PUBLICATIONSJournal of Geophysical Research: Biogeosciences
RESEARCH ARTICLE
10.1002/2014JG002767
Key Points:• A 7 year evapotranspiration (ET) ina tropical deciduous forest wasmeasured• Higher-order Taylor series expansions
were needed to explain ET controls• Rainfall variations exert physical andbiological controls on ET variation

Supporting Information:• Supporting Information S1
Correspondence to:Y. Igarashi,
igarashi@hyarc.nagoya-u.ac.jp

Separating physical and biological controls on long-term
evapotranspiration ﬂuctuations in a tropical deciduous
forest subjected to monsoonal rainfall
Yasunori Igarashi1, Gabriel G. Katul1,2,3, Tomo’omi Kumagai1, Natsuko Yoshifuji4, Takanori Sato5,
Nobuaki Tanaka6, Katsunori Tanaka7, Hatsuki Fujinami1, Masakazu Suzuki5, and Chatchai Tantasirin81

Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Aichi, Japan, 2Nicholas School of theEnvironment, Duke University, Durham, North Carolina, USA, 3Department of Civil and Environmental Engineering, Duke
University, Durham, North Carolina, USA, 4Graduate School of Agriculture, Kyoto University, Kyoto, Japan, 5Graduate Schoolof Agricultural and Life Sciences, University of Tokyo, Bunkyo-ku, Tokyo, Japan, 6Ecohydrology Research Institute, Universityof Tokyo Forests, Graduate School of Agricultural and Life Sciences, University of Tokyo, Seto, Japan, 7Department ofEnvironmental Geochemical Cycle Research, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan,
8
Faculty of Forestry, Kasetsart University, Bangkok, Thailand

Abstract Evapotranspiration (ET), especially in the mainland of the Indochina Peninsula, can impact and is
Citation:
Igarashi, Y., G. G. Katul, T.’o. Kumagai,
N. Yoshifuji, T. Sato, N. Tanaka, K. Tanaka,
H. Fujinami, M. Suzuki, and C. Tantasirin(2015), Separating physical and biologicalcontrols on long-term evapotranspiration
ﬂuctuations in a tropical deciduous forest
subjected to monsoonal rainfall,
J. Geophys. Res. Biogeosci., 120, 1262–1278,
doi:10.1002/2014JG002767.
Received 12 AUG 2014
Accepted 7 MAY 2015
Accepted article online 14 MAY 2015
Published online 23 JUL 2015

impacted by the Asian monsoonal (AM) system, thereby prompting interest in its long-term variability. To
separate the physical and biological factors controlling ET variability in a tropical deciduous forest under the
AM inﬂuence, 7 year eddy covariance and ancillary measurements were collected and analyzed. The 7 year
mean rainfall (Pr) and ET along with their standard deviations were 1335 ± 256 and 977 ± 108 mm (about 73%
of Pr), respectively, suggesting close coupling between these two hydrologic ﬂuxes. However, other physicaland biological drivers decouple seasonal and annual variations of ET from Pr. To explore them, a big-leaf
model complemented by perturbation analysis was employed. The big-leaf model agreed well with themeasured ET at daily to multiyear time scales, lending conﬁdence in its ability to separate biological andphysical controls on ET. Using this formulation, both ﬁrst-order and second-order Taylor series expansions of
the total ET derivatives were applied to the big-leaf model and compared with measured changes in ET (dET).
Higher-order and joint terms in the second-order expansion were necessary for matching measured andanalyzed dET. Vapor pressure deﬁcit (D) was the primary external physical controlling driver of ET. Leaf areaindex (LAI) and bulk stomatal conductance (gs) were shown to be the main signiﬁcant biological drivers of thetranspiration component of ET. It can be surmised that rainfall variability controls long-term ET throughphysical (mainly D) and biological (mainly LAI and gs) factors in this ecosystem.

1. Introduction©2015. American Geophysical Union.
All Rights Reserved.

IGARASHI ET AL.

The Asian monsoon, a large-scale circulation system driven by seasonal differential heating between the Eurasiancontinent and surrounding ocean, controls the seasonal and interannual rainfall variability in southeast (SE) Asia
[e.g., Yasunari et al., 2006; Yasunari, 2007]. There is now signiﬁcant interest in how the Asian monsoon maintains its
wet state and to what extent ongoing rapid (and projected) land cover changes impact the onset, strength, andduration of the Asian monsoon. Numerical modeling can provide information about the interplay between land
surface processes and the Asian monsoon such as the effects of deforestation on monsoonal rainfall. Using ageneral circulation model (GCM), earlier studies reported that the deforestation in SE Asia has less of an impact
on regional monsoonal precipitation than the deforestation in Amazonian rainforests [Henderson-Sellers et al.,
1993; Zhang et al., 1996]. However, recent numerical experiments using ﬁner-resolution models and realistic
land surface schemes have revealed that the deforestation in SE Asia can appreciably alter Asian monsoon
circulation and precipitation [Mabuchi et al., 2005], and the concomitant regional-scale precipitation [Kanae
et al., 2001]. Yasunari et al. [2006] also showed, from GCM experiments, that water vapor supplied from surface
vegetation plays a critical role in the formation of the Asian monsoon circulation, and subsequent precipitation
over the Asian monsoon region, suggesting the signiﬁcance of whole ecosystem evapotranspiration (ET). Usingsatellite-derived precipitation and atmospheric reanalysis data, a recent observational study revealed earlier
onset of the Asian monsoon in recent decades. The earlier arrival of the Asian monsoon appears to beconnected to the enhanced atmospheric warming over land trend in May (mainly through sensible heat), as

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this warming trend provides the key necessary environmental conditions for forming the Asian monsoonal
circulation [Kajikawa et al., 2012].
Given the tight coupling between sensible heat ﬂux and evapotranspiration over land, imposed by thesurface energy balance, it follows that low-frequency variability in evapotranspiration arising from possible
changes in physical or biological factors can directly contribute to the ﬂuctuation in sensible heat ﬂux.
Sensible heat ﬂux changes amplify the land-ocean heating contrast, thereby altering the atmospheric
convergence responsible for the formation of the Asia monsoon in this region. It is precisely these ﬁndingsthat motivated this investigation into what controls low-frequency variability in evapotranspiration. At
these low frequencies, biological and physical processes can interact to reshape the functional traits ofecosystems, particularly with regard to water and energy balances.
At the local spatial scale, ﬂuctuations in precipitation and other climate variables alter evapotranspiration
through multiple physical and biological pathways. For example, leaf phenology including timing of theleaf-on/off cycle is an important factor that determines seasonal and interannual variations ofevapotranspiration, as evidenced from ﬁeld studies of a tropical monsoonal deciduous forest in northern
Thailand [Yoshifuji et al., 2006; Tanaka et al., 2009; Yoshifuji et al., 2014]. To explore how these pathways
separately and jointly impact variability in evapotranspiration, a 7 year record of half-hourly
evapotranspiration measured by the eddy covariance method is analyzed, complemented by aperturbation analysis conducted on the Penman-Monteith equation [e.g., Wilson and Baldocchi, 2000;
Stoy et al., 2006]. The primary objective is to isolate the low-frequency contributions of physical andbiological factors controlling evapotranspiration in a tropical deciduous forest subjected to the Asianmonsoon climate. It is envisaged that such an analysis can “pinpoint” some of the slowly evolving
physical and biological variables that impact evapotranspiration on long time scales thereby assistingclimate models in projecting shifts in the Asian monsoon due to present and possible future
evapotranspiration variations.

2. Materials and Methods
2.1. Site Description
The experiments were carried out in an even-aged teak (Tectona grandis Linn. f.) plantation located in
the upper part of the Chao Phraya river basin, in Mae Mo district, Lampang Province, northern Thailand(18°25′N, 99°43′E, 380 m above sea level). Teak plantations have been widely established throughout thetropics, especially in Indochina and India, due to their high market value—a trend that is likely to continue
in the foreseeable future [Kollert and Cherubini, 2012]. A case in point is Thailand, where signiﬁcantdecreases in forest area were documented until the 1980s. Forest rehabilitation and plantation have since
been vigorously promoted, resulting in the establishment of large-scale teak plantations by the Forest
Industry Organization, one of which is used here as a case study.
The Mae Mo teak plantation was established in 1968. Tree density around the study site was 440 trees ha1 in2005 and, due to tree mortality, dropped to 433 trees ha1 in 2012. The mean stem diameter at breast height(DBH) was 22.4 cm (standard deviation (SD) = 7.3 cm, n = 308) in November 2005. This DBH increased to 24.2 cm(SD = 8.3 cm, n = 303) by February 2012. The mean tree height was 19.9 m (SD = 3.5 m, n = 308) in November
2005 and 23.8 m (SD = 5.4 m, n = 303) in February 2012. Various herbaceous plants and shrubs occupy theunderstory throughout the year, but their density is low. The study site is situated on ﬂat land where thestand structure is almost homogeneous. The soil is Loamy Paleustults (Thai classiﬁcation) or Ultisol (U.S.
Department of Agriculture, soil taxonomic classiﬁcation) with 35.5% sand, 43.5% silt, and 21.0% clay, with a
total porosity of 35–40%. Most ﬁne and coarse roots were detected in the top 0.4 m below the soil surface[Kume et al., 2013].
The climate is governed by the Asian monsoon and is characterized by two distinct seasons: a wet season(approximately May–October) and a dry (approximately November–April) season (Figure 1). This region
occasionally suffers from severe ﬂood events. For example, in 2011, the Chao Phraya river basin
experienced annual precipitation of 1439 mm, which was 143% more than the long-term average rainyseason precipitation (1982–2002) [Komori et al., 2012]. In fact, a record-level annual precipitation of1760 mm was measured in 2011 at this site. The mean annual temperature in the period of 2001 to 2010

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Figure 1. Year-long climatic data of (a) daily precipitation (Pr), (b) relative extractable soil water in the 0–60 cm soil layer (Θ),(c) daily total solar radiation (Rs), (d) daily mean air temperature (Ta), (e) daily mean vapor pressure deﬁcit (D), (f) leaf areaindex (LAI), and (g) midday-averaged above-canopy water vapor ﬂux under dry canopy condition (E). Shaded columns
represent periods of rainy season. Mean yearly cycles of (h) Pr, (i) Θ, (j) Rs, (k) Ta, (l) D, (m) LAI, and (n) E. For reference, one
standard deviation of the binned variables is also shown (gray zone).

was 25.4°C, with a minimum mean monthly value of 21–22°C (in December–January) and a maximum mean
monthly value of 29–30°C (in April).
2.2. Micrometeorological Measurements
A 40 m tall scaffold tower was constructed in the study area for conducting micrometeorological and eddy
covariance ﬂux measurements. Incident solar radiation above the canopy (Rs; W m2) and downward longwave radiation were measured with a pyranometer (CMP21, Kipp & Zonen, Delft, Netherlands) and apyrgeometer (Model PIR, Epply, Newport, RI and CG4, Kipp & Zonen before and after February 2007),
respectively, at a height of 41 m above the forest ﬂoor. Upward short-wave and long-wave radiations were
measured using a pyranometer (CMP21) and a pyrgeometer (Model PIR), respectively, installed upside down ata height of 35 m above the forest ﬂoor, respectively. The sensors were mounted away from the tower using a
3 m horizontal boom. The boom was ﬁxed on its south corner so as to minimize the effects of the tower.
These four radiation components were used to calculate net radiation (Rn; W m2). Air temperature (Ta; °C) andair relative humidity above the canopy were measured at 39 m above the forest ﬂoor with an air temperatureand humidity sensor (HMP45A and HMP155, Vaisala, Helsinki, Finland before and after June 2012, respectively).
Solar radiation (Sb; W m2) was also measured near the forest ﬂoor at a height of ~ 0.5 m, some 30 m from thetower, using a pyranometer (MS401, EKO, Tokyo, Japan and CMP21 before and after August 2008,
respectively). Wind speed was measured using a cup anemometer (AC750, Makino Instruments, Tokyo, Japan),which was installed on the tower at 39 m above the forest ﬂoor. All sensors were sampled every 5 s, and their
10 min averages were recorded by data loggers (CR10X, CR23X, and CR1000, Campbell Scientiﬁc, Logan, UT).

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Rainfall (Pr; mm) was measured with a tipping bucket rain gauge (No. 34T, Ohta Keiki, Tokyo, Japan)
connected to a data logger (Hobo Event, Onset Computer, Bourne, MA) in an open site about 500 m fromthe main meteorological tower.
Volumetric soil water content (θ; m3 m3) was measured about 40 m away from the meteorological tower at
depths of 10, 20, 40, and 60 cm using time domain reﬂectometers (CS-615, Campbell Scientiﬁc) below the soil
surface. Soil heat ﬂux (G; W m2) was also measured adjacent to the θ measurement array at a depth of 3 cm
using three soil heat ﬂux plates (MF-180, EKO, Tokyo, Japan), respectively. The data were recorded with a datalogger (CR10X) at 10 min intervals. The weighted average θ in the 0–60 cm soil layer was calculated as θ0–60 =(10θ10 + 20θ20 + 20θ40 + 10θ60)/60, where θ10, θ20, θ40, and θ60 are θ at depths of 10, 20, 40, and 60 cm,
respectively. The relative extractable water in the soil (Θ) was calculated using θ0–60 as Θ = (θ0–60  θr)/(θs  θr) [cf. Kumagai et al., 2005: Yoshifuji et al., 2006], where θs and θr are the saturated water content andthe residual water content, respectively, averaged in the 0–60 cm layer. The values of θs and θr were
determined as the maximum and minimum measured θ within the 0–60 cm soil column during the entire
experiment period (January 2006 to March 2013).
2.3. Eddy Covariance Measurements
The sensible (H; W m2) and latent (LE; W m2) heat ﬂuxes were measured using the eddy covariance technique. A
three-dimensional sonic anemometer (USA-1, METEK, Elmshorn, Germany) and an open-path CO2/H2O infrared
gas analyzer (LI-7500, Li-Cor, Lincoln, NE) were installed at 28.0 m above the forest ﬂoor. Wind speeds and gas
concentration time series were all sampled and stored at 10 Hz (CR5000 data logger, Campbell Scientiﬁc, andCR1000). Calibrations were routinely performed on the gas analyzer every 3–4 months using LI620 (Li-Cor), zerogas, and CO2 span gas (Thai Industrial Gases Public Company Limited, Bangkok, Thailand). Two large data gaps
(March 2007 to January 2008 and April–November 2009) occurred in the 7 year observation campaign due toequipment failures following lightning strikes and electric power supply problems.
Postprocessing of the data series was conducted as follows, Spikes, outliers, possible sensor/logger
malfunctions, and data discontinuities in the raw 10 Hz data were ﬁrst detected and removed [e.g.,
Moncrieff et al., 2005]. A double rotation method was then applied to the sonic anemometer velocities[McMillen, 1988; Kaimal and Finnigan, 1994]. Then, sensible and latent heat ﬂux were computed using the
30 min averaged covariances of the rotated 10 Hz vertical velocity, air temperature, and water vapor
concentration time series, respectively. Corrections for density effects for water vapor were also appliedprior to computing covariances with the vertical velocity [Webb et al., 1980]. The inﬂuence of the minimum
fetch, which was about 500 m, on the estimated scalar ﬂuxes was examined using a 2-D analytic solution
to the diffusion equation proposed by Schuepp et al. [1990]. The 80% cumulative footprint value [Schuepp
et al., 1990] decreased by 218 ± 98 m from 228 ± 92 m (mean ± SD) with increasing vegetation high during
the study period (from February–August 2006 to February–August 2012). However, in both periods, themain footprints (source area) of the ﬂux measurement were determined to be within the site border. The
in-stationarity test [Foken and Wichura, 1996] was employed, and if there was a difference of more than
60% between the 30 min averaged covariance and an average of six 5 min averaged covariances within
the 30 min run, the ﬂux calculated in the 30 min run was rejected. To minimize the effects of tower
distortions and other anomalies, sensible and latent heat ﬂux values measured during rain events andwhen the friction velocity was < 0.20 m s1 [see Saleska et al., 2003; Miller et al., 2004] were discarded, as
well as those runs where the mean wind direction originated from a sector bounded by 270–360° (whenthe wind ﬂow was distorted by the tower).
Using the postprocessed eddy covariance ﬂuxes with such ﬁlters, the energy balance was checked. It was
found that H + LE = 0.69 (Rn  G) reasonably represented (R2 = 0.70) the half-hourly energy balance closure.
Then, a high-frequency spectral correction was applied using standard surface layer transfer functions[Moncrieff et al., 1997]. As a result of applying this procedure, the lack of an energy balance closure was
improved from 69% to 72%.
We did not ﬁnd a signiﬁcant hysteresis relation between Rn  G and H + LE (data not shown) without any
adjustment by storage terms. This implies that the contribution of the storage ﬂux to the available energy
and the scalar ﬂuxes might be small [Leuning et al., 2012]. Storage ﬂuxes are not expected to be signiﬁcantin a canopy with low stem density, moderate leaf area index, and moderate canopy height conditions [seeOhkubo and Kosugi, 2008], especially when the friction velocity exceeds 0.2 m s1.

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Here the median of imbalance ratio ((H + LE)/(Rn  G)) in the morning (from 8:00 A.M. to 10:30 A.M., weakly
unstable condition) and midafternoon period (from 1:30 P.M. to 4:00 P.M., unstable condition) were 0.68
and 0.70, respectively, in the dry season (January–April). The difference in measured Bowen ratio between
the morning and the midafternoon period was also small (0.96 in the morning and 0.95 in the
midafternoon, respectively). On the other hand, the medians of the imbalance ratio in the morning andmidafternoon period were 0.74 and 0.68, respectively, in the wet season (June–September). The median of
the Bowen ratio in the morning and midafternoon period were large (0.40 and 0.24, respectively). These
results suggest that entrainment processes connected to boundary layer height and its temporal dynamics(leading to entrainment ﬂux differences during these two periods) are not the main culprit for the lack ofenergy balance closure as earlier suggested [Lee et al., 2004]. Therefore, we did not employ the Bowen
ratio correction for the ﬂuxes. However, further analyses will be needed to resolve all the reasons for theimbalance and the underestimation of the sensible and latent heat ﬂuxes measured by eddy covariance,
the degree of closure is well within the range reported for other forest sites. For example, mesoscale
circulation, which is driven in part by landscape level (approximately 20 km × 20 km) heterogeneity, might
affect the energy balance closure [Foken, 2008; Stoy et al., 2013].
2.4. Leaf Area Index Estimate
According to Beer’s law, the silhouette area index of leaves plus stems and branches (plant area index, PAI;m2 m2) can be expressed as [Holst et al., 2004]
1
PAI ¼ LAI þ b ¼  lnðRt Þ;k(1)where LAI is leaf area index (m2 m2), b is an empirical constant representing the ﬁxed contribution of stems
and branches to radiation extinction, k is the extinction coefﬁcient, and Rt is the radiative transmittancedeﬁned as Sb/Rs, where Sb and Rs are separately measured as described earlier every 30 min. Once theparameters b and k are determined, the temporal variation in leaf area index can be produced by equation(1) with continuous monitoring of radiative transmittance [see Maass et al., 1995; Holst et al., 2004; Wanget al., 2004]. The parameters b = 1.03 and k = 0.52 were determined by regressing periodic data of leaf areaindex estimated from litterfall collection and the Rt observation [Yoshifuji et al., 2014]. The value of k is
known to vary with zenith angle and cloudiness [Baldocchi et al., 1984; Holst et al., 2004]. To reduce dayby-day variation in the cloudiness or direct sunlight condition, we selected radiative transmittance values
measured during the period where the zenith angle was > 75° and used the 15 day moving average of thedaily radiative transmittance as an input into equation (1) [Yoshifuji et al., 2014]. On the other hand, theperiodic leaf area index data were calculated for dates in which litterfall measurements were made, during
the period from the day of the maximum radiative transmittance in the dry season to the day of theminimum radiative transmittance in the previous rainy season. Since the studied teak canopy wascompletely dormant (i.e., LAI = 0) on the day of the maximum radiative transmittance, the leaf area index
for each day in which litterfall measurements were taken was calculated by multiplying the total dry
weight of the litter lost from the teak canopy during the period between this day and the day of the
maximum radiative transmittance with the speciﬁc leaf area. Here the speciﬁc leaf area was estimated at
87.6 cm2 g1 using measurements of 40 sample leaves obtained at the site. Using a plant canopy analyzer(LAI-2000, Li-Cor), periodic checking was conducted to determine whether the leaf area index estimatesfrom equation (1) with b = 1.03 and k = 0.52 were reasonable. These checks suggest no large discrepancies
between the two leaf area index estimates throughout the studied period [Yoshifuji et al., 2014].
2.5. Models for Estimating Evapotranspiration
Daily evapotranspiration (ET; mm d1) was expressed as
ET ¼ E þ E i¼ E tc þ E ts þ E i ;(2)where Etc is canopy transpiration (mm d1), Ets is vegetation ﬂoor evaporation (mm d1), and Ei is canopy
interception (mm d1). Evapotranspiration was calculated with a big-leaf model [e.g., Kelliher et al., 1995].
We note that canopy transpiration and vegetation ﬂoor evaporation were calculated using the PenmanMonteith equation [e.g., Monteith and Unsworth, 1990] and a modiﬁed Priestley and Taylor’s equation[Priestley and Taylor, 1972] with half-hourly forcing data and aggregated to daily.

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Table 1. List of Abbreviations
Abbreviation
A
Ac
As
cA
Cp
D
d
dm
E
Ei
ET
Etc
Ets
G
Ga
Gc
gs
gsref
H
k1
k2
LAI
LE
m
Pr
Qmax
Rn
Rs
Rt
Sb
Ta
U
z0m
z0v
α
αPT
γ
Δ
ε
θ
Θ
θ0–60
κ
λ
ρ

Units

Deﬁnition

2

Daily total available energy
Daily total absorbed energy by the canopy
Daily total absorbed available energy by the vegetation ﬂoor
Extinction coefﬁcient determined
Speciﬁc heat of air at a constant pressure
Vapor pressure deﬁcit
Zero plane displacement
Model parameter (equation (7))
Daily above-canopy water vapor ﬂux under dry canopy conditions
Canopy interception
Total ecosystem evapotranspiration
Canopy transpiration
Vegetation ﬂoor evaporation
Soil heat ﬂux
Aerodynamic conductance
Canopy conductance
Bulk stomatal conductance
Model parameter (equation (6))
Sensible heat ﬂuxes
Model parameter (equation (6))
Model parameter (equation (6))
Leaf area index
Latent heat ﬂuxe
Model parameter (equation (6))
RainfallModel parameter (equation (6))
Net radiation
Incident solar radiation above the canopy
Radiative transmittanceSolar radiation near the forest ﬂoor
Air temperatureWind speed
Momentum roughness length
Roughness lengthInterception loss coefﬁcientPriestley-Taylor coefﬁcient
Psychometric constantRatio of charge of saturation water vapor pressure with temperature
Unit adjustment constant
Volumetric soil water content
Relative extractable water in the soil
Weighted average θ in the 0–60 cm soil layer
von Karman constantLatent heat of water vaporization
Density of dry air

Wm
2
Wm
2
Wm
1 1
J kg K
Pa
m
1
mm d
1
mm d
1
mm d
1
mm d
1
mm d
2
Wm
1
ms
1
ms
1
ms
1
ms
2
Wm
2
Wm
1
ln(Θ)
2 2
m m
2
Wm
1
ln(D)
1
mm d
2
Wm
2
Wm
2
Wm
2
Wm
°C
1
ms
m
m
1
Pa K
1
Pa K
2
1
1
mm m s kg 30 min
3 3
m m
3 3
m m
1
J kg
3
kg m

Canopy transpiration was calculated as follows:
E tc ¼εΔAc þ ρC p DGa;
λ Δ þ γ½1 þ Ga =ðgs LAIÞ(3)

where Δ is the ratio of charge of saturation water vapor pressure with temperature (Pa K1), ρ is the meandensity of dry air (kg m3), Cp is the speciﬁc heat of dry air at constant pressure (J kg1 K1), D is the vapor
pressure deﬁcit above the canopy (Pa), ε is the unit adjustment constant (1800 mm m2 s kg1 30 min1), γ
is the psychometric constant (66.5 Pa K1), and λ is the latent heat of water vaporization (2.5 × 106 J kg1).
The Ac is the absorbed energy by the canopy (W m2) given as
Ac ¼ ½1  expðcA LAIÞA;(4)

where cA is an extinction coefﬁcient determined to be 0.7 from net radiation measurements above and below
the canopy [Igarashi et al., 2015], and A is the available energy (W m2). Ga is the aerodynamic conductance
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10.1002/2014JG002767(m s1) in the absence of any thermal stratiﬁcation (a plausible assumption given the small sensible heat ﬂux,
the large friction velocity, and the small measurement heights above the canopy) as
Ga ¼

κ2 U
;
ln½ðz  dÞ=z0m ln½ðz  dÞ=z0v (5)where κ is the von Karman constant (= 0.41), U is the wind speed (m s1) at height z (= 39 m), where humidity
was also measured in this study, d is the zero plane displacement (= 20.7 m), z0m is the momentum roughness
length (= 1.93 m) [Igarashi et al., 2015]. Here z0v is the roughness length (m) governing the transfer of vapor
assumed to be the same as heat and 1/10 of z0m. The gs is the stomatal conductance (m s1), expressed byseries of multiplicative functions [e.g., Jarvis, 1976]:Rs ðQmax þ k 1 Þ
gs ¼ gsref
ð1  k 2 lnΘÞð1  mlnDÞ;(6)
Qmax ðRs þ k 1 Þ
where Rs (W m2), Θ, and D denote values of Rs, Θ, and D, respectively, and gsref (m s1), Qmax (W m2), k1(W m2), k2 (ln(Θ)1), and m (ln(D)1) are ﬁtting parameters determined so as to minimize the root-meansquare error (RMSE) between predicted and actual stomatal conductance. The derivation of the actual
stomatal conductance is reviewed in Appendix A.
A modiﬁed Priestley and Taylor’s equation [Priestley and Taylor, 1972] was used to compute vegetation ﬂoor
evaporation, given by
E ts ¼ αPT

ε Δ
As ð1 þ dm lnΘÞ;
λΔ þ γ(7)in which
As ¼ A  Ac ;(8)
2where As is the available energy absorbed by the vegetation ﬂoor (W m ). Priestley-Taylor coefﬁcient αPT and
dm are the parameter estimated using nonlinear least squares regression between vegetation ﬂoor
evaporation calculated from equation (7), and measured latent heat ﬂux, during the leaﬂess (i.e., LAI = 0)
conditions, where measured latent heat ﬂux can be assumed to be vegetation ﬂoor evaporation. αPT anddm were validated as 0.76 and 0.23, respectively. It should be noted that forest ﬂoor evaporation contains
not only soil evaporation but also transpiration from understory vegetation. It is assumed that herbaceous
plants and shrubs on the forest ﬂoor are active even in the dry season because of their green leaves.
From the throughfall and stemﬂow measurements at the study site, the canopy interception was determinedfrom daily rainfall (Pr; mm d1) as
E i ¼ αPr ;(9)where α is an interception loss coefﬁcient. It should be noted that throughfall ratios, which ranged from 0.89
to 0.93, showed no clear seasonality, despite different canopy phenophases (from leaﬂess to leaf-on) [Tanaka
et al., 2014]. Here α is estimated to be 0.075 (N. Tanaka et al., unpublished data, 2006). List of abbreviations
data are available, see Table 1.

3. Results
3.1. Variability in Physiological and Biological Drivers of Measured Latent Heat Flux
Daily measurements of environmental drivers, as well as leaf area index and measured latent heat ﬂux during
the study period (January 2006 to February 2013) are shown in Figures 1a–1g. The mean annual rainfall andthe SD during 2006–2012 were 1326 ± 268 mm. There was a clear seasonal variation in rainfall, rainy, and dry
seasons. The delineation of these seasons was determined from a 5 day mean rainfall criteria [Matsumoto,1997], which is commonly used in Asian monsoon region. The relative extractable water exceeded 0.5 in
the wet seasons and dropped to about 0.1 in the dry seasons. The annual average of daily cumulative
incident solar radiation and their SD were 17.6 ± 4.2 MJ m2 d1, but clear seasonal variations were also
noted. A decrease in incident solar radiation caused by increased cloud cover in the wet season wascompensated for by the maximal solar elevation. Hence, the mean incident solar radiation values in the
wet and dry seasons were similar. However, the maximum incident solar radiation occurred in the wet season.

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The maximum daytime mean air temperature surpassed 30°C in the late dry
seasons, and the minimum air temperature was around 20°C in the early dry seasons (November–December). The valueof vapor pressure deﬁcit showed adistinct seasonal variation with the highest daily mean vapor pressure deﬁcit(around 3.0–4.0 kPa) occurring in the latedry season (January–March) because of
the high air temperature and the lowest
vapor pressure deﬁcit values (≤1.0 kPa)
predictably occurring throughout thewet seasons. Seasonal variation in leaf
area index and the canopy duration(leaf-on) can be explained by seasonalvariation in rainfall and soil moistureavailability, and the leaf area index values
Figure 2. Comparison between eddy covariance-measured (observed E)
reached a plateau early in the wet seaand big-leaf modeled daily above-canopy water vapor ﬂux for dry
sons. It should be noted that the timing
canopy conditions (modeled E). The 95% upper and lower predictionof leaf-out and leaf-fall, i.e., the canopy
interval (dashed lines) and the 1:1 line (solid line) are also shown.
duration, differed considerably between
study years. Measured latent heat ﬂux,
which is averaged in midday (10:00–15:00), also showed clear seasonal variations. The increased measured
latent heat ﬂux and the timing of leaf-out corresponded to the start of the rainy season, but measured latent
heat ﬂux and leaf area index were not perfectly in phase, because of seasonal variation in physiological andbiological factors.
Interannual ﬂuctuation in environmental factors, leaf area index, and measured latent heat ﬂux are shownin Figures 1h–1n. Daily rainfall showed clear seasonality and reached almost zero in the dry season. The
value of the SD of rainfall increased as rainfall increased and was large in the rainy season. Some rainfall
events (premonsoon rainfall) during the late dry season also contributed to the rainfall SD in the dry
season. These rainfall events in the rainy season and premonsoon events were important in explaining
the interannual ﬂuctuations of other variables. The SD in the late dry season was larger than that in theearly dry season. This means that the beginning of the rainy season was quite variable, and the end time
was less so at this site. Relative extractable water also showed clear seasonality, reﬂecting theseasonality in rainfall. The clear seasonality in incident solar radiation was also evident; the incident solar
radiation peaked just before the rainy season due to the absence of clouds. The ﬂuctuations in incident
solar radiation were large in the rainy season and late dry season (January–March) due to the occurrenceof rainfall. The air temperature also showed seasonal trends, high in the late dry season (January–March)and low in December–January. However, seasonal and interannual variations in air temperature were
smaller than those in other variables. The SD of vapor pressure deﬁcit was large in the dry-rainy season
transition period due to the on-off nature of rainfall. The SD of the leaf area index was at maximum
during the period from day of year (DOY) 140–150 (from end of May to beginning of June). The SD ofdaily measured latent heat ﬂux increased with increasing daily measured latent heat ﬂux in the dry-rainyseason transition period but remained high in the rainy season. In contrast, some ﬂuctuations in daily
measured latent heat ﬂux were measured in the late dry season (February–March) due to the occurrenceof premonsoon rainfall.
3.2. Estimating Canopy Transpiration and Vegetation Floor Evaporation
Figure 2 compares half-hourly measured latent heat ﬂux from the eddy covariance method withmodeled E (sum of canopy transpiration and vegetation ﬂoor evaporation). The parameters inequation (6) were determined as gsref = 10.8 × 103 (m s1), Qmax = 660 (W m2), k1 = 450 (W m2),
k2 = 0.29 (ln(Θ)1), and m = 0.81 (ln(D)1), and the model reproduced the measurements well. It should
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Figure 3. Seasonal and interannual variations in (a) daily rainfall (Pr),(b) leaf area index (LAI), and (c) estimated canopy interception (Ei), canopy
transpiration (Etc), and vegetation ﬂoor evapotranspiration (Ets).

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be noted that these empirical parameters(equation (6)) showed insigniﬁcantdifferences between years and were
not affected by biases associated withspeciﬁc periods [Igarashi et al., 2015].
Hence, regardless of some uncertainty
introduced by measurement errors,
the big-leaf model is sufﬁciently
reliable to link physical and biologicaldrivers of evapotranspiration for the
purposes of this work (at least at thesespace and time scales). The meanannual rainfall and evapotranspiration,with their SD, during March 2006 to
February 2013 were 1335 ± 256 mm
and 977 ± 108 mm, respectively.

Daily rainfall, leaf area index, and evapotranspiration components computed using the big-leaf model are
shown in Figure 3. Because the leaf area index dropped to near zero during the period from DOY 60–70(beginning of March), increasing of canopy transpiration started in concurrence with rainfall in the rainyseason. Vegetation ﬂoor evaporation decreased with increasing leaf area index as expected. Hence, leaf
area index (a biological factor here) played a signiﬁcant role in partitioning evapotranspiration into canopy
transpiration and vegetation ﬂoor evaporation. The timing for increases in canopy transpiration anddecreases in vegetation ﬂoor evaporation in the leaf-out season corresponded to the start of the rainyseason, though did not perfectly correspond to the initial increase in leaf area index because of seasonaland interannual variations in physical (e.g., rainfall, vapor pressure deﬁcit, and aerodynamic conductance)and biological (e.g., stomatal conductance, and relative extractable water) factors.

4. Discussion
To address the study objectives, the discussion is presented in three sections. The ﬁrst reports the annual
evapotranspiration and the categorization of the record of each of the 7 years record based on the annual
rainfall and length of rainy season. The second presents the outcome of a multivariate higher-order
perturbation analysis separating the physical from biological drivers. The third considers physical andbiological factors affecting year-to-year differences in evapotranspiration for each of the categories,
thereby completing the study objectives.
4.1. Annual Evapotranspiration andYearly Classiﬁcation
The relation between annual rainfall and evapotranspiration is shown in Figure 4. When annual rainfall
was < 1400 mm, evapotranspiration increased withannual rainfall. On the other hand, when annual
rainfall > 1400 mm, evapotranspiration did notexhibit any signiﬁcant dependence on rainfall. This
result compares favorably with earlier metaanalytic
reviews that suggested that evapotranspiration
depends on rainfall when rainfall < 1000 mm (dueto water supply limitations) and plateaus or scatterswhen rainfall > 2000 mm [e.g., Zhang et al., 2001;
Komatsu et al., 2012].
Figure 4. Relation between annual rainfall (Pr) and annual
evapotranspiration (ET) during the study period 2006–2012.
2
The 1:1 and the regression (R = 0.83) lines are also shown.
The observation year are also listed along with the reference
year (2012) (see Figure 5).

IGARASHI ET AL.

Hence, one measure of “cycling” of water is given by
the mean evapotranspiration/rainfall (ET/Pr), which
was 0.73 for the entire study duration. This ratio
was reported to vary from 0.3 to 0.9 across some

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Figure 5. Relation between rainy season length and annual rainfall(Pr) for each year. The observation year is also shown. The observation years were categorized into three rainfall regimes, i.e., “Largeannual rainfall & Long rainy season,” “Small annual rainfall & Long
rainy season,” and “Small annual rainfall & Short rainy season.” Notethat 2012 is the reference year, and vertical and horizontal dashedlines represent mean rainy season length and mean Pr during thestudy duration, respectively.

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138 tropical forests that included seasonal
forests [Kume et al., 2011]. The value of
ET/Pr was also reported as 0.54 in an
evergreen forest in northern Thailand[Tanaka et al., 2003], 0.67 at lowland dry
evergreen forest in central Cambodia[Nobuhiro et al., 2009], and 0.66–0.71 at a
Eucalyptus plantation in southeast China[Lane et al., 2004]. The ET/Pr value at thissite was toward the upper end of the range
compared to other tropical forests—
suggesting that here a large part of theincident rainfall was consumed byevapotranspiration. Unsurprisingly, expanding
teak plantations might play a prominent rolein moisture supply to the atmosphere within
the mainland of the Indochina Peninsula
thereby impacting the Asian monsoon. As
shown earlier [Igarashi et al., 2015], the effectof interseasonal and interannual variations in
A available energy and Rs incident solar radiation to evapotranspiration was minor.

Based on the annual rainfall and rainy season length, each year was classiﬁed into one of three classes withthe year 2012 as a baseline reference (Figure 5). This base reference was selected because 2012 closely
approximates the long-term average annual rainfall and rainy season length. In fact, the annual
evapotranspiration in 2012 (984 mm) was close to the mean evapotranspiration during the 7 year study
period. Compared to 2012, years 2006 and 2011 were characterized by a “Large annual rainfall & Long
rainy season,” years 2008 and 2009 “Small annual rainfall & Long rainy season,” and years 2007 and 2010
“Small annual rainfall & Short rainy season” (hereinafter referred to as Large & Long, Small & Short, andSmall & Long, respectively). As noted earlier, a major ﬂood event occurred in 2011 resulting in the highest
rainfall on record.
4.2. Perturbation Analysis
The big-leaf model, which compared favorably with the eddy covariance-measured evapotranspiration at
daily time scales, is now used to analytically link evapotranspiration variations to variations in physical andbiological factors. Because of the analytical tractability of this model, it is ideal for conducting perturbation
analyses intended to isolate the relative contributions of physical and biological factors that give rise toseasonal and annual changes in evapotranspiration. Because of the multiple variables involved, a brief
review of perturbation analysis with thesimplest case of a two-variable function isprovided for illustration.
Consider a dependent variable u (e.g., representing evapotranspiration) and two independent variables x1 and x2 (e.g., representing
light and vapor pressure deﬁcit) linked to u by
a function f (e.g., represented by the big-leaf
model) so thatu ¼ f ðx 1 ; x 2 Þ:

IGARASHI ET AL.

(10)

Figure 6. Averaging time versus root-mean-square error (RMSE)
between observation and total change in evapotranspiration(dET). Closed and open circles represent computations from theﬁrst-order and the ﬁrst-order with second-order Taylor series
expansions, respectively. Vertical lines denote 30 days.

Total changes in u (= du) can be determinedfrom the total derivative of equation (10),which can be minimally represented by a ﬁrstorder Taylor series expansion given as

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du ¼

10.1002/2014JG002767∂u∂u
dx 1 þ
dx 2 :
∂x 1
∂x 2

(11)

Here the ∂u/∂xi (i = 1, 2) deﬁnes the overall sensitivity of the dependent variable u on each of the two
independent variables and dxi deﬁnes the changes (or perturbations) in the independent variables.
The total derivative of equation (10) can be generalized using higher-order Taylor series expansions. For
example, in the case of a second-order Taylor series expansion, du is given as




∂u
∂u
1 ∂2 u
∂ 2u
∂ 2u
2
2
du ¼
dx 1 þ
dx 2 þ
dx
þ
dx
þ
2
dx
dx
(12)
1
2
1
2 :
∂x 1
∂x 2
2! ∂x 1 2
∂x 2 2∂x 1 ∂x 2|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}|ﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ{zﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄﬄ}
First Order

Higher Order and Mixed Term

Here (∂2u/∂xi2)dxi2 and (∂2u/∂x1∂x2)dx1dx2 are higher-order and mixed terms, respectively. The mixed terms
signify “joint effects”—measuring how perturbations in x1 and x2 jointly (or in a multiplicative manner)
impact du above and beyond the linear sum characterizing the ﬁrst-order term. Higher-order and mixed
terms are commonly neglected when small perturbation analysis is assumed [e.g., Wilson and Baldocchi,
2000; Stoy et al., 2006]. However, there is no a priori reason to neglect them as they may be signiﬁcant(especially when extreme events such as the 2011 ﬂood exist in the record). Given the large seasonal andyear-to-year variability expected here (see Figures 1h–1n), higher-order and mixed terms are likely not tobe so small.
Averaging times also determine the size of these increments (i.e., dx1, dx2, dx12, dx22, and dx1dx2). Longer
averaging times (e.g., yearly and half yearly) may yield smaller increments when compared to shorter
averaging times (e.g., hourly). Deﬁning these increments at time scales where the function f accurately
links u to x1 and x2 is also imperative.
Using equations (2), (3), (7), and (9), the changes in evapotranspiration (= dET) can be expressed via a ﬁrstorder linear Taylor series expansion asdET ¼ dE tc þ dE ts þ dE i
∂E tc
∂E tc
∂E tc
∂E tc
∂E tc
¼
dGa þ
dg
dA þ
dD þ
dLAI þ
∂A
∂D
∂Ga
∂LAI
∂gs s
∂E ts
∂E ts
∂E ts
∂E i
þ
dPr :
dA þ
dLAI þ
dΘ þ
∂A
∂LAI
∂Θ
∂Pr

(13)

Here rainfall, available energy, vapor pressure deﬁcit, and aerodynamic conductance were deﬁned asphysical factors, while stomatal conductance, leaf area index, and relative extractable water were deﬁned
as biological factors, respectively. We note that perturbation analysis applied for a daily summed
evapotranspiration data and averaged forcing for daytime. While relative extractable water is generally aphysical factor, largely derived by precipitation characterizing the volume of water per unit volume of soil
available for evapotranspiration, it is categorized here as a biological factor. Because of the bimodality of
the relative extractable water (equation (7)) in wet and dry seasons, it may be considered to be abiological factor because it dictates the plant hydraulic capacity to uptake water on the time scales ofinterest (daily to seasonal). This categorization is based on the premise that during the dry season,
precipitation is small (equation (7)). The dET also can be expressed using the second-order Taylor series
expansion (equation (S11) in Text S1 in the supporting information).
To determine the optimal averaging times needed for calculating dET, both ﬁrst- and second-order
expansions were applied to the big-leaf model and compared with measured dET for several averaging
times (Figure 6). Root-mean-square error (RMSE) of dET between 2012 and other years was then calculated
for each averaging time and for both expansions. The higher-order expansion suggested minor reductions
in RMSE for averaging times > 30 days (<10 mm yr1). However, the RMSE in the ﬁrst-order analysis
remained large even at the annual time scale (= 39 mm yr1). These ﬁndings suggest that a second-order
expansion is necessary when explaining how biological and physical drivers explain long-term variability inevapotranspiration (>1 month). These results indicate that (i) the performance of the big-leaf model in
explaining evapotranspiration from physical and biological factors and (ii) the increments needed in theperturbation analysis depend on the averaging time with monthly time scales (or longer) are necessary.

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Unsurprisingly, truncating the dET
expansion at second order does not
perfectly agree with the measured dET
partly due to the fact that third- andhigher-order terms are ignored andpartly due to the fact that the big-leaf
model equation does not perfectly
describe measured evapotranspiration.
Nonetheless, what is important is the
improvement gained when expandingdET and retaining second-order terms(including the joint or interactive terms).
4.3. Physical Controls
on Evapotranspiration
Second-order perturbation analysis
revealed the relative importance ofeach variable on interannual dET on
the monthly time scale. In general, thesigniﬁcance of each variable for dET
were in the order vapor pressure deﬁcit rainfall > vapor pressure deﬁcit & aerodynamic conductance > available energy
Figure 7. Contribution of (a) physical, (b) biological, and (c) mixed physical (for vegetation ﬂoor evaporation) > availand biological drivers to total change in evapotranspiration (dET), categorized
able energy, aerodynamic conductance,
into three rainfall regimes, i.e., “Large annual rainfall & Long rainy season,”available energy & aerodynamic conduc“Small annual rainfall & Long rainy season,” and “Small annual rainfall & Shorttance (Figure 7a) (mix term effects arerainy season.” Variables in legend can be seen in text.
represented by “&”). Of cause, a small
rainfall event causes a decrease in evapotranspiration by saturating the leaf surface. At the same time, small rainfall events are associated with higher
vapor pressure deﬁcit when compared to intense rainfall periods. As a result, vapor pressure deﬁcit physicallydrives evapotranspiration. This impact becomes drastic on the Small & Short condition (Figure 7a), resulting inhigher positive contribution of physical effect to dET in 2007, 2009, and 2010 (Figure 8b). Although year 2008is classiﬁed as Small & Long, changes in vapor
pressure deﬁcit were not so large to increase
evapotranspiration; thus, small changes inrainfall could not alter evapotranspiration as
well (Figure 7a). With the other small but negative effects, the physical effect on dET in 2008
was overall negative (Figure 8b).

Figure 8. (a) Total change in evapotranspiration (dET) and (b) contribution of total physical, biological, and mixed physical and biological effects to dET, categorized into three rainfall regimes, i.e., “Largeannual rainfall & Long rainy season,” “Small annual rainfall & Long
rainy season,” and “Small annual rainfall & Short rainy season.”

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Large annual rainfall, i.e., in 2006 and 2011,provided a positive effect on evapotranspiration via the canopy interception process(Figure 7a). Here it should be noted that rainfall and vapor pressure deﬁcit are not always
coupled. For example, for the same Large &Long condition, vapor pressure deﬁcits in2006 and 2011 were high and low, respectively (see Figure 7a). These induced positive
and negative physical effects on dET in 2006
and 2011, respectively (Figure 8b).
As Wilson and Baldocchi [2000] reported intemperate deciduous forest, the atmospheric

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evaporative demand was the primary external driver on evapotranspiration rather than rainfall and/or
available energy. Other forests are shown not to be as sensitive to atmospheric water vapor demand alone[e.g., Kumagai et al., 2005; Stoy et al., 2006].
4.4. Biological Controls on Evapotranspiration
The dET was, in general, inﬂuenced by biological factors in the order of LAI > stomatal conductance > leaf
area index (for vegetation ﬂoor evaporation), relative extractable water (for vegetation ﬂoor
evaporation) > leaf area index & stomatal conductance, leaf area index & relative extractable water (forvegetation ﬂoor evaporation) (Figure 7b). A large amount of rainfall induced positive effect on LAI andincreased evapotranspiration (Figure 7b). The leaf area index effect was signiﬁcant during the Large &Long conditions, but insigniﬁcant in 2008 in the Small & Long condition.
Interestingly, the stomatal conductance effect decreased evapotranspiration throughout the study periodwith the exception of 2011 (Figure 7b). This suggested that low humidity and soil moisture deﬁcits in thestudied forest lower evapotranspiration. In the case of 2011, the Large & Long condition reduced theatmospheric demand and supplied sufﬁcient ecosystem water resources, resulting in an increase instomatal conductance (equation (6) and Figure 7b). Note that also, in 2011, both stomatal conductanceand LAI effects on dET were positive and thus raised the importance of the biological effect on
evapotranspiration (Figure 8b).
Small amounts of rainfall, especially during the Small &Short conditions, represented insufﬁcient ecosystemwater resources and reduced gs (Figure 7b). This caused negative biological effects on evapotranspiration
during such Small & Short conditions (Figure 8b).
The effects of leaf area index (for vegetation ﬂoor evaporation) and relative extractable water (for vegetation
ﬂoor evaporation) on evapotranspiration were not small (Figure 7b). During the Large & Long condition, their
negative effects compensated for the positive effect of leaf area index on dET, and thus, the biological effectin 2006 was almost zero (Figure 8b) though individually the changes in leaf area index and relative
extractable water.
Fluctuations in rainfall and other climatic variables alter evapotranspiration through multiple physical andbiological pathways. For example, leaf phenology including timing of the leaf-on/off cycle is an importantfactor that determines the seasonal and interannual variations in evapotranspiration, as evidenced by ﬁeld
studies of a tropical monsoonal deciduous forest in northern Thailand [Yoshifuji et al., 2006; Tanaka et al.,2009; Yoshifuji et al., 2014]. The interannual variations in the growing season length spanned several
months, even in the 40+ year old forest that did not experience any major disturbance during the study
period [Yoshifuji et al., 2006]. As a reference, these variations are considerably larger than interannual
variations previously reported in temperate deciduous forests [e.g., Black et al., 2000; Wilson and Baldocchi,
2000; Barr et al., 2004; Hirata et al., 2007].
Yoshifuji et al. [2014] reported that the canopy is nearly closed (i.e., LAI = ~ 3 m2 m2) during the wet season;
therefore, the effect of dLAI to dET can be small during this period due to leaf-shading effects [see Baldocchi
et al., 1984; Kelliher et al., 1995]. The teak plantation has much larger interannual variations in the leafy periodthat signiﬁcantly impact the length of the growing season [Yoshifuji et al., 2006] when compared totemperate and boreal deciduous forests [Black et al., 2000; Wilson and Baldocchi, 2000; Granier et al., 2002;
Barr et al., 2004], due to the large interannual variation in rainfall. Therefore, the contribution of theinterannual variability of leaf-out and leaf-off timing explains much of the interannual variability of dLAI. In
this context, estimation of leaf-out, lead-off timing and the length of growing season is needed forcomputing evapotranspiration in large-scale global climate and vegetation dynamics models [see Satoet al., 2007], especially for the Asian monsoon climate.
4.5. Effects of the Mixed Term on Evapotranspiration
The contributions of the mixed terms to dET with each of the physical and biological factors are shown in
Figure 7c. Each of the mixed term did not signiﬁcantly impact evapotranspiration when compared to its
physical or biological counterparts in the second-order expansion. However, since almost all the mixed
term effects are negative, their sum total on evapotranspiration for each year, especially in 2011, 2006,
2007, and 2010, was not so small (Figure 7c).

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4.6. Summary: Contribution of Physical and Biological Factors and Their Mix Term Controls on dET
It is evident that during the Large & Long and Small & Short conditions, the total derivatives of equation (13),dET (i.e., annual change in evapotranspiration), were determined to be positive and negative, respectively,
while during the Small & Long period, no clear sign direction was evident (Figure 8a). Here the negativedET during the Small & Short conditions was mainly due to large negative contributions to dET from
biological and mixed physical and biological effects. However, there was no dominant physical or
biological factor to explain the positive dET during the Large & Long conditions. Also, there were no
dominant factors for dET during the Small & Long periods.
Given the well-known logarithmic decrease in stomatal conductance with increasing vapor pressure deﬁcit[e.g., Oren et al., 1999] and the decrease in stomatal conductance with soil moisture in this studied forest[Igarashi et al., 2015]. It can be surmised that the negative, high stomatal conductance effect in 2007 and2010 during the Small & Short conditions was caused by a decrease in stomatal conductance with asimultaneous increase in vapor pressure deﬁcit and decrease in soil moisture. This is due to smallinterseasonal and interannual variations in incident solar radiation and stomatal conductance being
correlated with vapor pressure deﬁcit and soil moisture at the site [Igarashi et al., 2015]. The small
contribution of dA to dET (Figure 7) also means that incident solar radiation changes do not appreciably
impact stomatal conductance at these time scale. The other conditions for the remaining years (in 2006
and 2009) can also be explained by the same two pathways (Figures 7b and 8b), where vapor pressuredeﬁcit impacted both physical and biological drivers of evapotranspiration.
Thus, changes in vapor pressure deﬁcit cause opposing changes in physical and biological drivers: the increase invapor pressure deﬁcit increases evapotranspiration directly as a physical driver and reduces evapotranspiration
indirectly via a decrease in stomatal conductance as a biological driver, and vice versa. Cancelation was actuallydemonstrated in the analysis here between physical (i.e., the vapor pressure deﬁcit effect) and biological(i.e., the stomatal conductance effect) effects on ET in 2007 and 2010 during the Small & Short conditions.

5. Conclusion
The low-frequency physical and biological controls on evapotranspiration were examined in a teak plantationin northern Thailand subjected to Asia monsoon rainfall. A 7 year record of half-hourly eddy latent heat ﬂux
was complemented by a perturbation analysis conducted on the Penman-Monteith equation that “pinpointed” the key physical and biological controls impacting long-term variability in measured
evapotranspiration. The novelty of the analysis is the use of higher-order Taylor series expansions thatretain higher-order and joint effects among physical and biological drivers of evapotranspiration. The
analyzed big-leaf model suggested that higher-order terms were necessary for matching measured andanalyzed variation in evapotranspiration, when physical and biological factors exhibit large seasonal andinterannual variability. Fluctuations in atmospheric demand (through vapor pressure deﬁcit) were shown
to be the primary external physical driver of evapotranspiration perturbations, more signiﬁcant thanavailable energy or rainfall perturbations. Leaf area index and stomatal conductance were the main
biological drivers of canopy transpiration though evapotranspiration, though relative extractable water
played an important role in vegetation ﬂoor evaporation. Our analysis also suggested how water limitation
explains some of the controls on evapotranspiration through the vapor pressure deﬁcit, leaf area index,
and stomatal conductance in this ecosystem where evapotranspiration and rainfall are in near balance.
Moving forward into the future, the consensus from global climate models thus far is that this region will
experience longer rainy seasons and increased annual rainfall [e.g., Ueda et al., 2006; Kajikawa et al., 2012;
Kitoh et al., 2013]. Thus, the dET and its machinery (physical drivers) are likely to be shifting toward theLarge & Long conditions observed here (see Figures 7 and 8), which are quite different from the current
norms described by 2012. How this evapotranspiration shift might impact or “feed back” to the formationof the Asian monsoon is an interesting question for the future.

Appendix A: Estimation of Stomatal ConductanceThe canopy conductance (Gc; m s1), representing the efﬁciency of the latent heat ﬂux from the canopy trees(LEc; W m2), was calculated using the actual data of latent heat ﬂux above the canopy (LE; W m2) and the
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inverted Penman-Monteith equation [Dolman et al., 1991] implemented in the simple two-layer big-leaf
model [e.g., Kelliher et al., 1995] with some modiﬁcations:
 


Δ Ac
ρC p D1
Gc ¼
 1  1 Ga 1 þ
;
(A1)
γ LEc
LEc γ
in which
Ac ¼ ½1  expðcA LAIÞA;(A2)

where Δ is the ratio of charge of saturation water vapor pressure with temperature (Pa K1), γ is thepsychometric constant (Pa K1), ρ is the density of dry air (kg m3), Cp is the speciﬁc heat of air at aconstant pressure (J kg1 K1), and D is the vapor pressure deﬁcit above the canopy (Pa). Ga is the
aerodynamic conductance (m s1) and was estimated as u*2/U, where u* is the friction velocity (m s1) and
U is the wind speed (m s1) above the canopy. A is the available energy (W m2), Ac is the A absorbed by
the canopy (W m2), and cA is an extinction coefﬁcient determined to be 0.7 from the net radiation
observations above and below the canopy [Igarashi et al., 2015].
Latent heat ﬂux from the canopy trees can be expressed as a contribution from the canopy to the total
ecosystem latent heat ﬂux:
LEc ¼ LE  LEs ;(A3)

Δ
As ð1 þ d m lnΘÞ;
Δþγ

(A4)

in which
LEs ¼ αPTAs ¼ A  Ac ;
2

(A5)
2

where LEs and As are latent heat ﬂux (W m ) and available energy (W m ), respectively, at the vegetation
ﬂoor. αPT and dm are Priestley-Taylor coefﬁcient and an empirical parameter representing the effect of thesoil moisture deﬁcit on the latent heat ﬂux vegetation ﬂoor, respectively. The parameter αPT and dm wasestimated using nonlinear least squares regression between latent heat ﬂux vegetation ﬂoor calculated
from equation (A4) and the latent heat ﬂux above the canopy observed under the leaﬂess (i.e., LAI = 0)
conditions, where latent heat ﬂux above the canopy can be assumed to be equal to latent heat ﬂux
vegetation ﬂoor αPT and dm were validated as 0.76 and 0.23, respectively.
We assumed that vapor pressure deﬁcit is well mixed within the canopy and can represent the driving forceof evapotranspiration from the entire canopy and that canopy conductance can be regarded as the parallel
sum of the individual leaves’ stomatal conductances [e.g., Raupach and Finnigan, 1988; Raupach, 1995],
resulting in
Acknowledgments
The data for this paper are available
upon request to the corresponding
author: igarashi@hyarc.nagoya-u.ac.jp.
This work was supported by the
Ministry of Education, Culture, Sports,
Science and Technology in Japanthrough a project titled “Program forrisk information on climate change” andgrants-in-aid for Scientiﬁc Research(23405028, 24405031, and 23780161).
We thank Suwan Pakrat, Srinuan Tunjai,and the staff of Kasetsart University andUniversity of Tokyo for their assistancewith ﬁeldwork. We also acknowledge
the assistance of Rei Yamada andMayumi Yoshino. Katul dedicates thiswork in memory of his colleague andfriend, Antonio Martinez-Cob, who has
contributed numerous manuscripts on
the use of the Penman-Monteithequation in irrigation science and therole that stomata play inevapotranspiration models.

IGARASHI ET AL.

gs ¼

Gc
;
LAI

(A6)

where gs is bulk stomatal conductance per unit leaf area (m s1, bulk stomatal conductance: see Pataki andOren [2003]).
Note that for the quality control, we removed the gs values obtained on rainy days and when D ≤ 0.6 kPa[cf., Ewers and Oren, 2000] and LAI < 1.0 [cf., Kelliher et al., 1995] from the parameter determination to
avoid near singularities and obvious uncertainties in small D measurements.

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