclosures (fully fenced areas, partially fenced areas, and an unfenced area) were used in conjunction with plant life form (trees and grasses) were considered. Interaction effects of herbivory and plant life form on soil pH, electrical conductivity (EC), total nitrogen (TN), total carbon (TC), available phosphorus (available P), exchangeable cations (K+, Na+, Mg2+ and Ca2+) cation exchange capacity (CEC), organic matter (OM) and total microbial activity were determined on savanna soils in the Nkuhlu exclosures, KNP. Exclosures where herbivores were present had significantly higher soil pH, The presence of herbivores caused an increase in soil pH, EC, exchangeable Na, CEC, and OM. The influence of the tree canopy was significantly more pronounced in elevating total C and N, exchangeable K+, Mg2+ and Ca2+, CEC and OM than observed in the open grassland zones across all exclosures. The two-way interaction between herbivory and plant life form resulted in significant decreases in TN, TC, exchangeable K, Na and Mg in open grassland areas outside of herbivore exclosures where large animals had direct access, as compared to areas within the exclosures which was protected from animal entry herbivory. This data can be used by national parks as an indicator to increase their knowledge of environmental issues relating to maintaining and preserving landscape features of savannas. [ABSTRACT FROM AUTHOR]

Soil scientists can aid in an essential part of ecological conservation and rehabilitation by quantifying soil properties, such as soil organic carbon (SOC), and is stock (SOCs) SOC is crucial for providing ecosystem services, and, through effective C-sequestration, the effects of climate change can be mitigated. In remote mountainous areas with complex terrain, such as the northern Maloti-Drakensberg in South Africa and Lesotho, direct quantification of stocks or even obtaining sufficient data to construct predictive Digital Soil Mapping (DSM) models is a tedious and expensive task. Extrapolation of DSM model and algorithms from a relatively accessible area to remote areas could overcome these challenges. The aim of this study was to determine if calibrated DSM models for one headwater catchment (Tugela) can be extrapolated without re-training to other catchments in the Maloti-Drakensberg region with acceptable accuracy. The selected models were extrapolated to four different headwater catchments, which included three near the Motete River (M1, M2, and M3) in Lesotho and one in the Vemvane catchment adjacent to the Tugela. Predictions were compared to measured stocks from the soil sampling sites (n = 98) in the various catchments. Results showed that based on the mean results from Universal Kriging (R2 = 0.66, NRMSE = 0.200, and ρc = 0.72), least absolute shrinkage and selection operator or LASSO (R2 = 0.67, NRMSE = 0.191, and ρc = 0.73) and Regression Kriging with cubist models (R2 = 0.61, NRMSE = 0.184, and ρc = 0.65) had the most satisfactory outcome, whereas the soil-land inference models (SoLIM) struggled to predict stocks accurately. Models in the Vemvane performed the worst of all, showing that that close proximity does not necessarily equal good similarity. The study concluded that a model calibrated in one catchment can be extrapolated. However, the catchment selected for calibration should be a good representation of the greater area, otherwise a model might over- or under-predict SOCs. Successfully extrapolating models to remote areas will allow scientists to make predictions to aid in rehabilitation and conservation efforts of vulnerable areas. [ABSTRACT FROM AUTHOR]

In the Kruger National Park (KNP), the lower slopes of catenas have open patches referred to as sodic patches. Fire and herbivores are dominant mediators of vegetation in sodic patches. The effect of fire and herbivores on soil properties of sodic patches remains largely understudied. Moreover, the co-existence of trees and grasses and how they influence savanna soils is an important but poorly understood phenomenon in ecology. Therefore, the present study aimed to determine the influence of 20 years of fire, herbivores, vegetation type, and their interaction on soil biochemistry of sodic patches on the Nkuhlu exclosures in the Kruger National Park, South Africa. We found a higher main effect of fire on available phosphorus, cation exchange capacity, and soil organic matter. The presence of herbivores caused an increase in soil exchangeable cations (K+, Ca2+, Na+, and Mg2+), organic matter, cation exchange capacity, and microbial activity. Tree canopies had a higher effect on total nitrogen, exchangeable Ca and Mg, soil organic matter, and cation exchange capacity than open grassland zones. Our results indicate that changes in vegetation structure due to fire and herbivores and their secondary impact on soil properties should be taken into consideration in managing savannas. Moreover, fire and herbivores play an important role in the maintenance of vegetation type (trees and grasses) in sodic patches. [ABSTRACT FROM AUTHOR]

Reflecting internal catchment hydrological processes in hydrological models is important for accurate predictions of the impact of climate and land‐use change on water resources. Characterizing these processes is however difficult and expensive due to their dynamic nature and spatio‐temporal variability. Hydropedology is a relatively new discipline focusing on the synergistic integration of hydrology, soil physics and pedology. Hydropedological interpretations of soils and soil distribution can be used to characterize key hydrological processes, especially in areas with no or limited hydrometric measurements. Here we applied a hydropedological approach to reflect flowpaths through detailed routing in SWAT+ for a 157 ha catchment (Weatherley) in South Africa. We compared the hydropedological approach and a standard (no routing) approach against measured streamflow (two weirs) and soil water contents (13 locations). The catchment was treated as 'ungauged' and the model was not calibrated against hydrometric measurements in order to determine the direct contribution of hydropedology on modelling efficiency. Streamflow was predicted well without calibration (NSE > 0.8; R2 > 0.82) for both approaches at both weirs. The standard approach yielded slightly better streamflow predictions. The hydropedological approach resulted in considerable improvements in the simulation of soil water contents (R2 increased from 0.40 to 0.49 and PBIAS decreased from 40% to 20%). The routing capacity of SWAT+ as employed in the hydropedological approach reduced the underestimation of wetland water regimes drastically and resulted in a more accurate representation of the dominant hydrological processes in this catchment. We concluded that hydropedology can be a valuable source of 'soft data' to reflect internal catchment structure and processes and, potentially, for realistic calibrations in other studies, especially those conducted in areas with limited hydrometric measurements. [ABSTRACT FROM AUTHOR]

Heavy metals in water sources can threaten human life and the environment. The analysis time, need for chemical reagents, and sample amount per analysis assist in monitoring contaminants. Application of the Fourier Transform Infrared (FT-IR) Spectroscopy for the investigation of heavy metal elements has significantly developed due to its cost effectiveness and accuracy. Use of chemometric models such as Partial Least Square (PLS) and Principle Component Regression Analysis (PCA) relate the multiple spectral intensities from numerous calibration samples to the recognized analytes. This study focused on the FT-IR calibration and quantification of heavy metals (Ag, Cd, Cu, Pb and Zn) in surveyed water sources. FT-IR measurements were compared with the atomic absorption spectrometer (AAS) measurements. Quantitative analysis methods, PCA and PLS, were used in the FT-IR calibration. The spectral analyses were done using the Attenuated Total Reflectance (ATR-FTIR) technique on three river and four borehole water sources sampled within two seasons in QwaQwa, South Africa (SA). The PLS models had good R2 values ranging from 0.95 to 1 and the PCA models ranged from 0.98 to 0.99. Significant differences were seen at 0.001 and 0.05 levels between the PLS and PCA models for detecting Cd and Pb in the water samples. The PCA models detected Ag concentrations more (˂0 mg L−1 on selected sites). Both the PLS and PCA models had lower detection only for Zn ions mostly above 45 mg L−1 deviating from the AAS measurements (<0.020 mg L−1). The FT-IR spectroscopy demonstrated good potential for heavy metal determination purposes. [ABSTRACT FROM AUTHOR]

Soil erosion has serious off-site impacts caused by increased mobilization of sediment and delivery to water bodies causing siltation and pollution. To evaluate factors influencing soil erodibility at a proposed dam site, 21 soil samples collected were characterized. The soils were analyzed for soil organic carbon (SOC), exchangeable bases, exchangeable acidity, pH, electrical conductivities, mean weight diameter and soil particles’ size distribution. Cation exchange capacity, exchangeable sodium percentage, sodium adsorption ratio, dispersion ratio (DR), clay flocculation index (CFI), clay dispersion ratio (CDR) and Ca:Mg ratio were then calculated. Soil erodibility (K-factor) estimates were determined using SOC content and surface soil properties. Soil loss rates by splashing were determined under rainfall simulations at 360 mmh−1rainfall intensity. Soil loss was correlated to the measured chemical and physical soil properties. There were variations in soil form properties and erodibility indices showing influence on soil loss. The average soil erodibility and SOC values were 0.0734 t MJ−1 mm−1and 0.81%, respectively. SOC decreased with depth and soil loss increased with a decrease in SOC content. SOC significantly influenced soil loss, CDR, CFI and DR (P < .05). The soil loss rate was 5.60 t/ha per 8 minute rainstorm of 360 mmh−1. Addition of organic matter stabilize the soils against erosion. [ABSTRACT FROM AUTHOR]

• Sewage effluent and sludge contributes to water resource contamination. • Biochar has the ability to purify faecal wastewater and retain contaminants. • Soil-bed leaching columns actively monitors contaminants migration quicker. • Biochar adsorbents promotes soil recovery of nutrients from treated wastewater. • Biochar has a high potential to control contaminant movements. Biochar is a valuable treatment option for faecal sludge management (FSM). However, the sanitation application rates of biochar in FSM are not well established. There is also a gap in knowledge about the effect of actual raw sewage effluent and sludge on organic and inorganic contaminants migration of biochar treated soil. This study investigated the concentration and migration rates of N, P, E. coli and faecal coliform bacteria through different soil-bed biochar column treatments leached with raw faecal sludge and sewage effluent. Forty-four soil-bed leaching columns with pinewood biochar rates at 5, 10 and 20 t/ha were set at the Bloemspruit wastewater plant, South Africa. The pinewood biochar used had a pH of 10.21, total C composition of 92%, surface area of 517 m²/g, and a pore size of 1.7 nm. It was found that the 20 t biochar per ha treatment with faecal sludge increased water retention (flows of 33 mm/h. at 0 t/ha compared to 0.8 mm/h. at 20 t/ha) and leachates purification. High detections were observed for faecal coliforms and E.coli above 4331 CFU/100 mL from the effluent and faecal sludge in soils without biochar. Detection of E.coli at 20 t/ha decreased to 1 CFU/100 mL while the faecal coliforms still had counts above 10 CFU/ 100 mL. The results showed a decreasing rate of nitrates, phosphates, zinc and copper with an increasing biochar application rate. Pinewood biochar showed significant removal efficiencies of bacteria (between 89 and 98%) and nitrates and phosphates (between 68 and 98%). Significant differences were seen at P < 0.05 between the means of the treatments with and without biochar. The results from the study show that pinewood biochar applied at rates between 5 and 20 t/ha has a high organic and inorganic contaminants reduction potential for FSM. [Display omitted] [ABSTRACT FROM AUTHOR]

E horizons formed in soils by reduction and eluviation are considered to be an indicator of subsurface lateral flow (SLF) between the A and B horizons - a hydrological process important in generating streamflow. There is, however, uncertainty in the interpretation of the hydropedological behaviour of some E horizons. This study used a physical index (SLFI) to estimate the importance of SLF in profiles with E horizons, where SLFI isKsc/Ksc×(tanβ×L) . Data were obtained from the South African Land Type database. For criteria development, 156 profiles were used and an additional 80 profiles were used to validate the criteria. SLFI values were determined for the 156 profiles and then divided into 3 groups, with high, medium and low values. The basic hypothesis was that the individual quantifiable and qualitative soil and landscape properties influencing the pedogenesis of E horizons, and their integrated pedogenetic expression in soil forms, would be most and least strongly expressed in the profiles of the 'high' and 'low' SLFI groups, respectively. This concept was employed in a unique way to allocate numerical values expressing the estimated importance of the criteria with regard to SLF. In order to validate the pedological criteria the 80 test profiles were subjected to a similar procedure to that used to develop the criteria, resulting in an integrated pedological criterion value for each profile, which was then correlated against its SLFI value. Selected measured properties, i.e. organic matter, Fe, Mn and clay content, of the test profiles were also correlated against their SLFI values in the validation process. The results provide supporting evidence for the validity of the pedological criteria. [ABSTRACT FROM AUTHOR]

Macropores play an important role in the rapid transport of water, solutes and pollutants through the soil. Transport through these pores (>0.5 mm) is dominated by gravitational forces (i.e. matrix forces have low impact) resulting in flow rates orders of magnitude higher than rates that would be predicted, posing problems for modelling and understanding water and solute transport through soils. This study aimed to quantify the water conducting macroporosity (WCM) in a range of soils in South Africa and to develop three pedotransfer functions (PTFs) able to predict WCM. Saturated (Ks) and unsaturated (K30) conductivities were measured in situ on 120 soil profiles using double ring and tension infiltrometers methods. Differences between K5 and I(3~ in conjunction with Poiseuille's law and the capillary rise equation were used to calculate WCM. The first two multiple regression functions made use of all available soil properties influencing WCM using a 'best model' and 'backward' analysis approach respectively. The third model used only easily observable soil properties to predict the WCM. The functions were validated using a double-cross method. Results are encouraging with R2 values of 0.78, 0.74 and 0.69 for functions 1, 2 and 3 respectively. [ABSTRACT FROM AUTHOR]

There is an interactive relationship between soil and hydrology. Identifying and interpreting soil properties active in this relationship can enhance our understanding of the hydrological behaviour of soils and the hillslopes in which they occur. This study was conducted in the Weatherley research catchment, South Africa, where a hillslope in the upper part of the catchment was selected for detailed study. Soil properties and their spatial distribution in the hillslope were interpreted and related to their predicted hydrological response. From these interpretations a conceptual model of hillslope hydrological behaviour was developed. Vertical drainage was considered to be dominant in the upper areas of the hillslope as indicated by the presence of freely-drained apedal soils. These soils recharge the mid- and lower slope. Soils showing clear indications of interflow (A/B and soil/bedrock interface) dominate on the midslope. The valley bottom is covered by gleyed soils which is an indication of long periods of saturation. These saturated conditions favour overland flow due to saturation impairing infiltration. The conceptual model was then evaluated using hydrometric measurements in the form of tensiometers and streamflow hydrographs. Results confirm the reliability of the model and accentuate the contribution that soil science can make to the science of hydrology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Urbanization and hydrology have an interactive relationship, as urbanization changing the hydrology of a system and the hydrology commonly causing structural damage to the infrastructure. Hydrological modelling has been used to quantify the water causing structural impacts, and to provide solutions to the issues. However, in already-urbanized areas, creating a soil map to use as input in the modelling process is difficult, as observation positions are limited and visuals of the natural vegetation which indicate soil distribution are unnatural. This project used historical satellite images in combination with terrain parameters and digital soil mapping methods to produce an accurate (Kappa statistic = 0.81) hydropedology soil map for the Cosmo City suburb in Johannesburg, South Africa. The map was used as input into the HYDRUS 2D and SWAT hydrological models to quantify the water creating road damage at Kampala Crescent, a road within Cosmo City (using HYDRUS 2D), as well as the impact of urbanization on the hydrology of the area (using SWAT). HYDRUS 2D modelling showed that a subsurface drain installed at Kampala Crescent would need a carrying capacity of 0.3 m3·h−1·m−1 to alleviate the road damage, while SWAT modelling shows that surface runoff in Cosmo City will commence with as little rainfall as 2 mm·month−1. This project showcases the value of multidisciplinary work. The remote sensing was invaluable to the mapping, which informed the hydrological modelling and subsequently provided answers to the engineers, who could then mitigate the hydrology-related issues within Cosmo City. [ABSTRACT FROM AUTHOR]

In South Africa, the only soil resource available with full spatial coverage is the national resource inventory. Disaggregating this polygon-based inventory, is thus a logical step to create more detailed soil maps covering the entire country. The polygons are large in area encompassing complex soil-terrain patterns and research into disaggregation techniques has been limited. This study aimed to compare 10 algorithms, implemented through a modified DSMART ("Disaggregating and Harmonizing Soil Map Units Through Resampled Classification Trees") model, in their ability to disaggregate two polygons into soil associations in two environmentally contrasting locations. One site had high relief and strong catenal sequences (eastern KwaZulu-Natal Province) and the other site had low relief and a strong geological control of soil types (northern Eastern Cape Province). The algorithms compared were based on previous studies which included k-nearest neighbour, nearest shrunken centroid, discriminatory analysis, multinomial logistics regression, linear and radial support vector machines, decision trees, stochastic gradient boosting, random forest, and neural networks. The method involves stratifying the polygons with landform elements, randomly sampling the landform elements, allocating the soil classes based on the resource inventory, and predicting soil associations across a stack of covariates. This was done in an iterative process, creating multiple realisations of the soil distribution. The performance of each algorithm was based on their kappa and uncertainties. It was found that in general, robust linear models which either utilise an embedded feature selection or regularise covariates, performed best. In the area with high relief and clear toposequences, nearest shrunken centroid was the top performing algorithm with a kappa of 0.42 and an average uncertainty of 0.22. In the area with relatively low relief and complex geology, the results were unsatisfactory. However, a regularised multinomial regression was the top performing algorithm, achieving a kappa of 0.17 and an average uncertainty of 0.84. The results of this study highlight the versatility of a technique to disaggregate South Africa's national resource inventory, where algorithms can be chosen on expert knowledge, model averaging can be performed, the top performing algorithm can be chosen, and algorithm parameters can be optimised. • Ten algorithms were compared in diaggregating a national resource inventory. • The algorithms were compared through a modified DSMART model. • Robust linear algorithms, in general, achieved the best results. • In an area with clear toposequences, the model achieved satisfactory results. • In an area with more soil-geological relationships, the model needs improvement. [ABSTRACT FROM AUTHOR]

The interactive relationship between soil and water (hydropedology) was exploited to determine the hydrological behavior of 52 hillslopes in South Africa. These hillslopes were qualitatively grouped into six hillslope classes based on their dominant hydrological response derived from interpretations of their hydropedology. Applications of the classification system are presented. Soil has an interactive relationship with hydrology. It is a product of water related processes (physical and chemical) and a first order control of the destiny of rainwater. It is mapable with transfer functionality. These properties make it an appropriate entity for classification of hillslope hydrological responses. Hillslopes from all over South Africa were surveyed and hydropedologically interpreted. Soils were classified and based on the interpretation of the dominant hydrological pathway grouped into five hydrological soil types. The type and position of a hydrological soil types in a hillslope served as basis for the hillslope classification. Each of the hillslopes surveyed were assigned to one of six hillslopes classes. A flow diagram of the hydrology is presented. Arrows indicate the dominant flowpaths, and a hydrograph shows the anticipated impact on streamflow. The results made an impact on distributed modeling and land-use decisions, including land-use change to forestry and selection of on-site sanitation limiting water pollution. The composition and distribution of hydrological hillslope classes can serve as a basis for classification of catchments. [ABSTRACT FROM AUTHOR]

• Creation of a hydrological soil map of a macro-scale catchment in South Africa. • A method to handling imbalanced soil data using k-means clustering. • Downscaling spatially localised soil data improves digital soil mapping results. In South Africa, there is a growing demand for large scale detailed hydrological soil maps for modelling and management purposes. However, imbalanced legacy soil information often impedes the accurate creation of such maps by not being representative of the environmental complexity of large-scale catchments and containing imbalanced soil class distributions, often resulting in the loss of minority soil classes, which are often of great hydrological importance (e.g., wetland and riparian soils). In this study, we proposed a new downscaling approach to handle spatially localised legacy soil data within a larger low resolution legacy soil dataset to create an accurate hydrological soil map of the macro-scale (5790 km2) Sabie-Sand catchment using multinomial logistic regression (MNLR). The spatially localised legacy data was downscaled using k-means clustering and added to the broader legacy dataset. Five levels of legacy soil data were analysed in their representation of environmental covariates using QQ-plots and a Welsh's t -test and their mapping accuracy using confusion matrix's and Kappa coefficient statistics. However, MNLR also requires balanced soil classes. The value of the best performing legacy soil dataset was also compared to using all available soil information after both had their soil class distributions fully balanced using Synthetic Minority Oversampling Technique (SMOTE). The 500 ha/observation-SMOTE dataset resulted in the most accurate hydrological soil map with a validation point accuracy of 73% and a Kappa coefficient of 0.60, substantially outperforming the other downscaled soil maps as well as the SMOTE balanced dataset using all available soil information. This was due to the decreased variation between observations and catchment means, where the 500 ha/observation dataset yielded the least variation between soil observation and catchment datasets and well as reducing the class imbalance within the legacy soil data. Downscaling spatially localised legacy soil data for environmental representation is an effective tool to improve digital soil mapping accuracy using MNLR. [ABSTRACT FROM AUTHOR]