6.
6.1
TRANSPIRATION IN A SMALL FOREST PATCH
Introduction
The global rate of tropical deforestation exceeds 150 000 km2 yr-1 (Whitmore, 1997). This alarmingly rapid
land cover conversion raises concerns regarding reduction of plant and animal biodiversity, impacts on the
cultures of indigenous peoples, modification of atmospheric chemistry and consequent global climate
impacts, and regional to global climatic and hydrologic effects of changing land surface-atmosphere
interaction. The remaining forest in much of the tropics is confined to increasingly small patches of
remnant primary and secondary forest.
As Laurance and Bierregaard (1997) observe, "fragmented
landscape is becoming one of the most ubiquitous features of the tropical world--and indeed, of the entire
planet." Increasing fragmentation of tropical land cover is generally perceived to have negative ecological
impacts, exacerbating those of deforestation (Laurance et al., 1998). Effects of fragmentation on regional
and global climate are less well known. Microclimatic gradients observed near forest margins suggest that
regional climatic and hydrologic processes may be affected by the degree of land-cover fragmentation.
Forest clearing is known to disrupt surface atmosphere exchange of energy and mass by altering
the physical characteristics of the land surface.
Numerous investigations have been conducted
demonstrating the local effects of deforestation on hydrologic processes (Bruijnzeel, 1990; in press). In
general, deforestation increases surface albedo and reduces net radiation (e.g., Giambelluca et al., 1997;
1999). Forest removal affects evaporation by changing surface albedo, leaf area, aerodynamic roughness,
root depth, and stomatal behavior. Field studies have confirmed that evaporation is significantly reduced
when tropical forest is replaced by pasture (e.g., Jipp et al., 1998; Wright et al., 1992). As a result of
decreased evaporation, stream discharge increases following deforestation (Bruijnzeel, 1990; in press).
Land-atmosphere feedbacks may lead to regional changes in atmospheric circulation and rainfall. For
example, general circulation model (GCM) simulations of the complete conversion of the Amazon
rainforest to grassland, predict large reductions in basin precipitation (Henderson-Sellers and Gornitz,
1984; Lean and Warrilow, 1989; Shukla et al., 1990; Nobre et al., 1991; Henderson-Sellers et al., 1993;
Polcher and Laval, 1995; McGuffie et al., 1995; Xue et al., 1996; Hahmann and Dickinson, 1997). The
rainfall decrease is attributed, in part, to lower evaporation in the basin, and consequent reduction in
'recycling' of evaporated water into additional basin rainfall (Henderson-Sellers, et al., 1993).
Estimating evaporation for regions with heterogeneous land cover is an important part of the
problem of scaling energy, water, and momentum fluxes (Veen, et al., 1991), which has been undergoing
intensive research during the past decade (Kienitz et al., 1991; Stewart et al., 1996; Famiglietti and Wood,
1994; 1995). A simple mosaic approach can be used to take account of the relative proportions of the
dominant land cover types relatively easily by computing area-weighted averages of the fluxes over each
land cover type (e.g. Liang et al., 1994).
However, patch-scale fluxes are not independent of the
surroundings. Horizontal transfer of energy and water vapor in the atmosphere may significantly alter the
fluxes within a patch and hence invalidate a strictly one-dimensional approach to estimating regional
6-1
average fluxes. Such effects are greatest at the boundaries of dissimilar land covers (Veen, et al., 1991;
1996; Kruijt et al., 1991; Klaassen, 1992; 1996). Near the upwind margin of a forest patch, processes are
influenced by the advection of sensible energy generated in the clearing and by turbulence generated at
land-cover boundaries. Air entering a forest edge is relatively warm, dry, and turbulent, thus increasing
evaporation potential. This edge effect diminishes with distance toward the patch interior, but remains
significant for several tens of meters. As Veen et al. (1991) noted, "regional evaporation may be higher in
a landscape with many patches of forest (many edges) as compared with a landscape with the same total
forest concentrated in large blocks." Klaassen (1992) used a surface layer model to demonstrate the
dependency of regional latent energy flux on the scale of landscape fragmentation.
Measurements of transpiration near forest edges are sparse, due in part to the difficulties posed in
field measurements near surface discontinuities (c.f. Gash, 1986). The few field observations which have
been made generally give evidence supporting the depiction of the forest edge as a "special high-flux
environment" (Veen et al., 1996). For example, at a site 200 m downwind of a forest edge, Hutjes (1996
cited in Veen et al., 1996) observed turbulent energy fluxes to the atmosphere (sum of latent and sensible
energy fluxes) up to 25% greater than net radiation. Theory suggests that evaporation of intercepted
rainfall would be especially influenced by edge effect. In fact, simulations by Veen et al. (1991) suggested
that edge effects would be maximal for a wet canopy, while dry canopy transpiration would be affected
very little. Contrary to expectations, throughfall measurements (e.g. Neal, et al., 1993) show almost no
relationship with distance from the forest edge. Klaassen et al. (1996) concludes that proximity to the edge
affects both the interception storage capacity and the rate of evaporation of intercepted water, which cancel
one another. However, he speculates that the forest edge dries more quickly, allowing transpiration to
begin sooner after a storm.
Other researchers have found indirect evidence of greater evaporative flux near the forest edge.
Working in isolated forest reserves in central Amazonia, Kapos (1989) found lower soil moisture within 10
to 20 m of the forest margins. Studies of forest patch microclimate generally show significant gradients in
temperature, humidity, solar radiation, and wind speed at levels within and beneath the canopy (Matlack,
1993; Chen et al., 1993; Murcia, 1995; Kapos et al., 1997; Turton and Freiburger, 1997), which may
suggest trends in evaporation. Detectable effects generally were found to extend as far as 20 to 50 m into
the forest, with the extent sometimes dependent on edge aspect edge age, or patch size.
Most studies of edge microclimate and turbulent fluxes have been conducted over flat terrain.
This is done to minimize the effects arising from heterogeneities other than those associated with land
cover. Steep terrain further hampers the use of micrometeorological approaches to flux measurement and
complicates the interpretation of results. However, in parts of the tropics where landscape fragmentation is
most pronounced, such as montane Southeast Asia, studies on flat terrain are impossible and perhaps
irrelevant.
Theory strongly suggests that forest edges downwind of land with lower vegetation or bare soil
will experience higher rates of evaporation due to positive energy advection and enhanced turbulence. So
6-2
far, empirical evidence of this process is limited and sometimes contradictory. Efforts are intensifying to
understand the effects of spatial heterogeneity in and incorporate them into land surface-atmosphere
interaction and regional hydrologic models.
The need to understand and quantify edge effects on
transpiration increases as the tropical landscape continues to become more fragmented. With this in mind,
we conducted as field study of the spatial variations in microclimate and transpiration in a 12-ha forest
patch in Ban Tat hamlet, Hoa Binh, Vietnam. The objectives of this study were to:
1.
observe the microclimate of a small forest patch and its surroundings
2.
use sap flow techniques to measure rates of transpiration in trees at various distances from the edge
of the patch;
3.
determine whether edge effects varied by season (dry-wet)
4.
observe the effects of diurnal and day-to-day variations in atmospheric conditions on the spatial
pattern of transpiration;
6.2
Field Methodology
Our research strategy called for a measurement transect through a small forest patch oriented along the
prevailing wind direction (Figure 6.1). We selected a 12-ha patch of advanced secondary forest surrounded
on all sides by active or recently abandoned swidden fields.
We focused our observations on the
southwest-facing edge (Figure 6.2) because of its distinct boundary, the high contrast of its neighboring
patch, and the expectation of frequent wind southwest winds (regional wind direction during most of our
observations were dominantly southwest, however, terrain and local thermal influences produced mostly
northwesterly or northeasterly winds at the site). Other edge sites considered during an extensive ground
survey were rejected due to excessively steep slope.
To monitor microclimate variation with the patch, we installed stations at four sites along a SWNE transect trough the patch. Observations at each site are described in Table 6.1. Sensors were sampled
at a 10 s interval and statistics were recorded every 10 minutes with the exception of rainfall, which was
recorded minutely. Sensors are listed in Table 6.2. Vegetation characteristics at the microclimate stations
are given in Table 6.3.
Meteorological methods for estimating evaporation generally require a fetch of 100 m or more. In
the case of edge effect studies, the inhomogeneity which violates the assumptions of meteorological
methods, is precisely the subject of the research. For this reason, we sought an alternative method which
could be applied with equal reliability anywhere in a forest patch. Because the patch is composed of
secondary forest with 90% tree canopy cover, measurement of transpiration by representative trees can be
used to estimate the spatial pattern of total evaporative flux during dry canopy periods. Therefore, we
chose to estimate transpiration in sample trees by monitoring sap flow using the heat dissipation technique
(Granier, 1985, 1987). We installed two Dynamax (Houston, TX, USA) thermal dissipation probes into
each of 18 trees, nine each in near-edge and interior zones of the patch. Three of the most abundant tree
species were selected, Aluerites montana, Alphonsea tonkinensis, and Garcinia planchonii, with three
6-3
individuals of each species monitored at each of the two sap flow observation zones. Because of the very
high species diversity in the patch, we were unable to limit our selections to individuals with similar stem
and crown diameters.
Sapwood depth in each tree was estimated using dying and heat dissipation
techniques. Characteristics of sap flow trees are given in Table 6.4.
Table 6.1. Meteorological observations.
Station 301
302
Site
Swidden field
Forest edge
303
Forest interior
304
Secondary vegetation
Sensor Heights (m)
Rnet
2.7
Kd
2.85
Ku
2.85
Tir
2.7
Ta/RH 3.0
U/WD 3.25
RF
0.75
G
-0.08
Tsoil
-0.02 & -0.06
SM1
0.0 to -0.3
SM2
-0.5 to -0.8
SM3
-1.2 to -1.5
13.57
-13.5
13.5
14.05
14.25
--0.08
-0.02 & -0.06
0.0 to -0.3
-0.5 to -0.8
-1.2 to -1.5
---4.57
5.07
5.42
---0.0 to -0.3
-0.5 to -0.8
-1.2 to -1.5
June 30-July 12
March 27-June 20
July 2-July 12
March 28-June 20
Observation Periods
1997 June 29-July 12
1998 March 24-June 20
---12.1
12.3
12.55
---0.0 to -0.3
-0.5 to -0.8
-1.2 to -1.5
July 3-July 12
March 25-June 20
Note: Rnet = net radiation, Kd = downward shortwave radiation, Ku = reflected shortwave radiation, T ir =
infrared (surface) temperature, T a = air temperature, RH = relative humidity, U = wind speed, WD = wind
direction, ,RF = rainfall, G = soil heat conduction, T soil = soil temperature, SM1 = volumetric soil moisture
at level 1, SM2 = volumetric soil moisture at level 2, and SM3 = volumetric soil moisture at level 3.
We surveyed the locations, species, and diameter at breast height (DBH) of 328 trees (all trees
with DBH > 5 cm) within and around the sap flow monitoring zones (Table 6.5) summarizes the results.
We also measured light extinction using a Decagon (Pullman, WA, USA) Ceptometer, in order to estimate
the spatial pattern of leaf area index (LAI) within and between the sap flow monitoring zones. Stomatal
resistance was measured in selected sap flow trees using a Delta-T porometer. Soil samples taken at
various locations and depths were subsequently analyzed for soil moisture retention characteristics, bulk
density, and root length density.
Observations were conducted during two intensive field experiments during June-July 1997 and
March-June 1998. Results presented in this report will focus on the 1998 observation period. For the 1998
experiment, meteorological measurements were made continuously between 26 March and 20 June 1998;
sapflow measurements were made during and 1 April to18 June 1998. Only six of 18 sapflow probes were
maintained during 24 April through 5 June 1998, while investigators were busy at another field site. One
tree of each species was selected for monitoring (with one probe each) in the edge and forest interior zones.
6-4
Table 6.2. Sensors and recording systems.
Sensors
Company
Radiation Energy Balance Systems
Eppley Laboratory
Everest Interscience
Met-One
Met-One
Met-One
Campbell Scientific
Campbell Scientific
Campbell Scientific
Dynamax
Decagon
Delta-T
Location
Seattle, WA, USA
Newport, RI, USA
Fullerton, CA, USA
Grants Pass, OR, USA
Grants Pass, OR, USA
Grants Pass, OR, USA
Logan, UT, USA
Logan, UT, USA
Logan, UT, USA
Houston, TX, USA
Pullman, WA, USA
Cambridge, England
Data loggers and accessories
Model
Company
Location
Sites 301 and 303
Logger
21X
Data storage
SM192
Campbell Scientific
Campbell Scientific
Logan, UT, USA
Logan, UT, USA
Sites 302 and 304
Logger
10X
Data storage
SM192
Campbell Scientific
Campbell Scientific
Logan, UT, USA
Logan, UT, USA
Sites 305 and 306
Logger
Data storage
Multiplexer
Voltage Regulator
Campbell Scientific
Campbell Scientific
Campbell Scientific
Dynamax
Logan, UT, USA
Logan, UT, USA
Logan, UT, USA
Houston, TX, USA
Rnet
Kd/Ku
Tir
Ta/RH
U/WD
RF
G
Tsoil
SM
Sapflow
Ceptometer
Porometer
Model
Q*7
8-48
4000-4AGL
083C
034
HFT-3
CS615
TDP-30
CEP
AP4
10X
SM192
AM416
AVRD
Note: Rnet = net radiation, Kd = downward shortwave radiation, Ku = reflected shortwave radiation, T ir =
infrared (surface) temperature, T a = air temperature, RH = relative humidity, U = wind speed, WD = wind
direction, ,RF = rainfall, G = soil heat conduction, T soil = soil temperature, and SM = volumetric soil
moisture at level 1.
6-5
Table 6.3. Vegetation at meteorological stations.
301
302
(Swidden field)
(Forest edge)
303
(Forest interior)
304
(Secondary vegetation)
Vegetation Height (m)
0 to 1*
10 to 20
1 to 3*
10 to 20
*significant growth occurred during observation period
Representative Species
Musa paradisiaca
Musa coccinea
Zea mays L.
Melia aderazach
Cana orientalis
Saccharum spontaneum
Setaria palmifolia
Imperata cylindrica
Ageratum conyzoides
Eupatorium odoratum
Gynara crepidioides
Urena lobata
Rorippa indica
Solanum erianthum
Euphobia hirta
6.3
6.3.1
Pithecellobium clypearia Garcinia planchonii
Acanthopanax fragrans Ostodes paniculata
Garcinia planchonii
Schefflera octophylla
Macaranga auriculata Euodia triphylla
Aleurites montana
Aleurites montana
Alphonsea tonkinensis Amomum maximum
Cyathea podophylla
Phrinium capitatum
Lelaginella monospora Psychotria rubra
Miscanthus japonicus
Lelaginella monospora
Phrinium capitatum
Cyathea podophylla
Cyperus nutans
Ervatamia cambodiana
Psychotria rubra
Livistona saribus
Blastus cochinchinensis Dioscorea depauperata
Livistona saribus
Caryota urens
Ervatamia cambodiana Cyperus nutans
Miscanthus japonicus
Neohouzeaua dullooa
Alpinia bractata
Boehmeria macrophylla
Pithecellobium clypearia
Eupatorium odoratum
Ervatamia cambodiana
Mallotus philippensis
Litsea cubeba
Bambusa sp.
Cyperus nutans
Paspalum conjugatum
Microstegium vagans
Saccharum spontaneum
Cyclosorus baviensis
Analysis
Sap flow Analysis
The Granier (1985, 1987) sap flow method is analogous to the hot-wire anemometer technique for
measuring wind. Each probe consists of a pair of 1.2 mm (o.d.) stainless steel needles installed into the tree
stem about 4 cm apart in a vertical line. A constant voltage is applied to a resistor in the upper (heated)
needle. A copper-constantan thermocouple measures the temperature difference between the heated upper
needle and unheated lower reference needle. The flow of sap cools the heated needle. Laboratory
experiments have shown that a reliable relationship exists between the observed temperature difference and
the velocity of sap flow:
V = 0.0119 ((Tmax-T)/ T) 1.231
[6.1]
where V is average sap flow velocity along the length of the probe (cm s -1), T is the temperature
difference observed between the heated and reference needles, and T max is the value of T when sap flow
is zero (generally taken as the peak nighttime value of T).
6-6
Table 6.4. Characteristics of sapflow trees during 1998 experiment.
Species
Crown Area (m2) Stem radius
(m)
Height (m)
Edge Zone
E1
E2
E3
Aleurites montana
Aleurites montana
Aleurites montana
17.49
17.74
18.39
0.0698
0.1237
0.0762
14
22
17
E4
E5
E6
Alphonsea tonkinensis
Alphonsea tonkinensis
Alphonsea tonkinensis
18.33
15.49
16.37
0.0634
0.0587
0.0675
13
13
12
E7
E8
E9
Garcinia planchonii
Garcinia planchonii
Garcinia planchonii
12.83
27.07
32.12
0.0925
0.1175
0.1799
16
18
25
Forest Interior Zone
F1
F2
F3
Aleurites montana
Aleurites montana
Aleurites montana
57.52
25.15
60.13
0.1475
0.0748
0.1543
18
14
18
F4
F5
F6
Alphonsea tonkinensis
Alphonsea tonkinensis
Alphonsea tonkinensis
13.40
11.26
35.89
0.0800
0.0735
0.0822
12
15
15
F7
F8
F9
Garcinia planchonii
Garcinia planchonii
Garcinia planchonii
41.96
28.65
41.71
0.1575
0.1373
0.1269
23
20
19
Clearwater et al. (1999) recently confirmed the original Granier (1985) calibration in the stems of
tree tropical tree species. However, they showed that this calibration applied only when the entire length of
the probe was in contact with conducting xylem (sapwood). When the length of the heated probe exceeds
the thickness of conducting xylem, the original calibration underestimates sap velocity. They proposed a
correction for equation [6.1] in which the T of the sapwood (Tsw) is computed as
Tsw 
T - (b  Tmax )
a
[6.2]
where a and b are the proportions of the probe in sapwood and inactive xylem (b=1-a), respectively
(Clearwater et al., 1999). It can be readily seen that this correction becomes very important as sapwood
depth decreases below probe length. For many of the sample trees in our field study, this was the case.
Hence, we replaced T in equation [1] with Tsw calculated with equation [6.2].
6-7
Table 6.5. Summary of tree survey.
Edge Zone
Number of trees surveyed:
Number of tree species:
Basal area (m2)
Surveyed area (m2)
Abundant tree species (count)
Garcinia planchonii
Ostodes paniculata
Schefflera octophylla
Aleurites montana
Acanthopanax fragrans
Pithecellobium clypearia
Alphonsea tonkinensis
Euodia triphylla
Macaranga auriculata
Forest Interior Zone
Total
178
68
4.211
2200
148
66
5.408
2675
326
105
9.619
4875
9
0
5
7
9
17
7
3
8
15
12
8
7
5
4
3
7
0
24
12
13
14
14
21
10
10
8
Sap flux (volume per unit time) can be computed as
SF = V · Ax
[6.3]
where Ax is the cross-sectional area of active xylem (sap-conducting wood). Transpiration of an individual
tree can be estimated as
Tr = SF/Ac
[6.4]
where Ac is the projected ground area of the tree crown. Stand transpiration can be taken as the average
transpiration of monitored trees. Alternatively, stand transpiration can be estimated as
Tr 
V  Ax
As
[6.5]
where Vis thee average sap velocity of monitored trees, Ax is the total cross-sectional area of active
xylem for all trees in the stand, and As is the stand ground area. In practice, by measuring Ax in a
representative sample, a statistical relationship is developed between Ax and tree stem diameter. Using that
relationship, the ratio of Ax to total stem cross-sectional (basal) area (Ab) can be derived. A survey of
DBH in the stand is used to estimate the ratio of stem cross-sectional area to ground area. The product of
those two ratios gives Ax/As.
Ax Ax Ab


As
Ab As
[6.6]
6-8
6.3.2
Sapwood depth
In light of equation [6.2], determination of the sapwood depth in monitored trees is an essential prerequisite
for accurate interpretation of sap flow data. Until recently, the recommended procedure was to cut down
the tree or use an increment borer to extract a core for visual inspection of the wood coloration pattern.
Some researchers injected dye into the transpiring stem before coring or severing the stem above the
injecting site. We found natural wood coloration of cores to give very little evidence of the active xylem
region in our studied trees. During 1997 and 1998 field experiments, we injected dye into monitored trees.
Subsequent cores gave unambiguous results in only a few trees. Dye was very sparse or absent in the cores
of 7 out of 18 trees, including all 6 Garcinia individuals. Uncertainty in sapwood depth estimates is an
important issue in the use of Granier-type probes. In an effort to address this problem, Burns and Holbrook
(in review) developed a thermal dissipation probe in which a miniature heater and thermocouple are
thermally isolated at the tips of plastic tubing. With this design, the sensor response is limited to sap flow
in a narrow zone at the depth of the probe tips. By sequentially moving the probe to various depths, the
resulting T profile can be used to differentiate active and inactive xylem regions, and hence determine
sapwood depth. Botany Department, University of Hawaii (Honolulu, USA) and Hawaii Agricultural
Research Center (Honolulu, USA) staff built six 10-cm probes for our use at the Ban Tat study site. During
November 1999, 16 of the original 18 sap flow trees were resurveyed using the Burns-Holbrook-type
probes. (One tree had been felled, apparently to obtain fruits, the other had died). Combining the dye
injecting-coring results from June 1998 with the thermal dissipation probe results obtained in November
1999, a good relationship was developed between sapwood depth and stem radius (Figure 6.3). Data from
all three species were combined to obtain the linear equation shown in the figure. In tropical forest in
Panama, F.C. Meinzer (Hawaii Agricultural Research Center, Honolulu, USA) and G. Goldstein (Botany
Department, University of Hawaii, Honolulu, USA) (pers. comm., 1999) similarly found the sapwood
depth-stem radius relationship to be consistent throughout a stand, independent of species. In calculations
of sap velocity and transpiration, we used the relationship shown in Figure 6.3 to estimate sapwood depth
in each tree.
6.3.3
Evaporation methods
Over homogeneous vegetated surfaces, a one-dimensional energy balance approach can be used to estimate
total evaporation. The method can be expressed as (Monteith 1973):
λE  Rn - G 
ρCp T0 - Ta 
ra
[6.6]
where  is the volumetric latent heat of vaporization [J m-3], E is evaporation [m s-1], Rn is net radiation [W
m-2], G [W m-2] is soil heat conduction,  is air density [kg m-3], Cp is the specific heat of air at constant
pressure [J Kg-1 K-1], T0 is the temperature at the virtual source/sink height for sensible heat exchange [K],
Ta is the air temperature [K], and ra is the aerodynamic resistance (s m-1). Measured infrared surface
6-9
temperature may be substituted for T0 (Hatfield et al., 1984; Choudhury et al., 1986).
Aerodynamic resistance can be estimated using the stability corrections recommended by
Choudhury et al. (1986), for stable conditions:
  z-d 
 zd

  ψ  ln 
 ψ
ln 
z
z
 0 
   0h 

ra  
k2U
[6.7]
and for unstable conditions:
z -d z d
ln  u  ln  t

 z0   z0h 
ra 
k2U 1  η 
3
[6.8]
4
where
ψ
[6.9]
B - B2 - 41  η C 12
21  η 
z d
 zu - d 
B  ln  t
  2η ln 

 z0h 
 z0 
  z - d 
C  η ln  u  
  z0  
η
[6.10]
2
[6.11]
5 z - d  gT0  Ta 
U2 Ta
[6.12]
Net radiation and soil heat flux were measured at only two of four stations (301 and 303). Net
radiation was estimated at stations 302 and 303 as:
Rn  Kd - Ku  εA - εσT 04
[6.13]
where Kd = downward shortwave radiation, Ku = reflected shortwave radiation (measured at station 303), 
= emissivity of the surface, A = downward longwave radiation from the atmosphere, and  = 5.67E-8
(Stefan-Botlzmann constant). We assumed that solar radiation did not vary spatially over the study area;
therefore, Kd measured at 301 was used to estimate Rn at 302 and 304. The vegetated surfaces at 302 and
304 were assumed to have albedos similar to that of station 303, there for, K u measured at 303 was
substitued for Rn estimates at 302 and 304. Infrared measurements of surface temperature at 302 and 304
were used for T0 at the respective sites. We assumed that downward longwave radiation did not vary over
the study area, allowing us to estimate A for both 302 and 304 as:
6-10
εA  Rn  Kd  Ku  εσT 04
[6.14]
where Rn, Kd, Ku, and T0 were all measured at station 301.
Soil heat conduction (G) was estimated at each of two sites (301 and 303) on the basis of
measurements of two flux plates inserted at a depth of 8 cm and a four-sensor averaging soil temperature
probe with probes inserted at depths of 2 and 6 cm. G was estimated as the average of the two flux plate
measurements plus or minus the change in sensible heat in the 0- to 8-cm soil layer; soil specific heat was
estimated as a function of the measured soil moisture in the upper 30 cm. At stations 302 and 304, where G
was not measured, estimates from station 303 were substituted.
Land cover heterogeneity at the study site may reduce the reliability of meteorological estimates of
evaporation, such as equation [6.6]. For our clearing (station 301) and forest interior (station 302) sites,
fetch is adequate under the generally low velocity wind conditions and with typical daytime wind
directions. Although, the forest edge (302) and secondary vegetation (304) sites are often affected by the
nearby land cover discontinuity, we also apply this approach to those sites, with the understanding that
results should be considered uncertain.
6.3.4
Canopy resistance
Stomatal resistance varies temporally in response to changes in light, leaf temperature, leaf water potential,
vapor pressure deficit, and water availability in the root zone. When the rate of plant water loss is higher
than the rate of intake, plants experience increasing water stress and stomata close partially or completely,
thereby increasing stomatal resistance.
Stomatal response to water stress is dependent on species
physiology and the history of plant water availability, which changes throughout a life cycle. Canopy
resistance is a variable describing the aggregated effect of stomatal resistance on the evaporation rate of
the stand. The role of canopy resistance in regulating evaporation from a vegetated surface is described by
the Penman- Monteith equation (Monteith, 1965):
ρC
ΔRn  G   p es  e 
ra
λE 
[6.15]
r
 r 
Δ  γ a c 
 ra 
where  = the slope of the saturation vapor pressure versus temperature curve [mb K-1], es = the saturation
vapor pressure [mb], e = the ambient vapor pressure [mb],  = the psychrometric constant [mb K-1], and rc =
the canopy resistance [s m-1]. Substituting the sapflow-based transpiration estimate, Tr, for E, we can
invert equation [6.15] to obtain an estimate of canopy resistance of the overstory:
rc 
ΔRn  G   Δ  1
ρC p(e s  e)
 ra
γTr
γTr
γ
[6.16]
Alternatively, measurements of stomatal resistance and LAI can be used to estimate canopy
resistance as
6-11
rc 
[6.17]
rs
LAI
6.4
6.4.1
Observations
Meteorological conditions
Conditions at the study site during the 1998 measurement period are shown in Figure 6.4. With few
exceptions, the conditions were characterized by high humidity and light winds. Dew usually occurred
during the early morning hours and forest vegetation often remained wet until 0930 local time. Solar and
net radiation were frequently reduced by overcast. Hence, we would expect transpiration rates to be
relatively low. Although regional winds were dominantly southwest winds during most of the observation
period, surface wind direction was strongly influenced by the mechanical and thermal effects of local
topography and land cover. As a result, daytime winds were generally either northwesterly or northeasterly
and differed somewhat from site to site (Figure 6.5).
The observation period straddles the onset of the summer monsoon and therefore includes the
transition in moisture conditions associated with the increase in rainfall during mid-May 1998. Soil
moisture content (Figure 6.6) clearly reflects the abrupt monsoonal transition.
The effects of proximity to the forest edge can be seen in the gradients in air temperature,
humidity, wind,soil moisture content (Figure 6.7). These results confirm those of other studies of forest
patch microclimate. We present a detailed description of the meteorological conditions at the study site in
Giambelluca et al. (in preparation a).
6.4.2
Sapflow
Analysis of the 1997 sapflow observations was limited to 5 of the 6 Aluerites montana individuals used in
1998 (E1, E2, F1, F2, and F3). Transpiration in the sample trees was estimated using equation [6.4]. Mean
transpiration during 4-11 July 1997 is shown in Figure 6.8 as a function of distance from the edge of the
patch. These results suggested that transpiration was enhanced by proximity to the clearing, but were not
conclusive due to the limited number of sample trees, species, and the short duration of the measurements.
The 1998 observations were much more comprehensive in the number of individuals and the
period of observation. For the 1998 period, statistics of sap velocity, computed with equation [6.1], and
transpiration, based on equation [6.4], are summarized for each sample tree in Table 6.6. A sample of the
diurnal sapflow-derived transpiration cycle is given in Figure 6.9 for the six Alureites montana trees.
Diurnal variations in sap flow generally coincide with variations in net radiation, humidity, and wind.
Significant differences were found among species and among individual trees. Figure 6.10 gives a sample
of the diurnal variation of transpiration composited by species. Aluerites montana transpired at about twice
the rate of Alphonsea tonkinensis , and Garcinia planchonii. Transpiration of Alphonsea was 7% lower
than that of Garcinia during the dry period, and 18% higher during the wet period. Variation among
6-12
individuals of the same species may be attributed to differences in exposure due to (a) location relative to
the forest edge, (b) location relative to topographic features, and (c) vertical and horizontal position of
crown relative to neighboring tree crowns; and to differences in leaf area index among the different tree
crowns.
The hourly time series of average transpiration, computed as the mean of all individual tree
transpiration estimates (Figure 6.11) shows a trend of increasing transpiration during the observation
period, with June rates approximately double the April rates. This may be due in part to changes, in
atmospheric conditions. Net radiation increased during the period, but only by about 30%. Wind speed
decreased and there was no significant trend in humidity. The ratio of latent energy flux due to
transpiration to net radiation increased from 0.27 in April to 0.40 in June, suggesting that transpiration was
limited by high canopy resistance early in the observations period. Both soil moisture (Figure 6.6) and leaf
area (field observation) were relatively low at the start of observations April and increased following the
onset of persistent rains in mid-May.
6.4.3
Total evaporation
We estimated total evaporation at each of the four meteorological stations using Equation [6.6]. The time
series of daily mean evaporation and the fraction of net radiation used for evaporation at the four stations
are shown in Figure 6.12. Evaporation at all four stations is shown to increase during the study period, in
response to increasing water availability. Stations 302 and 304 have similar rates, with E/Rn increasing
from around 0.85 to over 1.0 during the study. The clearing (301) and forest interior (303) stations had
significantly lower rates than the other two stations, especially during late April, the driest part of the study.
6.4.4
Stomatal resistance and leaf area index
Stomatal resistance was measured at three times during the study period. Figure 6.13 shows the diurnal
patterns of stomatal resistance for two Alphonsea trees, one near the forest edge (E6) and one in the forest
interior (F6). Rains prior to the 8-9 April measurements, easing dry season conditions briefly. The driest
The 24 April observations were taken during the driest period of the study.
Comparing the April
observations with those of the 10-12 June (wet season) period, reduction of transpiration by droughtinduced stomatal resistance is strongly evident. Especially for 24 April, mid- to late-afternoon values are
very high. No consistent distinction between the edge and interior trees is evident in these observations.
The results of light-extinction-based LAI estimates are shown in Figure 6.14. LAI averaged 2.67
for the edge zone and 2.19 for the interior zone. Although we do not have sufficient measurements to
quantify the trend, leaf coverage in the canopy trees increased during the study period in response to the
onset of rainy conditions.
6-13
6-14
T (mm d-1)
Mean
SD
1.99
0.74
4.55
1.32
1.54
0.57
0.87
0.26
1.77
0.48
1.28
0.55
1.37
0.56
0.86
0.53
1.08
0.57
2.15
0.52
1.92
0.80
2.05
0.55
2.07
0.58
0.90
0.25
0.52
0.22
1.78
0.59
0.93
0.35
0.48
0.18
1.38
0.59
1.12
0.45
1.25
0.51
1.23
1.81
0.60
1.49
0.45
1.65
0.52
1.22
April 1-June 17
V (cm hr-1)
Mean
SD
16.65
6.28
8.92
4.93
7.83
5.25
6.99
3.69
14.10
7.07
8.74
4.76
4.04
2.08
4.26
2.11
1.97
1.19
9.47
5.22
18.89
8.59
10.31
4.12
8.31
3.59
3.53
1.52
5.31
2.73
5.56
2.85
3.04
1.26
2.53
1.18
9.88
4.15
9.08
4.10
9.48
4.01
1.09
8.40
4.27
7.30
3.70
7.85
3.97
1.15
T (mm d-1)
Mean
SD
1.68
0.63
2.86
1.58
0.91
0.61
0.57
0.30
1.15
0.58
0.90
0.49
0.98
0.50
0.80
0.40
0.74
0.45
1.34
0.74
1.50
0.67
1.52
0.61
1.48
0.64
0.64
0.27
0.36
0.19
1.21
0.62
0.74
0.31
0.36
0.17
1.13
0.48
0.87
0.38
1.00
0.42
1.30
1.21
0.62
1.01
0.49
1.11
0.55
1.20
Only the trees indicated with asterisks were monitored during the period April 24-June 5. Summary statistics are given for the three "select" trees over the full
observation
*
Table 6.6. Summary of sapflow velocity (V) and transpiration (T) estimates.
April 1- May 19
May 20-June 17
V (cm hr-1)
T (mm d-1)
V (cm hr-1)
Zone
Tree Species
Mean
SD
Mean
SD
Mean
SD
Edge
E1* Aleurites 14.78
4.72
1.49
0.48
19.74
7.33
E2
Aleurites 6.03
2.14
1.93
0.68
14.22
4.11
E3
Aleurites 4.80
1.93
0.56
0.23
13.40
4.84
E4
Alphonsea 4.94
1.94
0.40
0.16
10.74
3.15
E5
Alphonsea 10.02
3.31
0.82
0.27
21.59
5.90
E6* Alphonsea 6.56
2.64
0.68
0.27
12.35
5.30
E7
Garcinia 3.16
1.29
0.77
0.31
5.65
2.32
E8* Garcinia 4.06
1.55
0.76
0.29
4.59
2.81
E9
Garcinia 1.48
0.57
0.56
0.21
2.87
1.51
Forest Interior F1
Aleurites 6.23
2.39
0.88
0.34
15.19
3.66
F2*
Aleurites 15.05
5.19
1.25
0.43
25.25
9.38
F3
Aleurites 8.26
2.71
1.22
0.40
13.92
3.74
F4
Alphonsea 6.45
2.17
1.15
0.39
11.59
3.25
F5
Alphonsea 2.71
0.83
0.49
0.15
4.98
1.37
F6*
Alphonsea 3.93
1.10
0.27
0.08
7.58
3.10
F7
Garcinia 4.08
1.59
0.89
0.35
8.19
2.72
F8*
Garcinia 2.56
0.82
0.62
0.20
3.84
1.46
F9
Garcinia 2.03
0.77
0.29
0.11
3.41
1.28
*
Edge Zone Select (E1, E6, E8)
8.47
2.71
0.98
0.31
12.22
5.03
Forest Zone Select (F2, F6, F8)* 7.18
2.28
0.71
0.22
12.22
4.53
All Select Trees*
7.82
2.36
0.84
0.25
12.22
4.67
Ratio Edge to Forest Select*
1.18
1.37
1.00
Edge Zone (E1-E9)
6.10
2.21
0.88
0.32
12.60
3.96
Forest Zone (F1-F9)
5.29
1.87
0.75
0.26
10.99
3.40
All
5.70
2.01
0.81
0.29
11.79
3.64
Ratio Edge to Forest
1.15
1.17
1.15
6.5
Discussion
6.5.1
Edge effect
As a first means of evaluating edge effect on transpiration, we scaled up transpiration estimates from the
individual tree level to stand level for the edge and forest interior zones using equation [6.5]. Applying the
equation in Figure 6.3 to the tree survey data (Table 6.5), we estimated the ratio of total active xylem crosssectional area to total stem cross-sectional (basal) area (Ax/Ab) for edge and forest interior zones as
0.493 and 0.496, respectively. The tree survey data give the ratio of basal area to ground area (Ab/As) for
edge and forest interior zones as 0.00214 and 0.00212, respectively. Comparing the resulting times series
for the two groups of trees (Figure 6.15) shows that the edge trees had consistently higher rates of
transpiration.
The difference between the groups was greatest on days with relatively high rates of
transpiration, and was higher toward the latter (wetter) part of the observation period. This result is similar
to that found at the same site in 1997 for a shorter observation period and with a smaller sample of trees
(Giambelluca et al., in preparation b).
Correlation of daily edge and forest interior stand transpiration with daytime (0600-1900)
atmospheric forcing variables (Table 6.7) indicates that sapflow is responsive to variations in solar
radiation (Kd) net radiation (Rnet), infrared surface temperature (T ir), air temperature (Ta), and relative
humidity (RH). Note that transpiration in both stands is more highly correlated with R net, Ta, and RH
measured in the clearing than with corresponding measurements within the stands. This suggests that
higher transpiration in the forest patch occurs on relatively clear, sunny days when conditions in the
clearing are dry and hot. If transpiration is being influenced by the advection of warm, dry air from the
surroundings, as we hypothesize, then contrasts between edge and interior transpiration rates should be
correlated with conditions outside the patch (two right-hand columns in Table 6.7). These results give
evidence of edge effect especially on days during which we would expect higher positive heat advection.
With nine trees in each zone, we can examine the two-dimensional transpiration pattern using
spatial mapping software (Surfer, Golden Software, Golden, CO, USA). To remove the influence of
species
differences,
transpiration
was
normalized
by
the
species
mean.
Values
were
interpolated/extrapolated to a 1 m by 1 m grid using the Kriging option in Surfer. Default settings were
used for all Kriging options except error nugget, which was set at 0.05. Figure 6.16 shows the patterns of
transpiration for the dry period (2 April to 18 May 1998), wet period (19 May to 17 June), and for the
whole study period. Values were normalized by the means of the respective periods. The spatial patterns
shown in Figure 6.16 provide fairly convincing evidence of transpiration enhancement near the edge. The
pattern is almost identical for wet and dry periods. The spatial trend in transpiration extends through both
the edge and interior zones, indicating that transpiration edge effect extends at least 100 m from the
southwestern edge of the patch. The peak of the distribution is found at about 5 to 20 m from the edge with
some trees closer to the edge having slightly lower values. The decreasing trends to the northwest and
southeast of this peak are partly artifacts of extrapolation outside the observation areas.
6-15
Table 6.7. Correlation of daily sapflow-based transpiration versus meteorological variables.
Meteorological
Transpiration
Observations
Variable Site
Edge Zone Forest Interior All Trees
Edge:Forest Edge-Forest
Zone
Kd
Rnet
Rnet
Tir
Tir
Tir
Ta
Ta
Ta
RH
RH
RH
301
301
303
301
302
303
301
302
303
301
302
303
0.739
0.743
0.567
0.585
0.579
0.601
0.721
0.614
0.609
-0.523
-0.498
-0.490
0.682
0.691
0.535
0.584
0.604
0.625
0.734
0.636
0.632
-0.477
-0.438
-0.425
0.722
0.729
0.559
0.592
0.598
0.620
0.736
0.632
0.628
-0.508
-0.477
-0.467
0.355
0.351
0.202
0.104
0.000
0.005
0.098
0.019
0.019
-0.275
-0.273
-0.282
0.501
0.489
0.354
0.286
0.216
0.232
0.316
0.242
0.242
-0.367
-0.392
-0.401
Note: Kd = downward shortwave radiation, Rnet = net radiation, Tir = infrared (surface) temperature,
Ta = air temperature, RH = relative humidity
The mean transpiration patterns (Figure 6.16) suggest distinct enhancement of transpiration along
the southwestern edge of the patch despite the fact that the predominant wind directions were not into the
patch. We expect edge effect to be especially important where warmer drier air from the adjacent clearing
frequently enters moves into the patch. At the clearing station (located 45 m from the forest edge), daytime
wind direction indicated flow crossing the patch boundary moving into the patch during 32% of the
daytime observations. Wind direction at that station is strongly influenced by the local topography, tending
to flow parallel to the axis of the small valley there. Villagers who assisted us in the field maintained a
small hut just outside the forest edge. We often observed smoke from their cooking fire to move parallel to
the patch boundary, with eddies diffusing the smoke into the patch. We conclude that the frequency of air
movement crossing the southwest patch boundary from the clearing into the patch is higher than that
indicated by measured wind direction. To verify that the observed transpiration pattern was influenced by
the adjacent clearing , we examined variations in the pattern as it responded to different weather conditions,
including wind direction.
We selected two days, 4 and 23 April, with contrasting wind direction but otherwise having
similar weather conditions. Net radiation on the two days was 103 and 104 W m-2, respectively. The air
was warmer and drier on the 23rd, but wind speed was higher on the 4th. Daytime wind direction in the
clearing southwest of the edge was southerly (blowing into the edge) on the 4 th and northerly on the 23rd.
Figure 6.17 shows the spatial patterns of normalized transpiration for the two days.
Values were
normalized using the mean dry-period transpiration for each tree. In general, transpiration is higher
throughout the study area on the 23rd, probably in response to lower humidity. But the spatial patterns for
6-16
the two days are opposite. On 4 April, highest transpiration is found along the SW edge, decreasing toward
the interior zone. More variability is evident among the edge trees than the interior trees. On 23 April, the
lowest transpiration occurred along the southwest edge with higher values in the interior. Again the edge
trees were more variable. This comparison suggests that the spatial pattern of transpiration is highly
responsive to wind direction.
We analyzed the patterns of normalized transpiration for each hour during 8 April (Figure 6.18) to
determine whether response to short-term variation in wind direction could be observed in the spatial
pattern of transpiration. Again, values were normalized using the mean dry-period transpiration for each
tree. Also shown are solar radiation (yellow bar), surface temperature of the clearing (red bar), and wind
speed and direction in the clearing (black arrow). Conditions on 8 April were partly cloudy with moderate
humidity. During the morning, winds were SSW to S at the edge and E to ENE at the interior zone As the
day progressed, winds at the edge gradually shifted to the SE, and at the interior were E to ESE. Changes
in transpiration during the day follow the diurnal cycles of radiation and relative humidity, with the highest
values at mid-day. The pattern during the morning is bimodal with peaks near the edge and at the NE
corner of the interior zone. Enhancement at the edge peaks at midday. During the afternoon, the pattern
changes as wind direction at the edge shifts, until by mid-afternoon no edge enhancement is evident. The
diurnal variation in the pattern near the edge seems to confirm that edge effect is sensitive to edge
orientation and wind direction. The morning and mid-day interior peak is related to the easterly wind there.
While the patch extends several hundred meters east of this area, there is a gap in the canopy along the NE
portion of the interior zone, and a steep downward slope in that direction. Trees at the extreme NE corner
are exposed to easterly winds and normalized transpiration appears to have been enhanced as a result.
If enhanced transpiration near the forest edge is due in part to positive energy advection from the
nearby clearing, we should find greater edge effect on days favoring positive heat advection. On clear, dry
days, the soil surface of the surrounding clearings is more likely to become dry and hot. Therefore, we
should find more evidence of edge effect on clear, dry days. To investigate this, we selected the days with
the highest and lowest daytime net radiation recorded at the clearing station (301), and averaged the
normalized transpiration for those days to obtain a composite transpiration pattern (Figure 6.19). Similarly,
we constructed composite normalized transpiration patterns for days with high and low daytime relative
humidity (Figure 6.20), and high and low southwest wind component (Figure 6.21).
As expected,
transpiration appears enhanced along the SW edge on days with high net radiation, low relative humidity,
or southwesterly wind.
6.5.2
Saplfow vs. total evaporation
The energy equivalent of daily sapflow-based transpiration for edge and interior zones (equation [6.5]) is
compared with estimated total evaporation in each zone (equation [6.6]) in Figure 6.22. In each zone,
estimates of transpiration and total evaporation are highly correlated. However, the relative magnitude of
transpiration is quite low, averaging about 23% and 28% of total evaporation for edge and interior zones,
6-17
respectively. These lower than expected transpiration fractions may be explained in part by (1) frequent
canopy wetting from rain, fog, and dew; and (2) the open canopy structure, allowing relatively high
understory transpiration.
Errors in observations of sapflow, xylem depth, and basal area may have
contributed to underestimates of tree transpiration.
Likewise, meteorological observations, roughness
length estimates, and violation of model fetch requirements may have yielded overestimates of total
evaporation.
6.5.3
Canopy resistance
For the days during which we took stomatal resistance (rs) measurements in sample trees, we estimated
canopy resistance using two independent methods (equations [6.16] and [6.17]). Figure 6.23 shows the
daytime patterns of transpiration in the two sample trees (E6 and F6) for the rs observation days, and
corresponding estimates of canopy resistance using the two methods. The graphs in the right column show
the canopy resistance estimates. Comparing the two types of estimates, we note the relatively large scatter
of the stomatal resistance-based estimates (symbols). Although there is some agreement between the two
methods in discerning the effects of the dry season to wet season transition, differences between the two
sample trees are not clear due to the high variability in stomatal resistance among the sample leaves on
each tree.
Focusing on the transpiration-based estimates (lines), canopy resistance of the edge tree is greater
than that of the forest interior tree during the morning and similar to that of the forest tree in the afternoon,
on four of the six sample days (8 and 9 April and 10 and 11June). This pattern suggests that the typical wet
canopy conditions of early morning persists longer at the edge site on those days than in the forest interior
(wet canopy conditions would lower transpiration and result in a low estimate of rc derived from sapflow
measurement). We know that the foliage was wet enough to prevent stomatal resistance measurements
during the mornings of 8 April and 10 and 11June. This result, which is inconsistent with expectations of
higher interception evaporation rates for the edge trees, may reflect the specific characteristics of the two
sample trees (e.g. perhaps the edge tree has a large canopy interception storage capacity). Despite having
higher canopy resistance on those days, morning transpiration of the edge tree exceeded that of the interior
tree on 8 and 9 April and was only slightly lower on the other 2 days.
6.6
Conclusions
Our field observations within and near a small forest patch in Ban Tat provide evidence of the influences of
surrounding clearings on patch microclimate and transpiration. The effects of proximity to the forest edge
can be seen in the gradients in temperature, humidity, wind,soil moisture content. Sapflow measurements
in sample trees strongly indicate that transpiration rates are higher near the edge of the patch (edge effect).
This effect is seen in the averages for the whole study period, despite infrequent wind flow into the patch.
Edge effect is observed during both dry and wet periods. Edge effect is most apparent on days when solar
and net radiation are high, relative humidity is low, or wind direction is from the clearing into the forest
6-18
edge. These conditions are conducive to high positive heat advection from the clearing to the forest edge.
Transpiration in both edge and interior trees is highly correlated with conditions in the clearing.
The results obtained here suggest that greater fragmentation tends to increase regional evaporative
flux, i.e. fragmentation of remaining forested areas partly reverses the reduction in regional evaporation due
to deforestation. We can infer from the distance-to-edge dependency of transpiration that the magnitude of
this regional effect depends on the size, shape, and spatial distribution of landscape patches. It is also likely
that the replacement land cover and moisture status of the clearings affect this process. Although we found
slightly greater edge effect during the dry period of our observations, it is possible that under more
prolonged or severe dry conditions, the soil moisture storage at the forest edge would become depleted
leading to a reversal the transpiration pattern.
6.7
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