HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus GmbHGöttingen, Germany10.5194/hess-19-2513-2015Extending periodic eddy covariance latent heat fluxes through tree
sap-flow measurements to estimate long-term total evaporation in a peat swamp
forestClulowA. D.clulowa@ukzn.ac.zaEversonC. S.MengistuM. G.PriceJ. S.NicklessA.JewittG. P. W.https://orcid.org/0000-0002-7444-7855Centre for Water Resources Research, University of KwaZulu-Natal,
Pietermaritzburg, 3209, South AfricaDepartment of Geography and Environmental Management, University of
Waterloo, Waterloo, Ontario, N2L 3G1, CanadaDepartment of Statistical Sciences, University of Cape Town, Cape
Town, 7701, South Africanow at: South African Environmental Observational Network-Grasslands, Forests and
Wetlands Node, Pietermaritzburg, 3202, South AfricaA. D. Clulow (clulowa@ukzn.ac.za)28May20151952513253415October201412December20142May20155May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/19/2513/2015/hess-19-2513-2015.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/19/2513/2015/hess-19-2513-2015.pdf
A combination of measurement and modelling was used to find a pragmatic
solution to estimate the annual total evaporation from the rare and
indigenous Nkazana Peat Swamp Forest (PSF) on the east coast of Southern
Africa to improve the water balance estimates within the area. Actual total
evaporation (ETa) was measured during three window periods (between
7 and 9 days each) using an eddy covariance (EC) system on a
telescopic mast above the forest canopy. Sap flows of an understory tree and
an emergent tree were measured using a low-maintenance heat pulse velocity
system for an entire hydrological year (October 2009 to September 2010). An empirical
model was derived, describing the relationship between ETa from the
Nkazana PSF and sap-flow measurements. These overlapped during two of the
window periods (R2= 0.92 and 0.90), providing hourly estimates of
ETa from the Nkazana PSF for a year, totalling 1125 mm (while rainfall
was 650 mm). In building the empirical model, it was found that to include
the understory tree sap flow provided no benefit to the model performance. In
addition, the relationship between the emergent tree sap flow with ETa
between the two field campaigns was consistent and could be represented by a
single empirical model (R2= 0.90; RMSE = 0.08 mm h-1).
During the window periods of EC measurement, no single meteorological
variable was found to describe the Nkazana PSF ETa satisfactorily.
However, in terms of evaporation models, the hourly FAO Penman–Monteith
reference evaporation (ETo) best described ETa during
the August 2009 (R2= 0.75), November 2009 (R2= 0.85) and
March 2010 (R2= 0.76) field campaigns, compared to the
Priestley–Taylor potential evaporation (ETp) model
(R2= 0.54, 0.74 and 0.62 during the respective field campaigns).
From the extended record of ETa (derived in this study from
sap flow) and ETo, a monthly crop factor (Kc) was
derived for the Nkazana PSF, providing a method of estimating long-term swamp
forest water-use from meteorological data. The monthly Kc
indicated two distinct periods. From February to May, it was between 1.2 and
1.4 compared with June to January, when the crop factor was 0.8 to 1.0. The
derived monthly Kc values were verified as accurate (to one
significant digit) using historical data measured at the same site, also
using EC, from a previous study.
The measurements provided insights into the microclimate within a subtropical
peat swamp forest and the contrasting sap flow of emergent and understory
trees. They showed that expensive, high-maintenance equipment can be used
during manageable window periods in conjunction with low-maintenance systems,
dedicated to individual trees, to derive a model to estimate long-term
ETa over remote heterogeneous forests. In addition, the contrast
in annual ETa and rainfall emphasised the reliance of the Nkazana
PSF on groundwater.
Introduction
Severe water scarcity in parts of South Africa has threatened the health of
internationally recognised environmental areas such as the iSimangaliso
Wetland Park, a UNESCO world heritage site. To optimise the management of the
water balance and understand the functioning of the area, there has been a
need to quantify the water-use of the dominant vegetation types of the Park
such as the endangered Peat Swamp Forests (Grundling et al., 1998; Clulow et
al., 2012), a dominant plant type of the Mfabeni Mire. However, little is
known about the water-use characteristics of the species-diverse Peat Swamp
Forests (PSFs) both locally and internationally in terms of model
parametrisation. Despite significant improvements to measurement techniques
over vegetated surfaces (Savage et al., 1997), these have not been of benefit
for PSFs due to their remote and inaccessible nature. In addition, well-documented extreme events (such as the Demoina floods in 1987) pose a real
threat in the area. Sophisticated instruments are unfortunately vulnerable to
damage and malfunction in such environments and PSFs are therefore not good
locations for long-term deployment of sensitive equipment, a challenge facing
researchers internationally and particularly in developing countries.
There are numerous, complex evaporation sources, which interact and
contribute to actual total evaporation (ETa) in the Nkazana PSF.
The areas of open water fluctuate, depending on groundwater levels. Open
water evaporation is well described from the early work of Penman (1948) to
the more recent work of Finch (2001) and Rosenberry et al. (2007) but none
accounts for the effects of dense vegetation cover on radiative shading and
the prevention of convection over the water surface by a tall and dense
canopy. There are surface evaporation studies of peat (Nichols and Brown,
1980; Koerselman and Beltman, 1988; Lafleur and Roulet, 1992; Thompson et
al., 1999; Clulow et al., 2012), but none in the context of a subtropical
swamp forest. In addition the vegetated canopy is complex. There is a dense
cover of ferns, of which little is understood in terms of transpiration
(Andrade and Nobel, 1997). Above the ferns, the tree canopy consists of two
levels described below (understory and emergent trees) and there are
tree-climbing vines. Estimating ETa of the Nkazana PSF is clearly
multifaceted due to its diversity and our lack of understanding of the
water-use of the specific plants, together with the potential variation in
the evaporative demand within and above the canopy.
Within South Africa, the only comparable study took place over an evergreen
indigenous mixed forest in the Southern Cape near the coast. Dye et
al. (2008) measured ETa using eddy covariance (EC), scintillometry
and Bowen ratio over 18 days in total, during three different field
campaigns, representing three different seasons within the year. The periods
in-between were modelled using the FAO56 Penman–Monteith reference equation
of Allen et al. (1998), which generally underestimated ETa under
high evaporative conditions and overestimated under low evaporative
conditions. This was attributed to the assumption of a constant surface
resistance. The Penman–Monteith equation (Monteith, 1965) was found to give
the best match of modelled to observed daily ETa, but required
measurements or a submodel, accounting for variable canopy conductance. The
more complex WAVES (CSIRO, Canberra, Australia) process-based model simulated
canopy growth and water-use processes in much more detail. However,
successful parametrisation of the many model inputs was a significant
challenge and despite their best efforts, the WAVES output revealed an
overestimation of daily ETa under conditions of low evaporative
demand, which could not be corrected. They concluded that the best technique
for interpolating the periods between the three field campaigns would be the
Penman–Monteith equation despite the problem of the variable canopy
conductance and recommended that further research into understanding the
most appropriate techniques for interpolating measured data would be
necessary.
Flow diagram of the research strategy indicating the different
measurement techniques, their combination, and link to the aims of the
research.
Internationally, no studies were found with measurements over a comparable
subtropical peat swamp forest. However, Vourlitis et al. (2002) provide a
valuable study in which they attempted to measure the long-term ETa with an EC system over a tropical forest in Brazil. Despite the proximity
to the city of Sinop (offering a nearby base from which maintenance could be
conducted), power issues hampered the data collection and EC data were only
collected 26 % of the time. Meteorological data was therefore used to
estimate the latent energy flux (LE) using the Priestley–Taylor expression.
Since the beginning of the FLUXNET project, which was established to compile
long-term measurements of water vapour, carbon dioxide and energy exchanges
from a global network of EC systems, the problem of complete EC data sets and
gap filling of records was recognised and is still an ongoing challenge
(Baldocchi et al., 1996, 2001). Falge et al. (2001) found the average data
coverage for long-term EC systems to be only 65 % due to system failure
or data rejection with most of these located in developed countries. Clearly,
despite the benefit of EC systems, long-term, continuous records of observed
ETa data over indigenous subtropical and tropical forests are
improbable without significant research budgets allowing daily maintenance,
gap filling and the processing of data including complex spectral corrections, 3-D
corrections and coordinate rotation amongst others (Massman and Lee, 2002;
Finnigan et al., 2003; Hui et al., 2004). Intensive, short-term field
campaigns, offering reliable, continuous records, during different seasons
seem to provide an appropriate strategy to determine the annual cycle of
ETa. This is particularly the case in South Africa, where theft of
equipment and especially batteries from the foot of visible towers is a
severe limitation, although this is overcome by employing 24-h security
guarding services to protect the equipment during the short-term measurement
periods (Dye et al., 2008). However, this strategy only provides a viable
solution if the ETa during the in-between periods can be
adequately estimated.
Wilson et al. (2001) applied EC and sap-flow techniques in a deciduous forest
of the southeastern United States, and found that there was a qualitative
similarity between ETa, derived using the EC technique, and tree
transpiration. With the recent advances in sap-flow measurement techniques and
upscaling of individual tree transpiration measurements to canopy ETa, it is believed that sap-flow techniques offer a reliable, standalone,
long-term solution to estimating ETa in uniform tree stands
(Hatton and Wu, 1995; Meiresonne et al., 1999; Crosbie et al., 2007). There
are however, numerous complexities, bringing some doubt as to the accuracy of
the absolute sap-flow results, such as the anisotropic properties of sapwood
(Vandegehuchte et al., 2012), species composition effects (Wullschleger et
al., 2001), tree symmetry (Vertessy et al., 1997), radial patterns of sap flow
(Čermák and Nadezhdina, 1998) and changes in spatial patterns of
transpiration (Traver et al., 2010). In heterogeneous and complex canopies
such as the Nkazana PSF described above, sap-flow systems alone are
impractical for the prediction of stand ETa even with the recent
advances in process-based models of vegetation function such as the Measpa
model (Duursma and Medlyn, 2012). However, whether it is possible to use the
qualitative relationship of sap flow with measured ETa, as found by
Wilson et al. (2001), remains unknown.
For these reasons, a strategy to provide a measurement and modelling
framework was developed and tested, in which detailed water flux measurements
were recorded using EC instruments in an indigenous, heterogeneous forest
over three window periods in August 2009, November 2009 and March 2010
(Fig. 1). This minimised the cost and risk of damage to these expensive
systems, and provided continuous and reliable data from well-maintained
instruments, operated by a team of scientists, but were limited to three
window periods. Two of these window periods overlapped with long-term sap-flow
measurements, and a nearby weather station provided climatic data during the
full period (Fig. 1). The sap flow and weather station systems had lower
maintenance and power requirements, were less delicate, less visible, able to
withstand the harsh environment, and operated for longer periods unattended
(1–2 months) without compromising data quality. The aims were
therefore to (1) establish whether the long-term ETa of the
Nkazana PSF could be determined by this combination of EC window periods and
long-term sap-flow measurements, (2) to provide a means of modelling the
ETa of surrounding PSFs from nearby meteorological data and (3) to
investigate the controlling climatic variables and their influence on sap flow
as well as the energy fluxes and microclimate within the swamp forest
(Fig. 1).
The study area
The study area is located in Maputaland, South Africa, on the Eastern Shores area
of the iSimangaliso Wetland Park. It has held international status as a
UNESCO World Heritage Site since 1999 (Taylor et al., 2006) and falls within
the St Lucia Ramsar Site designated in 1986 (Taylor, 1991). It is one of the
largest protected aquatic systems in southern Africa and, due to its
biodiversity and natural beauty, has become an international tourist
destination and is now a `regional economic hub' (Whitfield and Taylor,
2009).
The Eastern Shores area has a subtropical climate and lies in a summer
rainfall area (Schulze et al., 2008). It has been reported that `the
rainfall gradient westwards from the coast is strong, with a precipitation at
Mission Rocks on the Indian Ocean coastal barrier dune exceeding
1200 mm yr-1 and decreasing to around 900 mm yr-1 at Fanies
Island on the western shoreline of the estuary' (Taylor et al., 2006).
However, Lynch (2004) provides mean annual precipitation values of 1056, 844
and 910 mm a-1 from the nearby Fanies Island, Charters Creek and St
Lucia respectively from a 125-year raster database, and the Agricultural
Research Council measured an average annual rainfall at St Lucia over a
22-year period of 975 mm a-1 (ARC-ISCW, 2011). Clearly rainfall in the
area is variable and figures depend on the length of the period in years over
which the rainfall was measured and the particular location. During this
study there was a well-reported drought in the region (Grundling et al.,
2014).
(a) Location of the Eastern Shores within South Africa, (b) the
Nkazana Peat Swamp Forest site (where the EC and sap-flow systems were
located) and the automatic weather station within the Mfabeni Mire on the
Eastern Shores (data from Mucina and Rutherford, 2006).
The Eastern Shores area is flanked by the Indian Ocean to the east and Lake
St Lucia to the west (Fig. 2a). It includes coastal dunes (dune forest) to
the east, the Embomveni Dunes (grassland) to the west and the Mfabeni Mire as
an interdunal drainage line through the middle. The perennial Nkazana Stream
drains from the Mfabeni Mire, providing freshwater to Lake St Lucia. This
stream was recognised by Vrdoljak and Hart (2007) as an ecologically
important source of freshwater to Lake St Lucia during droughts. Clulow et
al. (2013) state that `Organic matter and sediment have accumulated in the
Mfabeni Mire over the past 45 000 years, forming one of South Africa's
largest peatlands and one of the oldest active peatlands in the world
(Grundling et al., 1998)'. The Mfabeni Mire is approximately 8 km long
(north–south direction) and 4 km wide in places (east–west direction). It
comprises of subtropical freshwater wetland (SFW) with vegetation described
by Vaeret and Sokolic (2008) and with a variable canopy height averaging
approximately 0.8 m (Clulow et al., 2012). The Nkazana PSF is the other
dominant vegetation type that runs down the western side of the Mfabeni Mire
(Fig. 2b). The Nkazana PSF falls within the Indian Ocean Coastal Belt Biome,
and is described as being a `mixed, seasonal grassland community' (Mucina
and Rutherford, 2006). The Nkazana PSF is further classified by von Maltitz
et al. (2003) and Mucina and Rutherford (2006) as an Azonal Forest,
indicating its presence due to, and reliance on, the ground water surface
within the Mfabeni Mire.
Site description
The Swamp Forest site (28∘10.176′ S, 32∘30.070′ E)
posed significant logistical challenges due to the 20 m high tree canopy,
thick undergrowth, soft ground, dangerous animals and general inaccessibility
by road. The measurements were concentrated at its widest point
(approximately 1 km) to maximise the fetch for the flux measurements above
the tree canopy. Clulow et al. (2013) described previous botanical research,
explaining the structure of the Nkazana PSF and the vegetation in the
vicinity of the research site as follows:
Wessels (1997) classified the swamp forests of the area into three logical
subgroupings based on dominant species, stand density and basal areas. The
Syzygium cordatum subgroup is characterised by an irregular, broken
canopy of predominantly Syzygium cordatum trees (known locally as
the Water Berry) of up to 30 m, emerging above an intermediate canopy of
approximately 6–15 m. Dominant tree species found in the Swamp Forest and
in the vicinity of the site included: Macaranga capensis, Bridelia macrantha, Tarenna pavettoides and Stenochlaena tenuifolia. An
impenetrable fern (Nephrolepis biserrata) covers the forest floor
with a height of approximately 2.5 m and the Stenochlaena tenuifolia (Blechnaceae) fern grows up the tree stems to a height of
approximately 10 m.
The layer of peat at the Nkazana PSF site was approximately 2 m thick and
underlain by sand. The water table depth was < 1.0 m but at the
surface in low-lying areas of the forest. The leaf area index (LAI-2200,
LI-COR Inc., Lincoln, Nebraska, USA) beneath the ferns and trees was
approximately 7.2 and below the trees approximately 3.3.
Materials and methodsMicrometeorological measurements
An automatic weather station provided supporting meteorological data
(Fig. 1). It was located adjacent to the Nkazana PSF in the Mfabeni Mire over
a reed, sedge and grass dominated vegetation, described broadly as SFW
(Fig. 2b). Observations of rainfall (TE525, Texas Electronics Inc., Dallas,
TX, USA), air temperature and relative humidity (HMP45C, Vaisala Inc.,
Helsinki, Finland), solar irradiance (LI-200X, LI-COR, Lincoln, NB,
USA), net irradiance (NRLite, Kipp and Zonen, Delft, The Netherlands),
wind speed and direction (Model 03002, R. M. Young, Traverse City, MI,
USA) were made every 10 s. The appropriate statistical outputs were stored
on a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA) at
30 min intervals. Sensors were installed according to recommendations of the
World Meteorological Organisation (WMO, 2008) with the rain gauge orifice at
1.2 m and the remaining sensors 2 m above the ground. Vapour pressure
deficit (VPD) was calculated on the data logger from air temperature
(Tair) and relative humidity (RH) measurements according to
Savage et al. (1997).
Measurement of energy fluxes and actual total evaporation
The shortened energy balance equation is commonly used in evaporation studies
(Drexler et al. 2004) to describe the partitioning of energy at the Earth's
surface and provides an indirect method to determine ETa (Eq. 1).
The `shortened' version ignores those energies associated with
photosynthesis, respiration and energy stored in plant canopies. However,
these are considered small when compared with the other terms (Thom, 1975).
The shortened energy balance equation is written as
Rn=G+H+LE,
where Rn is the net irradiance, H is the sensible heat flux, G is the
ground heat flux and LE is the latent energy flux, which is the energy
equivalent of evaporation by conversion (Savage et al., 2004).
Eddy covariance is based on the estimation of the eddy flux which is
expressed as:
F=ρdw′s′‾,
where ρd is the density of dry air, w is vertical wind speed
(measured with the sonic anemometer described below) and s is the
concentration of the scalar of interest (water vapour in this case). The
primes indicate fluctuation from a temporal average (i.e. w′=w-w‾;
s′=s-s‾) and the overbar represents a time average. The averaging
period of the instantaneous fluctuations, of w′ and s′ should be long enough
(30 to 60 min) to capture all of the eddy motions that contribute to the
flux and fulfil the assumption of stationarity (Meyers and Baldocchi, 2005).
The vertical flux densities of H (ETa derived indirectly by the
shortened energy balance equation) and LE (ETa derived directly)
were estimated by calculating the mean covariance of sensible (Eq. 2) and
water vapour fluctuations respectively, with fluctuating vertical velocity
(Baldocchi et al., 1988).
Soil heat flux was measured using two soil heat flux plates (HFT-3, REBS,
Seattle, WA, USA) and a system of parallel thermocouples (Type E). The plates
were placed at a depth of 0.08 m below the peat surface. The thermocouples
were buried at 0.02 and 0.06 m and were used together with volumetric water
content (CS615, Campbell Scientific Inc., Logan, UT, USA) in the upper
0.06 m to estimate the heat stored above the soil heat flux plates. The
measurements were stored every 10 s on a data logger (CR23X, Campbell
Scientific Inc., Logan, UT, USA) and 30 min averages were computed.
During the measurements at the Nkazana Swamp Forest, the groundwater level
was deeper than 0.1 m below the surface and therefore, the total G was
determined using the calorimetric methodology described by Tanner (1960).
Over the corresponding time period, Rn was measured above the forest
canopy, using a 21.3 m telescopic mast (WT6, Clark Masts Systems Ltd,
Isle of Wight, UK). It was erected within the forest, on a fallen tree
stump approximately 2.5 m high (Fig. 3a). This formed a firm base for the 90
kg mast which was carried into the forest from the nearest road approximately
1 km away. The computer box for the EC system (In Situ Flux Systems AB,
Ockelbo, Sweden) was installed near the base of the mast (Fig. 3b) and a
generator that automatically charged a bank of four 100 Ah deep-cycle
lead–acid batteries (accumulators) was positioned approximately 50 m from
the site in a predominantly downwind direction (the northwest) to minimise
any possible influence from the exhaust fumes on the flux measurements. The
generator was controlled by a logger (CR10X, Campbell Scientific Inc., Logan,
UT, USA) which was set to activate the charging system (220VAC petrol
generator and 40 A 12 V charger) when the accumulators dropped below 12.4 V.
(a) Telescopic mast (21.3 m) erected in the swamp forest to raise
the eddy covariance instruments above the forest canopy, (b) the computer
installed at the swamp forest, housed in a temperature controlled enclosure
and (c) the instruments attached to the head of the mast.
A `SATI-3VX' style, three-dimensional (3-D) sonic anemometer (Applied
Technologies, Inc., Longmont, CO, USA) and open-path infrared gas analyser
(LI7500, LI-COR, Lincoln, NE, USA) were mounted on the head of the mast
(0.089 m diameter) orientated to face the east (predominant wind direction)
to avoid air-flow distortion from the mast (Fig. 3c). In addition,
Tair (PT-10, Peak Sensors Ltd, Chesterfield, UK) and Rn
(NRLite, Kipp and Zonen, Delft, the Netherlands) were measured at the head of
the mast. Data collection and analyses of the system was made in real time by
the ECOFLUX software fully described by Grelle and Lindroth (1996) using a
Flux Computer (In Situ Flux Systems AB, Ockelbo, Sweden). The system operated
with a sampling rate of 10 Hz and the average fluxes were calculated every
30 min. The raw data were also stored for further processing. All the
necessary corrections for air-density effects and 3-D coordinate rotation
were performed on the Flux Computer to determine H (Grelle and Lindroth,
1996).
The Bowen ratio (β) has historical significance in evaporation
studies and is defined as
β=HLE
for a specified time period (Bowen, 1926). It informs on the dominance of
H or LE and was calculated at a daily time interval in this study, providing
a useful means of showing changes in the distribution and weighting of the
energy balance components within and between field campaigns.
Energy balance closure
If each component of the energy balance is measured
accurately and independently, then Eq. (1) should be satisfied, and
closure is considered satisfied. However, energy balance closure could still
be achieved if two or more terms have incorrect values and the terms in Eq. (1) still sum to zero (Savage et al., 2004). If the components of the
shortened energy balance equation are measured independently then Rn-G-H- LE =c, where c is termed the energy balance closure (W m-2),
and closure is satisfied if c= 0 W m-2. By rearranging Eq. (1),
closure is not achieved if the available energy Rn-G
does not equal the turbulent fluxes H+ LE. Another measure of the
lack of closure is the closure ratio or the energy balance closure
discrepancy D defined by Twine et al. (2000) as
D=H+LERn-G,
in which a D of 1 indicates perfect closure. Several studies using
numerous techniques over various surfaces have failed to achieve closure by
up to 20 or 30 % (Wilson et al., 2001, 2002; Barr et al.,
2006). The vast majority have found higher energy input by radiation fluxes
than loss by turbulent fluxes (H and LE) and G (Oncley et al.,
2007). Therefore, the measured fluxes should be corrected or the
uncertainties in the measured fluxes accepted (Twine et al., 2000). Several
reasons for lack of energy balance closure have been discussed by Twine et
al. (2000), Wilson et al. (2002), and Cava et al. (2008). These reasons
include: (1) sampling errors associated with different measurement
source areas for the terms in Eq. 1, (2) a systematic bias in
instrumentation, (3) neglected energy sinks, (4) the loss of
low- and/or high-frequency contributions to the turbulent flux, (5)
neglected advection of scalars, (6) measurement errors related to
sensor separation, alignment problems, interference from tower or
instrument-mounting structure, and (7) errors in the measurement of
Rn and/or G. Despite concerns that the direct method of determining
total evaporation (ETec) by measuring water vapour concentrations using
an Infrared Gas Analyser may result in underestimates or overestimates of LE,
in this study, it was considered that some of the closure pitfalls of the
shortened energy balance method, such as (3) and (7) in
particular, could be significant due to the tall canopy at the site
(3) and point measurement location (7). Therefore, all
ETa results reported in this paper were calculated by the direct
method. Energy balance closure discrepancy was determined during the daytime
period (Rn > 0) due to the potentially large nocturnal
influences reported by Wilson et al. (2002).
Measurement of tree sap flow
A heat pulse velocity system based on the heat ratio method (Burgess et al.,
2001) was used to measure sap flow at various depths across the sapwood of
two trees over 20 months from September 2009 to early May 2011 which
overlapped with the November 2009 and March 2010 field campaigns (Fig. 1).
The trees measured were located approximately 40 m from the mast where the
EC and energy balance sensors were installed. Representative trees, in terms
of species, stem diameter, canopy height and proximity to each other, were
selected given the cable length limitations of the HPV system. The
Syzigium cordatum tree selected was approximately 22.5 m tall and
had a breast height stem diameter of 0.430 m. Sap flow was measured at four
depths across the sapwood on both the eastern and western sides of the stem
to account for differences in the sapwood depth around the tree. Sap flow was
also measured in a nearby understory tree (Shirakiopsis elliptica)
with a smaller stem diameter (0.081 m) at four depths within the sapwood. Air
temperature and relative humidity (HMP45C, Vaisala Inc., Helsinki, Finland)
within the canopy, at a height of 2 m above the ground, and soil volumetric
water content (θ) at the Syzigium cordatum tree (where the
roots were most dense at a depth of 0.075 m) were also measured. These were recorded hourly to coincide with the sap-flow measurements. Further details of
the installation, equipment used, wounding corrections applied and
calculations to derive the tree sap flow are documented in Clulow et
al. (2013). In this paper, following Dye et al. (2008), sap flow is
assumed to equate with tree transpiration and tree water-use.
Modelling actual total evaporation from sap flow
Polynomial regression (second order) analysis in the Genstat software (VSN
International, 2011) was used to describe the relationship between measured
ETa and sap flow of the emergent and understory trees during the
overlapping periods of the November 2009 and March 2010 field campaigns in
order to understand the possibility of extending the record of ETa from
the Nkazana PSF using the long-term sap-flow records. The hourly ETa and
sap-flow data were checked for homoscedasticity and required a square root
transformation to correct the variance distribution. The model derived was
applied over a full year of sap-flow data (October 2009 to September 2010) to
obtain an annual ETa (Fig. 1).
Evaporation models assessed
Two well-recognised evaporation models were tested for applicability of
modelling ETa from the Nkazana PSF (Fig. 1). After assessment of the
models at an hourly temporal resolution, over the three field campaigns, the
most applicable model was applied to the long-term ETa discussed above
and verified using historic data collected by the Council for Scientific and
Industrial Research (CSIR) during a preliminary study over the Nkazana PSF
from 8 to 12 August 2008 and 12 to 20 November 2008 (unpublished). The CSIR
measured ETa with the identical EC equipment used during the field
campaigns in 2009 and 2010 described above and at the same site in the
Nkazana PSF making the data ideal for verification of the models.
FAO Penman–Monteith reference evaporation
The original Penman
evaporation model (Penman, 1948), assumed an absence of any control on
evaporation at the Earth's surface – in effect, an open water or wet surface
situation. This was extended by Monteith (1965) to incorporate surface and
aerodynamic resistance functions applicable to vegetated surfaces and was
widely used in this form as the Penman–Monteith model. It is however, highly
data intensive (Mao et al., 2002; Drexler et al., 2004) and the model was
therefore standardised by the Food and Agriculture Organisation in Irrigation
and Drainage Paper No. 56 (Allen et al., 1998) into a form known as the FAO56
Penman–Monteith model that could be applied at both hourly and daily time
intervals. The model received favourable acceptance internationally in
establishing a reference evaporation (ETo) index (atmospheric
evaporative demand) as a function of weather variables measured at most
standard weather station systems. The definition of a reference crop over
which the weather variables should be measured was a `hypothetical crop with
an assumed height of 0.12 m having a surface resistance of 70 s m-1 and
an albedo of 0.23, closely resembling the evaporation of an extensive surface
of green grass of uniform height, actively growing and adequately watered'
(Allen et al., 1998). A nearby crop ETa is calculated by adjusting
ETo by a crop factor (Kc) in the form
ETa=ETo⋅Kc,
where the crop is not water stressed. In Allen et al. (1998), values of
Kc have been compiled for different vegetation types at different
stages in crop development. Since recommendations by the American Society of
Civil Engineers Evapotranspiration in Irrigation and Hydrology Committee
(Allen, et al., 2000) and the work by Irmak et al. (2005) and Allen et
al. (2006) amongst others, the tall crop reference (alfalfa height = 0.5 m)
and separate daytime (r= 50 s m-1) and night-time
(r= 200 s m-1) resistances for hourly calculations were
introduced. It was this most recent form of the equation, now referred to as
the FAO Penman–Monteith ETo, that was applied in the current
study.
Using the FAO Penman–Monteith ETo in combination with the
long-term ETa (from sap flow), Kc was calculated (Eq. 5)
for the Nkazana PSF at an hourly interval (while Rn > 0 and
ETa > 0.1 mm h-1) and summed to daily totals
as recommended by Irmak et al. (2005). The reference evaporation approach has
been successful internationally, partly due to technological advances leading
to improvements in temporal and spatial data availability, but also because
it provides a method for estimating ETa, which is transferrable
and can be applied to different vegetation types and locations across the
world.
Priestley–Taylor potential evaporation
Priestley and Taylor (1972) simplified the theoretical Penman equation for
specific conditions. They reasoned that, as an air mass moves over an
expansive, short, well-watered canopy, evaporation would eventually reach a
rate of equilibrium. In this case, where humid air moves over a wet surface,
the aerodynamic resistances become negligible, while irradiance dominates,
and the rate of evaporation would be equal to the potential evaporation
(ETp) which is written as
ETp=αLv⋅ΔΔ+γ⋅Rn-G,
where α is a constant, Lv is the specific latent heat of
vaporisation of water (2.45 MJ kg-1), Δ is the slope of the
saturation water vapour pressure versus Tair, and γ is the
psychometric constant.
The definition of the Priestley–Taylor model makes it suitable for estimation
of evaporation from open water areas and wetlands (Price, 1992; Souch et al.,
1996; Mao et al., 2002) but it has been applied over numerous other surfaces
such as forests (Shuttleworth and Calder, 1979), cropped surfaces (Davies and
Allen, 1973; Utset et al., 2004), pastures (Sumner and Jacobs, 2005) and even
soil water limited conditions in forest clearcuts (Flint and Childs, 1991)
with varied success and deviations from the originally proposed estimate for
α of 1.26. In this study it was applied in the form described by
Savage et al. (1997) where Δ / (Δ+γ) was estimated by
ΔΔ+γ=0.413188419+0.0157973⋅Tair-0.00011505⋅Tair2,
where Tair is average air temperature over the interval of
calculation (hourly in this study). By rearranging Eq. (6), and substituting
ETa for ETp, α was estimated in the same way as
Kc above.
Investigating climatic controls and drivers of sap flow
Sap flow was compared by simple linear regression to climatic variables
(Fig. 1) generally considered to control sap flow in trees such as solar
irradiance (Is) and VPD (Albaugh et al., 2013). Sap flow was also
compared by multiple regression analysis to the micrometeorological
parameters including Is, Tair, RH and soil volumetric
water content (θ) to determine individual and combined drivers of
sap flow. The log of the sap-flow measurements was modelled, as the variance of
the measurements themselves was not homoscedastic and therefore required a
variance stabilising transformation. Significance of variables, with up to
four-way interactions were considered. In addition, the predictor variables
(Is, RH, Tair and θ) were broken up into sets
of data with different ranges using regression tree analysis. In regression
tree analysis a different model is applied to individual ranges of data
rather than a global model (such as in regression analysis), in which a
single model is applied to the entire range of each variable. The
relationship for a linear regression model is assumed to be the same no
matter what the value of any of the predictor variables is. The consequence
thereof, is that a good midday relationship between sap-flow and a variable
(such as Is) for example, may be missed due to a poor relationship
during the early morning and late afternoon periods. The regression tree
analysis provides an alternative approach, in which the predictor variables
are broken up into different sets, and a different model applied to each
individual set. In the regression tree analysis output, which is represented
by a hierarchical tree diagram – the longer the line, the greater the
difference between the two subsets, and the higher in the hierarchy a split
occurs, the more significant is the split.
Summary of weather conditions during the August 2009, November 2009
and March 2010 field campaigns.
DateSolar radiantdensity (MJ m-2)Wind speed (m s-1)Wind direction (∘)VPD (kPa)Air temperature (∘C) RH (%) Rain(mm)MaxMinMaxMin13/08/200914.24.61991.521.913.088.733.714/08/200915.42.41941.221.89.895.543.315/08/200915.31.9831.122.47.098.950.716/08/200914.92.5741.123.311.297.046.517/08/200915.93.2381.022.610.498.148.90.818/08/200914.84.3331.023.014.195.052.519/08/200915.55.5281.224.513.296.345.8Average15.13.51.222.811.195.645.904/11/200922.02.11150.923.813.296.451.905/11/200916.74.2400.625.217.793.366.706/11/200918.15.3370.625.819.993.373.007/11/200919.06.9360.625.921.693.270.508/11/200925.37.4370.726.421.390.569.009/11/200921.16.2340.725.321.094.769.25.310/11/200916.24.42230.624.719.495.661.60.311/11/200921.22.3470.526.115.597.164.6Average22.83.10.828.318.494.958.216/03/201019.63.5661.027.920.092.865.21.517/03/201014.62.32450.828.718.496.060.34.618/03/201017.22.22140.826.417.794.165.519/03/201020.23.0580.828.616.196.862.620/03/201020.63.8661.030.022.193.259.021/03/201016.21.9901.028.621.992.859.922/03/201022.51.7970.828.916.496.759.60.323/03/201019.92.32341.028.816.897.363.924/03/201021.22.1791.030.218.696.756.6Average19.12.51.128.718.795.261.4ResultsWeather conditions during the study
The daily radiant densities (integrated solar irradiance over a day) were
lowest in August 2009 (∼15 MJ m-2) and most consistent (Table 1),
whereas in November 2009 and March 2010 they were higher and more variable
(between ∼16 and ∼25 MJ m-2), particularly in November 2009
(Table 1). The daily maximum temperatures were highest in March 2010
(∼29 ∘C) and lowest in August 2009 (22.8 ∘C). Average
minimum RH was lowest in August 2009 (∼34 %) and the average daytime
VPD was highest (1.2 kPa). Average daily wind speeds were notably high in
November 2009 (>7 m s-1) and the dominant wind direction
for the site was from the north-east and the south. Some rainfall (<7 mm) occurred during the field campaigns but fortunately fell at night and
did not affect the daytime flux measurements.
The difference in the monthly daytime (09:00 LT to 15:00 LT) vapour pressure
deficit and difference between the monthly average dawn air temperatures
measured in the subtropical freshwater wetland area of the Mfabeni Mire
(reeds, sedges and grasses) and within the canopy of the Nkazana Peat Swamp
Forest site.
The average of the half-hourly energy fluxes, with error bars
indicating the standard error, measured at the Nkazana Swamp Forest in
(a) August 2009, (b) November 2009 and (c) March 2010.
Daily total energy densities (while Rn > 0) measured
at the Nkazana Swamp Forest in (a) August 2009, (b) November 2009 and
(c) March 2010.
Daily actual total evaporation (ETa) measured over the Nkazana
Swamp Forest during three representative periods.
The microclimate within the Nkazana PSF was noticeably different to the
adjacent SFW areas. The VPD within the Nkazana PSF canopy was consistently
lower than the SFW where the automatic weather station was located
approximately 3 km away, with the larger differences occurring from March to
August, which is the winter period (Fig. 4). A difference in dawn
Tair between the Nkazana PSF and the adjacent area was also
noted. The difference was lowest in summer and highest in winter with the
Nkazana PSF being up to 6 ∘C warmer on some mornings in June 2010.
Eddy covariance flux measurements
Despite the apparent consistency in the daily radiant density during August
2009 noted above (Table 1), the 30 min net irradiance flux data showed
that all field campaigns were affected by cloud during the daytime, as
indicated by the standard error bars of the net irradiance (Fig. 5a, b and
c). Even the August 2009 data, despite being in the middle of the dry season,
were influenced by cloud during 6 out of the 7 days of measurement (not
shown). During the August 2009 and March 2010 field campaigns, there was a
noticeable dip in the average Rn at approximately 11:00 LT. with large
standard errors (>90 W m-2) due to cloud cover. In
November, the dip occurred at approximately 13:00 LT., also accompanied by
large standard errors (>90 W m-2). The cloud affected
pattern of Rn was translated through to H and LE, which were positive
during the day, and with largest standard errors coinciding with those of the
Rn except for the early morning observed LE in August 2009, which was
attributed to the evaporation of dew on some days. The maximum rates of LE
were approximately 400 W m-2 in August 2009, 600 W m-2 in
November 2009 and 700 W m-2 in March 2010 (not shown). The pattern of
G fluctuated diurnally but due to attenuation (sensors were below the soil
surface) the pattern was smoother than the other fluxes during the course of
the day.
During the August 2009 field campaign the daily net radiant density, between
10.2 and 11.8 MJ m-2, was reasonably consistent at a daily level
(Fig. 6a), despite the irregularity observed from the 30 min data. During
the November 2009 (11.4 to 18.3 MJ m-2) and the March 2010 (9.0 to
14.4 MJ m-2) field campaigns, the daily net radiant density was more
variable (Fig. 6b and c). This variability at a daily level was translated
through to the H and LE results, which during August 2009 were fairly
consistent, but irregular during November 2009 and March 2010. The average
daily net radiant density was lowest in August 2009 (11.2 MJ m-2),
highest in November 2009 (15.1 MJ m-2) and in-between during March
2010 (12.7 MJ m-2). The average daily soil heat flux did not mirror
the pattern of Rn and was highest in March 2010 at approximately
11 % of Rn (up to 1.8 MJ m-2), lower in August 2009 at
5 % of Rn (0.7 MJ m-2) and lowest in November 2009 at 1 %
of Rn (up to 0.3 MJ m-2).
The daily total LE was higher than H in August 2009 (Fig. 6a), with a daily
average β ratio of 0.7 (0.4 to 0.9). In November 2009 (Fig. 6b) the
daily average β ratio was higher with a daily average of 0.9 (0.5 to
1.3) but in March (Fig. 6c) however, LE dominated the energy balance with an
average β ratio of 0.4 (0.1 to 0.6).
Closure discrepancy was different for each field campaign. In August 2009 the
D was 0.98 indicating exceedingly good closure. However, the second and
third field campaigns produced a D of 1.18 and 1.33 in November 2009 and
March 2010, respectively, indicating (1) either an overestimation of LE
and/or H, and/or an underestimation of the available energy (Rn –
G) and/or (2) unaccounted energy such as advection or storage in the canopy
biomass.
Measured actual total evaporation
The mean daily ETa over the three field campaigns was
significantly different (based on their 95 % confidence interval). The
daily ETa (Fig. 7) was lowest in the August 2009 (winter) and
increased progressively through November 2009 (early summer) to March 2010
(late summer). The standard deviation (SD) for all field campaigns was
similar (0.3 to 0.4 mm) but the coefficient of variation (not shown)
differed with the highest in November 2009 (12.0) and August 2009 (11.0) and
lowest in March 2010 (8.8).
Relationship between sap flow and actual total evaporation measured during
two field campaigns
The diurnal courses of the sap flow from the emergent and understory trees
were surprisingly smooth in comparison to the ETa results (Fig. 8a–d).
The ETa is an integrated measure of soil evaporation and
transpiration from numerous plants at different levels within the canopy over
the contributing area described by the footprint, whereas the transpiration
measurements (assumed to equal sap flow) describe the physiology of a single
tree. The Rn, frequently considered a significant driver of tree
physiology, fluctuated due to cloud cover (Fig. 5a, b and c). These
fluctuations were not translated into fluctuations in tree sap flow but are
evident in the ETa results particularly over the midday period. A
similar pattern was observed in the March 2010 ETa data (Fig. 8c
and d). It is recommended that G be measured at numerous positions under
swamp forest canopies in order to capture the variability in G and a
representative average.
Despite the greater midday variability of the ETa data, the
polynomial regression (least squares) between hourly ETa and tree
sap flow showed a strong relationship in November 2009 for the emergent tree
(RMSE = 0.05 mm h-1) as well as the understory tree
(RMSE = 0.06 mm h-1). The polynomial regression was convex
(R2= 0.89) rather than linear (R2= 0.87) in the case of
the emergent tree (Fig. 9a) and concave (R2= 0.92) rather than
linear (R2= 0.90) in the case of the understory tree (Fig. 9b). The
increase in the rate of sap flow of the emergent tree was exponential for
lower values of ETa (morning and evening) but the rate of sap flow
versus ETa for higher values of ETa slowed down as the
tree reached its peak transpiration rate. In contrast the understory sap flow
rate increased gradually per unit increase in ETa at lower values
but at higher values of ETa the increase in sap flow was
exponential. In March 2010 the results were similar with RMSEs of 0.07 and
0.08 mm h-1 for the emergent and understory trees, respectively.
Convex and concave trend lines again fitted the data best (Fig. 9c and d).
Lagging the sap flow by 1 h as suggested by Granier et al. (2000) did not
improve the regression of sap flow on ETa.
Summary of the hourly crop coefficient Kc and advective
term α with standard deviation and root mean square error (RMSE) for
each of the three field campaigns.
Diurnal course of the hourly actual total evaporation (ETa) and
sap flow in November 2009 (a and b) and March 2010 (c and d) for the emergent
and understory trees, respectively.
Polynomial regressions of actual total evaporation (ETa)
against the hourly sap flow for the (a) emergent and (b) understory trees
during November 2009, and the (c) emergent and (d) understory trees in March
2010.
Comparison of the FAO Penman–Monteith reference evaporation versus the
Priestley–Taylor potential evaporation during the three field campaigns
The linear regression (least squares) of the hourly ETo against
hourly ETa explained 75, 85 and 76 % of the fluctuations in
ETa during the August 2009, November 2009 and March 2010 field
campaigns, respectively (Table 2). The Priestley–Taylor model did not perform
as well, accounting for 54, 74 and 62 % of the variation in ETa during the August 2009, November 2009 and March 2010 field campaigns,
respectively (Table 2).
The slope of the linear regression (Kc) varied between field
campaigns (Table 2) and was highest in March (1.3), and lower in November
2009 (1.1) and August 2009 (0.8). The α, also estimated by the slope
of the linear regression, was similar during the August (1.0) and November
2009 (1.0) field campaigns (Table 2) while during March 2010, α was
slightly higher (1.1). The standard deviations and root mean square errors of
α were higher than those of the Kc (Table 2). Therefore,
the FAO Penman–Monteith ETo model was adopted as most suitable for
use over the Nkazana PSF in this study.
The time interval (hourly and daily) at which the FAO Penman–Monteith
ETo and Priestley–Taylor models were computed resulted in
different Kc and α estimates. Daily computations used
average daytime Tair, typically derived from an average of
maximum and minimum daily Tair. In this research the models were
run hourly and the average Tair derived from 10 s measurements of
Tair (while Rn > 0) accurately representing that
hour. However, using hourly data produced outliers in the calculation of
Kc and α at the beginning or end of a day where the
measured or modelled results are very small numbers, producing, from
division, erroneous estimates of Kc and α (Eqs. 5 and 6).
These typically occurred near sunset or sunrise and were filtered out of the
data as they represented outliers. In addition, due to the vastly different
canopy structures and heights within the Mfabeni Mire, of the SFW (∼0.8
m) and Nkazana PSF (∼20 m), climatic data from above the forest was used
as an input to the models to determine whether the SDs of Kc and
α could be minimised, but no significant improvement was found. This
indicated that the nearby weather station data (from within the SFW) was a
suitable input for both models, supporting the application of these models
using the standard FAO Penman–Monteith ETo weather station sensor
heights of 2 m (Allen et al., 2006).
Modelling long-term actual total evaporation and monthly crop factors
The long-term ETa (October 2009 to September 2010) was modelled
through the relationship between the observed ETa and observed
sap flow over the November 2009 and March 2010 field campaigns. In regressions
of the emergent tree sap flow with ETa over the two field campaigns
(Fig. 9a and c), it was found that there was little gain in using separate
linear models for the two periods (R2= 0.92 and 0.89;
RMSE = 0.05 mm h-1 and 0.06 mm h-1) as a single, combined
model described ETa equally well (R2= 0.90;
RMSE = 0.07 mm h-1). A similar result was found for the understory tree,
indicating that for both trees a single relationship between ETa
and sap flow represented both field campaigns.
Monthly crop factors Kc for the Nkazana Peat Swamp Forest.
In addition, a multiple regression, including the emergent and understory
trees as predictors of ETa (R2= 0.91;
RMSE = 0.08 mm h-1), provided insufficient benefit over the use of
the single model based on only the emergent tree (R2= 0.90;
RMSE = 0.08 mm h-1). The understory tree sap flow was considerably
less (by 85 %) than that of the emergent tree and the density of the
understory trees within the Nkazana PSF is much lower than the emergent
trees. These results support the omission of the understory tree from the
prediction of ETa, and the use of the following model to estimate
the ETa of the Nkazana PSF from hourly sap-flow data:
ETa=(0.16341⋅Tr+0.06)2,
where ETa is the actual total evaporation (mm h-1) and
Tr the emergent tree sap flow (L h1).
The annual ETa (October 2009 to September 2010) from the Nkazana
PSF was 1125 mm, over which period the rainfall was 650 mm (well below the
long-term average, reported to be between 844 and 1200 mm a-1 for the
area). Finally, Kc was calculated at a daily interval from the
extended ETa and ETo (Eq. 5), and averaged for each
month of the year (Fig. 10). These results equated well with the results of
Kc calculated during the field campaigns (Table 2) which were 0.8,
1.0 and 1.3 in August, November and March, respectively. During a distinct
period from February to May, Kc was between 1.2 and 1.4 while for
the rest of the year it was 0.8 to 1.0. When Kc= 1 the
Nkazana PSF ETa equals the evaporative demand, or in other words
ETo. However, a Kc of <1 or >1
indicates that the PSF ETa is less than or greater
than the ETo, respectively. Figure 10 shows that the Nkazana PSF
ETa is at or just less than ETo for 8 months of the
year (June to January) and greater than ETo for 4 months of the
year (February to May).
The derived crop factors were verified using independent measurements of
ETa over the Nkazana PSF collected during window periods at the same
site from 8 to 12 August 2008 and 12 to 20 November 2008 in an experimental
unpublished study conducted by the CSIR. The surface conditions during 2008
within the Nkazana PSF were much wetter as the water table was close to the
surface with open water in low-lying areas whereas in 2009 and 2010 the dry
period had caused the water level to drop resulting in only a few areas of
open water within the forest. Despite this difference in groundwater level,
the Kc was 0.8 in August of 2008 and 0.9 during November 2008,
validating the results derived for Kc (from the extended record of
ETa modelled from sap flow of the emergent tree), thus confirming that
the Kc derived was applicable across wetter and drier years.
A regression tree analysis for sap flow showing the optimal splits
of solar irradiance (W m-2) and relative humidity (%). Air
temperature and volumetric water content were included, but these variables
were not required for the optimal splits. The percentage of the total data
at each split is also shown.
Response of sap flow to climatic variables
Equation (8) enabled the derivation of ETa over the period during
which there were sap-flow measurements (October 2009 to September 2010). The
purpose for this was to better understand the relationship between important
climatic variables and ETa. Three statistical approaches were used
to determine these relationships with sap flow, which were directly related to
ETa and the climatic variables. The simple linear regressions of
daily sap flow were considered with radiant flux density and VPD and it was
found that these were poor, with coefficients of determination of only 0.51
and 0.52 respectively (not shown). Clearly the relationship between climatic
conditions and sap flow is more complex. By applying multiple regression
analysis Is, RH, Tair and θ at 0.075 m were
found to be significant (p<0.001) with up to four-way interactions.
Finally, a regression tree analysis was applied of hourly log-transformed
sap flow with the meteorological variables Is, RH, Tair
and θ (Fig. 11). This showed again that the relationships are complex
but that Tair and θ were not required for the optimal
split for the Nkazana PSF emergent tree sap flow. The most important split was
between data with Is of less than 55.7 W m-2 and data with
Is greater than 55.7 W m-2. Solar irradiance was clearly a
key variable to include and the first split observed, essentially separates
day- and night-time data. Solar irradiance was also highly correlated with
Tair, which may be the reason Tair was not found to
be an additionally required variable. The next important splits were for RH
above and below 93.2 % for the night-time data (essentially when it is
raining and when it is not) and an additional split for Is above
and below 279.2 W m-2 for the daytime data; therefore splitting daytime data during high and low irradiance periods. At night the logged sap flow
was found to be negative, with the greatest negative average logged sap flow
when the RH was less than 96.4 %. The greatest average positive logged
sap flow was found to be when Is was greater than
279.2 W m-2, and this occurred 28 % of the time.
Discussion
The EC method is recognised internationally to be a suitable and accurate
technique for estimating ETa over vegetated surfaces, and long-term
EC measurements over the Nkazana PSF could provide the data required to
understand the annual cycles of ETa. However, EC systems have
relatively high power requirements and need careful and frequent attendance
as well as data checking, correction and analysis for complete records. The
remote location of the Nkazana PSF, with no road access and difficult access
on foot, high wind speeds and dangerous wild animals such as buffalo,
rhinoceros, hippopotamus and crocodiles, prompted a research strategy to
characterise the ETa of the Nkazana PSF during field campaigns
conducted in representative seasons, as it was impractical to maintain a full
EC system over an extended period of time (such as a year). There was a risk
that a period of unusual weather could have coincided with the window periods
(between 7 and 9 consecutive days at a time). However, the weather
conditions during the field campaigns showed that a range of climatic
conditions were captured that were representative of the seasons (Table 1).
With this approach, field campaigns could be extended should unusual weather
conditions be encountered over the planned measurement period.
The challenge remained in interpolating and extrapolating the ETa
results from the EC system to annual ETa. In long-term evaporation
studies where gaps occur or where window periods have been used, and
interpolation of the ETa record is required, meteorological models are
typically used. Total evaporation has been estimated using models that are
computationally simple such as the Priestley–Taylor model (Priestley and
Taylor, 1972; Shuttleworth and Calder, 1979) to more complex models using
multi-layer approaches within the canopy, but still based on the
Penman–Monteith approach (Roberts et al., 1993; Harding et al., 1992), with
significant deviations between measurements and modelled results. These
meteorological models are, however, uncoupled from the transpiring
vegetation and therefore the pattern of actual tree sap flow was considered
in this study as a predictor of ETa.
External regulation of sap flow has been described by numerous variables
including the readily available soil water of the rooting area (Oren and
Pataki, 2001), the micrometeorological conditions of the atmosphere
(Lundblad and Lindroth, 2002), leaf area (Granier et al., 2000), canopy
conductance (Granier et al., 2000), aerodynamic resistance (Jacobs and De
Bruin, 1992; Hall, 2002), shading of lower leaves (Cienciala et al., 2000)
and wind stress (Kim et al., 2014). However, it has been found that trees
can have several mechanisms of internal regulation related to species-specific morphology and physiology that is partially uncoupled from the
external conditions (Zweifel et al., 2005). Nevertheless, in most trees with
actively transpiring leaves and some readily available soil water, a diurnal
pattern of sap-flow results from a combination of internal and external
conditions, which determines how a tree contributes to the ETa of a
forest stand.
With advances in sap-flow measurement techniques, long-term forest ETa has been estimated by up-scaling from tree transpiration to forest
ETa using various techniques generally based on sapwood area
(Čermák et al., 2004). However, the large majority of these studies,
especially where tree transpiration has been up-scaled, have been conducted
in uniform forest stands (Oren et al., 1999; Wilson et al., 2001) and much of
the work has taken place in temperate boreal stands (Lundblad and Lindroth,
2002; Launiainen et al., 2011) and their applicability to other climatic
zones needs consideration. In addition, it has also been recognised that
transpiration often varies amongst species (Oren and Pataki, 2001; Ewers et
al., 2002; Bowden and Bauerle, 2008) and up-scaling to forest transpiration
in species-rich indigenous forests is complex.
The results from this study showed that the hourly sap flow of a single
emergent tree, selected as a dominant species, correlated well with the
hourly ETa measured over two window periods. In species-rich
forests, measuring the sap flow of the different vegetation types (including
the ferns, vines, understory and emergent trees) would be challenging, and
up-scaling questionable, due to the variety of plant structures within the
canopy and our lack of information on the plant physiologies. Therefore, the
empirical relationship between the single tree and ETa provided an
ideal opportunity to derive the annual ETa of a vegetation type
for which there is no information of the water-use characteristics. This
relationship indicated that the emergent canopy trees are the main
contributors to ETa. The other contributors to ETa,
including open water, peat, ferns, vines and understory trees were either
(1) insignificant contributors due to the low irradiance and VPD below the
emergent tree canopy (supported by the low measured sap-flow rate of the
understory tree), or (2) follow similar diurnal trends in evaporation and
sap flow as the emergent tree (also supported by the diurnal trend in the
sap-flow rate of the understory tree) and are therefore captured in the
empirical model of the emergent tree.
Variation of the energy balance closure discrepancy (D) occurred between
field campaigns, despite replication of the same instrumentation at the same
site and with the same data processing procedures. Only the placement of the
soil heat flux sensors changed slightly within the vicinity of the site
between field campaigns. However, the soil heat fluxes (as a percent of net
irradiance) fluctuated from 1 to 11 %, likely due to the specific
placement of the sensors within the Nkazana PSF in a predominantly shaded
area in contrast to a sunlit location due to gaps in the canopy. During
August 2009 when D= 1 (i.e. perfect closure of the energy balance),
the soil heat flux was approximately 5 % of Rn and was likely to be
the most representative result for G for a forested area, so agreeing with
Dye et al. (2008). In March, G was 11 % of Rn and may have
contributed to the poorest result of D= 1.33. Wilson et al. (2002)
found that energy balance closure, especially over forests, is seldom
achieved. However, in most cases the magnitude of the long-term turbulent
fluxes is lower than the available energy (Twine et al., 2000; Oliphant et
al., 2004), which was not the case in the Nkazana PSF study where D
increased with increasing ETa from August 2009 through November
2009 to March 2010.
An important observation, made over the three field campaigns, was that the
average ETa measured during March 2010 (4.4 mm day-1) did
not correspond to the period of highest Rn (November 2009), which is
commonly accepted to be one of the main driving variables in the process of
ETa (Albaugh et al., 2013). This may indicate a lag in the
ETa of the Nkazana PSF in relation to the maximum Rn,
possibly explaining the poor relationships observed between tree sap flow and
climatic variables (such as Rn). This lag was also observed in the high
Kc values from February to May, where the ETa of the
PSF was higher relative to ETo. Typically, Kc is higher
while vegetation is more actively transpiring and is associated with higher
Is and water availability, which in the Nkazana PSF would coincide
with the summer period (October to March). However, the period of higher
Kc values in the Nkazana PSF occurred quite late (February to May)
in the summer season (Fig. 10). Clulow et al. (2013) showed the Nkazana PSF
sap flow to be relatively consistent between seasons but that ETo
rapidly decreased from February to May (4.2 mm day-1 to
2.4 mm day-1). The high Kc is therefore likely a result of
decreasing ETo measured at the meteorological station, while
transpiration rates in the Nkazana PSF were maintained into the late autumn
period. A number of reasons may be attributed to this including the
microclimate of the Nkazana PSF. For example, the lower energy loss at night
from the ground and within the canopy, due to the combined effect of high
water vapour levels (a greenhouse gas) and reduced infrared emission as a
result of canopy absorbance, reflectance and re-emission downwards, compared
to areas outside the PSF with shorter canopies, resulted in higher minimum
daily temperatures (Fig. 4). The area adjacent to the Nkazana PSF where the
automatic weather station was located (with a shorter canopy of approximately
1 m in height) experienced lower daily minimum temperatures (Fig. 4). The
importance of this result is that Tair affects biochemical
processes such as photosynthesis and senescence. This Tair
difference, although greatest in winter, starts to build in January and could
play a role in influencing the ETa in relation to the summer
season as well as the period of higher Kc values in the latter
half of summer.
Two important points regarding the weather station data and model
calculations were noted. Firstly, where possible, hourly model time intervals
should be used, which concurs with Irmak et al. (2005). However, this
frequently resulted in outliers in Kc and α at the
beginning or end of a day where the measured or modelled results were small
numbers, producing, through division, erroneous estimates. It was therefore
favourable to sum the hourly ETo and ETa data for each
day (while Rn > 0) and calculate the daily Kc
(which was then averaged for each month). Secondly, when calculating the
Kc and α coefficients, there was no benefit in using the
climatic data from above the tree canopy rather than the climatic data from
the adjacent SFW of the Mfabeni Mire, which sufficiently represented the
microclimate for the model calculations. This showed that data from nearby
weather stations can be used with the Kc to estimate the
ETa although this may only hold in humid environments where there
is little difference in the VPD of the boundary layer conditions over the
Nkazana PSF and the surrounding wetland areas, which are likely to all be at,
or near, equilibrium evaporation.
The shape of the regressions of hourly ETa versus hourly sap flow
(Fig. 9) during the window periods showed that sap flow of the emergent tree
responded rapidly for low conditions of ETa. These conditions
occurred most frequently in the early morning and late afternoon when the
angle of the Is was low but still incident upon the emergent tree
leaves. At higher rates of ETa the sap flow peaked as the
physiology of the tree limited the sap-flow rates. In contrast, the understory
tree sap-flow rate increased slowly relative to ETa, while
ETa was low, and exponentially for higher values of ETa. This is likely due to shading of the understory trees for low sun angles
(early morning and late afternoon) with Is limiting transpiration
(together with the low VPD discussed above) with maximum rates occurring when
shading by the emergent trees was at a minimum (noon) and ETa was
at a maximum. These different responses of the trees indicated that a model
to derive ETa from sap flow would require the inclusion of both
emergent and understory trees. However, the sap flow and density of the
understory trees was much lower than the emergent trees and therefore its
inclusion in the empirical model was not found to significantly improve the
relationship between entire canopy ETa and sap flow. This
conclusion applies specifically to the Nkazana PSF. Some models such as the
WAVES model permits two canopy simulations due to the importance of the
understory canopy in some forest sites (Dye et al., 2008).
Within South Africa, the study by Dye et al. (2008) measured daily ETa of between 2 and 6 mm on clear days over three field campaigns during
February, June and October 2004, which are comparable with the results from
the Nkazana PSF of between 2.2 mm (August 2009) and 5.1 mm (March 2010).
Internationally, no results of ETa or modelling guidelines for
peat swamp forests were found, signifying the unique contribution of this
study.
The comparison of meteorological variables with sap flow revealed that it is
unlikely that a single climatic variable is able to determine sap flow, and
in turn ETa. The relationships were revealed to be non-linear, and thus
to model sap flow accurately, data need to be subset into different
periods – at least into day and night.
Conclusions and opportunities for further research
This study has portrayed the difficulties of using the most advanced systems
available to measure ETa, such as EC, in remote and difficult-to-access
areas. It has shown that intensive window period measurements using high-maintenance EC systems provide reliable and continuous measurements of
ETa but require a method to determine the ETa during the
in-between periods to be able to estimate long-term ETa. This was
overcome by measuring the long-term sap flow of an emergent canopy tree and
deriving a qualitative model for ETa based on sap-flow measurements.
Further research on the benefit of measuring multiple emergent trees and the
possible variability of transpiration within different species and the
extent to which this could improve the long-term estimate of forest ETa
together with window periods of EC data would be beneficial.
Energy balance closure discrepancy (D) remains an unresolved matter which
affects flux measurements such as ETa and CO2. Corrections
suggested in research studies can be applied but without conclusively
identifying the source of the error in the observations. In contrast to most
studies reported, the closure discrepancy of the energy balance over the
Nkazana PSF was greater than 1 for two of the field campaigns. Although
attributed in part to unrepresentative G measurements, D increased as ETa
increased.
The model used to derive the annual ETa from sap flow (Eq. 8), and
then monthly crop factors, was verified with data from two independent field
campaigns in 2008, when conditions were much wetter and there were larger
areas of open water within the forest. The much wetter conditions in 2008 did
not alter the Kc thus indicating that the relationship between
ETa and ETo remained constant and that the Kc derived can be applied over a range of climatic conditions. In addition,
it indicates that the humid, low-VPD environment within the forest canopy
minimises the contribution of open water evaporation within the forest to
ETa. However, the general dearth of information on the ETa of subtropical indigenous forests internationally allows little
comparison of the results obtained from the Nkazana PSF and similar forest
types and knowledge of the extent to which these crop factors can be
extrapolated geographically and to similar forests would benefit from further
comparisons.
The Mfabeni Mire is actively managed by the iSimangaliso Wetland Park. These
results provide the basis for improved estimates of the ETa
component of the Nkazana PSF water balance and the environmental water
requirements. Water is critical to the functioning of this ecosystem for
biotic and abiotic life, the sequestration or release of carbon from the Mire
and also to the spread of fires. The annual ETa estimated in this
study (1125 mm) was even higher than the range (844 to 1200 mm yr-1)
of reported estimates of mean annual precipitation for the area (Lynch, 2004;
Taylor et al., 2006; ARC-ISCW, 2011).
The difference between ETa and rainfall highlights the importance of
the groundwater contributions and the critical role it plays in assuring the
survival of this groundwater-dependant ecosystem. The groundwater available
to the Mfabeni Mire is in part determined by the management of the upstream
catchments and the groundwater levels of the greater Zululand Coastal
Aquifer, emphasising the need for an integrated catchment management
approach to the area.
Acknowledgements
This research was funded by Key Strategic Area 2 (i.e. Water-Linked
Ecosystems) of the Water Research Commission (WRC) of South Africa and the
Council for Scientific and Industrial Research and forms part of an
unsolicited research project (Evapotranspiration from the Nkazana Swamp
Forest and Mfabeni Mire). The iSimangaliso Wetland Park are acknowledged for
their support in providing access to the research sites. Craig Morris
provided invaluable statistical analysis and support. Assistance in the field
by Piet-Louis Grundling, Siphiwe Mfeka, Scott Ketcheson, David Clulow,
Lelethu Sinuka and the late Joshua Xaba is much appreciated.Edited
by: A. van Griensven
ReferencesAlbaugh, J. M., Dye, P. J., and King, J. S.: Eucalyptus and Water Use in
South Africa, Int. J. For. Res., 11, 852540, 10.1155/2013/852540, 2013.
Allen, R., Walter, I., Elliott, R., Mecham, B., Jensen, M., Itenfisu, D.,
Howell, T., Snyder, R., Brown, P., and Echings, S.: Issues, requirements and
challenges in selecting and specifying a standardized ET equation, Proc., 4th
National Irrigation Symp, 201–208, 2000.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration: Guidelines for computing crop water requirements, FAO
Irrigation and Drainage Paper 56, Food and Agriculture Organization of the
United Nations, Rome, Italy, 1998.
Allen, R. G., Pruitt, W. O., Wright, J. L., Howell, T. A., Ventura, F.,
Snyder, R., Itenfisu, D., Steduto, P., Berengena, J., Yrisarry, J. B., Smith,
M., Pereira, L. S., Raes, D., Perrier, A., Alves, I., Walter, I., and
Elliott, R.: A recommendation on standardized surface resistance for hourly
calculation of reference ETo by the FAO56 Penman-Monteith method, Agric.
Water Manage., 81, 1–22, 2006.
Andrade, J. L. and Nobel, P. S.: Microhabitats and Water Relations of
Epiphytic Cacti and Ferns in a Lowland Neotropical Forest1, Biotropica, 29,
261–270, 1997.
ARC-ISCW (Agricultural Research Council-Institute for Soil, Climate and
Water): National AgroMet, Climate Databank, ARC-ISCW, Pretoria, South Africa,
2011.
Baldocchi, D. D., Hincks, B. B., and Meyers, T. P.: Measuring
Biosphere-Atmosphere Exchanges of Biologically Related Gases with
Micrometeorological Methods, Ecology, 69, 1331–1340, 1988.
Baldocchi, D., Valentini, R., Running, S., Oechel, W., and Dahlman, R.:
Strategies for measuring and modelling carbon dioxide and water vapour fluxes
over terrestrial ecosystems, Global Change Biol., 2, 159–168, 1996.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A.,
Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W.,
Paw, K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S., Vesala,
T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the Temporal and
Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and
Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2001.Barr, A. G., Morgenstern, K., Black, T. A., McCaughey, J. H., and Nesic, Z.:
Surface energy balance closure by the eddy-covariance method above three
boreal forest stands and implications for the measurement of the CO2
flux, Agr. For. Meteorol., 140, 322–337, 2006.
Bowen, I. S.: The ratio of heat losses by conduction and by evaporation from
any water surface, Phys. Rev., 27, 779–787, 1926.
Bowden, J. D. and Bauerle, W. L.: Measuring and modeling the variation in
species-specific transpiration in temperate deciduous hardwoods, Tree
Physiol., 28, 1675–1683, 2008.
Burgess, S. S. O., Adams, M. A., Turner, N. C., Beverly, C. R., Ong, C. K.,
Khan, A. A. H., and Bleby, T. M.: An improved heat pulse method to measure
low and reverse rates of sap flow in woody plants, Tree Physiol., 21,
589–598, 2001.
Cava, D., Contini, D., Donateo, A., and Martano, P.: Analysis of short-term
closure of the surface energy balance above short vegetation, Agr. For.
Meteorol., 148, 82–93, 2008.
Čermák, J., Kučera, J., and Nadezhdina, N.: Sap flow measurement
with some thermodynamic methods, flow integration within trees and scaling up
from sample trees to entire forest stands, Trees, 18, 529–546, 2004.
Čermák, J. and Nadezhdina, N.: Sapwood as the scaling
parameter-defining according to xylem water content or radial pattern of sap
flow?, Ann. For. Sci., 55, 509–521, 1998.Cienciala, E., Kučera, J., and Malmer, A.: Tree sap flow and stand
transpiration of two Acacia mangium plantations in Sabah, Borneo,
Journal of Hydrology, 236, 109–120, 2000.Clulow, A. D., Everson, C. S., Mengistu, M. G., Jarmain, C., Jewitt, G. P.
W., Price, J. S., and Grundling, P.-L.: Measurement and modelling of
evaporation from a coastal wetland in Maputaland, South Africa, Hydrol. Earth
Syst. Sci., 16, 3233–3247, 10.5194/hess-16-3233-2012, 2012.Clulow, A. D., Everson, C. S., Price, J. S., Jewitt, G. P. W., and
Scott-Shaw, B. C.: Water-use dynamics of a peat swamp forest and a dune
forest in Maputaland, South Africa, Hydrol. Earth Syst. Sci., 17, 2053–2067,
10.5194/hess-17-2053-2013, 2013.
Crosbie, R., Wilson, B., Hughes, J., and McCulloch, C.: The upscaling of
transpiration from individual trees to areal transpiration in tree belts,
Plant Soil, 297, 223–232, 2007.
Davies, J. A., and Allen, C. D.: Equilibrium, Potential and Actual
Evaporation from Cropped Surfaces in Southern Ontario, J. Appl. Meteorol., 12, 649–657, 1973.
Drexler, J. Z., Snyder, R. L., Spano, D., and Paw U. K. T.: A review of
models and micrometeorological methods used to estimate wetland
evapotranspiration, Hydrol. Process., 18, 2071–2101, 2004.Duursma, R. A. and Medlyn, B. E.: MAESPA: a model to study interactions
between water limitation, environmental drivers and vegetation function at
tree and stand levels, with an example application to [CO2] ×
drought interactions, Geosci. Model Dev., 5, 919–940,
10.5194/gmd-5-919-2012, 2012.
Dye, P. J., Gush, M. B., Everson, C. S., Jarmain, C., Clulow, A., Mengistu,
M., Geldenhuys, C. J., Wise, R., Scholes, R. J., Archibald, S., and Savage,
M. J.: Water-use in relation to biomass of indigenous tree species in
woodland, forest and/or plantation conditions, Water Research Commission
Report No. 361/08, ISBN 978-1-77005-744-9, Pretoria, South Africa, 156 pp.,
2008.
Ewers, B. E., Mackay, D. S., Gower, S. T., Ahl, D. E., Burrows, S. N., and
Samanta, S. S.: Tree species effects on stand transpiration in northern
Wisconsin, Water Resour. Res., 38, 8-1–8-11, 2002.
Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C.,
Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P.,
Grünwald, T., Hollinger, D., Jensen, N.-O., Katul, G., Keronen, P.,
Kowalski, A., Lai, C. T., Law, B. E., Meyers, T., Moncrieff, J., Moors, E.,
Munger, J. W., Pilegaard, K., Rannik, Ü., Rebmann, C., Suyker, A.,
Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: Gap
filling strategies for defensible annual sums of net ecosystem exchange, Agr.
For. Meteorol., 107, 43–69, 2001.
Finch, J. W.: A comparison between measured and modelled open water
evaporation from a reservoir in south-east England, Hydrol. Proc., 15,
2771–2778, 2001.
Finnigan, J. J., Clement, R., Malhi, Y., Leuning, R., and Cleugh, H. A.: A
Re-Evaluation of Long-Term Flux Measurement Techniques Part I: Averaging and
Coordinate Rotation, Bound.-Layer Meteorol., 107, 1-48, 2003.
Flint, A. L. and Childs, S. W.: Use of the Priestley-Taylor evaporation
equation for soil water limited conditions in a small forest clearcut, Agr.
For. Meteorol., 56, 247–260, 1991.
Granier, A., Loustau, D., and Bréda, N.: A generic model of forest canopy
conductance dependent on climate, soil water availability and leaf area
index, Ann. For. Sci., 57, 755–765, 2000.Grelle, A. and Lindroth, A.: Eddy-correlation system for long-term monitoring
of fluxes of heat, water vapour and CO2, Global Change Biol., 2,
297–307, 1996.
Grundling, A. T., van den Berg, E. C., and Pretorius, M. L.: Influence of
regional environmental factors on the distribution, characteristics and
functioning of hydrogeomorphic wetland types on the Maputaland Coastal Plain,
KwaZulu-Natal, South Africa, Water Research Commission Report No. 1923/1/13,
ISBN 978-1-4312-0492-2, Pretoria, South Africa, 156 pp., 2014.
Grundling, P.-L., Mazus, H., and Baartman, L.: Peat resources in northern
KwaZulu-Natal wetlands: Maputaland, Department of Environmental Affairs and
Tourism Pretoria, South Africa, 102 pp., 1998.
Hall, R. L.: Aerodynamic resistance of coppiced poplar, Agr. For. Meteorol.,
114, 83–102, 2002.
Harding, R. J., Hall, R. L., Neal, C., Roberts, J. M., Rosier, P. T. W., and
Kinniburgh, D. G.: Hydrological impacts of broad leaf wood lands:
implications for water use and water quality. Project report 115/03/ST,
National Rivers Authority, Bristol, 135 pp., 1992.
Hatton, T. J. and Wu, H.-I.: Scaling theory to extrapolate individual tree
water use to stand water use, Hydrol. Proc., 9, 527–540, 1995.
Hui, D., Wan, S., Su, B., Katul, G., Monson, R., and Luo, Y.: Gap-filling
missing data in eddy covariance measurements using multiple imputation (MI)
for annual estimations, Agr. For. Meteorol., 121, 93–111, 2004.
Irmak, S., Howell, T. A. , Allen, R. G. , Payero, J. O., and Martin, D. L.:
Standardized ASCE-Penman-Monteith: Impact of sum-of-hourly vs. 24-hr-timestep
computations at Reference Weather Station Sites, Trans. ASABE, 48,
1063–1077, 2005.
Jacobs, C. M. J. and De Bruin, H. A. R.: The sensitivity of regional
transpiration to land-surface characteristics: significance of feedback, J.
Climate, 5, 683–698, 1992.
Kim, D., Oren, R., Oishi, A. C., Hsieh, C.-I., Phillips, N., Novick, K. A.,
and Stoy, P. C.: Sensitivity of stand transpiration to wind velocity in a
mixed broadleaved deciduous forest, Agr. For. Meteorol., 187, 62–71, 2014.
Koerselman, W. and Beltman, B.: Evapotranspiration from fens in relation to
Penman's potential free water evaporation (EO) and pan evaporation, Aquat.
Bot., 31, 307–320, 1988.
Lafleur, P. M. and Roulet, N. T.: A comparison of evaporation rates from two
fens of the Hudson Bay Lowland, Aquat. Bot., 44, 59–69, 1992.Launiainen, S., Katul, G. G., Kolari, P., Vesala, T., and Hari, P.: Empirical
and optimal stomatal controls on leaf and ecosystem level CO2 and
H2O exchange rates, Agr. For. Meteorol., 151, 1672–1689, 2011.
Lundblad, M. and Lindroth, A.: Stand transpiration and sapflow density in
relation to weather, soil moisture and stand characteristics, Basic Appl.
Ecol., 3, 229–243, 2002.
Lynch, S. D.: Development of a raster database of annual, monthly and daily
rainfall for Southern Africa, Report to the Water Research Commission, ISBN
Number: 1-77005-250-X, Water Research Commission, Pretoria, South Africa,
2004.
Mao, L. M., Bergman, M. J., and Tai, C. C.: Evapotranspiration measurement
and estimation of three wetland environments in the upper St. Johns River
Basin, Florida, J. Am. Water Res. Ass., 38, 1271–1285, 2002.
Massman, W. J. and Lee, X.: Eddy covariance flux corrections and
uncertainties in long-term studies of carbon and energy exchanges, Agr. For.
Meteorol., 113, 121–144, 2002.
Meiresonne, L., Nadezhdin, N., Čermák, J., Van Slycken, J., and
Ceulemans, R.: Measured sap flow and simulated transpiration from a poplar
stand in Flanders (Belgium), Agr. For. Meteorol., 96, 165–179, 1999.
Meyers, T. P. and Baldocchi, D. D.: Current Micrometeorological Flux
Methodologies with Applications in Agriculture, in: Micrometeorology in
Agricultural Systems, edited by: Hatfield, J. L., and Baker, J. M., Agronomy
Monograph no. 47, American Society of Agronomy, Crop Science Society of
America, and Soil Science Society of America, 381–396, 2005.
Monteith, J. L.: Evaporation and environment: the state and movement of water
in living organisms, Symp. Soc. Exp. Biol., 19, 205–234, 1965.
Mucina, L. and Rutherford, M. C. (Eds.): The Vegetation of South Africa,
Lesotho and Swaziland, Strelitzia 19, South African National Biodiversity
Institute, Pretoria, South Africa, 2006.
Nichols, D. S. and Brown, J. M.: Evaporation from a sphagnum moss surface, J.
Hydrol., 48, 289–302, 1980.
Oliphant, A. J., Grimmond, C. S. B., Zutter, H. N., Schmid, H. P., Su, H. B.,
Scott, S. L., Offerle, B., Randolph, J. C., and Ehman, J.: Heat storage and
energy balance fluxes for a temperate deciduous forest, Agr. For. Meteorol.,
126, 185–201, 2004.
Oncley, S., Foken, T., Vogt, R., Kohsiek, W., DeBruin, H. A. R., Bernhofer,
C., Christen, A., Gorsel, E., Grantz, D., Feigenwinter, C., Lehner, I.,
Liebethal, C., Liu, H., Mauder, M., Pitacco, A., Ribeiro, L., and Weidinger,
T.: The Energy Balance Experiment EBEX-2000. Part I: overview and energy
balance, Bound.-Layer Meteorol., 123, 1–28, 2007.
Oren, R. and Pataki, D. E.: Transpiration in Response to Variation in
Microclimate and Soil Moisture in Southeastern Deciduous Forests, Oecologia,
127, 549–559, 2001.
Oren, R., Phillips, N., Ewers, B. E., Pataki, D. E., and Megonigal, J. P.:
Sap-flux-scaled transpiration responses to light, vapor pressure deficit, and
leaf area reduction in a flooded Taxodium distichum forest, Tree Physiol.,
19, 337–347, 1999.
Penman, H. L.: Natural Evaporation from Open Water, Bare Soil and Grass,
Proc. Roy. Soc. Ldn. A Math. Phys. Sci., 193, 120–145, 1948.
Price, J. S.: Blanket bog in Newfoundland: Part 2. Hydrological processes, J.
Hydrol., 135, 103–119, 1992.
Priestley, C. H. B. and Taylor, R. J.: On the Assessment of Surface Heat Flux
and Evaporation Using Large-Scale Parameters, Mon. Weather Rev., 100, 81–92,
1972.
Roberts, J., Cabral, O. M. R., Fisch, G., Molion, L. C. B., Moore, C. J., and
Shuttleworth, W. J.: Transpiration from an Amazonian rainforest calculated
from stomatal conductance measurements, Agr. For. Meteorol., 65, 175–196,
1993.
Rosenberry, D. O., Winter, T. C., Buso, D. C., and Likens, G. E.: Comparison
of 15 evaporation methods applied to a small mountain lake in the
northeastern USA, J. Hydrol., 340, 149–166, 2007.
Savage, M. J., Everson, C. S., and Metelerkamp, B. R.: Evaporation
measurement above vegetated surfaces using micrometeorological techniques,
Water Research Commission Report No. 349/1/97, ISBN 1-86845 363 4, Water
Research Commission, Pretoria, South Africa, 248 pp., 1997.
Savage, M. J., Everson, C. S., Odhiambo, G. O., Mengistu. M. G., and Jarmain.
C.: Theory and practice of evaporation measurement, with a special focus on
SLS as an operational tool for the estimation of spatially-averaged
evaporation, Water Research Commission Report No. 1335/1/04, ISBN
1-77005-247-X, Pretoria, South Africa, 204 pp., 2004.
Schulze, R. E., Maharaj, M., Warburton, M. L., Gers, C. J., Horan, M. J. C.,
Kunz, R. P., and Clark, D. J.: South African Atlas of Climatology and
Agrohydrology, Water Research Commission Report No. 1489/1/08, Water Research
Commission, Pretoria, South Africa, 2008.
Shuttleworth, W. J. and Calder, I. R.: Has the Priestley-Taylor Equation Any
Relevance to Forest Evaporation?, J. Appl. Meteorol., 18, 639–646, 1979.
Souch, C., Wolfe, C. P., and Grimmtind, C. S. B.: Wetland evaporation and
energy partitioning: Indiana Dunes National Lakeshore, J. Hydrol., 184,
189–208, 1996.
Sumner, D. M. and Jacobs, J. M.: Utility of Penman–Monteith,
Priestley–Taylor, reference evapotranspiration, and pan evaporation methods
to estimate pasture evapotranspiration, J. Hydrol., 308, 81–104, 2005.
Tanner, C. B.: Energy balance approach to evapotranspiration from crops, Soil
Sci. Soc. Am. Proc., 24, 1–9, 1960.
Taylor, R.: The Greater St Lucia Wetland Park, Parke-Davis for Natal Parks
Board, Pietermaritzburg, South Africa, 1991.
Taylor, R., Kelbe, B., Haldorsen, S., Botha, G. A., Wejden, B., Vaeret, L.,
and Simonsen, M. B.: Groundwater-dependent ecology of the shoreline of the
subtropical Lake St Lucia estuary, Environ. Geol., 49, 586–600, 2006.
Thom, A. S.: Momentum, mass and heat exchange in plant communities, in:
Vegetation and the Atmosphere, Vol. 1, Principals, edited by: Monteith, J.
L., Acad. Press., London, 57–109, 1975.
Thompson, M. A., Campbell, D. I., and Spronken-Smith, R. A.: Evaporation from
natural and modified raised peat bogs in New Zealand, Agr. For. Meteorol.,
95, 85–98, 1999.
Traver, E., Ewers, B. E., Mackay, D. S., and Loranty, M. M.: Tree
transpiration varies spatially in response to atmospheric but not edaphic
conditions, Funct. Ecol., 24, 273–282, 2010.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P. R.,
Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L.: Correcting
eddy-covariance flux underestimates over a grassland, Agr. For. Meteorol.,
103, 279–300, 2000.
Utset, A., Farré, I., Martínez-Cob, A., and Cavero, J.: Comparing
Penman–Monteith and Priestley–Taylor approaches as
reference-evapotranspiration inputs for modeling maize water-use under
Mediterranean conditions, Agric. Water Manage., 66, 205–219, 2004.
Vaeret, L. and Sokolic, F.: Methods for studying the distribution of
groundwater-dependent wetlands: a case study from Eastern Shores, St Lucia,
South Africa, in: Responses to global change and management actions in
coastal groundwater resources, edited by: Vaeret, L., Maputaland, southeast
Africa, PhD Thesis, Norwegian University of Life Sciences, Norway, 2008.Vandegehuchte, M. W. and Steppe, K.: Use of the correct heat
conduction–convection equation as basis for heat-pulse sap flow methods in
anisotropic wood, J. Exp. Bot., 63, 2833–2839, 10.1093/jxb/ers041, 2012.
Vertessy, R. A., Hatton, T. J., Reece, P., O'Sullivan, S. K., and Benyon, R.
G.: Estimating stand water use of large mountain ash trees and validation of
the sap flow measurement technique, Tree Physiol., 17, 747–756, 1997.
von Maltitz, G., Mucina, L., Geldenhuys, C. J., Lawes, M. J., Eeley, H.,
Aidie, H., Vink, D., Fleming, G., and Bailey, C.: Classification system for
South African Indigenous Forests, An objective classification for the
Department of Water Affairs and Forestry, Unpublished report, No. ENV-P-C
2003-017, Environmentek, CSIR, Pretoria, 275 pp., 2003.
Vourlitis, G. L., Filho, N. P., Hayashi, M. M. S., de S. Nogueira, J.,
Caseiro, F. T., and Campelo, J. H.: Seasonal variations in the
evapotranspiration of a transitional tropical forest of Mato Grosso, Brazil,
Water Resources Research, 38, 30-1–30-11, 2002.
Vrdoljak, S. M. and Hart, R. C.: Groundwater seeps as potentially important
refugia for freshwater fishes on the Eastern Shores of Lake St Lucia,
KwaZulu-Natal, South Africa, Afr. J. Aquat. Sci., 32, 125–132, 2007.VSN International: GenStat for Windows 14th Edition, VSN International, Hemel
Hempstead, UK, available at: http://GenStat.co.uk (last access: 24 January 2013), 2011.
Wessels, N. G.: Aspects of the ecology and conservation of swamp forests in
South Africa, unpublished M. Tech thesis, Port Elizabeth Technikon, Port
Elizabeth, South Africa, 155 pp., 1997.
Whitfield, A. K. and Taylor, R. H.: A review of the importance of freshwater
inflow to the future conservation of Lake St Lucia, Aquat. Conserv. Mar.
Freshwat. Ecosyst., 19, 838–848, 2009.
Wilson, K. B., Hanson, P. J., Mulholland, P. J., Baldocchi, D. D., and
Wullschleger, S. D.: A comparison of methods for determining forest
evapotranspiration and its components: sap-flow, soil water budget, eddy
covariance and catchment water balance, Agr. For. Meteorol., 106, 153–168,
2001.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier,
P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom,
A., Law, B. E., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel,
W., Tenhunen, J., Valentini, R., and Verma, S.: Energy balance closure at
FLUXNET sites, Agr. For. Meteorol., 113, 223–243, 2002.WMO: Guide to Meteorological Instruments and Methods of Observation,
WMO-No.8, 7th Edn., Geneva, 2008.
Wullschleger, S. D., Hanson, P. J., and Todd, D. E.: Transpiration from a
multi-species deciduous forest as estimated by xylem sap flow techniques,
For. Ecol. Manage., 143, 205–213, 2001.
Zweifel, R., Zimmermann, L., and Newbery, D. M.: Modeling tree water deficit
from microclimate: an approach to quantifying drought stress, Tree Physiol.,
25, 147–156, 2005.