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In a context of water scarcity, efforts to increase landscape production should focus on improving water productivity. This requires an appreciation of the various components of evapotranspiration (ET), including soil evaporation (Es) because the latter reflects ‘unproductive’ water loss. Both complex and simple algorithms have been developed to determine ET. In data scarce areas, developing and testing parsimonious algorithms is useful. This study sought to improve a simple single layer ET model by incorporating an Es component. Empirical methods were also explored to predict ET from vegetation indices (VIs), leaf area index (LAI) and reference evapotranspiration (ET0). A large aperture scintillometer and an eddy covariance (EC) system were used to validate the proposed algorithm at three sites over Grasslands and Albany Thicket biomes in the Eastern Cape, South Africa. There was good agreement between the observed and predicted ET with RMSE of 0.30–0.58 mm d⁻¹ when average daily observed ET was 0.43–3.24 mm. The VIs had moderate correlations with the observed data due to the significant role played by Es (65%–84%) across the sites and stomatal conductance at the Albany Thicket site. The simple algorithms developed would make determining ET easier in data scarce regions.
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African Journal of Range & Forage Science
ISSN: 1022-0119 (Print) 1727-9380 (Online) Journal homepage: http://www.tandfonline.com/loi/tarf20
Estimating evapotranspiration in semi-arid
rangelands: connecting reference to actual
evapotranspiration and the role of soil
evaporation
Onalenna Gwate, Sukhmani K Mantel, Andiswa Finca, Lesley A Gibson, Zahn
Munch & Anthony R Palmer
To cite this article: Onalenna Gwate, Sukhmani K Mantel, Andiswa Finca, Lesley A Gibson,
Zahn Munch & Anthony R Palmer (2018): Estimating evapotranspiration in semi-arid rangelands:
connecting reference to actual evapotranspiration and the role of soil evaporation, African Journal
of Range & Forage Science, DOI: 10.2989/10220119.2018.1505779
To link to this article: https://doi.org/10.2989/10220119.2018.1505779
Published online: 23 Nov 2018.
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African Journal of Range & Forage Science 2018: 1–9
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AFRICAN JOURNAL OF
RANGE & FORAGE SCIENCE
ISSN 1022-0119 EISSN 1727-9380
https://doi.org/10.2989/10220119.2018.1505779
African Journal of Range & Forage Science is co-published by NISC (Pty) Ltd and Informa UK Limited (trading as Taylor & Francis Group)
This is the nal version of the article that is
published ahead of the print and online issue
Evapotranspiration (ET) is one of the most important
components of the hydrological cycle in terms of global
change studies but remains one of the most difficult to
determine. Evapotranspiration comprises water loss from
various surfaces and transpiration through plant leaves.
Transpiration (T) is closely coupled with plant produc-
tion, while Es reflects the so-called ‘unproductive’ loss of
water to the atmosphere (Kool et al. 2014). As many parts
of the world are increasingly becoming water limited, the
need to account for ET and its various components cannot
be overemphasised. More importantly, the increasing
world population requires improvements in water produc-
tivity, especially in water-limited environments (Molden et
al. 2010; Kool et al. 2014). Water productivity is the ratio of
the net benefits from agricultural activities to the amount of
water consumed to yield those benefits (Molden et al. 2010).
Therefore, it is prudent to enhance productive water use and
reduce the so-called ‘unproductive’ evaporation. Partitioning
of ET into its various components such as Es, and T
provides a sound starting point for enhancing water produc-
tivity. Consequently, several algorithms, ranging from data
intense to parsimonious ones exist to account for total ET.
In data scarce areas, simple models using readily available
input data are useful for ET modelling. Models have been
developed that are based on empirical relationships between
observed ET and another independent variable, such as
the leaf area index (LAI) or vegetation indices (VIs) that are
known to correlate with ET, for example, the normalised
difference vegetation index (NDVI) and the enhanced
vegetation index (EVI) (Nagler et al. 2005a, 2013; Palmer et
al. 2014). Consequently, models have been developed trying
to link water vapour fluxes to canopy attributes. These have
mainly revolved around connecting ET0 to actual evapotrans-
piration (AET) by using crop coefficients, or using LAI or VI
as scalars (Allen et al. 1998; Nagler et al. 2013; Palmer et al.
2014). The empirical relations take the following forms:
ET = KcET0 (1)
where Kc is the crop coefficient and ET0 is the reference
evapotranspiration.
Alternatively, vegetation indices could be used to connect
ET0 to AET, whereby Kc is replaced by VIs:
ET = f (VI)*ET0 (2)
These approaches of determining ET have been widely
applied in crops and natural ecosystems (Allen et al.
1998; Nagler et al. 2013). However, there are a number
of challenges and uncertainties related to the use of LAI
or VIs approaches to determine ET. For example, they
are unable to detect changes in stomatal behaviour. The
Estimating evapotranspiration in semi-arid rangelands: connecting
reference to actual evapotranspiration and the role of soil evaporation
Onalenna Gwate1* , Sukhmani K Mantel1 , Andiswa Finca2, Lesley A Gibson3 , Zahn Munch4 and Anthony R
Palmer1,2
1 Institute for Water Research, Rhodes University, Grahamstown, South Africa
2 Agricultural Research Council–Animal Production Institute, Grahamstown, South Africa
3 School of Engineering, University of Edinburgh, Edinburgh, EH9 3JL, UK
4 Department of Geography and Environmental Studies, Stellenbosch University, Stellenbosch, South Africa
* Corresponding author, email: onalennag37@gmail.com
In a context of water scarcity, efforts to increase landscape production should focus on improving water
productivity. This requires an appreciation of the various components of evapotranspiration (ET), including
soil evaporation (Es) because the latter reflects ‘unproductive’ water loss. Both complex and simple algorithms
have been developed to determine ET. In data scarce areas, developing and testing parsimonious algorithms is
useful. This study sought to improve a simple single layer ET model by incorporating an Es component. Empirical
methods were also explored to predict ET from vegetation indices (VIs), leaf area index (LAI) and reference
evapotranspiration (ET0). A large aperture scintillometer and an eddy covariance (EC) system were used to validate
the proposed algorithm at three sites over Grasslands and Albany Thicket biomes in the Eastern Cape, South
Africa. There was good agreement between the observed and predicted ET with RMSE of 0.30–0.58 mm d−1 when
average daily observed ET was 0.43–3.24 mm. The VIs had moderate correlations with the observed data due to the
significant role played by Es (65%–84%) across the sites and stomatal conductance at the Albany Thicket site. The
simple algorithms developed would make determining ET easier in data scarce regions.
Keywords: Grassland, leaf area index, Thicket, vegetation indices
Introduction
Gwate, Mantel, Finca, Gibson, Munch and Palmer2
dynamics in stomatal behaviour greatly influence the carbon
and water vapour exchanges over canopies, which in turn
affect the partitioning of total ET into Es and T. In addition,
approaches employing LAI and VIs to connect potential
ET and actual ET are not able to determine Es. However,
it is well established that Es is significant in drylands with
LAI < 2.5 and can contribute ~80% of total ET (Leuning et
al. 2008; Mu et al. 2011; Morillas et al. 2013). Therefore, in
such systems, there is a need for algorithms that can also
capture Es in order to accurately characterise total ET.
Other approaches tend to combine these VIs with other
meteorological data, such as air temperature and net
radiation (Rn), in simple or non-linear and multiple linear
regressions in order to develop predictive equations (see
review by Glenn et al. 2008). These empirical relations are
very crucial in data scarce areas and where there is no ET
validation equipment. In South Africa, there are relatively few
micrometeorological observation towers, although scientific-
grade weather stations are becoming common. Therefore,
the development of such simple empirical algorithms may be
useful in deriving ET for all biomes of the country.
The main aim of this study was to explore an improved
Penman–Monteith-based model formulation over the
grasslands and Albany Thicket vegetation by advancing
the preliminary work of Palmer et al. (2014), conveniently
called the Penman–Monteith–Palmer (PMP) algorithm.
Palmer et al. (2014) reckoned the need for developing and
testing parsimonious ET models in data scarce areas such
as rangelands of southern Africa. This study adds an Es
component to the preliminary work of Palmer et al. (2014)
in order to improve ET estimation in semi-arid rangelands
characterised by short and open canopies. The proposed
algorithm has the advantage of using widely available
weather data, surface albedo as well as LAI and it does not
require observed ET to calibrate some parameters and this
is crucial in data scarce areas. Secondly, this study sought
to develop simple empirical algorithms for predicting ET in
rangelands using observed ET on one hand and ET0 as
well as LAI on the other. Such empirical relations may help
in scaling up ET from point observations to the landscape
across the biomes of interest.
Theory
Based on the resource optimisation theory (Glenn et al.
2008), plants have evolved to scale foliage density in line
with resources availability. Hence, in vegetated surfaces,
AET approaches ET0 under ideal conditions of plentiful soil
moisture and soil fertility when plant root systems are able
to supply water to the atmosphere via stomata at a rate
almost corresponding to demand. Details of the PMP model
can be found in Palmer et al. (2014).
Modelling Es is quite a delicate issue because a good
model should be able to reproduce the rate of soil moisture
changes over time. However, it is well established that the
link between groundwater and the upper soil moisture is
one of the least understood hydrological processes (Wilcox
2010) and this exacerbates the problem. It is recognised
that the rate of Es follows three stages as a result of soil
physical and atmospheric characteristics (Kool et al. 2014).
Stage 1 denotes a period where Es is limited by available
energy (A) to drive ET and stage 2 where water loss is
strongly coupled with soil characteristics such as soil
moisture, soil hydraulic properties on one hand, and vapour
pressure deficit on the other. Stage 3 relates to a context
where Es is nil due to unavailable soil water (Morillas et
al. 2013). In order to capture these dynamics, the ratio of
equilibrium evaporation to precipitation method was adopted
(Zhang et al. 2010; Morillas et al. 2013). Many studies have
demonstrated that this approach was able to capture the
soil drying process (Zhang et al. 2010; Morillas et al. 2013;
Zhang et al. 2016). The approach has the advantage that
it simply uses rainfall and equilibrium evaporation data to
parameterise the fraction (f) of Es and there is no need for
parameter fine-tuning. Therefore, such simple models are
relevant in data scarce areas and can be used with sparsely
distributed weather stations to enable accounting for ET.
The proposed improved algorithm is presented as:
(3)
s
0
max
LAI
ET ET
LAI 1
A
f
ε
= ∗+
ε+
15
Zhang
eq, ,
15
min ,1
i
i
i
i
si
i
P
f
E


=

NIR red
NIR red
NDVI ρ −ρ
=ρ +ρ
NIR red
NIR red blue
EVI 2.5 6 7.5 1
ρ −ρ
=ρ +ρ − ρ +
where As is energy available to the soil, and the logistics of
its derivation can be found in Allen et al. (1998) and Morillas
et al. (2013); LAImax is the maximum leaf area index, ε is
the slope () of the curve relating saturation water vapour
pressure to temperature divided by the psychrometric
constant (γ), and f is a factor modulating potential evapora-
tion from the soil and ranges between 0 and 1.
For the f value, this study adopted the precipitation and
equilibrium evaporation ratio method conveniently called the
fZhang (Zhang et al. 2010; Morillas et al. 2013):
s
0
max
LAI
ET ET
LAI 1
A
fε
= ∗+
ε+
15
Zhang
eq, ,
15
min ,1
i
i
i
i
si
i
P
f
E


=


NIR red
NIR red
NDVI ρ −ρ
=ρ +ρ
NIR red
NIR red blue
EVI 2.5 6 7.5 1
ρ −ρ
=ρ +ρ − ρ +
(4)
where pi is the accumulated daily precipitation and Eeq,s,i is
the daily soil equilibrium evaporation rate for day i over a
number of days (N); this study used N = 16 days (day i and
15 preceeding days).
Materials and methods
Study site
Three study sites were selected for testing and validating
the algorithm. These included two sites in the grassland
biome, one in the Southern Drakensberg Highland
Grassland (Truro farm) and the other in the East Griqualand
Grassland (Mucina et al. 2006), i.e. Somerton farm in the
northern Eastern Cape province, South Africa (Figure 1,
Table 1). The two grassland sites are situated approxi-
mately 14 km apart on freehold land where mixed farming is
practised. Extensive cattle and sheep production as well as
rainfed crop cultivation are key farming activities. The third
study site is situated in the Great Fish Thicket in the Albany
Thicket (AT) Biome on the eZulu Game Reserve (EGR) in
the south-western Eastern Cape. The succulent Thicket
vegetation at this study site has been used to support
extensive livestock farming (sheep and goats) since the
early 1800s and has since 1998 been converted to wildlife
ranching. There is no dryland crop cultivation, but irrigated
cultivation does occur along the Great Fish River.
African Journal of Range & Forage Science 2018: 1–9 3
Measurement of ET and micrometeorological variables
At the EGR site, ET was measured by an Integrated CO2/
H2O Open-Path Gas Analyser and 3D Sonic Anemometer
(IRGASON, Campbell Scientific Inc., Logan, UT, USA). The
EC system and details of instrumentation and data analysis
can be found in Gwate et al. (2016). At the grassland sites,
a Large Aperture Scintillometer (LAS; LAS MkII, Kipp and
Zonen BV, Delft, The Netherlands) was used for validating
the new algorithm. Details of the instrumental set up, data
processing and quality checks can be found in Gwate
(2018). Given that the proposed algorithm does not require
any calibration, the measurement and modelling periods
were similar at each study site (Table 1).
Meteorological data
To test the utility of sparsely located weather stations in ET
estimation over wide areas, daily meteorological data were
obtained from an automatic weather station (AWS) to derive
ET0 and As (Table 2). At Truro farm, there was no weather
station and as such the Agricultural Research Council’s
Somerton station was used, which was approximately
14 km away. However, a rainfall data set combining ground
and remotely sensed data called Tropical Applications
of Meteorology Using Satellite Data and Ground-Based
Observations (TAMSAT; Maidment et al. 2014; Tarnavsky
et al. 2014) was used in the calculation of the f values at
the Truro farm site since there was no weather station.
Gwate (2018) showed that TASMSAT rainfall data were
similar with those from an AWS at Somerton farm. Details
for deriving other meteorological data, such as atmospheric
pressure, vapour pressure and the procedures for gap-filling
meteorological data, can be found in Gwate (2018).
MOD 15A2 FPAR/ LAI product
The MODerate-resolution Imaging Spectroradiometer
(MODIS) provides 1 km spatial resolution data every day
in 36 spectral bands and these have been used to develop
several products. The MOD15A2 (FPAR/LAI) product
was acquired from the Oak Ridge National Laboratory
Distributed Active Archive Center (ORNL DAAC) website
(https://lpdaac.usgs.gov/dataset_discovery/modis/modis_
products_table/mod15a2). The 8-day average LAI was
extracted for the three areas of interest from the year 2000
to day of year (DoY) 318 in 2016. This provided the LAI
values used in the algorithm (Equation 3) to derive the ET.
MODIS vegetation indices (MOD13A2)
Vegetation indices are empirical measures of vegeta-
tion performance and include NDVI and EVI. Vegetation
indices are essentially indicative of the integrative
functions of a vegetated surface (Huete et al. 2002). The
16-day MOD13A2 product with a spatial resolution of
1 km was also used during this study and was acquired
from the ORNLP DAAC website (https://lpdaac.usgs.
gov/data_access/data_pool). The NDVI and EVI values
coinciding with the study period were extracted. The NDVI
is a normalised transform of the near infrared (NIR) to red
reflectance ratio, designed to standardise VI values to
between −1 and +1 and is expressed as:
NIR red
=ρ +ρ
(5)
where ρ is the full or partially atmospheric-corrected
(for Rayleigh scattering and ozone absorption) surface
reflectance.
The EVI is an improvement on NDVI and it incorporates
an algorithm to reduce the effects of atmospheric scattering,
canopy background reflection and does not saturate in high
biomass areas as opposed to the NDVI (Huete et al. 2002).
The EVI formula is expressed as:
s
0
max
LAI
ET ET
LAI 1
A
fε
= ∗+
ε+
15
Zhang
eq, ,
15
min ,1
i
i
i
i
si
i
P
f
E


=

NIR red
NIR red
NDVI ρ −ρ
=ρ +ρ
NIR red
NIR red blue
EVI 2.5 6 7.5 1
ρ −ρ
=
ρ +ρ − ρ +
(6)
AFRICA
South
Africa
SOUTH
AFRICA
24° E
33° S
31° S
29° S
26° E 28° E 30° E
170 km
Truro
Somerton
eZulu
Queenstown
Grahamstown


Eastern
Cape
INDIAN OCEAN
Site name Latitude/Longitude ET data period Instrument Vegetation type Elevation (m) MAR (mm)
Somerton 31°09′02″ S, 28°23′03″ E DoY 309, 2015–
DoY 101, 2016
LAS East Griqualand Grassland 1 257 756
Truro 31°04′10″ S, 28°17′25″ E DoY 265–
DoY 308, 2015
LAS Southern Drakensberg
Highland Grassland
1 471 786
eZulu Game Reserve 33°01′08″ S, 26°04′47″ E DoY 283, 2015–
DoY 318, 2016
EC Great Fish Thicket (Albany
Thicket biome)
554 400
Table 1: Location and characteristics of study sites. MAR = mean annual rainfall
Figure 1: Location of the study sites at eZulu Game Reserve, Truro
farm and Somerton farm in Eastern Cape province, South Africa
Gwate, Mantel, Finca, Gibson, Munch and Palmer4
Nadir Bidirectional Reflectance Distribution Function
Adjusted Reflectance (NBRDF) product (MCD43B4)
The MCD43B product (Strahler and Muller 1999) was
used to obtain the surface albedo. This product provides
1 km reflectance data adjusted using the bidirectional
reflectance distribution function of MCD43B1 to model
values as if they were acquired from a nadir view (Strahler
and Muller 1999). Shortwave albedo is required in the
calculation of net radiation when the new algorithm is
applied. Hence, the MCD43B4 product was acquired
from the ORNL DAAC website (https://lpdaac.usgs.gov/
data_access/data_pool) and average 8-day albedo values
that coincided with the study period were extracted.
Subsequently, the equation developed by Liang (2001)
was applied to compute surface albedo.
Model evaluation
The mean absolute error (MAE) and root mean square
error (RMSE) were chosen as metrics to evaluate the
new algorithm. These indices indicate the extent of the
error in the simulated and measured ET and they have
the advantage of using units similar to variables under
consideration. The MAE is suitable for uniformly distributed
errors as it gives the same weight to all errors, whereas
the RMSE gives errors with larger absolute values more
weight, and hence it is necessary for evaluating data that
are normally distributed such as model errors (Chai and
Draxler 2014).The RMSE-observations standard deviation
ratio (RSR), which standardises RMSE using the observa-
tions standard deviation, was also used in order to give
insights as to what should be considered as low RMSE
(Moriasi et al. 2007). The percent bias (PBIAS) was
also computed to help decipher model over- and under-
estimation bias. Finally, simple linear regression using the
ordinary least square regression method was prepared
between the observed and predicted ET. The r 2, slope and
intercept of the linear regression between the observed
and modelled ET were also reported. These were chosen
because they are reflective of the extent to which simulated
ET reproduces the measured ET, while the r 2 shows the
proportion of variance in measured ET that is explained by
the model (Moriasi et al. 2007).
Development of predictive equations
Vegetation indices have been widely used to predict ET
over wide areas (Nagler et al. 2005b; Glenn et al. 2010;
Nagler et al. 2013). Data from the two grassland sites
were combined and the EGR data were used separately
in developing regressions to explore the possibility of
estimating ET in these biomes using VIs, LAI and ET0.
Simple linear, non-linear regression and the linear correla-
tion (r) of VIs (NDVI and EVI) against ET were also
generated to explore the nature of the relationship between
the observed ET and VIs. In this case 16-day ET was
summed in order to coincide with the availability of VI.
This relationship enabled the study to make a determi-
nation as to whether VIs can be used to predict ET in
rangelands similar to the study site. Furthermore, multiple
linear regressions of measured ET0 and LAI against ET
were developed for the grassland and the AT sites, respec-
tively, to develop simple algorithms for estimating ET in
data scarce areas.
Results
Environmental characteristics across the study sites
The environmental conditions varied greatly at each experi-
mental site (Table 3). The LAI was < 2 across the sites but
differed significantly, as the Kruskal–Wallis test revealed (p
< 0.05), with the lowest LAI being observed at the EGR site
(Table 3).
Model performance
The improved PMP algorithm resulted in a RMSE of
0.58 mm d−1 at Somerton and 0.39 mm d−1 at Truro in
a context where the observed daily mean ET was 3.24
and 2.23 mm, respectively. At the EGR the RMSE was
0.50 mm d−1 and this was largely unsystematic (Table 4) in
a context of a daily mean of 0.76 mm. The RSR was similar
(0.08–0.13) across sites (Table 4). The model tended to
slightly overestimate and underestimate observed ET at the
Truro and Somerton sites, respectively, as shown by the
PBIAS (Table 4). The unsystematic RMSE was relatively
higher than the systematic RMSE across the sites. When
data from the two grassland sites were combined, the
RMSE was within 18% of the observed mean daily ET
(3.01 ± 1.23 mm) against the modelled value of 3 ± 1.55 mm
(Table 4). Modelled soil evaporation accounted for 69%,
65% and 84% of the total modelled ET at Truro, Somerton
and EGR, respectively. At the EGR the growing season
(August–April) and non-growing season (May–July) RMSE
were 0.51 and 0.3 mm d−1, respectively, and the PBIAS was
negative for both seasons (Table 5). In the non-growing
season, the average EC ET was 0.43 ± 0.49, whereas in the
growing season it was 0.85 ± 0.66.
At the Somerton site, the improved algorithm under-
estimated ET at the beginning of the validation period but
intermittently overestimated ET after DoY 21, 2016. The
model also underestimated ET between DoY 89 and 94
(Figure 2a and b). A similar pattern was observed at the
Truro farm (Figure 2c and d). The underestimation coincided
with periods of reduced rainfall, whereas the overestimation
bias occurred during periods after rainfall events. A similar
pattern was observed at the EGR site (Figure 2e and f).
Weather parameter Instrument
Solar radiation (MJ m−2) Pyranometer (LI-200SA)
Relative humidity (%) and air temperature(°C) Vaisala HMP60 Temp/Humidity probe (HMP60)
Wind speed (m s−1) and direction (°) RM Young wind sentry wind set (10FT LEAD, Model 03001)
Rainfall (mm) TE525MM-L Texas Electronics Rain Gage 0.1 mm (0.00394 inch)
Table 2: Summary of instruments at the automated weather station
African Journal of Range & Forage Science 2018: 1–9 5
The linear regression between the observed and modelled
ET was significant (p < 0.001). The combined grassland
data set yielded a slope of 1.04 (r 2 = 0.73), whereas at
the EGR site a slope of 1.02 was obtained (r 2 = 0.52). At
both sites there was no positive autocorrelation (p > 0.05).
When the EGR data were separately analysed by seasons,
a slope of 1.02 and intercept of 1.1 mm were obtained for
the growing season (r 2 = 0.49). For the non-growing season,
a slope of 0.92 and intercept of 1.1 mm (r 2 = 0.49) were
obtained. The scatter plots of the relationships between the
observed and predicted ET over the entire validation periods
for the grasslands and EGR sites are presented (Figure 3).
The ET data were accumulated over 8-day periods to
coincide with each new MODIS 8-day LAI.
Predicting ET from vegetation indices and LAI
Using linear and non-linear regression, the relationship
between VIs and ET was weak (r 2 0.3). The correla-
tion between ET and VI at the study sites is presented
(Table 6). The NDVI had a better correlation with ET
(p < 0.05), whereas the relationship between EVI and ET
was non-significant at EGR (p > 0.05). Using multiple
linear regressions, the 8-day average LAI and 8-day
accumulated ET0 were regressed against the observed
8-day accumulated ET. At the EGR, strong relations were
observed (r 2 = 0.35, F = 11.92, p < 0.001, N = 51, 8-day
periods). The equation is expressed as:
ET = 16*LAI + 0.004*ET0 − 0.17 (7)
Using the combined data from the grassland sites, signifi-
cant relationships were also found (r 2 = 0.65, F = 16.73,
p < 0.001, N = 20, 8-day periods) and the equation was:
ET = 14.19*LAI + 0.58*ET0 − 14.1 (8)
Discussion
Validity of observed ET
This study sought to advance the work of Palmer et al.
(2014) in order to accurately estimate total ET in semi-arid
rangelands. Although the two grassland sites were adjacent
to each other, environmental conditions differed during the
respective validation periods. Owing to the field campaign
approach adopted and logistical constraints, micrometeoro-
logical measurements could not be conducted across
the growing and non-growing season over the grassland
sites. Suffice to note that rainfall predominantly occurs
during the growing season months in the grasslands
study sites and much of ET takes place during this period.
The data collected were essentially for the wet growing
season although environmental conditions varied. For the
EGR site, data were collected for an entire year and as
such the experiment captured the seasonal water vapour
fluxes over the study area. The environmental conditions
at the grassland sites and the EGR site are different. For
example, long-term modelled mean annual rainfall are
756–786 and 400 mm y−1 at the grassland and the EGR
sites (Schulze 1997), respectively. Therefore, the valida-
tion took place under varied environmental conditions
and the improved model was largely able to capture the
dynamics of measured ET. In addition, uncertainties
associated with the inputs, especially MOD15A2 LAI and
MCD43B surface albedo, may have influenced the fluxes
derived. For example, errors in the MOD15A2 LAI introduce
uncertainties in the modelled ET and the tendency by
MOD15A2 LAI to overestimate LAI by up to 0.25 units has
been recognised (Huemmrich et al. 2005).
Model performance and evaluation
The model performed better over grassland as shown by
the RMSE, but the RSR was low across the grassland
and AT, suggesting that the model simulation was good in
both biomes. At the grassland sites, validation was done
during a generally wet period and the improved model
Environmental
parameter
Somerton
(N = 104)
Truro
(N = 29)
EGR
(N = 401)
Air temperature (°C) 20.22 ± 3.68 18.2 ± 2.5 19.5 ± 4.7
Relative humidity (%) 67.5 ± 29.4 58.2 ± 16.3 62.24 ± 12.84
Wind speed (m s−1) 1.41 ± 0.37 3.2 ± 0.78 1.73 ± 0.75
Net radiation (W m−2) 127.3 ± 55.6 99.3 ± 35 71.8 ± 52
ET (mm) 3.24 ± 1.24 2.23 ± 0.85 0.76 ± 0.65
ET0 (mm) 4.5 ± 2.1 4.8 ± 1.3 3.24 ± 1.48
SWC (m3 m−3) 0.22 ± 0.11 0.14 ± 0.02 0.09 ± 0.02
Rainfall (mm) 2.95 ± 5.86 1.7 ± 3.2 0.78 ± 2.5
LAI (m2 m−2) 1.32 ± 0.23 0.77 ± 0.14 0.39 ± 0.14
Table 3: Environmental conditions during the experiments at
Somerton and Truro farms and eZulu Game Reserve (EGR). Values
are the mean ± SD. ET = Evapotranspiration, ET0 = reference
evapotranspiration, SWC = soil water content, LAI = leaf area index
Statistic Somerton Truro Grassland
Combined EGR
MAE 0.50 0.19 0.45 0.32
RMSE 0.58 0.39 0.55 0.5
RSR 0.08 0.11 0.08 0.13
PBIAS 0.11 –0.38 0.04 –2.6
Systematic RMSE 20 14 24 18
Unsystematic
RMSE
31 13.5 31 63
Modelled ET
(mean ± SD)
3.21 ± 2.7 2.28 ± 0.62 3 ± 1.55 0.9 ± 0.77
Table 4: Model evaluation at Somerton farm (N = 104 d), Truro
farm (N = 29 d) and eZulu Game Reserve (EGR; N = 401 d).
MAE = Mean absolute error, RMSE = root mean square error,
RSR = RMSE-observations SD ratio, PBIAS = percent bias, ET =
evapotranspiration
Statistic Summer Winter
MAE 0.30 0.23
RMSE 0.51 0.30
RSR 0.13 0.08
PBIAS −2.3 −2.5
Systematic RMSE 19 22
Unsystematic RMSE 58 61
Modelled ET (mean ± SD) 1.03 ± 0.8 0.45 ± 0.38
Table 5: Model performance in summer (N = 312 d) and winter
(N = 89 d) at eZulu Game Reserve. MAE = Mean absolute error,
RMSE = root mean square error, RSR = RMSE-observations SD
ratio, PBIAS = percent bias, ET = evapotranspiration
Gwate, Mantel, Finca, Gibson, Munch and Palmer6
1
2
3
4
5
6
7
309
104
316
323
330
337
344
35
42
49
56
69
76
83
90
97
ET (mm)
2015 2016 2015
2015 2015
2016
(a)
LAS ET
Improved PM P ET
LAS ET
Improved PM P ET
5
10
15
20
25
30
35
309
316
323
330
337
344
35
42
49
56
69
76
83
90
97
RAINFALL (mm)
2015 20162015 2016
(b)
0.5
1
1.5
2
2.5
3
3.5
4
266
268
270
272
274
276
278
280
282
284
286
288
290
292
294
(c)
Improved PM P ET
2
4
6
8
10
12
266
268
270
272
274
276
278
280
282
284
286
288
290
292
294
(d)
1
2
3
0
4
283
309
335
361
22
48
74
100
126
152
178
204
230
256
282
308
(e) EC ET
5
0
10
15
20
283
309
335
361
22
48
74
100
126
152
178
204
230
256
282
308
(f)
DAY OF THE YEAR
Figure 2: Variation in measured evapotranspiration (ET), modelled ET and rainfall at (a and b) Somerton Farm, (c and d) Truro farm and
(e and f) eZulu Game Reserve. LAS = Large Aperture Scintillometer, PMP = Penman–Monteith–Palmer algorithm, EC = eddy covariance
was largely able to reproduce observed ET. However,
the underestimation bias at the beginning of the valida-
tion period could possibly be due to low modelled f values.
The tendency to overestimate ET at the EGR site was
due to overestimated Es and possibly the underestimated
latent heat flux as shown by the poor energy balance
closure reported in Gwate (2018). In addition, the LAI that
is used to connect ET0 to T in the model was relatively
stable. However, in the AT on the EGR it is suggested that
the dominant shrub, Portulacaria afra, exercises greater
stomatal control, resulting in high water use efficiency
(Mills and Cowling 2006). Admittedly, grasslands may
also exercise great stomatal control over ET (Snyman et
al. 2013; Favaretto et al. 2015), but P. afra has a higher
water use efficiency and hence its widespread environ-
mental plantings in South Africa under the auspices of the
Clean Development Mechanism (Mills and Cowling 2006).
Therefore, the available leaf area may not necessarily
be reflective of ET taking place as changes in stomatal
behaviour greatly influence the water vapour flux. This
African Journal of Range & Forage Science 2018: 1–9 7
result was not surprising because the LAI, which represents
the phenological characteristics of the vegetation in the
model, cannot detect variations in stomatal behaviour that
influence the total flux over vegetated surfaces. Glenn et al.
(2010) observed that ET models based on VIs are not able
to estimate Es and stomatal conductance, which affect total
ET. Hence, the overestimation bias could be indicative of the
model’s limitations in reproducing the stomatal behaviour.
The overestimation by the model can be reduced by
a careful choice of the number of days (N) to be used in
the determination of the f value. Sensitivity analysis of
such approaches has shown that increasing N reduces
overestimation and the optimum N lies between 16 and
20 days (Morillas et al. 2013). However, despite the
overestimation, on an annual basis, the model reproduced
the measured ET with a relatively low RMSE from the EGR
site despite the complex nature of the environment. In
addition, across sites, the RMSE was largely unsystematic
and this suggests that the proposed algorithm is robust
(Willmott 1981; Leuning et al. 2008). Willmott (1981) warned
that models that had a relatively high systematic RMSE
were not good enough and should not be accepted despite
a seemingly good fit. Therefore, the results from this study
confirm that the improved algorithm is robust. Using similar
approaches of modelling Es, good agreement between
flux tower observed ET and modelled ET across many
catchments have been recorded (Zhang et al. 2010; Morillas
et al. 2013; Zhang et al. 2016). The good performance of
the proposed algorithm is very important particularly for
data scarce areas. The model allows for ET to be calculated
using routine meteorological data, surface albedo and LAI
without the need for fine-tuning with observed data. These
data are readily available in sparsely distributed weather
stations and from remote sensing. This work, therefore,
has advanced the preliminary work by Palmer et al. (2014)
to develop parsimonious models for predicting ET in data
scarce areas at a fine resolution.
Predicting wide-area ET from VIs
In line with the objective of developing simple algorithms for
estimating ET in drylands where data is scarce, the relation-
ship between VIs and ET were investigated. Positive correla-
tions between ET and VIs were observed. However, across
sites, NDVI had better correlations with measured ET than
EVI. These results were consistent with those of Haynes
and Senay (2012), who found even lower correlations
of 0.14 during the winter season in the USA. In addition,
Helman et al. (2015) found that NDVI provided better model
fit than EVI when VIs were regressed against observed ET
in the Mediterranean regions. However, the results from the
present study were in sharp contrast with results from the
USA that found EVI to be a more useful scalar in connecting
potential ET to actual ET (Nagler et al. 2005a, 2007; Glenn
et al. 2008). The results from the present study sites were
probably due to the low VIs and LAI (0.1–1.8). It is well
established that in areas with LAI < 2.5, Es is crucial and
could account for ~80% of total evaporation (Leuning et al.
2008; Morillas et al. 2013). This suggests that much of the
water is consumed through the so-called ‘unproductive’ or
‘white’ evaporation and hence there is scope for improving
water productivity across the study sites. At the same
time, VIs can neither estimate Es nor dynamics in stomatal
conductance (Glenn et al. 2010). Therefore, results from
this study are not surprising since Es accounted for between
65% and 84% of total ET across sites. This is consistent
with literature values of between 30% and 80% reported in
rangelands (Kool et al. 2014). Thus, the moderate relation-
ship was likely caused by the influence of Es and stomatal
control of ET in the study sites. Owing to these moderate
relations, the study could not go on to develop predictive
0 5 10 15
0
5
10
15
20
EC ET (mm)
10 15 20 25 30 35
10
15
20
25
30
35
LAS ET (mm)
IMPROVED PMP ET (mm)
(a) (b)
y = 1.0 4x 0.88
r2 = 0.73
p < 0.001
y = 1.0 2x + 1.04
r2 = 0.52
p < 0.001
Figure 3: Relationship between a) accumulated 8-day observed ET from the large aperture scintillometer (ET LAS) and the improved
algorithm over the grassland (N = 20, 8-day periods) and b) accumulated 8-day observed ET from the eddy covariance system (ET EC) and
the improved algorithm over the Albany Thicket (N = 51, 8-day periods)
Study site EVI NDVI
Somerton and Truro farm 0.47 0.53
EGR 0.31 0.47
Table 6: Correlation coefficients between ET and vegetation
indices.
Gwate, Mantel, Finca, Gibson, Munch and Palmer8
equations. Hence, the objective of developing robust but
simple algorithms using VIs was not successful.
However, robust relations were developed through
multivariate regression of ET0 and LAI against measured
ET. The r 2 at EGR was relatively lower due to the great
stomatal control of the ET process. Therefore, the study
succeeded in further developing simplified algorithms since
credible strong relations were developed. These algorithms
may be used to predict biome-specific ET, an approach that
is becoming common in ecohydrological studies (Fang et
al. 2016). The strong relationships developed are crucial
particularly for South Africa over the AT because this biome
is critical in global change studies owing to its perceived
high water use efficiency. This makes the task of estimating
ET in such drylands easier by simply using ET0 and LAI in
the empirical relationship developed in this work. However,
such algorithms need further validation.
Model uncertainties
The study improved the PMP model by introducing an Es
component, making it a two-layer model. Model uncertain-
ties stem from the calculation of f values and general
input data. It should be noted that the movement of water
between the upper soil layers and groundwater is not well
understood (Wilcox 2010). Wu et al. (2015) found that even
shallow-rooted plants such as grasses can use ground-
water by exploiting the capillary rise fluxes. The study
sites are underlain by Beaufort Series sandstones of the
Karoo Supergroup (Mucina et al. 2006) and hence the
accompanying shale and mudstones could be creating
peached water tables, causing capillary action to be signifi-
cant and this possibly leads to groundwater becoming
available to even short-rooted vegetation. Therefore, these
factors may introduce minor errors in the subsequent
f values calculated. However, results from this study
and elsewhere (Zhang et al. 2010; Morillas et al. 2013;
Zhang et al. 2016) suggest that the approach is robust in
reproducing dynamics in ET. Other uncertainties are linked
to the MODIS LAI (Myneni et al. 2002) used. For example,
McColl et al. (2011) found that MODIS overestimated LAI
over patchy vegetation (LAI < 0.6) and underestimated
LAI in densely vegetated areas. Serbin et al. (2013) also
reported moderate inconsistency between measured
and MODIS LAI in Manitoba. Therefore, there is a distinct
possibility that these patterns could also be playing out at
the present study sites as LAI was relatively low. Hence,
estimation biases observed in this work could also be linked
to uncertainties in MODIS LAI. The process of selecting
the highest LAI for a particular pixel is also vital and could
result in model uncertainties. In situations where land cover
was not persistent, this could be problematic and great
care should be taken to ensure that maximum LAI relevant
to the particular land-cover type is retrieved. At the Truro
farm, the land was previously invaded by woody invasive
alien species (IAPs) and, hence, a graph of LAI trajectories
since MOD15A became available helped the study in differ-
entiating IAPs signal from that of the grassland. Hence,
the modelled fluxes are a true reflection of dynamics of
ET over the landscape. The use of maximum LAI values
for the same vegetation type found in other areas could
also be useful.
Conclusion
The study developed credible algorithms for estimating
ET in semi-arid areas by advancing the preliminary work
of Palmer et al. (2014). The addition of the soil evapora-
tion component resulted in good agreement between the
observed and modelled ET. The simple two-layer model
described in this study will make it possible to estimate ET
in data scarce areas by using widely available meteoro-
logical data, MODIS LAI and surface albedo without the
need for reverse engineering. This is particularly crucial in
regions where there are no networks of flux tower stations
for validation purposes. However, the model had limitations
in reproducing stomatal behaviour over specific vegeta-
tion species. In semi-arid areas, accounting for Es is crucial
because it contributes a significant proportion to total ET.
Consequently, attempts to develop algorithms based on VIs
were not successful because these indices cannot estimate
Es. The LAI and ET0 were useful predictors of ET across
the sites and this enabled the development of algorithms
for predicting ET on a biome scale and this is vital for data
scarce areas. The empiral algorithms developed in this
study need further refinement once a larger database of ET
has been accumulated.
Acknowledgements — This work was supported by the South
African Water Research Commission (K5/2400/4) and the National
Equipment Programme (Grant 93213). Host farmers in the selected
study sites are also profoundly acknowledged.
ORCID
Onalenna Gwate https://orcid.org/0000-0003-0237-4316
Sukhmani Mantel https://orcid.org/0000-0003-2086-6912
Lesley Gibson https://orcid.org/0000-0002-0824-7927
Zahn Munch https://orcid.org/0000-0003-0691-7920
Anthony Palmer https://orcid.org/0000-0001-9179-2725
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Associate Editor: Craig Morris
... Dye et al. (2001) noted that removal of riparian wattle, an invasive alien tree will lead to significant reductions in annual ET which in turn will enhance streamflow. So far, studies on ET over AT include: measurement of ET using an EC system (Gwate et al., 2016); development of ET predictive model to be applied at biome scale (Gwate et al., 2018); validation of improved simple single layer ET model (Penman-Monteith-Palmer (PMP)) using large aperture scintillometer (LAS) and EC system data (2015)(2016) (Gwate et al., 2019). Gwate et al. (2016) noted that during the experiment period between 2015-2016, the observed ET was more than the amount of rainfall received, possibly because the vegetation may be accessing groundwater in addition to the high water storage capacity of the vegetation. ...
... is the slope of the curve linking saturation water vapor pressure to air temperature divided by the psychrometric constant (γ), f regulates potential evaporation rate at the soil surface and ranges between 0 and 1 Gwate et al., 2019). It can be estimated as a function of i) soil water content (SWC); ii) precipitation and equilibrium evaporation ratio; and iii) soil drying after precipitation. ...
... RMSE and MAE show the level of the errors between the measured and simulated ET while RSR technique normalizes RMSE using standard deviation of the observed. According to Gwate et al. (2019), MAE technique is appropriate for errors that are uniformly distributed while RMSE technique is good for evaluating normally distributed data. The RSR varies from 0 to a large positive value which indicates zero RMSE. ...
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The Albany Thicket (AT) biome contains outstanding global biodiversity as well as the potential to achieve carbon credits associated with water-efficient Crasslucean acid metabolism (CAM). Understanding the water fluxes in the AT is crucial to determining carbon (C) sequestration rates and water-use efficiency. Despite large variation in water fluxes across the AT, only a few studies have been conducted in this region with their results validated against short periods of observed data. This study aims to evaluate three models of water fluxes over AT against data from an eddy covariance (EC) system active from October 2015 to May 2018. ET was modelled using the BioGeoChemistry Management (BGC-MAN) model, a biophysical model (Penman-Monteith-Leuning (PML)) and a remotely-sensed product (MOD16), and their results compared with that from the EC system. More than three decades of rainfall data from Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) was used to assess some rainfall characteristics of the region. The mean annual rainfall is 404 mm and mean monthly rainfall ranges from 16.0–50.7 mm, with minima likely to occur in winter period (between May and July) and monthly maxima in the summer period (between October and March). Among the three hydrological years in this study, total ET for 2016-2017 exceeded rainfall received by about 7% which shows that AT is likely to be supported by groundwater at some point but this requires further investigations. Generally, the three models applied in this study performed reasonably well when compared with the measured ET. The cumulative ET from BGC-MAN was slightly higher than that from EC by 16% and 8% in 2015-2016 and 2017-2018 hydrological years respectively while PML was slightly lower by 3% and 17% in 2016-2017 and 2017-2018; additionally, MODIS was slightly lower by 14% and 7% in 2016-2017 and 2017-2018, respectively. However, the correlation between the ET from EC and simulated ET from the three models was significant at p < 0.01.
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ABSTRACT It is imperative to understand the strong coupling between the carbon capture process and water use to sustainably manage rangelands. Woody encroachment is undermining rangelands grass production. Evapotranspiration (ET) highlights the links between ecosystem carbon capture process and water use. It forms the biggest flux of the hydrological cycle after precipitation yet it is not well understood. The Grassland and the Albany Thicket (AT) biomes in the Eastern Cape, South Africa, provide an interesting space to study the dynamics in rangelands biomass production and the associated water use. Therefore, the main purpose of this study was to contribute towards management of rangelands by understanding the dynamics in rangeland grass production and water use. To achieve this aim, the impact of Acacia mearnsii, an invasive alien plant, on soil chemical properties and rangelands grass production was investigated. This was achieved by analysing the biophysical attributes of A. mearnsii as they related to grass production. Secondly, selected soil variables that could be used as a prognosis for landscape recovery or deterioration were evaluated. In addition, aboveground grass biomass was measured in areas cleared of A. mearnsii and regression equations were prepared to help model aboveground grass biomass in areas cleared of A. mearnsi. The thesis also explored dynamics in water vapour and energy fluxes in these two biomes using an eddy covariance system. Consequently, water vapour and energy fluxes were evaluated in order to understand landscape water use and energy partitioning in the landscape. The study also tested the application of Penman-Monteith equation based algorithms for estimating ET with micrometeorological techniques used for validation. Pursuant to this, the Penman-Monteith-Leuning (PML) and Penman-Monteith-Palmer (PMP) equations were applied. In addition, some effort was devoted to improving the estimates of ET from the PMP by incorporating a direct soil evaporation component. Finally, the influence of local changes in catchment characteristics on ET was explored through the application of a variant of the Budyko framework and investigating dynamics in the evaporative index as well as applying tests for trends and shifts on ET and rainfall data to detect changes in mean quaternary catchment rainfall and ET. Results revealed that A. mearnsii affected soil chemical properties and impaired grass production in rangelands. Hence, thinning of canopies provided an optimal solution for enhanced landscape water use to sequestrate carbon, provide shade, grazing, and also wood fuel. It was also shown that across sites, ET was water limited since differences between reference ET and actual ET were large. ET was largely sensitive to vapour pressure deficit and surface conductance than to net radiation, indicating that the canopies were strongly coupled with the boundary layer. Rangeland ET was successfully simulated and evaporation from the soil was the dominant flux, hence there is scope for reducing the so-called ‘unproductive’ water use. Further, it was shown that the PML was better able to simulate ET compared to the PMP model as revealed by different model evaluation metrics such as the root mean square error, absolute mean square error and the root mean square observations standard deviation ratio. The incorporation of a soil evaporation component in the PMP model improved estimates of ET as revealed by the root mean square error. The results also indicated that both the catchment parameter (w) and the evaporative index were important in highlighting the impacts of land cover change on ET. It was also shown that, despite changes in the local environment such as catchment characteristics, global forces also affected ET at a local scale. Overall, the study demonstrated that combining remote sensing and ground based observations was important to better understand rangeland grass production and water use dynamics.
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Evapotranspiration (ET) is the process by which liquid water becomes water vapor and energetically this accounts for much of incoming solar radiation. If this ET did not occur temperatures would be higher, so understanding ET trends is crucial to predict future temperatures. Recent studies have reported prolonged declines in ET in recent decades, although these declines may relate to climate variability. Here, we used a well-validated diagnostic model to estimate daily ET during 1981–2012, and its three components: transpiration from vegetation (Et), direct evaporation from the soil (Es) and vaporization of intercepted rainfall from vegetation (Ei). During this period, ET over land has increased significantly (p < 0.01), caused by increases in Et and Ei, which are partially counteracted by Es decreasing. These contrasting trends are primarily driven by increases in vegetation leaf area index, dominated by greening. The overall increase in Et over land is about twofold of the decrease in Es. These opposing trends are not simulated by most Coupled Model Intercomparison Project phase 5 (CMIP5) models, and highlight the importance of realistically representing vegetation changes in earth system models for predicting future changes in the energy and water cycle.
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This is an extract of this Grassland Biome chapter from the pre-publication PDF of the book Mucina, L., & Rutherford, M.C. (eds). Reprint 2011. The Vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19. South African Biodiversity Institute, Pretoria. ISBN: 978-1919976-21-1
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We present a model to retrieve actual evapotranspiration (ET) from satellites' vegetation indices (Parameterization of Vegetation Indices for ET estimation model, or PaVI-E) for the eastern Mediterranean (EM) at a spatial resolution of 250 m. The model is based on the empirical relationship between satellites' vegetation indices (normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from MODIS) and total annual ET (ETAnnual) estimated at 16 FLUXNET sites, representing a wide range of plant functional types and ETAnnual. Empirical relationships were first examined separately for (a) annual vegetation systems (i.e. croplands and grasslands) and (b) systems with combined annual and perennial vegetation (i.e. woodlands, forests, savannah and shrublands). Vegetation indices explained most of the variance in ETAnnual in those systems (71 % for annuals, and 88 % for combined annual and perennial systems), while adding land surface temperature data in a multiple-variable regression and a modified version of the Temperature and Greenness model did not result in better correlations (p > 0.1). After establishing empirical relationships, PaVI-E was used to retrieve ETAnnual for the EM from 2000 to 2014. Models' estimates were highly correlated (R = 0.92, p < 0.01) with ETAnnual calculated from water catchment balances along rainfall gradient of the EM. They were also comparable to the coarser-resolution ET products of the Land Surface Analysis Satellite Applications Facility (LSA-SAF MSG ETa, 3.1 km) and MODIS (MOD16, 1 km) at 148 EM basins with R of 0.75 and 0.77 and relative biases of 5.2 and −5.2 %, respectively (p < 0.001 for both). In the absence of high-resolution (< 1 km) ET models for the EM the proposed model is expected to contribute to the hydrological study of this region, assisting in water resource management, which is one of the most valuable resources of this region.
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Eragrostis plana Nees (Poaceae), known commonly as tough lovegrass, is the most abundant invasive plant found in the rangelands of Southern Brazil. This study is the first to document the anatomical and histochemical features of the leaves and roots of this grass. Five leaf blades and five roots were collected, fixed in formaldehyde, glacial acetic acid, ethanol 70° (FAA 70), sectioned, stained, and photographed under a light microscope. Anatomical analysis of the leaves was supplemented with observations under a scanning electron microscope. Manual cuts were made into fresh material, which was subjected to specific reagents to determine the lipid, phenol, tannin, lignin, alkaloid, and starch presence. The analyses revealed that the leaves are amphistomatic with paracytic stomata, and the epidermis has lignified cells, a Kranz structure, collateral vascular bundles of two sizes, tectors, unicellular and crystalline trichomes. The roots are polyarc with air gaps on the cortical parenchyma, U-thickened endodermal cell walls and a parenchymatic pith with starch storage cells. Leaves and roots contain lipids, phenols, lignins, and alkaloids; starch is only present in the roots. Anatomical and histochemical traits of tough lovegrass suggest that the plant has adapted to survive under biotic and abiotic stress, which enhances its performance in relation to native plants.
Conference Paper
Determining water and carbon fluxes over a vegetated surface is important in a context of global environmental changes and the fluxes help in understanding ecosystem functioning. Pursuant to this, the study measured evapotranspiration (ET) using an eddy covariance (EC) system installed over an intact example of the Albany Thicket (AT) vegetation in the Eastern Cape, South Africa. Environmental constraints to ET were also assessed by examining the response of ET to biotic and abiotic factors. The EC system comprised of an open path Infrared Gas Analyser and Sonic anemometer and an attendant weather station to measure bi-meteorological variables. Post processing of eddy covariance data was conducted using EddyPro software. Quality assessment of fluxes was also performed and rejected and missing data were filled using the method of mean diurnal variations (MDV). Much of the variation in ET was accounted for by the leaf area index (LAI, p < 0.001, 41%) and soil moisture content (SWC, p < 0.001, 32%). Total measured ET during the experiment was greater than total rainfall received owing to the high water storage capacity of the vegetation and the possibility of vegetation accessing ground water. Most of the net radiation was consumed by sensible heat flux and this means that ET in the area is essentially water limited since abundant energy was available to drive turbulent transfers of energy. Understanding the environmental constraints to ET is crucial in predicting the ecosystem response to environmental forces such as climate change.
Article
The Horqin sandy steppe is an ecosystem where groundwater-dependent grasslands are main components. To study the dynamics of soil water and evapotranspiration, and its partition into transpiration and soil evaporation, the SIMDualKc model was applied to two sites in the Agula eco-hydrological study area, Inner Mongolia, China. This model adopts the dual crop coefficient approach to compute daily crop evapotranspiration (ETc) and uses a parametric function to compute capillary rise from a shallow watertable. The application refers to three years of field observations, 2008 with high rainfall and higher watertable, 2009 with low rainfall and lower watertable, and 2011 with average rainfall and watertable conditions. Model calibration and validation were performed comparing simulated against observed soil water content throughout the vegetation seasons, with regression coefficients close to 1.0 and small root mean square errors (RMSE<0.013cm3cm-3). The groundwater contribution represented 41-47% of the actual ETc in the higher rainfall year and 56-59% in the dry year, which clearly shows the role of groundwater in sustaining grasslands in the Horqin steppe. The ratio between transpiration and actual ETc ranged 88 to 94%, which is much higher than observations in other grasslands because capillary rise contributes to extraction by crop roots, not to soil evaporation. Simulations relative to lowering the watertable show that groundwater contribution decreases up to 25% for a dry year with a consequent decrease of transpiration of 24%. Hence, there is the need for developing appropriate groundwater protection measures to preserve the Horqin landscape.
Article
Accurately measuring evapotranspiration (ET) is essential if we are to derive reasonable estimates of production and water use for semi-arid savannas. Estimates of ET are also important in defining the health of an ecosystem and the quantity of water used by the vegetation when preparing a catchment-scale water balance. We derived ET0 from an automatic weather station 30km west of Skukuza using the Penman- Monteith equation, and then used the MODIS LAI to inform the model of canopy phenological dynamics. This result was compared with 173 days of ET measurements from the eddy covariance (ETec) system near Skukuza in 2007 as well as from the ET recorded by a large aperture scintillometer at the same site in 2005. The model compared favourably with both sets of measured data and enabled us to fill gaps in the eddy covariance record, predicting a total ET of 332mm for the semi-arid savanna around the Skukuza flux site in 2007. When used independently of the eddy covariance data, ETMODIS predicted an annual ET of 378 mm in 2007.
Article
Evapotranspiration (ET) is arguably the most uncertain ecohydrologic variable for quantifying watershed water budgets. Although numerous ET and hydrological models exist, accurately predicting the effects of global change on water use and availability remains challenging due to model deficiency and/or a lack of input parameters. The objective of this study was to create a new set of monthly ET models that can better quantify landscape-level ET with readily available meteorological and biophysical information. We integrated eddy covariance flux measurements from over 200 sites, multiple year remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS), and statistical modeling. Through examining the key biophysical controls on ET by land cover type (i.e., shrubland, cropland, deciduous forest, evergreen forest, mixed forest, grassland, and savannas), we created unique ET regression models for each land cover type using different combinations of biophysical independent factors. Leaf area index and net radiation explained most of the variability of observed ET for shrubland, cropland, grassland, savannas and evergreen forest ecosystems. In contrast, potential evapotranspiration (PET) as estimated by the temperature-based Hamon method was most useful for estimating monthly ET for deciduous and mixed forests. The more data- demanding PET method, FAO Reference ET model, had similar power as the simpler Hamon PET method for estimating actual ET. We developed three sets of monthly ET models by land cover type for different practical applications with different data availability. Our models may be used to improve water balance estimates for large basins or regions with mixed land cover types. This article is protected by copyright. All rights reserved.