Modelling the potential of land use change to mitigate the impacts of climate change on future drought in the Western Cape, South Africa

Abstract

Several studies have shown that climate change may enhance the severity of droughts over the Western Cape (South Africa) in the future, but there is a dearth of information on how to reduce the impacts of climate change on water yields. This study investigates the extent to which land-use changes can reduce the projected impacts of climate change on hydrological droughts in the Western Cape catchments. For the study, the Soil Water Assessment Tool (SWAT +) model was calibrated and evaluated over several river catchments, and the climate simulation dataset from the COordinated Regional Downscaling EXperiment (CORDEX) was bias-corrected. Using the bias-corrected climate data as a forcing, the SWAT + was used to project the impacts of future climate change on water yield in the catchments and to quantify the sensitivity of the projection to four feasible land-use change scenarios in the catchments. The land-use scenarios are the spread of forest (FOMI), the restoration of shrubland (SHRB), the expansion of cropland (CRDY), and the restoration of grassland (GRSL).
The model evaluation shows a good agreement between the simulated and observed monthly streamflows at four stations, and the bias correction of the CORDEX dataset improved the hydrological simulations. The climate change projection features an increase in temperature and potential evaporation, but a decrease in precipitation and all the hydrological variables. The drying occurs across the Western Cape, with the magnitude increasing with higher global warming levels (GWLs). The land-use changes alter these climate change impacts through changes in the hydrological water balance. FOMI increases streamflow and decreases runoff, while SHRB decreases streamflow and runoff. The influence of CRDY and GRSL are more complex. However, all the impacts of land-use changes are negligible compared to the impacts of climate change. Hence, land-use changes in the Western Cape may not be the most efficient strategies for mitigating the impacts of climate change on hydrological droughts over the region. The results of the study have application towards improving water security in the Western Cape river catchments.

Full text

Vol.:(0123456789)
Theoretical and Applied Climatology (2024) 155:6371–6392
https://doi.org/10.1007/s00704-024-04995-7
RESEARCH
Modelling thepotential ofland use change tomitigate theimpacts
ofclimate change onfuture drought intheWestern Cape, South Africa
MyraNaik1· BabatundeJ.Abiodun1,2
Received: 6 January 2024 / Accepted: 28 April 2024 / Published online: 16 May 2024
© The Author(s) 2024
Abstract
Several studies have shown that climate change may enhance the severity of droughts over the Western Cape (South Africa)
in the future, but there is a dearth of information on how to reduce the impacts of climate change on water yields. This
study investigates the extent to which land-use changes can reduce the projected impacts of climate change on hydrological
droughts in the Western Cape catchments. For the study, the Soil Water Assessment Tool (SWAT +) model was calibrated
and evaluated over several river catchments, and the climate simulation dataset from the COordinated Regional Downscaling
EXperiment (CORDEX) was bias-corrected. Using the bias-corrected climate data as a forcing, the SWAT + was used to
project the impacts of future climate change on water yield in the catchments and to quantify the sensitivity of the projection
to four feasible land-use change scenarios in the catchments. The land-use scenarios are the spread of forest (FOMI), the
restoration of shrubland (SHRB), the expansion of cropland (CRDY), and the restoration of grassland (GRSL).
The model evaluation shows a good agreement between the simulated and observed monthly streamflows at four stations, and
the bias correction of the CORDEX dataset improved the hydrological simulations. The climate change projection features
an increase in temperature and potential evaporation, but a decrease in precipitation and all the hydrological variables. The
drying occurs across the Western Cape, with the magnitude increasing with higher global warming levels (GWLs). The
land-use changes alter these climate change impacts through changes in the hydrological water balance. FOMI increases
streamflow and decreases runoff, while SHRB decreases streamflow and runoff. The influence ofCRDY and GRSL are more
complex. However, all the impacts ofland-use changes are negligible compared to the impacts ofclimate change. Hence,
land-use changes in the Western Cape may not be the most efficient strategies for mitigating the impacts of climate change
on hydrological droughts over the region. The results of the study have application towards improving water security in the
Western Cape river catchments.
1 Introduction
The Western Cape Province (in South Africa) is a water-
scarce region. The region experienced one of its worst multi-
year droughts in 2015–2017, when a meteorological drought
cascaded to agricultural, hydrological, and socio-economic
impacts. During this extended drought, the water storage
levels of the Western Cape’s major dams deteriorated to
about 23%; meanwhile, the last 12% of the dam water was
unusable (Botai etal. 2017). The province was declared a
disaster region, and the crisis triggered the local government
to impose severe water restrictions on agricultural, urban,
and industrial consumers, while exploring strategies to avert
a situation where the taps ran completely dry. The drought
had a significant impact on agriculture, livelihoods, and
communities. For instance, the agricultural sector suffered
economic losses estimated at ZAR 5.9 billion, and at least
30 000 jobs were lost (Green Cape 2019). Moreover, the
Western Cape is likely to become drier, and to experience
moderate to strong warming in the near future (e.g., Field
etal. 2014; Midgley etal. 2007). By 2050, it is projected
that Western Cape rainfall may decrease by about 30% from
current levels (Roux 2018). Such reductions in rainfall may
impact dam levels and surface water budgets, with serious
implications for agriculture and industry. Some climate
* Myra Naik
myranaik@gmail.com
1 Climate System Analysis Group, Department
ofEnvironmental andGeographical Science, University
ofCape Town, CapeTown, SouthAfrica
2 Nansen-Tutu Centre forMarine Environmental Research,
Department Oceanography, University ofCape Town,
CapeTown, SouthAfrica
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6372 M.Naik, B.J.Abiodun
projections already suggest that future drought across the
region may be more severe and more frequent (see Naik
and Abiodun 2020). Thus, along with rising temperatures
and increasing evaporation, the implications of drought and
climate change for long-term water security may be serious.
The need to minimize future drought impacts on water avail-
ability in the province has necessitated studies on mitigating
the impacts of climate change on droughts.
Land-use and land-cover (LULC) changes play a cru-
cial role in influencing the hydrology of a basin. They may
have positive or negative impacts on water cycle and water
yield in the basin by influencing processes like evapotran-
spiration, vegetation interception, and surface infiltration
(Guo etal. 2014). However, the magnitude and direction of
these hydrological impacts depend on the type and extent
of the land-use change (Lumsden and Schulze 2003). For
instance, forestation may reduce soil moisture and surface
runoff, while urbanization and human settlement may give
rise to higher storm flow and enhanced discharge peaks. Jia
etal. (2017) showed that removal of forest reduced the soil
moisture across the Loess Plateau (in China). Farley etal.
(2005) reported reductions in runoff following the foresta-
tion of grassland/shrubland and indicated that the reduc-
tion may be higher in drier regions. Youpeng etal. (2010)
found that the expansion of urban areas increased surface
runoff over the Yangtze River Delta, China. Holder etal.
(2019) also showed that conversion of temperate grassland
to crops in the western United Kingdom decreased surface
runoff. To date, however, only few empirical studies (e.g.,
Albhaisi etal. 2013; Gyamfi etal. 2016a; Schütte & Schulze
2017; Warburton etal. 2012) have modelled LULC changes
impacts on hydrological processes over South Africa, and
there remains a dearth of information on how LULC changes
may influence future hydrological drought in the Western
Cape river catchments.
Some studies have speculated that removal of invasive
alien vegetation from the Western Cape river catchments
may reduce the severity of hydrological droughts. This is
because the invasive alien vegetation reduces runoff into
the major dams that supply the Western Cape Water Supply
System. Some estimates suggest that more than 100 million
litres per day (about 20% of Cape Town’s water requirement)
are lost due to invasive alien vegetation in these catchments
(Ground Up 2018). Hence, there is some consensus (e.g., Le
Maitre etal. 2016, 2019; Rostorfer etal. 2015; Turpie 2018)
that the removal of invasive alien vegetation often found in
old forestry plantations surrounding these river catchments
(e.g., Steenbras, Berg and Theewaterskloof) may increase
surface water flows, and thereby offer an economically cheap
and environmentally low risk option to improve water secu-
rity for the greater Cape Town area. Indeed, it may even be
that removal of alien vegetation may also reduce the sever-
ity of hydrological drought due to future climate change.
Catchment rehabilitation studies that re-establish and protect
natural vegetation (after clearance of invasive alien vegeta-
tion) may prove beneficial both from an economic and water
security perspective (e.g., Le Maitre etal. 2019; Rostorfer
etal. 2015; Turpie 2018). Previous research by Tizora etal.
(2016) examined historical land-use in the Western Cape
Province and found that there has been a decrease in for-
est plantations, grasslands, wetlands, and barren lands over
recent years, but also increases in urbanization, mines and
quarries, water bodies, woodlands, thicket and shrubland.
While a previous study in the region also assessed the poten-
tial impacts of future drought in river catchments (Naik and
Abiodun 2020), there remains a dearth of information on
how land-use change may influence the risk of future hydro-
logical droughts in the Western Cape.
For this study we used the Soil and Water Assessment
Tool Plus (SWAT +), the new generation of the SWAT–a
public domain model, jointly developed by the USDA Agri-
cultural Research Service (USDA-ARS) and Texas A&M
AgriLife Research (Arnold, etal. 1998; 2012). SWAT is
widely used, for example, in assessing soil erosion preven-
tion and control, non-point source pollution control and
regional management in watersheds (Krysanova and White
2015; Tan etal. 2019; van Griensven etal. 2012). SWAT is
suitable for hydrological modelling streamflow conditions
over the South African domain (Andersson etal. 2011;
Mengistu etal. 2019; Scott-Shaw etal. 2020; Thavhana
etal. 2018) and has been applied and calibrated over South
African catchments (e.g., Gyamfi etal. 2016a; Ncube and
Taigbenu 2005), as well as extensively used for studying the
impact of land-use changes. However, there remains limited
application of SWAT + to examine on how LULC changes
may influence future hydrological drought. The aim of the
present study is to investigate the extent to which some land-
use changes can mitigate the impact of future droughts in the
Western Cape river catchments. This study is structured as
follows: Section.2 describes the data and methods used in
the study, Section.3 presents the results and discussion, and
Section.4 gives the conclusions of the study.
2 Methodology
2.1 Study region
The Western Cape Province (about 30oS–35oS; 17oE–25oE)
is located in the southernmost part of Southern Africa
(Fig.1a). The economic activities in the province include
manufacturing, construction, mining, and agriculture. The
Mediterranean climate of the region offers warm dry sum-
mers (reaching about 15 to 27°C) and cold winters (about
5 to 22°C), but highly variable rainfall. The Western Cape
is one of South Africa's driest regions, with only 350mm
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6373
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
of rain per year, far less than the national annual average
of 500mm (Dennis & Dennis, 2012). In contrast to much
of southern Africa, which experiences summer rains and
dry winters, the Western Cape is unique in that it receives
its highest precipitation during the austral winter months
of June-July–August (JJA). Rainfall is, however, highly
heterogeneous, varying considerably with the region’s
complex topography (Fig.1c). Mean annual rainfall var-
ies significantly, with mountainous regions receiving up
to 3000mm of rain, while low-lying regions (40m above
sea level) receive less than 200mm (Lakhraj-Govender
& Grab 2019). The prominent Cape Fold Mountain Belt
extends along the length of the province (1300km), form-
ing an L-shaped mountain range that extends north–south,
along the western coast, and east–west along the south-
ern coast. This mountain range acts as an orographic bar-
rier that creates dry interior conditions and augments the
rainfall (through orographic rain) in the coastal parts. The
Western Cape Province has three major rainfall zones,
including the winter, late summer, and all-year seasonality
regimes. The winter rainfall zone occurs along the south-
western and western parts of the province (West Coast),
where annual precipitation ranges from less than 200mm
(in the north) to over 1000mm (over the mountainous
regions). This zone typically receives winter rainfall (May
to August) from mid-latitudecyclones originating over the
South Atlantic, and it is affected by the combination of
the cold Benguela current and the northward displacement
of high-pressure systems (Du Plessis & Scholms 2017).
The late summer (February–March) rainfall zone occurs
over the north-east part of the province (Great Karoo) and
is bound inland by South Africa’s interior plateau. The
all-year rainfall zone occurs along the southern coastline,
stretching eastward from Cape Agulhas (South Coast
and Little Karoo), where the lower annual precipitation
ranges from between 200 and 400mm throughout the year,
though often with a summer maximum, and often in the
form of thunderstorms (Mahlalela etal. 2019; Van Niekerk
Fig. 1 The characteristics of the Western Cape catchments as used in
the study: (a) the four river catchments (Berg, Breede, Olifants and
Gouritz, with red dots to indicate major dams); (b) land-use (the per-
centage of each land-cover type is indicated in brackets); (c) topog-
raphy (Digital Elevation Model); (d) the SWAT + delineations of the
catchments: streams (with streamflow observation stations), subbasin
and channels; and (e) soil types
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6374 M.Naik, B.J.Abiodun
& Joubert 2011). The climate of this zone is influenced by
the movement of warm, moist air from the Indian Ocean,
producing all-year rainfall (Du Plessis and Scholms 2017).
This region's climatic pattern is largely due to the subcon-
tinent's location relative to low-pressure systems (between
40° and 50°S) (Midgley etal. 2005). Seasonally, when the
westerly waves shift northward, these low-pressure sys-
tems bring rainfall to the southwestern part of the coun-
try in the form of cold fronts (Louw 2007). Thus, rainfall
in the Western Cape is typically caused by cold fronts
and associated extratropical cyclones, or by rare westerly
disturbances such as cut-off lows, which frequently cause
extreme rainfall events in the spring and autumn (Midgley
etal. 2005). The Western Cape region is also influenced
by coastal low-pressure systems, which generate hot, dry
‘berg' winds that blow from the interior and cause above-
average warm conditions in the spring and late winter
(Louw 2007). However, it is the large variability in this
dry climate that induces severe droughts in Western Cape.
The dominant soil types comprise sedimentary rocks
(of the Malmesbury Group), which provide rich soil for
agriculture (e.g., viticulture and fruit farming under irriga-
tion and rain-fed wheat cultivation) (DEADP 2011). The
major land cover types include fynbos, renosterveld and
succulent Karoo ecosystems (Lechmere-Oertel 1998). In
this study, we focus on the Western Cape region’s four
river catchments: the Breede (12,348 km2), Berg (7,715
km2), Gouritz (45,715 km2) and Olifants (46,220 km2)
(Fig.1a). Additionally, The Breede basin accommodates
theTheewaterskloof Dam (TWT) that serves as a crucial
water source for the Western Cape, particularly for Cape
Town. As the largest dam within the Western Cape Water
Supply System, it boasts a storage capacity of 480 million
cubic meters. This accounts for approximately 41% of the
water accessible to Cape Town (Marais etal. 2021). The
four Western Cape river catchments are rich in biodiver-
sity and have high ecological importance but have been
severely impacted by land-use change (DEADP 2011). For
example, the Berg River is affected by urban effluent (near
Cape Town) and high salinity while the Breede is affected
by commercial forestation and alien invasive vegetation
(Le Maitre etal. 2000).
2.2 Data
Several types of datasets were used to set up the model for
this study. The datasets include observational data, reanal-
ysis data, and model simulation data. We used QGIS 3.4
(Madeira) with QSWAT + plugin (version 1.2.2) to perform
pre-processing of the SWAT + model input. We obtained
global Digital Elevation Model (DEM) data from the Shuttle
Radar Topography Mission (SRTM; CGIAR Consortium for
Spatial Information; Jarvis etal. 2008) with a 90m × 90m
resolution, digital soil data from the Food and Agriculture
Organization’s (FAO 2004; UNESCO available from Water-
base.Org) global soil databases (version 3.6), and digital
land-use and land cover maps from the USGS Global Land
Cover Characterization (GLCC; United States Geological
Survey National Centre for Earth Resources; available from
the Waterbase.Org) database (Table1). Both observational
and climate model simulation datasets were also analysed for
this study. The observational data for this study includes the
Global Meteorological Forcing Dataset (GMFD; version 3.0;
Sheffield etal. 2006) that has been developed by Princeton
University to drive models of land surface hydrology. The
GMFD dataset is constructed by combining a suite of global
observation-based datasets and it provides near-surface
meteorological data. The dataset provides high resolution
(0.5 degree) daily climate data that is available globally for
1901–2012. Climate model simulation data from the RCMs
in the Coordinated Regional Climate Downscaling Experi-
ment (CORDEX-Africa database website; Giorgi etal. 2009)
were used (Nikulin etal. 2018). This consists of RCM data
at a grid spacing of 0.44° X 0.44° (approximately 50km)
over the African continent. We used seven downscaled
CMIP5 (Coupled Model Intercomparison Project Phase 5;
Taylor etal. 2012) GCMs simulations for past and future
climates (Table2). Furthermore, to minimize the bias in
Table 1 Main SWAT + input data
Input Data Source Description Reference
Digital Elevation Model (DEM) Shuttle Radar Topography Mission;
CGIAR Consortium for Spatial Informa-
tion
90m × 90m resolution Jarvis etal. 2008
Weather Global Meteorological Forcing Dataset 0.5-degree, daily temperature, precipita-
tion, relative humidity, solar radiation,
wind
Sheffield etal. 2006
Stream network Water Resources 2012 Stream network burn in (shapefile) Herold & Bailey 2016
Land Use / vegetation cover USGS Global Land Cover Characteriza-
tion (v3.6)
Digital land-use and land cover maps
(shapefile)
Waterbase.Org
Soil Food and Agriculture Organization Digital soil data (shapefile) Waterbase.Org
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6375
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
the CORDEX dataset, we applied multivariate bias correc-
tion (N-dimensional probability density function transform;
hereafter, MBCn) on the dataset using the method proposed
by Cannon etal. (2015, 2018).
2.2.1 Characterizing droughts
Meteorological drought is typically defined by the amount
of dryness (in comparison to some "normal" or average
amount) and the length of the dry period (months or years)
(NDMC 2021). Hydrological drought is associated with
the effects of periods of precipitation deficits on surface
and subsurface waters (i.e., streamflow, reservoir and lake
levels, and groundwater) (Wilhite 2005). For this study,
meteorological drought is characterized using Standard-
ized Precipitation Index (McKee etal. 1993) and Stand-
ardized Precipitation Evapotranspiration Index (SPEI;
Vicente-Serrano etal. 2010). Both SPI and SPEI are multi-
scalar meteorological drought indices, meaning they can
provide drought information at various timescales. This
versatility makes them suitable for detecting different
types of droughts. Further details on the equation formula-
tion and calculation for the drought index can be found in
previous studies(e.g., McKee etal. 1993; Vicente-Serrano
etal. 2010). Unlike SPI, SPEI can help identify droughts
caused by a decrease in rainfall, higher atmosphericwater
demand (i.e., potentialevaporation), or both. In addition,
four types of hydrological drought were identified using
soil water index (SWI), percolation index (PERCI), runoff
index (RFI) and water yield index (WYLDI) indices over
the Western Cape river basins. All the hydrological indi-
ces were calculated following a similar procedure as for
SPI, but rainfall data were substituted with the relevant
hydrological variable (see e.g., Dutra etal. 2008; Shef-
field etal. 2004; Shukla and Wood 2008). Input data for
hydrological drought computation were derived from the
SWAT + model. SWAT + calculates WYLD as the sum of
surface runoff, lateral flow, and groundwater contributions,
less transmission losses. Only theWYLDI for Theewater-
skloof dam (TWT)is analysed in the study.
2.3 SWAT +
2.3.1 SWAT + description andmodel set‑up
We used the Soil and Water Assessment Tool Plus
(SWAT +). Compared to SWAT, SWAT + offers more flex-
ibility, better independent modules (modularization), is more
computationally efficient, and may result in enhanced model
performance (Bieger, etal. 2017). SWAT is a spatially dis-
tributed, process-based hydrological model (Arnold etal.
2012). It is a small watershed to river basin-scale model and
has been applied to a wide variety of watershed applications
(see Akoko etal. 2021). As a time-continuous model with
multiple components (including, for example, hydrology,
soil, plant growth, nutrient transport, land management), it
is designed to simulate the quality and quantity of surface
and groundwater, and to predict the environmental impact
of land-use, land management practices, and climate change.
The SWAT model simulates watershed processes at the sub-
basin scale, which is then further divided into Hydrological
Response Units (HRUs). These are discrete areas of the same
land-use, slope, and soil characteristics within the sub-basin.
The SWAT + was set up following the procedure
described by Dile etal. (2016). We used the QGIS interface
with the QSWAT plugin and default SQLite database (for
data management), to delineate the basins, create HRUs and
edit the climate inputs before running the model. QSWAT
uses TauDEM (Terrain analysis using Digital Elevation
Models) for watershed delineation. TauDEM provides a suite
of geoprocessing capabilities (e.g., such as pit depression
removal using the flooding approach, calculation of flow
paths and slopes, calculation of contributing areas using
single and multiple flow direction methods, delineation of
stream networks using contributing area threshold, delinea-
tion of watersheds and subbasins; see Tarboton 1997). The
standard specification library of Message Passing Interface
(MPI; Gropp etal. 1996) was used to reduce the processing
times of the DEMs. QSWAT was run with MPI installed
and 20 processors selected. The delineation of the basins
was generated using a burn-in vector file for a predefined
stream network to accurately identify stream positions, and
default threshold values (or number of DEM cells). The four
Table 2 The CMIP5 GCMs simulations used in the study and the
corresponding 30-year period for various global warming levels
(1.5 °C, 2 °C, 2.5 °C and 3.0 °C) under RCP8.5 scenario. All the
GCM simulations were downscaled with the RCA4 model in the
CORDEX and bias corrected with MBCn in this study. Adapted
from Déqué etal. 2017 in Naik and Abiodun 2020 (also see Appen-
dix TableA1)
GCMs Period of global warming levels (GWLs)
1.5°C 2°C 2.5°C 3°C
CanESM2 1999 – 2028 2012 – 2041 2024—2053 2034—2063
CNRM-
CM5
2015 – 2044 2029 – 2058 2041—2070 2052—2081
CSIRO-
Mk3-6–0
2018 – 2047 2030 – 2059 2040—2069 2050—2079
IPSL-
CM5A-
MR
2002—2031 2016 – 2045 2027—2056 2036—2065
MIROC5 2019—2048 2034 – 2063 2047—2076 2058—2087
MPI-ESM-
LR
2004—2033 2021 – 2050 2034—2063 2046—2075
NorESM1-
M
2019 – 2048 2034 – 206 2047—2076 2059—2088
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6376 M.Naik, B.J.Abiodun
primary catchments in the Western Cape were divided into
26 sub-basins and 234 landscape units (LSUs) and 2731
HRUs (Fig.1d). Point sources were automatically added to
each sub-catchment. The sub-basins were delineated with a
299 km2 channel and 299 km2 stream threshold. No merg-
ing of sub-basins was applied. We inserted a slope band
of 10%, and no filtering was used in generating the HRUs.
After the HRU generation, the SWAT + model assimilated
the climate input (i.e., precipitation, air temperature, rela-
tive humidity, wind speed, and solar radiation) and used the
Penman–Monteith approach to calculate potential evapotran-
spiration (PET). A map of land-use characteristics (Fig.1b)
shows that the region is dominated by cropland/grassland
mosaic (CRGR, 30.74%), shrubland (SHRB, 27.89%), and
grassland (GRSL, 23.13%). The remaining area is comprised
of dryland cropland and pasture (CDRY; 9.02%), barren or
sparsely vegetated (BSVG, 4.95%), savanna (SAVA, 1.69%),
cropland/woodland mosaic (CRWO, 1.03%), evergreen
broadleaf forest (FOEB, 0.69%), evergreen deciduous for-
est (FODB, 0.31%), mixed forest (FOMI, 0.43%), urban
(URMD; 0.09%), or water (WATR; 0.02%). Soils in the
Western Cape region are variants of loam and sandy loam
(Fig.1e). Loam predominates in the northern and eastern
catchments (Olifants and Gouritz), whereas sandy loam is
the prevailing soil type in the southern and western regions
(Berg and Breede).
2.3.2 SWAT + calibration andvalidation
Model calibrationis the process of estimatingmodelparam-
eter values to enable ahydrologic modelto match observa-
tions. Model validation, however, is the process of demon-
strating that a given site-specific model can make sufficiently
accurate simulations (Arnold etal. 2012; Refsgaard 1997).
For this study, daily streamflow data from 1 January 1980
to 31 December 1990 (11-year period) were used for cal-
ibration, and the remaining data from 1 January 1991 to
31 December 2005 (16-year period) were used to validate
the model performance. For each catchment, the calibra-
tion and validation of the model were conducted over four
hydrological stations across the region (E2H003, G1H013,
H7H006 and J1H019; Fig.1d; Herold, and Bailey 2016).
The stations were chosen by considering the data quality and
quantity for the calibration and validation periods. Valida-
tion ensures that the set of calibrated parameters performs
reasonably well under an independent data set, i.e., with-
out any further adjustment at different spatial and tempo-
ral scales (Neitsch etal. 2002). The calibration years were
chosen because of the completeness of their observed data
record and the inclusion of representative stations across
the Western Cape’s four primary river catchments (Berg,
Breede, Olifants and Gouritz). The model parameters were
selected for the calibration using the ± 30% change (from the
default values) for 300 model simulations (after Yen etal.
2019). We examined the performance of the SWAT + model
by comparison with observed data. Both graphical model
evaluation techniques and statistical measures were applied.
The statistical metrics used in the evaluations include the
Nash–Sutcliffe model efficiency coefficient (NSE), per-
centage bias (PBIAS), root mean square error (RMSE), and
coefficient of determination (R2). The monthly calibration
and validation of the SWAT + model for streamflow was
performed using an automated Fortran program, following
the Integrated Parameter Estimation and Uncertainty Analy-
sis Tool + framework + (IPEAT + ; Yen etal. 2019). In this
study ten model parameters (CN2, ESCO, EPCO, AWC, K,
SURLAG, DELAY, REVAP_CO, REVAP_MIN; Table3)
were selected for the calibration using Nash–Sutcliffe model
Table 3 Selected model parameters for the SWAT + calibration and their associated best values based on Integrate Parameter Estimation and
Uncertainty Analysis Tool framework (IPEAT + ; Yen etal. 2019) optimization using a range of ± 30% change in the default values
Parameter Description Unit Object type Best value
(% change in
the default
value)
CN2 SCS runoff curve number unit HRU -6.2
ESCO Soil evaporation compensation factor unit HRU -1.9
EPCO Plant evaporation compensation factor unit HRU -7.4
SOL_AWC Soil available water storage capacity mm_H20/mm SOL 8.2
SOL_K Saturated hydraulic conductivity mm/hr SOL 12.4
GW_DELAY Groundwater delay, time required for water leaving bottom of the root zone
to reach the shallow aquifer
days GW 1.6
FLO_MIN Water table depth for return flow mm GW -12.9
REVAP_CO Groundwater “revap” coefficient unit GW 12.4
REVAP_MIN Threshold water depth in shallow aquifer for return to reach to occur mm GW 0.7
SURLAG Surface lag coefficient, controls fraction of water entering reach in one day days BSN 7.6
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6377
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
Efficiency coefficient (i.e., 1-NSE; hereafter NSE) as the
objective function.
2.4 Climate change projection experiments
We applied the SWAT + model to perform climate change
simulations for seven CORDEX experiments. These sim-
ulations are driven by the Representative Concentration
Pathway (RCP) 8.5 scenario, which is the most realistic
scenario of greenhouse gas emissions (Riahi etal. 2011).
Here, the greenhouse gas emissions in the simulation’s sce-
nario increase considerably over time, leading to a radiative
forcing of 8.5 W m−2 at the end of the century. The high-
emissions scenario is frequently referred to as “business as
usual” and represents a likely outcome if society does not
make concerted efforts to reduce greenhouse gas emissions.
Further information about the simulations can be found in
Table1 (see Appendix; Table A1). We assessed the impacts
of climate change at various GWLs (i.e., 1.5°C, 2.0°C,
2.5°C, and 3.0°C), and calculated the difference between
the climate data in the reference period (1971–2000) and the
GWL periods (i.e., GWL minus reference).
2.5 Land‑cover change experiments
SWAT + was applied to perform five experiments (Table4).
The first experiment (CTRL) represents the control scenario,
i.e., using the SWAT + default land-use pattern (Fig.1b).
The SWAT + land-use file was then modified to perform
four additional experiments (Fig.2a-d). The second experi-
ment (FOMI) represents a future land-use scenario with an
increase in invasive alien vegetation. Hence, mixed forest
(represents alien invasive tree species; abbreviated FOMI)
“invades'' catchment areas outside their current distributional
range. The main species that comprise these invasive species
include eucalypts, pines, wattles, and particularly non-native
species that have been identified as problematic to river
catchments. These "alien" species often outcompete indig-
enous vegetation, disrupt ecosystems, and adversely impact
biodiversity (Le Maitre etal. 2016; Kotzé etal. 2010). The
area of land-use change for this scenario was delineated
after consideration of the Working for Water programme
that mapped the distribution of alien invasive tree species
and identified invaded areas (riparian and non-riparian)
across South Africa’s quaternary catchments (Kotzé etal.
2010). The third experiment of land-use change (SHRB)
describes a restoration of shrubland in areas covered by bare
ground/sparse vegetation. The fourth scenario of vegetation
change (CRDY) represents the expansion of crop (agricul-
tural) practices to replace semi-natural vegetation (GRSL,
CRGR and CRWO), while the fifth scenario describes a
restoration of grassland to replace cropland areas (GRSL;
Table4). These experiments were not designed to simulate
accurate scenarios of vegetation change, but rather to assess
the magnitude and type of regional hydrological impact that
might occur from a hypothetical, albeit potential, change in
vegetation. Each change in LULC was implemented as a
step-change to assess its impact. All the simulations were
run for 148years (1951–2099), but the simulation of the first
five years was discarded as the spin-up period, and only the
simulations of the remaining 143years were analysed. The
CTRL simulation was analysed to evaluate the performance
of the hydrological model in simulating the Western Cape’s
climate, and to ascertain the projected future hydrological
impacts that could be due to climate change and resulting
changes in vegetation. All future projections of land cover
change are provided as an anomaly (i.e., those of FOMI,
SHRB, CRDY and GRSL are relative to that of CTRL).
Table 4 Summary of land-use
change experiments in the
Western Cape river catchments
No Experiments Description of LULC pattern Reference
1 CTRL Control, SWAT + (default) land use Figure1b
2 FOMI Increase in mixed forest (i.e., eucalypt, pine, and black
wattle trees invade river catchments)
FOMI occupies 14.45% of the basin
Figure2a
3 SHRB Restoration of shrubland, shrubland replaces bare ground/
sparsely vegetation areas
SHRB replaces BSVG
SHRB covers 32.74% of the basin
Figure2b
4 CRDY Expansion of (rainfed) agriculture cropland
CRDY replaces GRAS, CRGR and CRWO
CRDY covers 63.98% of the basin
Figure2c
5 GRSL Restoration of grassland, grassland replaces crop
GRAS replaces CRDY, CRGR and CRWO
GRAS occupies 63.98% of the basin
Figure2d
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6378 M.Naik, B.J.Abiodun
3 Results andDiscussion
3.1 Evaluation ofthehydrological model (SWAT +)
There is a good agreement between the simulated and
observed monthly streamflow at the four hydrological
stations (i.e., G1H013, H7H006, E2H003, and J1H019;
Fig.1d) during the calibration period (1980–1990) (Fig.3).
The simulated streamflow values closely track the observed
values in reproducing seasonal and annual variations of the
streamflow. The model also captures the observed peaks
and their inter-annual variability. In the calibration period
(1980–1990), the simulation spread often envelopes the
observation, especially at H7H006 and E2H003. At two sta-
tions (i.e., G1H013 and H7H006), the correlation between
the simulated and observed flows is more than 0.5 and the
NSE is also more than 0.5. The model also captures the
differences in the magnitude of the streamflow across the
stations. For instance, it agrees with the observation that,
among the four stations, the streamflow is largest at H7H006
(i.e., Breede river) and G1H013 (i.e., Berg river) and lowest
at J1H019 (i.e., Gouritz river). However, there are some
notable model biases in the simulation. The model overesti-
mates the magnitude of the peaks in some years and under-
estimates them in others. For instance, it overestimates the
observed streamflow by more than 40 m3 s−1 at H7H006 in
1986 and underestimates itby up to 120 m3 s−1 at H7H006
in 1983. The model percentage bias (PBIAS) ranges from
-43% (at G1H013) to 73% (at J1H09). Several reasons can
be attributed to the model biases, ranging from shortcom-
ings in model development to uncertainties in model input
and set-up. For example, the underestimation of the stream-
flow conditions may be due to overestimation of evapora-
tion. Unfortunately, there is no evaporation data for further
analysis on the biases. The biases may also be due to errors
thatexist within the climate and hydrological datasets. The
resolution of the GMFD reanalysis may be insufficient to
fully resolve all the climate processes in the Western Cape’s
complex topography. The resolution of the soil and land-use
dataset may also be too coarse to represent all the hydro-
logical processes in the catchments. This problem has also
been reported and identified in previous studies that applied
Fig. 2 The land use patterns used for each experiment: (a) FOMI, (b)
SHRB (c) CRDY (d) GRSL. The land-use types are urban residen-
tial medium density (URMD), cropland/dryland and pasture (CRDY),
cropland/grassland mosaic (CRGR), cropland/woodland mosaic
(CRWO), grassland (GRSL), shrubland (SHRB), savanna (SAVA),
forest—deciduous broadleaf (FODB), forest—evergreen broad-
leaf (FOEB), forest—mixed (FOMI), water (WATR), bare ground,
sparsely vegetated (BSVG). Each land-use type (as a percentage
across Western Cape) is indicated in brackets
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Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
SWAT over South African catchments (e.g., Dabrowski
2014). The biases may also occur from using naturalised
flow data as observed streamflow, since this removes the
effects of human impacts (e.g., reservoirs, dams) to estimate
the unimpaired flow hydrology.
Nevertheless, the model's performance in this catchment
is deemed satisfactory. In most cases, the model perfor-
mance is better (or the same) during the validation period
(1991–2005) than in the calibration period (1980–1990)
(Fig.3). For instance, at station G1H013, the model perfor-
mance improves for NSE (from -0.4 to 0.5), PBIAS (-43.2%
to -40.2%), and RMSE (from 17.5 m3 s−1 to 17.2 m3 s−1).
Also, at station E2H006, it improves for PBIAS (from -25%
to -14%), RMSE (34.2 m3 s−1 to 30.7 m3 s−1), and for R2
(from 0.5 to 0.6). However, the model improvement depends
on statistical metrics used in the performance evaluation. For
example, at station H7H003, while the model performance
improves for NSE (from 0.5 to 0.6) and R2 (0.5 to 0.7), it
deteriorates for PBIAS (-30.8% to -31.2%) and RMSE (14.6
m3 s−1 to 15.4 m3 s−1). The difference in model performance
in the two periods may be due to differences in dominant
processes or differences in the lengths of the observation
data gaps in the periods, as evident in station J1H019, where
the paucity of observation data makes the results unreliable.
In accordance with established model evaluation criteria out-
lined by Moriasi etal. (2007; 2015), the NSE exceeds 0.50,
and the PBIAS remains within + 25% for streamflow, par-
ticularly notable for Stations H7H006 and E2H003. Results
obtained from the evaluation of the SWAT model in this
study are consistent with findings from previous studies con-
ducted over other river basins in South Africa. These studies
show good transferability of the SWAT for understanding
hydrological processes as show good agreement between the
simulated and observed streamflow with satisfactory model
performance. Andersson etal. (2011) showed the value of
model calibration reduced the predictive uncertainty of the
Fig. 3 Comparison of the observation and SWAT + simulated stream-
flow during the calibration and evaluation periods at four hydrologi-
cal stations (i.e., G1H013, H7H006, E2H003 and J1H019) in the
Western Cape river catchments. The values of the statistical metrics
(i.e., NSE, PBIAS, RMSE, and R2) used in quantifying the compari-
son are indicated
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6380 M.Naik, B.J.Abiodun
model over the Thukela River Basin. Welderufaelet al.
(2013) also showed good performance of SWAT in simulat-
ing the monthly streamflow over the Modder River Basin
(NSE of 0.57 for the calibration). Gyamfi etal. (2016b)
found satisfactory model performance in both the calibration
and validation periods (i.e., NSE and R2 values were greater
than 0.6, and PBIAS values in the range of ± 10%), but in
contrast to our findings however, SWAT performed better
(or the same) during calibration period than in the valida-
tion period. Nevertheless, the generally good performance
of the SWAT + model at the stations suggests that the model
provides a reliable representation of hydrological processes
in Western Cape catchments, as is needed in this study.
3.2 Evaluation oftheclimate simulation datasets
(CORDEX)
To provide confidence on the quality of the climate simula-
tion datasets (CORDEX), which are used as input data in
the SWAT + hydrological projections, we examine the per-
formance of the CORDEX simulations (with and without
bias-correction) during the historical period (1971–2000).
In the evaluation, we compare the performance of the origi-
nalCORDEX dataset (CORDEX_ORG) with the multivari-
ate bias corrected CORDEX dataset (CORDEX_MBCn).
With reference to the reanalysis data (GMFD) results, the
model evaluation focuses on how well the dataset reproduces
different climate variables (e.g., mean temperature (Tmean),
precipitation (PRE), potential evaporation (PET) and evap-
oration (ET)), and how accurately SWAT + replicates the
Fig. 4 Spatial distribution of climate variables over the Western Cape river catchments as simulated by SWAT + using GMFD
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6381
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
hydrological variables (e.g., soil water (SW), percolation,
runoff, streamflow, and stream ET) over the catchments.
The CORDEX_ORG simulation captures the spatial dis-
tribution of climate variables across the Western Cape, but
with some notable biases (Fig.4). The MBCn bias correc-
tion reduces the errors (PBIAS) and improves the correlation
(r) between the CORDEX and GMFD results. For example,
the bias correction reduces Tmean PBIAS from -13% to
-0.6% and increases the r from 0.87 to 1.0. Although it does
not improve on PRE PBIAS, it increases the r from 0.75 to
1.0. However, the corrected dataset (i.e., CORDEX_MBCn)
generally improves on the original dataset (CORDEX_ORG)
and reproduces the spatial distribution of climate variables
better. In agreement with GMFD, CORDEX_MBCn simu-
lates the highest temperature over the north-western part
of the basin and the lowest temperature over the mountain
range at the centre of the study domain. It also captures the
location of the maximum PRE on the south-western side of
the mountain. Both datasets (GMFD and CORDEX_MBCn)
agree on the difference in spatial distribution of PET and ET
over the region. While PET increases from south to north,
ET increases from north to south, suggesting the spatial
distribution of evaporation of the basin is more driven by
water availability (PRE) rather than by atmospheric demand
(PET). However, in both datasets, the capability of the West-
ern Cape catchments in meeting the atmospheric demand
(i.e., ET/PET) decreases from south-west to north-east in
accordance with PRE distribution. Generally, the capabil-
ity of catchments to meet the atmospheric water demand is
Fig. 5 Same as Fig.4 but for hydroclimate variables
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6382 M.Naik, B.J.Abiodun
higher over the Berg and Breede than over the Olifants and
Gouritz.
The bias correction of the CORDEX dataset also
improves the hydrological simulations over the catchments,
but the performance of CORDEX_MBCn is lower for hydro-
logical variables (i.e., 29% ≤ PBIAS ≤ 116%) than for cli-
mate variables (-6% ≤ PBIAS ≤ -0.6%) (Fig. 5). However,
in reference to GMFD results, the CORDEX_MBCn still
captures the spatial distribution of hydrological variables
across Western Cape well. For instance, in both datasets,
the spatial distributions of SW, percolation, and runoff
follow that of PRE. In contrast to CORDEX_ORG, COR-
DEX_MBCn agrees with GFMD that the Breede catchment
features the highest streamflow, while the Gouritz river has
the lowest. Nevertheless, in both datasets, the ET is higher
over the Gouritz than over the Breede. This is because PET
is higher over the former than the latter, and as long as there
is water in a river, the ET over the river is not limited by a
lack of precipitation or by low soil moisture.
3.3 Impacts ofclimate change onclimate
andhydrological variables
Figure6 shows the timeseries of projected climate changes
across the Western Cape for the period 1980–2100.
These projections show a gradual increase in mean tem-
perature (Tmean), from about 1°C in 2030, and reaching
about + 4 °C by the end of the century. The spatial dis-
tribution of the warming is generally uniform across the
region, but the magnitude of the warming increases with
the GWLs (Fig.7). For example, the temperature increase
is projected to be between 1.5 – 2.5°C under GWL1.5 and
GWL2.0, or as high as 3.0 – 4.5°C under GWL2.5 and
GWL3.0 across the catchments. These climate projections
for temperature are in line with those of previous studies,
Fig. 6 Time series of hydroclimate variables over the Western Cape (1971—2099)
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6383
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
which suggests increased temperatures (up to 4°C) could
occur in this region before the end of the century (Haensler
etal. 2011; Field etal., 2014; Naik and Abiodun 2016). Note
that for higher GWLs, increased warming tends to occur
further inland, over the north-eastern regions. For example,
under GWL3.0, the increase is about 4.5°C, over the north-
eastern parts of the Olifants catchment, but about 2.5°C
along southern parts of the Berg and Breede. This is in line
with previous projections and may be due to the moderating
effects of the ocean on temperature along the coastline (e.g.,
Mbokodo etal. 2020; Naik and Abiodun 2020). This pro-
jected increase in temperature leads to an increase in poten-
tial evaporation (PET), which also shows a gradual increase
by the end of the century (from about + 10mm mon−1 in
2030, and to about + 20mm mon−1 by 2100; Fig.6). The
areas where the largest changes in PET occur compare well
spatially to areas where the largest changes in Tmean occur
(Fig.7), and the magnitude of the PET change increases
with higher GWLs (2.0, 2.5 and 3.0). The largest increase
occurs over north-eastern parts of the Olifants and northern
Gouritz catchments, where PET is projected toincrease by
+ 10mm mon−1 (GWL1.5) to more than + 20mm mon−1
(GWL3.0), respectively. Several studies confirm that PET
rates may increase with higher temperatures in the future.
This is because global warming (and associated higher tem-
peratures) lead to an increase in the vapor pressure deficit
of air and increased atmospheric evaporative demand (Dai
etal. 2018; Yuan and Bai 2018).
The projected changes in precipitation (PRE) are more
complex than those of Tmean. PRE projections show no
Fig. 7 Spatial distribution of projected changes in climate variables
over the Western Cape catchments at four global warming levels
(GWL1.5, GWL2.0, GWL2.5, and GWL3.0). The vertical strip (|)
indicates where at least 80% of the simulations agree on the sign of
the changes, while horizontal strip ( −) indicates where at least 80%
of the simulations agree that the projected change is statistically sig-
nificant (at 99% confidence level). The cross ( +) shows where both
conditions are satisfied; that is, the change is robust
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6384 M.Naik, B.J.Abiodun
discernible change in the trend before 2040, but a decreasing
trend afterwards until the end of the century. Nonetheless,
the pattern of the PRE change differs spatially across the
Western Cape catchments. While there is a general decrease
in PRE across the region, the projections under GWL 1.5
suggest more drying (about -2mm month−1) may occur over
some areas, like parts of the southern Gouritz and the south-
eastern Breede, while only modest change occurs over other
areas, like the central Olifants. Generally, this pattern is sim-
ilar across the GWLs, only that the magnitude of the change
increases under higher GWLs and that the spatial extent of
the drying increases. For instance, the area of maximum
drying includes the southern margin of the Gouritz and
Berg (-1.5mm mon−1) for GLW2.0, and it extends cross the
entire Gouritz, Breede, and parts of the Berg and Olifants,
where it exceeds -2.5mm month−1 for GWL 3.0. Only a few
areas show wetter conditions over the Western Cape in the
future. The exception to the general drying over the region
occurs over a limited area of the central Berg (near the large
dams), where increased PRE (+ 2mm mon−1) occurs, and
over central-southern parts of the Olifants (+ 1mm mon−1).
However, this occurs for GWL1.5, but not under higher
GWLs, when the drying signal dominates. The large-scale
forcing mechanisms potentially responsible for the decreased
rainfall across the Western Cape region may include an
increase in the intensity and frequency of upper-level highs,
and migration of subtropical anticyclones towards the mid-
latitudes across the Southern African subcontinent (Sousa
etal. 2018). This poleward shift of the Southern Hemisphere
moisture corridor and the displacement of the South Atlantic
storm-track may create significantly drier conditions under
conditions of future climate change. The projected evapora-
tion (ET) can be linked to the changes in PRE. While there
is no discernible trend in future ET timeseries projections
over the Western Cape (Fig.6), there are changes spatially
over the Western Cape. The spatial pattern of the ET change
is not uniform across the Western Cape for GWL1.5. ET
may decrease over catchments, such as the Gouritz (-2mm
mon−1), but increase over others, such as the south-central
Olifants (+ 1mm mon−1) or the Berg (+ 1mm mon−1). Gen-
erally, however, the areas where the largest changes in ET
occur are comparable to the areas where PRE changes occur.
The magnitude of the ET change also increases with higher
GWLs (2.0, 2.5 and 3.0). For example, the projections show
that, for higher GWLs, ET may decrease over much of the
region (-1.5mm mon−1), and particularly over the south-
western Gouritz and central Olifants. This decrease may be
up to -1.5mm mon−1 under GWL1.5, -2mm mon−1 under
GWL2.0 and exceed -2.5mm mon−1 under GWL3.0.
The projected decrease in hydrological variables can be
linked to the decrease in PRE (Figs.6 and 7). The spatial
distribution of these hydrological changes is consistent with
that of PRE. For example, the projections show a general
decrease in soil water (SW) across the region (-10mm
mon−1), that is enhanced over the eastern Olifants (-20mm
mon−1) and the central Berg (-20 mm mon−1), under
GWL3.0. The spatial distribution of the drying is compara-
ble for higher GWLs, except that the magnitude of the dry-
ing increases with the GWLs. This is because the projected
decrease in PRE negatively affects the overall soil water
budget of the affected areas. With time, this causes a gradual
decrease in volumetric water content of soil over the region
(SW; about -10mm) from 2050–2100 (Fig.6). This negative
change in storage (i.e., SW content) occurs when outputs,
such as percolation (PERC) and surface runoff (RUNOFF)
exceed PRE. As seen in Fig.7, the distribution of the future
drying is spatially comparable for these variables. Since the
soil does not reach saturation, reduced PERC occurs and
there is also a consistent decrease in overland RUNOFF.
(Note the differences in PERC and runoff are comparable
to the spatial variation in soil types (Fig.2e), because loam
and sandy loam textures differ in their water-holding capac-
ity.) This is consistent with our projected changes in hydro-
logical variables over channels (Figs.8 and 9). There is a
decrease in channel precipitation over many streamareas of
the Western Cape (StreamPRE; -10mm mon−1), and this
drying increases under higher GWLs. For instance, drying
of -8.4mm mon−1 occurs along channels of the southern
Gouritz and Breede, under GLW2.0, and it extends across
the entire Western Cape, where it exceeds up to 16.9mm
mon−1 for GWL 3.0. The projected increase in PET leads
to an increase in stream ET, which occurs for all GWLS,
and over all the Western Cape catchments, but is enhanced
over the Olifants and Gouritz catchments (+ 19mm mon−1).
Since more water is lost through evaporation, there is an
associated decrease in streamflow over many of the region’s
channels (+ 10mm mon−1), but particularly over the Olifants
and Gouritz catchment (+ 19mm mon−1). This is consistent
with the decreasing trend in future projection of channel
discharge entering the largest dam in the Western Cape at
Theewaterskloof (TWT; Fig.6) and reaching about -2 m3 s−1
by the end of the century. While there are few studies on the
hydrological projections for the Western Cape, several stud-
ies recognize that this region has historically experienced
increased surface aridity due to decreased precipitation. The
hydrological projections here are in line with historical anal-
ysis, which found increased surface temperature, decreases
in precipitation, and occurrence of dry spells, which have
depleted water resources in the Southwest Cape (Jury 2018).
3.4 Impacts ofland‑use change onhydrological
variables
The land-use changes have different impacts on the future
hydrological water balance due to their influence on the
curve number (CN; Fig.10). With FOMI, there is a decrease
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6385
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
in CN, which is consistent with the area of mixed forest
land-use change (CN of about -3, Figs.10 and2a). The
lower runoff potential is linked to more permeable soil sur-
face and hence, there is less runoff from rainfall. As such,
FOMI increases the amount of soil water (SW) available
over the catchment. For instance, SW increases occur over
parts of the southern Berg (+ 2 mm mon−1) catchment,
and the southwest Breede (+ 2mm mon−1) catchment. The
increase in SW may be due to the deeper rooting network
of trees, which alters the physical properties of the upper
soil layers, improving infiltration and percolation. As the
amount of available SW at the surface increases, more water
is available for conversion through evapotranspiration (ET).
Thus, for FOMI, ET also increases over these parts of the
Berg and Breede (+ 0.5mm mon−1) that are spatially com-
parable to that of SW. Although FOMI generally decreases
runoff over the region (-0.2mm mon−1), particularly over
parts of the central Berg and western Breede (-0.5mm
mon−1), it also increases the streamflow over most catch-
ments. The largest increases in streamflow occur over parts
of the western Olifants, the northern Berg, and the south-
eastern Gouritz (about + 61.9 m3 s−1; Fig.10). Decreases
in streamflow occur only in some sub-basins of the west-
ern Breede and southern Berg (about -61.9 m3 s−1). These
hydrological changes suggest thatthe increase in streamflow
is unlikely due to surface runoff (from precipitation run-
off over the landscape i.e., overland flow) but rather due
to percolation of water past the soil profile (subsurface) to
become groundwater recharge, or via lateral movement in
the profile (interflow), which eventually reaches streamflow
(not shown). Several studies confirm that forests may have a
positive impact on soil hydraulic properties, by functioning
as water harvesters and contributing to infiltration, deeper
Fig. 8 Same as Fig.8, except for hydrological variables
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6386 M.Naik, B.J.Abiodun
drainage, and groundwater recharge (e.g., Bargués Tobella
etal. 2014; Luo etal. 2020).
The hydrological impact of SHRB is similar to that of
FOMI. With SHRB, there is a decrease in CN (of about
8) over the areas where shrubland restoration replaces
bare ground (Figs.10 and 2b). This occurs particularly
over sub-basins of the northern and central Olifants and
northern Gouritz. Over the central and northern Olifants
sub-basins, SHRB increases SW (+ 2.0 mm mon−1) and
increases ET (+ 0.4mm mon−1). But SHRB decreases run-
off over the region (-0.2mm mon−1), particularly over the
central Olifants and northern Gouritz (-0.5mm mon−1).
However, unlike FOMI, SHRB decreases water yield over
most Western Cape catchments. The streamflow decreases
(about -61.9 m3 s−1) occur over the Olifants, Gouritz and
Breede. However, SHRB does not influence water yield over
the Berg since no land-use change occurred in this catch-
ment. Overall, these hydrological changes suggest that the
restoration of shrubland may decrease streamflow and may
have negative impacts on the water security of the region.
Nevertheless, it is crucial to acknowledge the limitations in
extending these findings to implications for regional ecology
conservation. Removing shrubland vegetation may lead to
adverse impacts, as it eliminates the protective barrier and
leaving bare ground exposed to erosion.
The response of CRDY is complex, as it includes both
increases and decreases in hydrological conditions. CRDY
(i.e., expansion of cropland/dryland area) increases the
curve number (CN), such that the area of LUC is spatially
consistent with the area of higher CN (about + 5; Figs.10
and2c). While CRDY decreases SW over some sub-basins,
it increases it in other sub-basins. For instance, it decreases
SW over the eastern and southern Berg (-2mm mon−1),
the northern and southern Breede (-2mm mon−1) and the
eastern Gouritz (-1.6mm mon−1), but it increases SW over
the southwestern Olifants (+ 0.8 mm mon−1). However,
the hydrological response of CRDY differs from FOMI, in
that, while the SW changes are spatially consistent with ET
in some catchments, they are not consistent in others. For
instance, the decrease in ET (-2mm mon−1) over the Breede
is consistent with the decrease in SW over the catchment, and
the increase in ET (+ 0.4mm mon−1) over the southwestern
Olifants also agrees with the increase in SW over this sub-
basin. However, the increase in ET (+ 0.2mm mon−1) over
Fig. 9 Same as Fig.8, except for river channel variables
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6387
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
the southern Breede is not consistent with the decrease in
SW over the sub-basins. Moreover, CRDY increases runoff
and streamflow over the southern Olifants and eastern Gour-
itz (+ 0.5mm mon−1 and about + 61.9 m3 s−1) but decreases
them over much of the Breede (-0.5mm mon−1 and up to
about -123.8 m3 s−1, respectively). Unlike with FOMI, the
increase in the streamflow obtained with CRDY is likely
due to an increase in surface runoff over the landscape. It
may be that the percolation rate is slower with CRDY than
with FOMI. Nevertheless, the CRDY changes have complex
impacts on the hydrology of the region, possibly due to the
complex topography and local soil conditions.
The hydrological response to GRSL is also complex.
GRSL (i.e., restoration of grassland) generally decreases
the curve number (CN), with areas of lowest CN spatially
consistent with areas converted from CRDY to GRSL (CN
about -4; Figs.10 and2d). GRSL is associated with a
Fig. 10 Impact of land use changes on hydrological variables in the
Western Cape catchments. The vertical strip (|) indicates where at
least 80% of the simulations agree on the sign of the changes, while
horizontal strip ( −) indicates where at least 80% of the simulations
agree that the projected change is statistically significant (at 99% con-
fidence level). The cross ( +) shows where both conditions are satis-
fied; that is, the change is robust
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

6388 M.Naik, B.J.Abiodun
lower curve number (compared to CRDY) and hence, less
runoff from rainfall. Compared to CRDY, GRSL increases
SW over many catchments. For example, SW increases
occur over the southwestern Olifants (+ 2mm mon−1), the
eastern Gouritz (+ 2mm mon−1) and several sub-basins of
the Berg. Although, like CRDY, for GRSL SW decreases
occur over the southern margins of the Breede (-2mm
mon−1). Even so, the changes in ET for the two land-uses
(CRDY and GRSL) are spatially comparable. Neverthe-
less, unlike CRDY, GRSL reduces runoff (-0.5mm mon−1)
over many subbasin areas of the four catchments. And,
while changes in streamflow for the two land-uses are sim-
ilar over the Olifants and Gouritz catchments (increases),
and over the Breede (decreases), unlike CRDY, GRSL
reduces streamflow over the Berg (-61.9 m3 s−1).
3.5 A comparison oftheimpacts ofland‑use change
withclimate change projections
The impacts of projected future climate change resulted in
decreased hydrological variables over the catchments stud-
ied herein, but the percentage decrease varies (Fig.11a).
For instance, while the ET decreases by only 10%, the
percolation (PERC) decreases by more than 40%. The
decrease in ET may be linked to the projected decreases in
PRE, which makes less water available for evaporation over
the catchments. The capability of the basin to meet atmos-
pheric water demand (ET/PET) also decreases (by -20%),
not only because the ET decreases, but because PET, the
atmospheric water demand, is also projected to increase
across the catchments (Fig.8). These changes drive the
decrease in other hydrological variables because they alter
the overall soil water budget of the catchment areas, induc-
ing a 30% decrease in soil water (SW), a 40% decrease in
percolation (PERC) and a 30% decrease in runoff. Water
yield also decreases as less water percolates into deep soil
layers. Hence, there is a decrease in water yield (-30%) and
discharge (-40%) at the Theewaterskloof dam (TWT). The
impacts of some land-use changes offset the climate change
impacts on the mean hydrological variables (Fig.11a). For
example, CRDY could result in increased runoff by 10%.
This could mitigate the impacts of climate change on run-
off by about 30%. CRDY may also offer potential climate
change offset due to its impact on water yield at the Theewa-
terskloof dam (TWT; + 5%), where CRDY may increase dis-
charge. Flörke etal. (2018) also determined that, in the con-
text of future climate change, enhancements in agricultural
water usage could potentially release enough water to meet
the needs of urban areas. Nevertheless, for FOMI, SHRB,
and GRSL land-use changes, the impact on the hydrological
variables is almost negligible relative to the impact of future
climate change.
Fig. 11 A comparison of
impact of land cover change
and climate change projection
on hydrological variables in
Western Cape catchments
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6389
Modelling thepotential ofland use change tomitigate theimpacts ofclimate change onfuture…
The changes in the hydrological variables are linked to
changes in drought. The projections indicate that impacts
of climate change could increase drought frequency in
the future. Climate change may increase both Stand-
ardized Precipitation Evapotranspiration Index (SPEI)
(about + 80months decade-1) and Standardized Precipita-
tion Index (SPI) (+ 20months decade-1) drought frequency
(Fig.11c). Note that for both indices, a moderate drought
is defined as a condition in which the drought index (SPI or
SPEI) is less than or equal to − 1.0(i.e., moderate drought).
These projections are in line with those of a previous study,
which suggests that changes in the intensity and frequency
of droughts are weaker when using the SPI than the SPEI,
and that SPI projections may in fact underestimate the
influence of global warming on drought because they do
not account for the influence of PET (Naik and Abiodun
2020). The changes in the SPEI/SPI drought provide the
upper/lower range that is linked to changes in hydrological
drought for other variables. For instance, climate change
may increase the frequency of the soil water drought index
(SWI) by + 38months decade−1, the percolation drought
index (PERCI) by + 30months decade−1, the runoff drought
index (RFI) by + 10months decade−1, and the water yield
drought index (WYLDI) by + 12months decade−1. Some
land-use changes may offset the climate change impacts on
the hydrological drought variables (Fig.11). Most nota-
bly, since FOMI may increase the soil water index (SWI)
and the percolation index (PERCI), it may thus reduce
the impacts of climate change on runoff drought by about
-5months decade−1. Similarly, since CRDY may increase
runoff (RFI), it also offers a climate change offset due to
its impact on decreasing the frequency of runoff drought
by about -5months decade−1. At the Theewaterskloof dam
(TWT), CRDY may increase discharge, and partially off-
set the frequency of discharge drought by about -3months
decade−1. For GRSL land-use changes, the impact of the
hydrological variables on drought is almost negligible rela-
tive to the impact of future climate change drought or it may
even enhance it (e.g., for RFI).
4 Conclusion
In this study, the SWAT + was used to perform hydrologi-
cal simulations over four Western Cape river catchments.
We calibrated the SWAT + and evaluated its performance
in reproducing the hydrological variables over the region.
We then examined the MBCn bias correction method and
its influence on the climate dataset, quantified the potential
impacts of future climate change on drought across various
global warming levels (GWLs 1.5, 2.0, 2.5, 3.0°C), and
examined the impact of four land-use changes on hydrologi-
cal drought. We used the Standardized Precipitation Index
(SPI) and the Standardized Precipitation Evapotranspiration
Index (SPEI) to quantify drought frequency and intensity.
The results of the study can be summarized as follows:
• Model evaluation shows good agreement between the
simulated and observed monthly streamflows at the four
hydrological stations (i.e., G1H013, H7H006, E2H003,
and J1H019) during the calibration period (1980–1990).
The simulated streamflow also tracked the observed
values in reproducing seasonal and annual variation of
the streamflow, and the model captured the inter-annual
variability. Generally, the model provided a satisfactory
representation of hydrological processes in Western Cape
rivercatchments.
• The MBCn bias correction of the CORDEX dataset
improved the hydrological simulations over the catch-
ments, though the performance of CORDEX_MBCn was
lower for hydrological variables than for climate vari-
ables.
• The timeseries of projected climate changes across the
region suggests a gradual increase in temperature, and
this led to an increase in potential evaporation (PET).
However, the projected changes in precipitation (PRE)
are more complex.
• The spatial distribution of the warming is generally con-
sistentacross the region, but the magnitude of the warm-
ing increases with the GWLs and increases further inland
(especially over the north-eastern regions). The spatial
distribution of these hydrological changes is consistent
with that of PRE.
• The projected decrease in hydrological variables can be
linked to the decrease in PRE. There is a decrease in
channel precipitation over many areas of the Western
Cape, and this drying increases under higher GWLs. The
projected increase in PET leads to an increase in stream
evaporation, which occurs underall GWLs, and acrossall
the Western Cape catchments, but is enhanced over the
Olifants and Gouritz catchments.
• The different land-uses alter the future hydrological water
balance. While FOMI increases SW and ET, it decreases
runoff (about -0.2mm mon-1) and increases streamflow
(about + 61.9 m3 s-1). SHRB increases SW and ET but
decreases runoff (about -0.2mm mon-1) and stream-
flow (about -61.9 m3 s-1). The hydrological responses of
CRDY and GRSL are more complex, as it includes both
increases and decreases in hydrological conditions.
• The impacts of FOMI, SHRB, CRDY and GRSL on the
hydrological variables and drought are negligible relative
to the impact of future climate change.
The results of the study suggest that these land-use
changes may not be the most efficient strategies for mitigat-
ing the impacts of climate change on hydrological droughts
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

6390 M.Naik, B.J.Abiodun
over the Western Cape region. Nevertheless, the local
impacts of the land-use changes on hydrological variables
are subjective and alsolikely dependent on the end user
upstream/downstream in each catchment area. The approach
used in this study only offers insight into the potential hydro-
logical impacts of land-use changes in Western Cape river
catchments. It is by no means intended to be prescriptive
with regard to regionalland-use activities, thoughit may
assist stakeholders and decision makers in making better
decisions for land management and water resource plan-
ning in a future impacted by drought due to climate change.
Further research is required for an improved understand-
ing of the potential for land-use to mitigate drought due to
climate change. For instance, it may be that, as ensemble
climate change projections (like CORDEX datasets here)
improve, SWAT + may better simulate the local hydrol-
ogy. Future research avenues may also incorporate different
drought indices (since indices have different strengths and
weaknesses), consider different future emissions trajecto-
ries (e.g., a newly built range of the “Shared Socioeconomic
Pathways”; SSPs), or consider how other relevantalternative
land-use changes (e.g., by the expansion of viticulture) may
affect futureprojections of droughts in the Western Cape.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00704- 024- 04995-7.
Author contributions Bothauthors designed the study, performed the
simulations, and analysed the data.MN drafted the manuscript, and
bothauthors revised it.
Funding Open access funding provided by University of Cape Town.
This work was supported with grants from the Water Research Com-
mission (WRC, South Africa)andfrom the South African Research
Chairs Initiative of the Department of Science and Technology and
National Research Foundation.The Centre for High Performance Com-
puting (CHPC, South Africa) provided the computing facility used for
the study.
Data availability All observational and reanalysis data used in
this study are publicly available at no charge and with unrestricted
access.The simulation data used in the study are freely available on
request.
Declarations
Ethics approval & consent to participate Not applicable.
Consent for publication The authors agreed with the content and gave
explicit consent to submit.
Competing interests The authors declare no competing interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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