6. TRANSPIRATION IN A SMALL FOREST PATCH

6.1 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

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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

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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 SW-

NE 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

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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 301 302 303 304Site Swidden field Forest edge Forest interior Secondary vegetation

Sensor Heights (m)Rnet 2.7 -- 13.57 --Kd 2.85 -- -- --Ku 2.85 -- 13.5 --Tir 2.7 12.1 13.5 4.57Ta/RH 3.0 12.3 14.05 5.07U/WD 3.25 12.55 14.25 5.42RF 0.75 -- -- --G -0.08 -- -0.08 --Tsoil -0.02 & -0.06 -- -0.02 & -0.06 --SM1 0.0 to -0.3 0.0 to -0.3 0.0 to -0.3 0.0 to -0.3SM2 -0.5 to -0.8 -0.5 to -0.8 -0.5 to -0.8 -0.5 to -0.8SM3 -1.2 to -1.5 -1.2 to -1.5 -1.2 to -1.5 -1.2 to -1.5

Observation Periods1997 June 29-July 12 July 3-July 12 June 30-July 12 July 2-July 121998 March 24-June 20 March 25-June 20 March 27-June 20 March 28-June 20

Note: Rnet = net radiation, Kd = downward shortwave radiation, Ku = reflected shortwave radiation, Tir = infrared (surface) temperature, Ta = air temperature, RH = relative humidity, U = wind speed, WD = wind direction, ,RF = rainfall, G = soil heat conduction, Tsoil = 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.

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Table 6.2. Sensors and recording systems.

SensorsModel Company Location

Rnet Q*7 Radiation Energy Balance Systems Seattle, WA, USAKd/Ku 8-48 Eppley Laboratory Newport, RI, USATir 4000-4AGL Everest Interscience Fullerton, CA, USATa/RH 083C Met-One Grants Pass, OR, USAU/WD 034 Met-One Grants Pass, OR, USARF Met-One Grants Pass, OR, USAG HFT-3 Campbell Scientific Logan, UT, USATsoil Campbell Scientific Logan, UT, USASM CS615 Campbell Scientific Logan, UT, USASapflow TDP-30 Dynamax Houston, TX, USACeptometer CEP Decagon Pullman, WA, USAPorometer AP4 Delta-T Cambridge, England

Data loggers and accessoriesModel Company Location

Sites 301 and 303Logger 21X Campbell Scientific Logan, UT, USAData storage SM192 Campbell Scientific Logan, UT, USA

Sites 302 and 304Logger 10X Campbell Scientific Logan, UT, USAData storage SM192 Campbell Scientific Logan, UT, USA

Sites 305 and 306Logger 10X Campbell Scientific Logan, UT, USAData storage SM192 Campbell Scientific Logan, UT, USAMultiplexer AM416 Campbell Scientific Logan, UT, USAVoltage Regulator AVRD Dynamax Houston, TX, USA

Note: Rnet = net radiation, Kd = downward shortwave radiation, Ku = reflected shortwave radiation, Tir = infrared (surface) temperature, Ta = air temperature, RH = relative humidity, U = wind speed, WD = wind direction, ,RF = rainfall, G = soil heat conduction, Tsoil = soil temperature, and SM = volumetric soil moisture at level 1.

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Table 6.3. Vegetation at meteorological stations.301 302 303 304

(Swidden field) (Forest edge) (Forest interior) (Secondary vegetation)

Vegetation Height (m)0 to 1* 10 to 20 10 to 20 1 to 3*

*significant growth occurred during observation period

Representative SpeciesMusa paradisiaca Pithecellobium clypearia Garcinia planchonii Boehmeria macrophyllaMusa coccinea Acanthopanax fragrans Ostodes paniculata Pithecellobium clypeariaZea mays L. Garcinia planchonii Schefflera octophylla Eupatorium odoratumMelia aderazach Macaranga auriculata Euodia triphylla Ervatamia cambodianaCana orientalis Aleurites montana Aleurites montana Mallotus philippensisSaccharum spontaneum Alphonsea tonkinensis Amomum maximum Litsea cubebaSetaria palmifolia Cyathea podophylla Phrinium capitatum Bambusa sp.Imperata cylindrica Lelaginella monospora Psychotria rubra Cyperus nutansAgeratum conyzoides Miscanthus japonicus Lelaginella monospora Paspalum conjugatumEupatorium odoratum Phrinium capitatum Cyathea podophylla Microstegium vagansGynara crepidioides Cyperus nutans Ervatamia cambodiana Saccharum spontaneumUrena lobata Psychotria rubra Livistona saribus Cyclosorus baviensisRorippa indica Blastus cochinchinensis Dioscorea depauperataSolanum erianthum Livistona saribus Caryota urensEuphobia hirta Ervatamia cambodiana Cyperus nutans

Miscanthus japonicusNeohouzeaua dullooaAlpinia bractata

6.3 Analysis

6.3.1 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).

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Table 6.4. Characteristics of sapflow trees during 1998 experiment.Species Crown Area (m2) Stem radius

(m)Height (m)

Edge Zone

E1 Aleurites montana 17.49 0.0698 14E2 Aleurites montana 17.74 0.1237 22E3 Aleurites montana 18.39 0.0762 17

E4 Alphonsea tonkinensis 18.33 0.0634 13E5 Alphonsea tonkinensis 15.49 0.0587 13E6 Alphonsea tonkinensis 16.37 0.0675 12

E7 Garcinia planchonii 12.83 0.0925 16E8 Garcinia planchonii 27.07 0.1175 18E9 Garcinia planchonii 32.12 0.1799 25

Forest Interior Zone

F1 Aleurites montana 57.52 0.1475 18F2 Aleurites montana 25.15 0.0748 14F3 Aleurites montana 60.13 0.1543 18

F4 Alphonsea tonkinensis 13.40 0.0800 12F5 Alphonsea tonkinensis 11.26 0.0735 15F6 Alphonsea tonkinensis 35.89 0.0822 15

F7 Garcinia planchonii 41.96 0.1575 23F8 Garcinia planchonii 28.65 0.1373 20F9 Garcinia planchonii 41.71 0.1269 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

[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].

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Table 6.5. Summary of tree survey.

Edge Zone Forest Interior Zone Total

Number of trees surveyed: 178 148 326Number of tree species: 68 66 105Basal area (m2) 4.211 5.408 9.619Surveyed area (m2) 2200 2675 4875Abundant tree species (count)

Garcinia planchonii 9 15 24Ostodes paniculata 0 12 12Schefflera octophylla 5 8 13Aleurites montana 7 7 14Acanthopanax fragrans 9 5 14Pithecellobium clypearia 17 4 21Alphonsea tonkinensis 7 3 10Euodia triphylla 3 7 10Macaranga auriculata 8 0 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

[6.5]

where is 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.

[6.6]

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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):

[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],

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Ta is the air temperature [K], and ra is the aerodynamic resistance (s m-1). Measured infrared surface

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:

[6.7]

and for unstable conditions:

[6.8]

where

[6.9]

[6.10]

[6.11]

[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:

[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:

10

[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):

[6.15]

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:

[6.16]

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Alternatively, measurements of stomatal resistance and LAI can be used to estimate canopy

resistance as

[6.17]

6.4 Observations

6.4.1 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

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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

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 drought-

induced 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.

13

14

15

Tabl

e 6.

6. S

umm

ary

of sa

pflo

w v

eloc

ity (V

) and

tran

spir

atio

n (T

) est

imat

es.

Apr

il 1-

May

19

May

20-

June

17

Apr

il 1-

June

17

V (c

m h

r-1)

T (m

m d

-1)

V (c

m h

r-1)

T (m

m d

-1)

V (c

m h

r-1)

T (m

m d

-1)

Zon

eTr

eeSp

ecie

sM

ean

SDM

ean

SDM

ean

SDM

ean

SDM

ean

SDM

ean

SDEd

geE1

*Al

euri

tes

14.7

84.

721.

490.

4819

.74

7.33

1.99

0.74

16.6

56.

281.

680.

63E2

Aleu

rite

s6.

032.

141.

930.

6814

.22

4.11

4.55

1.32

8.92

4.93

2.86

1.58

E3Al

euri

tes

4.80

1.93

0.56

0.23

13.4

04.

841.

540.

577.

835.

250.

910.

61E4

Alph

onse

a4.

941.

940.

400.

1610

.74

3.15

0.87

0.26

6.99

3.69

0.57

0.30

E5Al

phon

sea

10.0

23.

310.

820.

2721

.59

5.90

1.77

0.48

14.1

07.

071.

150.

58E6

*Al

phon

sea

6.56

2.64

0.68

0.27

12.3

55.

301.

280.

558.

744.

760.

900.

49E7

Gar

cini

a3.

161.

290.

770.

315.

652.

321.

370.

564.

042.

080.

980.

50E8

*G

arci

nia

4.06

1.55

0.76

0.29

4.59

2.81

0.86

0.53

4.26

2.11

0.80

0.40

E9G

arci

nia

1.48

0.57

0.56

0.21

2.87

1.51

1.08

0.57

1.97

1.19

0.74

0.45

Fore

st In

terio

rF1

Aleu

rite

s6.

232.

390.

880.

3415

.19

3.66

2.15

0.52

9.47

5.22

1.34

0.74

F2*

Aleu

rite

s15

.05

5.19

1.25

0.43

25.2

59.

381.

920.

8018

.89

8.59

1.50

0.67

F3Al

euri

tes

8.26

2.71

1.22

0.40

13.9

23.

742.

050.

5510

.31

4.12

1.52

0.61

F4Al

phon

sea

6.45

2.17

1.15

0.39

11.5

93.

252.

070.

588.

313.

591.

480.

64F5

Alph

onse

a2.

710.

830.

490.

154.

981.

370.

900.

253.

531.

520.

640.

27F6

*Al

phon

sea

3.93

1.10

0.27

0.08

7.58

3.10

0.52

0.22

5.31

2.73

0.36

0.19

F7G

arci

nia

4.08

1.59

0.89

0.35

8.19

2.72

1.78

0.59

5.56

2.85

1.21

0.62

F8*

Gar

cini

a2.

560.

820.

620.

203.

841.

460.

930.

353.

041.

260.

740.

31F9

Gar

cini

a2.

030.

770.

290.

113.

411.

280.

480.

182.

531.

180.

360.

17Ed

ge Z

one

Sele

ct (E

1, E

6, E

8)*

8.47

2.71

0.98

0.31

12.2

25.

031.

380.

599.

884.

151.

130.

48Fo

rest

Zon

e Se

lect

(F2,

F6,

F8)

*7.

182.

280.

710.

2212

.22

4.53

1.12

0.45

9.08

4.10

0.87

0.38

All

Sele

ct T

rees

*7.

822.

360.

840.

2512

.22

4.67

1.25

0.51

9.48

4.01

1.00

0.42

Rat

io E

dge

to F

ores

t Sel

ect*

1.18

1.37

1.00

1.23

1.09

1.30

Edge

Zon

e (E

1-E9

)6.

102.

210.

880.

3212

.60

3.96

1.81

0.60

8.40

4.27

1.21

0.62

Fore

st Z

one

(F1-

F9)

5.29

1.87

0.75

0.26

10.9

93.

401.

490.

457.

303.

701.

010.

49A

ll 5.

702.

010.

810.

2911

.79

3.64

1.65

0.52

7.85

3.97

1.11

0.55

Rat

io E

dge

to F

ores

t1.

151.

171.

151.

221.

151.

20

* Onl

y th

e tre

es in

dica

ted

with

ast

eris

ks w

ere

mon

itore

d du

ring

the

perio

d A

pril

24-J

une

5. S

umm

ary

stat

istic

s are

giv

en fo

r the

thre

e "s

elec

t" tr

ees o

ver t

he fu

ll ob

serv

atio

n

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 cross-

sectional 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 (Tir), 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.

16

Table 6.7. Correlation of daily sapflow-based transpiration versus meteorological variables.Meteorological Observations

Transpiration

Variable Site Edge Zone Forest Interior Zone

All Trees Edge:Forest Edge-Forest

Kd 301 0.739 0.682 0.722 0.355 0.501Rnet 301 0.743 0.691 0.729 0.351 0.489Rnet 303 0.567 0.535 0.559 0.202 0.354Tir 301 0.585 0.584 0.592 0.104 0.286Tir 302 0.579 0.604 0.598 0.000 0.216Tir 303 0.601 0.625 0.620 0.005 0.232Ta 301 0.721 0.734 0.736 0.098 0.316Ta 302 0.614 0.636 0.632 0.019 0.242Ta 303 0.609 0.632 0.628 0.019 0.242

RH 301 -0.523 -0.477 -0.508 -0.275 -0.367RH 302 -0.498 -0.438 -0.477 -0.273 -0.392RH 303 -0.490 -0.425 -0.467 -0.282 -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

17

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,

18

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 r s 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

19

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.

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