The estimation of soil loss and sediment transport is important for
effective management of catchments. A model for semi-arid catchments in
southern Africa has been developed; however, simplification of the model
parameters and further testing are required. Soil loss is calculated through
the Modified Universal Soil Loss Equation (MUSLE). The aims of the current
study were to: (1) regionalise the MUSLE erodibility factors and; (2) perform
a sensitivity analysis and validate the soil loss outputs against
independently-estimated measures. The regionalisation was developed using
Geographic Information Systems (GIS) coverages. The model was applied to a
high erosion semi-arid region in the Eastern Cape, South Africa. Sensitivity
analysis indicated model outputs to be more sensitive to the vegetation
cover factor. The simulated soil loss estimates of
40 t ha
Soil erosion is a threat to agriculture and the environment, and water-borne sediment disrupts aquatic ecosystem functionality and compromises the quality of water (Msadala et al., 2010). In addition, reservoir sedimentation is a major offsite impact associated with soil erosion (Kusimi et al., 2015). Soil erosion is therefore a critical environmental problem on a global scale, and is also one of the most important environmental problems facing South Africa, particularly in high soil erosion risk areas such the Eastern Cape Province (Le Roux et al., 2008).
Quantifying the rate of soil loss and sediment delivery as well as identifying major contributing factors is important for the effective and sustainable management of catchments. The development of models that estimate erosion and sediment transport is therefore necessary as models enable planners to gain a better understanding of complex natural processes (Xu, 2002) and the data generated can be used to complement scarce observed sedimentation data.
Erosion modelling has commonly been conducted using the Universal Soil Loss Equation (USLE) (Mishra et al., 2006) or models that are based on a similar conceptual understanding (Rabia, 2012). Modifications of the USLE over the years include the Modified USLE (MUSLE) and the Revised USLE (RUSLE) (Mishra et al., 2006). However, most existing erosion models have been developed for European or North American conditions and may not be reliable or appropriate to represent the dynamics of semi-arid catchments in southern Africa. The spatial and temporal variations of erosion processes in semi-arid catchments complicate the process of simplifying patterns of runoff generation and sediment transfer (Hughes, 2008). This relates to catchments in southern Africa that experience extreme hydrological variability, characterised by low annual precipitation and high evaporative losses. Stored sediment loads in these regions can be abruptly flushed out by sporadic high-intensity storms and flash floods.
Internationally-developed models may also require more observed data for model calibration than are typically available for southern African catchments. To address this problem, Bryson (2015) used MUSLE in conjunction with flow input from the Pitman model (Pitman, 1973) to develop a simple erosion and sediment delivery model (WQSED) to effectively represent the sediment dynamics of South African semi-arid catchments. However, the model is characterised by a large number of parameter requirements. In this context, the aim of the current study was to simplify and reduce the parameter requirements and perform a sensitivity analysis, as well as test the erosion and sediment delivery model. This was aimed at improving/developing a model that can provide simulations of soil loss at appropriate spatial and temporal scales. In this regard, Geographic Information Systems (GIS) analyses of readily-available spatial data were explored for regionalising model parameters.
The Tsitsa River catchment is part of the larger Umzimvubu River catchment
and is located in the Eastern Cape Province of South Africa. The present
study concentrates on the lower quaternary sub catchment of the Tsitsa River
catchment, labelled T35E (Fig. 1), which has an area of 492 km
The catchment varies considerably in geology, with areas of high elevation along the escarpment consisting of basaltic lava from the Drakensberg Formation (Jurassic), underlain by a stratum of Triassic sandstone and mudstone (Le Roux et al., 2015). Soil depth is limited on steep slopes and gradually deepens towards the foot slopes and floodplain areas due to colluvium and alluvial deposits. The thin soils on steeper slopes become highly erodible when vegetation is degraded (Dollar and Rowntree, 1995), and this progressively worsens as livestock graze on the slopes.
The climate of the area is characterised by a distinct seasonality in
rainfall and temperatures. Most rainfall (
The Tsitsa River catchment is dominated by the grassland biome, and valley bushveld thrives along river channels in the lower reaches of the catchment (Mucina and Rutherford, 2006). The natural vegetation is largely influenced by altitude and burning (Le Roux et al., 2015); therefore, small patches of Afromontane forest occur along drainage lines and ravines where fire has minimal effect.
Location of study area: Tsitsa River Catchment, Eastern Cape, South Africa.
The MUSLE was used to estimate soil erosion. The MUSLE is given in the
general form of:
In the application of the MUSLE in the present study, the runoff data
consists of a monthly discharge record extending from 1920 to 1990. The lack
of more recent data records influenced the choice of data that were used
within this study. To enable application within MUSLE, the monthly flows
from the Pitman Model (Pitman, 1973) were disaggregated to daily. The Pitman
Model is one of the most widely used moisture accounting models in southern
Africa (Hughes, 2008). Slaughter et al. (2015) present a detailed account of
the flow disaggregation method used for the present study. The disaggregated
flows were used to obtain the volume (m
A sensitivity analysis was performed to determine the changes in model output that occur when different inputs are used in the model (Loucks and Van Beek, 2005). A simple deterministic sensitivity analysis (Benaman, 2003) was used to measure the response of the model output to changes in values of each factor. Minimum and maximum possible values for the study area were used for each factor. According to Loucks and Van Beek (2005), such a range may reflect differences in model outputs between maximum and minimum values for each factor. The model was initially run using catchment parameter values; these were used as the baseline parameters. The next step included routinely running the model using parameters set to the minimum and maximum values respectively. The process involved testing the parameters one at a time so as to evaluate the variations in model output.
The results of the current study were compared to that by Msadala et al. (2010),
who predicted sediment yield for South Africa. It is an improvement
to the Rooseboom and Lotriet (1992) erosion prediction map for South Africa,
and is the largest recent erosion study and a widely-used reference for soil
loss in South African catchments. The Le Roux et al. (2015) sediment yield
results for the Mzimvubu River catchment were also used for comparison
between model outputs as the Tsitsa River is a tributary of the Mzimvubu
catchment. The aforementioned studies used the Soil and Water Assessment
Tool (SWAT) (Neitsch et al., 2005) and RUSLE (Renard et al., 1997) models to
estimate soil loss. The only limitation is that the outputs of the previous
studies are at a coarse spatial scale and provided as mean annual soil loss
ranges in t km
Soil erodibility refers to the susceptibility of the soil to erosional processes and is dependent on soil
characteristics such as structure and texture, which are important
determinants of the aggregate soil strength and water infiltration capacity.
The
The soil type distribution for South Africa was obtained from
readily-available shapefiles from the South African Atlas of Climatology and
Agro-hydrology (Schulze et al., 2007). These data are made available by the Water
Research Commission of South Africa (WRC) and contain the distribution of
soil types and related
Cover factor (
The LS factor was determined using an STRM 30 m
digital elevation model (DEM) in a GIS environment. The DEM was clipped to
the catchment using a mask extraction tool from the ArcGIS toolbox. The DEM
was further conditioned to be depressionless using the “fill sink” command
to determine the maximum downhill slope and the flow direction (e.g., Jain
and Das, 2010). The slope and flow accumulation were derived from the
depressionless DEM. The LS factor map was generated in ArcGIS using the raster
calculator (Jain and Das, 2010) by using the LS equation:
The
Practice factor (
The cover management factor was determined using the National Land Cover
data (NLC, 2014). This is the national-scale grid-mapped land cover and land
use across South Africa. Catchment-specific cover properties were extracted
from the grid by using ArcMap 10.3.1 to clip out catchment-specific data
from the NLC map. The attribute table containing land cover categories was
exported to a Microsoft Excel (2013) spreadsheet where
The management practice factor relates
to conservation methods that are implemented to reduce the rate of soil loss
from agricultural lands (Tiruneh and Ayalew, 2015). The
Using land cover/use maps is a relatively easy and efficient method of
determining the
Modified Universal Soil Loss Equation (MUSLE) erodibility values for the study area.
Sensitivity of soil loss simulations (in t
The result of the sensitivity analysis, summarised in Table 4, shows that
the model was more sensitive to the parameter relating to vegetation cover (
The simulated results showed that the cumulative amount of soil lost due to
erosion in the 492 km
Correlation of runoff and soil loss.
The rate of soil loss correlated well with runoff (Fig. 2). High flows are typically accompanied by increased soil loss. The modelled time series runoff (Fig. 3) shows the typical “flashiness” associated with arid catchments where periods of dryness are followed by large storm events. This triggers rapid erosion, as displayed by the years 1976 and 1977 (Fig. 3). The model output for soil loss also shows the impact of low flows associated with droughts that affected South Africa. The severe drought period of 1980–1983 (Masih et al., 2014) was associated with low flows and reduced soil loss (Fig. 3).
Based on the experience and findings of the present study, regionalisation of the MUSLE inputs using available GIS datasets reduced data requirements of the model. An effective, simple and low input model is essential for southern African catchments with limited observed data. This supports further development of the soil erosion model. Although the use of readily-available datasets to parameterise the model has been shown to yield reasonable results, a disadvantage of this approach is that temporal variations in vegetation cover have not been considered. Accounting for temporal variations in vegetation cover would likely further improve model performance, and should be considered in the future development of the erosion and sediment transport model.
The study examined the use of readily-available GIS coverages to derive
values for MUSLE factors. An a priori regionalisation procedure was used and values
for the LS,
Model output for runoff and soil loss for the Ntabelanga Dam catchment.
The data used in this study are available at
The supplement related to this article is available online at:
The authors declare that they have no conflict of interest.
This article is part of the special issue “Water quality and sediment transport issues in surface water”. It is a result of the IAHS Scientific Assembly 2017, Port Elizabeth, South Africa, 10–14 July 2017.
This research was funded by the Water Research Commission (WRC) South Africa. The data were provided by the Department of Water and Sanitation, Department of Environment Affairs, USGS explorer and the WRC. They are thanked for making the GIS data and flow data readily available and free to access. Edited by: Kate Heal Reviewed by: Seyed Hamidreza Sadeghi and Ju Qian