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WATER RESOURCES RESEARCH VOL 44 W03412 doi1010292006WR005526 2008

Energy balance and canopy conductance of a tropical semi-deciduous forest of the southern Amazon Basin

George L Vourlitis1 Jose de Souza Nogueira2 Francisco de Almeida Lobo3

Kerrie M Sendall1 Se rgio Roberto de Paulo2 Carlos Alberto Antunes Dias2

Osvaldo Borges Pinto Jr2 and Nara Luısa Reis de Andrade2

Received 13 September 2006 revised 8 October 2007 accepted 11 December 2007 published 12 March 2008

[1] Deforestation and climate change have the capacity to alter rainfall regimes water availability and surface-atmosphere flux of water and energy of tropical forests especially in ecotonal semi-deciduous tropical forests of the southern Amazon Basin which have experienced rapid regional warming and deforestation over the last three decades To reduce uncertainty regarding current and future energy and water flux micrometeorological measurements of latent (Qe) and sensible heat flux (Qh) and canopy conductance (Gc) were combined with measurements of sap flux density (Fd) and maximum leaf conductance (gsmax) to characterize the seasonal controls on mass (H2O) and energy exchange of an ecotonal semi-deciduous forest in northern Mato Grosso Brazil over the 2005-2006 annual cycle Average diel patterns and daily rates of energy flux and conductance declined during the dry season however the decline in Fd and Qe

was smaller andor more gradual than Gc and gsmax Weekly averages of transpiration calculated from sap flow measurements during the dry-wet season transition period were positively correlated (r2 = 047 p lt 005 n = 11) with estimates of leaf area index (LAI) derived from the Modis-Aqua satellite platform while estimates of evapotranspiration ET derived from eddy covariance were not presumably because these estimates also include an evaporation component Overall our results suggest that access to deep water reserves can support high rates of Fd and Qe during the dry season but because of high evaporative demand declines in plant water potential lead to a corresponding decline in Gc Furthermore seasonal variations in LAI that are likely to be controlled in part by plant water status and phenology constrain tree and stand transpiration Thus the consistency of Qe over the annual cycle appears to be the result of trade-offs between water availability (rainfall soil moisture water potential) canopy structural properties (LAI) and meteorological conditions including vapor pressure deficit and net radiation

Citation Vourlitis G L J de Souza Nogueira F de Almeida Lobo K M Sendall S R de Paulo C A Antunes Dias

O B Pinto Jr and N L R de Andrade (2008) Energy balance and canopy conductance of a tropical semi-deciduous forest

of the southern Amazon Basin Water Resour Res 44 W03412 doi1010292006WR005526

1 Introduction [2] Tropical forests and woodlands (savanna) exchange

large amounts of water and energy with the atmosphere and are important in controlling regional and global climate [Nobre et al 1991 Grace 1992 Meir and Grace 2005] However substantial variation in rainfall and dry season duration across the Amazon Basin leads to concomitant variation in the magnitude and seasonality of energy exshychange [Shuttleworth et al 1984a Roberts et al 1993 Grace et al 1995 Miranda et al 1997 Vourlitis et al

1Biological Sciences Department California State University San Marcos California USA

2Departamento de Fısica Universidade Federal de Mato Grosso Cuiaba Mato Grosso Brazil

3Departamento de Solos e Engenharia Rural Universidade Federal de Mato Grosso Cuiaba Mato Grosso Brazil

2002 2005 Malhi et al 2002 Rocha et al 2002 Priante Filho et al 2004] For example little seasonal variation in evapotranspiration (Qe) has been observed for rain forest of the northern and central Amazon Basin owing to small variation in rainfall and seasonal trade-offs between availshyable energy and evaporative demand [Shuttleworth et al 1984b Roberts et al 1993 Grace et al 1995 Malhi et al 2002 Rocha et al 2004] In contrast tropical savanna and semi-deciduous forest of the rain forest-sava nna ecotone experience larger seasonal variation in Qe presumably beshycause of higher temporal variability in rainfall and a more pronounced dry season [Grace 1992 Miranda et al 1997 Vourlitis et al 2002 Rocha et al 2002] [3] In the Amazon Basin semi-deciduo us forest occupies

a climatic transition between the rain forest of the central Amazon Basin and the tropical savanna of eastern and southern Amazonia Ecotonal communities are hypothetishycally more sensitive to climate change than zonal commushynities due to their transitional nature [Grace 1992 Longman

Copyright 2008 by the American Geophysical Union and Jenik 1992 Arris and Eagleson 1994] In the state of 0043-1397082006WR005526$0900

W03412 1 of 14

W03412 VOURLITIS ET AL TROPICAL FOREST ENERGY BALANCE W03412

Mato Grosso Brazil this transition lies between 9ndash 14degS latitude [Ratter et al 1978 Ackerly et al 1989] which also coincides with the lsquolsquoarc of deforestationrsquorsquo where rapid deforestation has taken place over the last three decades [Skole and Tucker 1993 Moran et al 1994 Nepstad et al 1999] Given the current lsquolsquobusiness as usualrsquorsquo land manageshyment scenario these forests are expected decline by 70 ndash 80 by the year 2050 [Soares-Filho et al 2006] Furthermore semi-deciduous forests of the SE Amazon Basin have experienced larger increases in temperature over the last 30 years than other regions within the Amazon Basin however trends in rainfall have been equivocal suggesting little change in the rainfall regime [Malhi and Wright 2005] The rapid deforestation [Skole and Tucker 1993 Nepstad et al 1999 2004] and climate change [Giorgi et al 2001 Malhi and Wright 2005] have the capacity to destabilize regional rainfall regimes surface water availability and surface-atmo sphere flux of water and energy [Nobre et al 1991 Wright et al 1992 Hodnett et al 1995 Culf et al 1996 Manzi and Planton 1996 Cramer et al 2005 Laurance 2005] highlighting the urgent need to understand energy balance and water cycling dynamics of these semi-deciduous ecotonal forests [4] Aside from modeling [Grace 1992] and short-term

field studies [Vourlitis et al 2002 2005 Priante Filho et al 2004] the surface-atmosphere exchange of water and energy of ecotonal forests has been poorly described To reduce uncertainty regarding tropical semi-deciduous forest energy and water flux measure ments of mass (H2O vapor) and energy exchange were combined with measure ments of sap flux density and maximum leaf conductance to characterize the seasonal patterns of and controls on Qe and Qh over the 2005-06 annual cycle

2 Methods 21 Site Description

[5] The study was conducted 50 km NE of Sinop Mato Grosso Brazil (11deg24750S 55deg19500W) in a 25 ndash 28 m tall intact mature terra firme tropical semi-deciduous forest 423 m above sea level Tree species at our study site are typical of semi-deciduous Amazonian forest [Ackerly et al 1989 Lorenzi 2000 2002] and include Protium sagotianum Marchland Dialium guianense (Aubl) Sandwith Hevea brasiliensis Mu ll Arg Brosimum lactescens (S Moore) CC Berg Cordia alliodora (Ruiz amp Pav) Oken Tovomita schomburgkii Planch amp Triana and Qualea paraensis Ducke There are approximately 80 species and 35 families of trees with a diameter 10 cm however nearly 50 of all individuals are in the families Burseraceae (P sagotianum) Clusiaceae (T schomburgkii) and Moraceae (B lactescens) Leaf area index (LAI) estimated from measureme nts of the extinction of photosynthetic photon flux density by the forest canopy [Goudriaan 1988] reaches a maximum of 50 m2m2 during the wet season (February) and a minimum of 25 m2m2 during the dry season (July) [Vourlitis et al 2004] The soil is a quartzarenic neosol characterized by a sandy texture ( 90 sand) which has high porosity and drains rapidly following rainfall events (ie within 4-7 days) [6] The 30-year mean annual temperature in the Sinop

area is 24degC with little seasonal variation and rainfall is

approximately 2000 mma with a 4 ndash 5 month dry season between May ndash September The seasonal climatology for the ecotonal semi-deciduo us forest is similar to rain forest and savan na howeve r the semi-decidu ous forest typica lly receives about 200 mm less rainfall per year than rain forest in northern Mato Grosso and eastern Rondonia and 500 mm more rainfall than savanna near Brasilia [Vourlitis et al 2002] Average air temperature is similar for semi-deciduo us and rain forest however savanna is typically 2 ndash 3degC cooler than the semi-deciduous forest

22 Micrometeorological Measurements

[7] Latent (Qe) and sensible heat flux (Qh) were quantishyfied using tower -based eddy covariance betwe en July 2005 ndash 2006 This micrometeorolo gical technique directly quantifies the surface-atmosphere exchange of mass and energy by measuring the turbulent transport of H2O vapor and heat [Baldocchi et al 1988] Eddy covariance sensors were mounted at a height of 42 m above ground level or 12ndash 14 m above the forest canopy Wind direction was typically out of the SSW and SE and analysis of the fetch or the upwind distance sampled by the eddy covariance system [Schuepp et al 1990] indicated that more than 90 of the flux originated within 1 km upwind of the tower [Vourlitis et al 2004] [8] The eddy covariance system utilized 3-dimensional

sonic anemometer-thermometer (CSAT-3 Campbell Scienshytific Inc Logan UT USA) and an open-path infrared gas analyzer (LI-7500 LI-COR Inc Lincoln NE USA) to measure the mean and fluctuating quantities of wind speed and temperature and H2O vapor respectively Both sensors sampled and outputted data at 10 Hz and were physically oriented into the direction of the mean wind at the upwind side of the tower to minimize the potential for flow distortion Raw (10 Hz) data and 30-min average fluxes of latent (Qe) and sensible heat flux (Qh) obtained from the eddy covariance array were stored and processed using a solid-state data logger (CR5000 Campbell Scientific Inc Logan UT USA) Average fluxes of Qe and Qh were obtained by calculating the covariance between the fluctuashytions in vertical wind speed and H2O vapor density and temperature respectively over a 30-min interval following a coordinate rotation of the wind vectors [McMillen 1988] Water vapor flux was corrected for the simultaneous flucshytuations in heat [Webb et al 1980] [9] Net radiation (Q) was measured above the canopy

(40 m above ground level) using a net radiometer (NR-LITE Kipp amp Zonen Bohemia NY USA) Soil heat flux (Qg) was measure d using heat flux transducers (n = 2) buried approxshyimately 2 cm into the surface litter layer (HFT-31 REBS Inc Seattle WA USA) Air temperature and atmospheric water vapor density were measured at the top of the tower (42 m above ground level) using a sonic anemometer and open-path gas analyzer respectively (described earlier) and the atmospheric vapor pressure deficit (D) was calculated as the difference between the saturation and actual vapor pressures derived from temperature and humidity measureshyments Micrometeorological data were averaged every 30 min from observations made every 60 s and stored using a solid-state data logger (CR5000 Campbell Scientific Inc Logan UT USA)

2 of 14


23 Soil Moisture and Precipitation

[10] Volumetric soil water content (VSWC) was measured at 5 25 and 75 cm below the soil surface (n = 1 probe per depth) using time domain reflectometer (TDR) probes (CSshy616 Campbell Scientific Inc Logan UT USA) The output of the probes (in milliseconds) was regressed against volushymetric soil moisture measure d from soil samples collected in the upper 5 cm soil layer to derive a site-specific calibration Fixed-volume soil samples were obtained from 20 points on a monthly basis using a metal cylinder Fresh samples were weighed dried at 100degC and re-weighed to determine the mass of water in the field soil Gravimetric soil moisture (g H2Og dry soil) was converted to volumetric soil moisture following Dingman [1994] and linear regression was used to relate the measured VSWC to the period output of the TDR probe (P) at 5 cm for the same time period that the soil samples were collected (VSWC = 112P ndash 085 r 2 = 080 n = 10 sample dates) The same calibration equation was used to calculate VSWC for the 25 and 75 cm depths which is appropriate given that the upper 1 m of soil is composed almost entirely of sand TDR readings were averaged over 30-min intervals from observations made every 60 s and stored using a solid-state data logger (CR5000 Campbell Scientific Inc Logan UT USA) [11] Water table depth was measured in 3 polyvinyl

chloride (PVC) water wells installed within approximately 100 m of the eddy flux tower Water wells were ca 5 cm in diameter and installed to a depth of 5 m Measurements were made periodically (7 times) over the study period using an electronic water level meter [12] Precipitation was measured every 30 min at the top

of the eddy flux tower using a tipping-bucket rainfall gauge (TE-525 Texas Electronics Inc Dallas TX USA) Howshyever gaps in data collection precluded use of the rainfall data measure d on site and data obtained from a manual rain gauge that was read daily at the Fazenda Continental located 5 km E of the study site was used instead These data were highly correlated to data collected on-site and linear reshygression between existing rainfall data derived from the study site (independent variable) and Fazenda Continental (dependent variable) yielded a mean (plusmn95 CI) slope of 098 plusmn 018 and a y-intercept that was not significantly different from zero (r2 = 091 n = 7 months)

24 Sap Flux Density Measurements

[13] Sap flux density of understorey (Tovomita schomshyburgkii n = 4) and canopy trees (Brosimum lactescens and Qualea paraensis n = 1 each) was measured between August and November 2005 using thermal dissipation probes (TDP-50 Dynamax Inc Huston TX USA) Each probe consisted of a pair of 50 mm long needles that were affixed with thermocouple junctions one of which also contained a resistance heating element that provided a continuous source of heat The heated element was inshystalled 40 mm above the unheated element and sap flux density was measured as a function of the temperature difference between the heated and unheated elements using a dimensionless sap flux index (K) = (dTM-dT)dT where dTM is the maximum temperature difference (observed at night) and dT is the instantaneous temperature difference between the heated and reference elements [Granier 1985]

-2 -1Sap flux density per unit sapwood area (Fd g m s ) was

1231calculated using an empirical function where Fd = 119K[Granier 1987] [14] Probes were installed approximately 15 m above

ground level One probe per tree was installed in small understorey trees (diameter 10ndash 25 cm) while two probes per tree were installed in larger (diameter gt 40 cm) canopy trees to minimize the potential for radial variations in sap flux density [Dynamax 1997] Probes were secured to each tree using polystyrene hemispheres and modeling clay to seal the probes from rainfall-induced stemflow and reflecshytive bubble-wrap covered the entire probe assembly to minimize external thermal gradients [Dynamax 1997] Data were averaged over 30-min intervals from observations made every 10 s and stored using a solid-state data logger (CR10X Campbell Scientific Inc Logan UT USA)

25 Stomatal Conductance Measurements

[15] Maximum stomatal conductance (gsmax) was meashysured using a portable photosynthesis system (model LIshy6400 LI-COR Lincoln NE USA) during the dry (July 2005) and wet (January 2006) seasons One B lactescens and three T schomburgkii individuals were sampled because these trees were accessible either from the forest floor or from the tower at varying heights Measurements were taken throughout the canopy at heights of 28 20 12 10 and 1 m above ground level and approximately 12 measure ments were made at each height during both the dry and wet seasons In order to account for day-to-day variation in rates of photosynthesis measurem ents began at different heights throughout the canopy each day Leaves were exposed to 400 mmolmol CO2 2000 mmol

-2 -1quanta m s 28degC and 40 and 70 relative humidity (n = 6 per species humidity canopy height and season) which is consistent the ambient humidity that leaves experience during the dry and wet seasons respectively [Vourlitis et al 2004] Maximum stomatal conductance was calculated using the photosynthesis system software [16] Xylem water potential (Y) was measured using a

pressure chamber (model 670 PMS Instrument Company Albany OR USA) in conjunction with measureme nts of gsmax Leaves used for gas exchange measurements (n = 102 in the dry season n = 83 in the wet season) were detached and placed into the pressure chamber with the petiole protruding through a seal Nitrogen gas was then pumped into the chamber to exert pressure on the leaf The amount of pressure necessa ry to force the water column back to the cut surface of the petiole was proportional to Y [Scholander et al 1965]

26 Statistical Analysis and Derived Quantities [17] Average daytime (0800-1600 h) bulk canopy conshy

ductance (Gc) was estimated by inversion of the Penman-Monteith equation [Monteith 1981 Harris et al 2004]

-1DQ thorn rCpDGa D Gc frac14 Ga - - 1 eth1THORN

gQe g

where Ga is the aerodynamic conductance (ms described below) D is the slope of the saturation vapor pressure vs temperature curve (kPaK) r is the density of dry air (gm3)

-1 -1Cp is the specific heat capacity (J g K ) D is the atmospheric vapor pressure deficit (kPa) g is the psychroshy

-1metric constant (kPaK) and Q = Q - Qg (J m-2 s )

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Figure 1 (a) The sum of the instantaneous (30-min average) sensible and latent energy flux density (Qh + Qe y axis) as a function of the difference in instantaneous net radiation and ground heat flux density (Q - Qg x axis) (b) Average daily sum of the sensible and latent energy flux density (Qh + Qe y axis) as a function of the difference between the average daily net radiation and ground heat flux density (Q - Qg x axis) Also shown are the 11 (dottedshyline) and the best fit lines (solid line) slope and coefficient of determination (r 2) calculated using linear regression

] -1Aerodynamic conductance (Ga) was calculated as [u(u)2

and corrected for atmospheric stability [Malhi et al 2002] where u = wind speed measured from the tri-axial sonic anemomete r and u = frictional velocity calculated from eddy covariance measurements of momentum flux [Baldocchi et al 1991] The average daytime (0600-1800 h) lsquolsquode-coupling factor rsquorsquo (W) [Jarvis and McNaughton 1986] was calculated as

-1 g rcW frac14 1thorn eth2THORND thorn g ra

where rc is the canopy resistance (sm) and ra is the aerodynamic resistance (sm) Values of W vary between 0ndash 1 and lower values indicate that the canopy is more coupled to the overlying atmosphere [Jarvis and McNaughton 1986] In highly coupled canopies such as tall aerodynamically rough forests Ga is substantiall y larger than Gc and variations in stomatal conductance (gs) exert a relatively large effect on Gc while in shorter aerodynamishy

cally smooth canopies Ga is similar in magnitude to Gc and variations in gs have a smaller effect on Gc and Qe [Jarvis and McNaughton 1986 Meinzer et al 1993] [18] Daily averages andor totals were summarized as

mean (plusmnsd) values calculated over weekly intervals unless specified Diel (24-h) averages of energy flux micrometeshyorology conductance and sap flux density were calculated over seasonal intervals by averaging each 30-min datum for a particular time (eg 0900 ndash 0930 h) This averaging process was conducted to provide an indication of how diel trends varied over seasonal periods [19] Variations in maximum stomatal conductance (gsmax)

were assessed as a function of season humidity and canopy height using a 3-way ANOVA while variations in water potential were assessed as a function of season and canopy height using a 2-way ANOVA Data were tested for normality and heteroscedasticity prior to analyses and response variables violating these assumptions were LN-transformed [20] Sensor andor infrastructure (ie power) failures

caused unavoidable gaps in data collection while short-term events such as driving rainfall andor poor turbulent mixing lead to the rejection of data Given these limitations flux data recovery was on the order of 70 for the measurem ent system Gaps in micrometeorologic al data were filled using a moving average technique that filled the data gap for a given 30 min period as the average of the last 7 days for the appropriate 30 min period (ie 0900 h for the previous 7 d) [Falge et al 2001a] Longer gaps (gt 1 day) were filled using linear regression where the missing data series (ie Q) was estimated from a similar variable (solar radiation or photoshysynthetically active radiation) if available If similar variables were not available gaps in time series were filled using auto-regressive integrated moving average (ARIMA) models which exploit underlying temporal autocorrela tion in time series data to forecast andor interpolate time series based on previous values [Edwards and Coull 1987] ARIMA modshyels were fit to the time series using an iterative Box-Jenkins approach where (1) autocorrelation and partial autocorrelashytion analysis were used to identify whether auto-regressiv e moving-average or mixed models were required for the given time series (2) coefficients of the model were calculated using maximum likelihoo d techniques and (3) autocorrelation plots of model residuals were interroshygated for additional structure [Hintze 2004] A model was accepted when the underlying structure of the time series was eliminated [Hintze 2004]

3 Results and Discussion 31 Eddy Covariance System Performance [21] System performance was assessed from energy

balance closure [McMillen 1988] Under perfect closure and presumably no errors in measurem ent the slope of the regression between sensible plus latent heat flux (Qh + Qe) measure d from eddy covariance versus net radiation minus ground heat flux (Q - Qg) measured from the meteoroshylogical sensors should be unity [McMillen 1988] Using instantaneous (ie 30 min average) measurements Qh + Qe

accounted for only about 74 of Q - Qg and there was a significant y-intercept (Figure 1a) Thus the eddy covariance data tended to underestimate the net energy loss at night and

4 of 14


Figure 2 (a) Total rainfall (b) mean (plusmnsd) volumetric soil water content for 5 25 and 75 cm below the soil surface and (c) mean (plusmnsd) water table depth between July 2005 ndash 2006 Data for rainfall (Figure 2a) represent weekly totals while data for volumetric water content (Figure 2b) represent daily average values that were averaged over weekly intervals

the net energy gain by the forest during the day This degree of closure is poor but comparable to other estimates of energy balance closure for temperate and tropical forest eddy covariance systems [Aubinet et al 2000 Araujo et al 2002 Malhi et al 2002] However this definition neglects instantaneous energy storage within plant biomass litter andor soil but over daily time periods the storage term can be ignored because energy stored during the day is balanced by energy loss at night [Falge et al 2001b] Thus daily totals or averages of Qh + Qe and Q - Qg rather than instantaneous values may provide a better estimate of system performance from energy balance closure Using least squares linear regression of the daily average of Q -Qg (independent variable) and daily average of Qh + Qe

(dependent variable) the mean (plusmn 95 CI) energy balance closure was 087 plusmn 002 (R2 = 074 n = 263 d Figure 1b) These data suggest that there was still appreciable underesshytimation of Q - Qg by the eddy flux system at the highest levels of Q - Qg Variation andor errors in the degree of energy balance closure may arise from random andor systematic errors caused by inadequate performance of the eddy covariance system andor scale mismatches between the eddy covariance and micrometeo rological samples [McMillen 1988]

32 Seasonal Variations in Rainfall Soil Water Content and Microclimate

[22] The rainfall distribution exhibited a strong seasonal trend with maximum rainfall observed between December 2005 and March 2006 and minimum rainfall observed in July ndash August 2005 and May ndash June 2006 (Figure 2a) While December is historically the wettest month in this region nearly 550 mm of rainfall was observed in December 2005 which is 170 mm higher than the long-term (30 year) average [Vourlitis et al 2002] In contrast rainfall in January ndash February 2006 (455 mm) was more than 200 mm lower than the long-term average especially during the first week of January indicating substantial variability in the wet season rainfall regime during the study period In contrast no measurable rainfall was recorded during the months of Mayndash August which is 45 mm lower than the combined average rainfall typically recorded during these months but consistent with the 4-month duration of the dry season in this area [Vourlitis et al 2002 2005] Overall annual rainfall was 1772 mm during the study period compared to the long-term average of 2037 mm [Vourlitis et al 2002] [23] Seasonal variation in soil moisture followed the

seasonal trend in rainfall closely (Figure 2b) Volumetric soil water content (VSWC) increased rapidly at the onset of rainfall during the September-November dry-wet season transition Regardless of depth VSWC peaked in December in response to the abundant rainfall declined in January reached a secondary peak between February and April and declined in May to values similar to that observed during the dry season Depending on depth VSWC averaged over weekly intervals reached a peak of 020ndash 025 m3m3

following the high rainfall in December 2005 but instantashyneous values for the surface 5 and 25 cm profiles often exceeded 035 m 3m3 following heavy rainfall events which is approaching saturation [Dingman 1994] VSWC reached a minimum of 006 ndash 010 m 3m3 during the dry season (Figure 2b) [24] Soil moisture also varied substantiall y as a function

of depth (Figure 2b) Soil water content was consistently higher at the 25 cm soil depth than at the 5 and 75 cm depths and this vertical pattern is qualitatively similar to that described for a tropical forest of the SE Amazon Basin and is presumably indicative of vertical variations in root density and surface evaporation [Hodnett et al 1995 Souza et al 1996] For example rapid evaporation at the litter-soil interface can cause surface drying (5 cm) while increases in root density deeper in the soil profile can cause the soil water content at depth to decline relative to shallower soil profiles [Hodnett et al 1995] [25] While soil moisture varied substantially over the

annual cycle water table depth remained relatively constant over the study period (Figure 2c) Water table depth varied between -30 m below the soil surface in May 2006 to as low as -36 m in November 2005 The minimum value observed in November is striking in light of the nearly 320 mm of rainfall observed during the previous 25 months (Figure 2a) Similar time lags between rainfall and groundshywater recharge have been observed for other Brazilian tropical forests [Hodnett et al 1995] but unfortunately gaps in the water table depth time series reported here limit the ability to quantify potential lags between rainfall and groundwater recharge in our forest

5 of 14


Figure 3 Mean (plusmnsd) average daily wind speed (a) air temperature (b) and the atmospheric vapor pressure deficit (D c) calculated over weekly intervals between July 2005 ndash 2006

[26] Weekly variations in average daily wind speed were small and in general wind speed was higher during the dry season (Figure 3a) Average daily wind speed was generally gt 15 ms during the dry season and lt15 ms during the wet season however storm events such as those observed in late-November and December caused large transient increases in wind speed The large error bars (plusmnsd) about the average weekly values indicate large day-toshyday variations in wind speed highlighting the sporadic nature of wind speed in the Brazilian Amazon [Carswell et al 2002] Weekly variations in average temperature exhibited consistent seasonal trends with the lowest average daily air temperature observed during the dry season (Figure 3b) However large weekly variations in air temperature were also observed in the wet season when cloud cover and rainfall were frequent (eg December 2005) and in the dry season (first weeks of May and June 2006) when cold air transported by fronts out of the south (friagens) can persist for several days [Grace et al 1996] Weekly trends in the atmospheric vapor pressure deficit (D) also varied over seasonal scales with the highest average D (15 ndash 17 kPa) observed during the dry season and dry-wet season transishytion periods (September-October) and the lowest (04ndash 05 kPa) observed during the wet season in February and March (Figure 3c) These seasonal trends are consistent with a variety of tropical forests of the Amazon Basin [Culf et al 1996 Rocha et al 2004]

33 Seasonal Variation in Energy Flux Density and Conductance

[27] Average diel patterns of net radiation (Q) sensible (Qh) and latent heat (Qe) flux were similar in magnitude

during the wet and dry seasons (Figures 4a and 4b) but average daytime (0800 ndash 1600 h) patterns of bulk canopy conductance (Gc) differed markedly (Figures 4c and 4d) Energy flux density increased during the morning peaked at noon local time and declined during the afternoon and in general peak midday values of Qh and Qe were slightly higher during the dry season owing to slightly higher values of midday Q (Figures 4a and 4b) However the error term (plusmnsd) associated with the mean diel average energy flux densities was substantiall y higher during the wet season which is consistent with frequent and variable cloud cover during the wet season [Shuttleworth et al 1984b Roberts et al 1993 Grace et al 1995 Malhi et al 2002 Rocha et al 2004] Daytime values of Gc were typically highest during the early morning (0800 h) and later afternoon hours (1600 h) and lowest during the midday hours (1200-1400 h) when the vapor pressure deficit (D) and temperature reached a daily maximum (Figures 4e and 4f) A midday depression in Gc has been observed in a variety of tropical forests of the Amazon Basin [Carswell et al 2002 Malhi et al 2002 Harris et al 2004] and is presumably due to stomatal closure in response to high D that develops during the warm midday period [McWilliam et al 1996 Sa et al 1996] Midday rates of Gc during the dry season (00035 ms) were 30 lower than midday values of Gc during wet season (00055 ms) in part becau se of a substantially higher midday D and lower soil moisture (Figure 2b) during the dry season [28] Average weekly values of Qh and Qe followed trends

in Q closely (Figure 5a) Q increased from the dry season into the wet season until about the second week of November (Figure 5a) when rainfall increased markedly

6 of 14


Figure 4 Mean (plusmnsd) diel (24-h) net radiation (Q) and sensible (Qh) and latent (Qe) heat flux (a and b) daytime (0800 ndash 1600 h) canopy conductance (Gc c and d) and the diel vapor pressure deficit (D) and air temperature (e and f) for the wet season (September 2005 ndash April 2006 left-hand-side panels) and dry season (July ndash August 2005 and May ndash June 2006 right-hand-side panels)

(Figure 2a) Frequent cloud cover in December caused Q to decline by gt3-fold compared to November values but after the December rainy period Q increased to on average 170 Wm2 in January and exhibited a declining trend into the dry season of 2006 Thus large week-to-week variability and the low values of Q observed for December notwithstanding average daily Q was slightly higher during the wet season in spite of the increased frequency of cloud cover which is presumably in response to the annual variation in solar angle This seasonal trend in Q is similar to that reported for a savanna near Sao Paulo [Rocha et al 2002] but in contrast to that reported for rain forest near Manaus [Malhi et al 2002] [29] Qe increased into the wet season in response to the

trend in Q declined by gt 50 in December and exhibited the highest average daily rates in April toward the end of the wet season (Figure 5a) Maximum values of Qe (120 ndash 130 Wm2) were observed during the wet season between January and April while minimum values were observed in December (30-40 Wm2) and overall this seasonal trend appeared to be driven almost completely by Q Similar trends were observed for Qh however values of Qh were typically less than half of that observed for Qe (Figure 5a) The rates of Qh and Qe reported here are comparable to those previously published for this [Vourlitis et al 2002 Priante Filho et al 2004] and other tropical forests of the

Brazilian Amazon [Shuttleworth 1988 Roberts et al 1993 Malhi et al 2002 Rocha et al 2004] [30] Qe was not significantly correlated with VSWC in

the upper 75 cm soil profiles (data not shown) and when normalize d by Q the ratio of QeQ was slightly lower during the wet season except in April when Qe composed on average 60 ndash 70 of Q (Figure 6a) The peak in Qe and QeQ observed at the end of the wet season in April (Figures 5a and 6a) appears consistently in composite data sets from the same site [Priante Filho et al 2004 Vourlitis et al 2005] and has been reported in rain forest [Malhi et al 2002] and savanna [Rocha et al 2002] systems and is presumably in response to leaf production following the wet season [Meir and Grace 2005] Seasonal variations in the amount of Q dissipated by Qh were smaller than that observed for Qe and on average Qh comprised approxishymately 20ndash 25 of Q except in November-December when Qh only accounted for 8 ndash 15 of Q (Figure 6b) The Bowen ratio (QhQe) failed to show any dramatic season trend and was on average 03 ndash 05 over the study period (Figure 6c) [31] In contrast aerodynamic (Ga) and canopy conducshy

tance (Gc) exhibited larger and more consistent seasonal trends (Figure 5b) Seasonal patterns in Ga followed seasonal trends in wind speed closely (Figure 3a) and on average Ga

was higher during the dry season with the exception of lateshy

7 of 14


Figure 5 Mean (plusmnsd) daily net radiation (Q) and sensible (Qh) and latent (Qe) heat flux (a) daytime (0800 ndash 1600 h) aerodynamic (Ga) and canopy (Gc) conductance (b) and the de-coupling factor (W c) calculated over weekly intervals between July 2005 ndash 2006

Figure 6 Mean (plusmnsd) daily ratio of latent heat flux to net radiation (QeQ a) sensible heat flux to net radiation (QhQ b) and the Bowen ratio (QhQe c) calculated over weekly intervals between July 2005 ndash 2006

8 of 14


Figure 7 Mean daily canopy conductance (Gc) as a function of soil water content at 5 25 and 75 cm below the soil surface Data are calculated over weekly intervals Also shown is the linear regression line for the relationship between Gc and soil water content at 5 (solid line) 25 (dotted line) and 75 cm (dashed line) below the soil surface and the corresponding coefficient of determination (r2) value of each regression

November and December owing to the high frequency of storms (Figure 5b) Gc was consistently lower during the dry season and reached a seasonal peak in late-November and December presumably in response to rainfall (Figure 2a) high VSWC (Figure 2b) andor low D (Figure 3c) Surface soil water content and D are negatively correlated over seasonal timescales and thus it is difficult to determine which variable is more important in controlling seasonal variations in Gc [Carswell et al 2002 Malhi et al 2002 Harris et al 2004] However sensitivity analyses suggest that seasonal variations in soil water content exert a stronger control on Gc than D [Malhi et al 2002 Harris et al 2004] Daytime rates of Gc averaged over weekly time periods were significantly positively correlated with VSWC at all depths (Figure 7) highlighting the importance of soil water availshyability in limiting Gc [32] Seasonal variations in the lsquolsquode-coupling factor rsquorsquo (W)

[Jarvis and McNaughton 1986] exhibited qualitatively simshyilar trends as Gc (Figure 5c) and were 1 ndash 2 units higher

during the wet season Average values of W ranged between 02 and 03 during the dry season to nearly 05 during the wet season (December) The relatively low values indicate that the forest was highly coupled with the atmosphere [Jarvis and McNaughton 1986] which is expected given the tall and aerodynamically rough forest canopy and the fact that Ga was substantiall y larger (ie 2 ndash 10 times) than Gc (Figure 5b) This high coupling indicates that variations in Gc were affected more by variations in stomatal conducshytance (gs) [Jarvis and McNaughton 1986 Meinzer et al 1993] especially during the dry season when W was lowest

34 Leaf and Whole-Plant Controls on Evapotranspiration and Conductance

[33] Sap flux density (Fd) data collected during the dry and the dry-wet transition seasons in 2005 indicate that temporal variations in Fd for canopy and understorey trees lagged behind temporal variations in rainfall and VSWC by 1ndash 2 months (Figure 8) For example maximum midday rates of Fd (per unit sapwood area) were 459 ndash 483 g m -2 s -1

for canopy trees during the August 2005 dry season when soil moisture was at a seasonal minimum (Figure 2b) Sap flux density declined by approximately 30 following the onset of rainfall in September but showed consistent recovery in October and November (Figure 8) when an additional 347 mm of rainfall was recorded (Figure 2a) Seasonal trends in Fd of understorey trees were similar to that observed for canopy trees but the absolute rate was nearly 4-times lower than canopy trees (Figure 8) The difference in Fd between canopy and understorey trees was presumably due to tree size [Granier et al 2000] where larger trees have a correspondingly larger sap wood area and leaf area index and canopy leaves are subjected to higher evaporative demand associated with warmer drier upper canopy micro-climate [Cabral et al 1996 OrsquoBrien et al 2004] [34] Measurements of sap flow can be used to provide

a measure ment of evapotranspiration (ET) for the whole stand by multiplying sap flux density by the distribution of sapwood volume for the stand [Granier et al 1996] This approach requires estimates of sapwood area for trees equipped with sap flow gauges and a means to estimate sapwood area for the entire stand Following Granier et al [1996] sapwood area of individual trees was estimated from cores obtained using an increment

Figure 8 Mean (plusmnsd) diel (24 h) sap flux density between 13 August and 7 November for canopy (n = 2 trees) and understorey trees (n = 4 trees)

9 of 14


Figure 9 Total daily evapotranspiration calculated from eddy covariance and sap flow measurements between 13 August and 7 November

borer to derive a relationship between sapwood area and trunk circumference at breast height (sapwood area = 41circumferenc e ndash 134 r 2 = 099 n = 9 trees) Using this relationship and the distribution of tree basal area measured from twenty-314 m2 plots randomly located near the eddy flux tower ET derived from sap flow ranged

d -1 d -1between 2 ndash 42 kg m -2 (1 kg m -2 1 mmd) in August which was similar to the values calculated from eddy covariance (Figure 9) During this time surface soil water content remained constant and since there was no recorded rainfall in August the source of the water for ET must have come from the water table Root systems as deep as 8 m are not unprecedented for tropical rain forest trees of the Amazon Basin [Nepstad et al 1994 Hodnett et al 1996] suggesting that the water table which is on the order of 3 ndash 35 m below

the soil surface in this semi-deciduo us forest (Figure 2c) provides an accessible water reserve for trees during the dry season Our data support this interpretation and over the August 2005 dry season the average daily rate of ET (301 and 307 mmd for the sap flow and eddy covariance estimates respectively) was comparable to the average daily drop in water table depth (311 mmd) [35] After rainfall ensued in September the ET values

estimated from sap flow began to diverge from those derived from eddy covariance (Figure 9) ET estimated from eddy covariance was consistently higher than that derived from

d -1sap flow and was on average 05 kg m -2 higher in mid-d -1September and as much as 2 kg m -2 higher by early

November The reason for this discrepancy is thought to be due to the fact that ET derived eddy covariance is sensitive to

Figure 10 Mean (plusmnsd) maximum rate of stomatal conductance (gsmax) for Brosimum lactescens (top panels) and Tovomita schomburgkii (bottom panels) during the wet (black bars) and dry (shaded bars) seasons measured under 70 relative humidity (left panels) and 40 relative humidity (right panels) N = 6 measurements per season canopy height humidity and species combination

10 of 14


Table 1 Three-Factor Analysis of Variance (ANOVA) Results for the Response of Tovomita Schomburgkii and Brosimum Lactescens Maximum Stomatal Conductance (gsmax) to Variations in Height

Season and Humiditya

T Schomburgkii F df p

Height 2664 384 lt0001 Season 10203 184 lt0001 Humidity 061 184 044

B Lactescens F df p


aNote that only direct effects are displayed

transpiration and evaporation from plant and soil surfaces while sap flow only characterizes stand-level transpiration [Williams et al 2004] Thus rainfall intercepted by leaves stems and soil surfaces would not be adequately quantified from scaled sap flow measurements [36] Rates of ET derived from sap flow remained

relatively constant after mid-September (Figure 9) even though substantial rainfall (289 mm) was recorded into mid-Novembe r (Figure 2a) These data suggest that stand transpiration was limited by something other than water availability as surface soil moisture increased during that period and roots apparently were deep enough to access water from the water table Leaf area index (LAI) typically reaches an annual minimum at the end of the dry season [Vourlitis et al 2004 Sanches et al 2005] and LAI lags behind the onset of rainfall by approximately 1 ndash 2 months [Poveda et al 2001 Vourlitis et al 2004] Thus low LAI may limit stand transpirati on during the dry-wet season transition even with adequate rainfall To test this hypothesis 8-day average estimates of LAI derived from the Modis-Aqua satellite platform (httpmodisgsfcnasagov) were derived during the period when both sap flow and eddy covariance measure ments were conducted to determine the correlation between ET and LAI Sap flow estimates of ET were significantly correlated with the Modis-derive d LAI (ET = 024LAI + 128 r2 = 047 p lt 005 n = 11 observations) while ET derived from eddy covariance was not presumably because the ET from eddy covariance also contained an evaporation component that may not be highly correlated with LAI These data suggest that seasonal variashytions in LAI which are in part driven by water availability plant water status and phenology [Eamus 1999 Poveda et al 2001 Meir and Grace 2005] are important in controlling rates of stand transpiration [37] At the leaf scale measurements of maximum (light-

saturated) stomatal conductance (gsmax) during the dry seashyson were significantly lower than wet season rates of gsmax

(Figure 10 and Table 1) During the dry season gsmax was -1 -1consistently less than 65 mmol m -2 s (1 mmol m -2 s =

0025 mms) at all canopy heights however during the wet season gsmax was considerably higher in leaves that were higher in the canopy and exposed to increased levels of

-2 -1irradiance reaching levels of 112ndash 181 mmol m s

(Figure 10) Upper-canopy (20 and 28 m) B lactescens and mid-canopy (12 m) T schomburgkii leaves had the largest wet season increase in gsmax which was 62ndash 71 higher than that observed during the dry season Mid-canopy (12 m) B lactescens leaves exhibited the next largest increase in gsmax (52ndash 57) while the understory leaves of T schomburgkii had the smallest increase of 18 ndash 42 Chamber humidity (ie D) did not significantly affect rates of gsmax (Table 1) during the wet or dry seasons further supporting that notion that seasonal variations in soil water availability were important for controlling rates of stomatal conductance and Gc [Malhi et al 2002 Harris et al 2004] [38] The dry season declines in gsmax were coincident

with a significant decline in xylem water potential (Y) (Figure 11 and Table 2) suggesting that the dry season declines in rainfall and soil moisture were sufficient to cause water stress in canopy and understory trees and ultimately a decline in gsmax [Eamus 1999 Meir and Grace 2005] Understory trees experienced smaller seasonal declines in Y than mid-canopy and upper-canopy trees (Figure 11) preshysumably because understory trees were exposed to lower radiation andor D than upper-canopy trees [Cabral et al 1996 OrsquoBrien et al 2004] During the dry season high transpiration can lead to a decline in Y especially under

Figure 11 Mean (plusmnsd) water potential for Brosimum lactescens (top panel) and Tovomita schomburgkii (bottom panel) during the wet (black bars) and dry (shaded bars) seasons N = 12 measurem ents per season canopy height and species combination

11 of 14


Table 2 Two-Factor Analysis of Variance (ANOVA) Results for the Response of Tovomita Schomburgkii and Brosimum Lactescens Xylem Water Potential to Variations in Height and Season


Height 217 380 010 Season 446 180 lt005 Height Season 124 380 030

B Lactescens F df p


high D which causes a decline in gs (or Gc) because the atmospheric demand for transpiration exceeds plant hydraushylic capacity [Eamus 1999 Meir and Grace 2005]

4 Conclusions [39] The data presented for the semi-deciduous tropical

forest near Sinop Mato Grosso Brazil indicate that seasonal variations in energy flux density (Qh and Qe) were relatively more stable over the annual cycle than canopy conductance (Gc) despite large seasonal variations in rainfall and soil moisture The small seasonality in Qh and Qe during the 2005 ndash 2006 study period is in contrast to that observed with a more limited (7 month) data set from 1999 ndash 2000 [Vourlitis et al 2002] but consistent with composite data sets comshypiled over 2ndash 3 years of measure ments from the same site [Priante Filho et al 2004 Vourlitis et al 2005] Seasonal variations in Gc were large and positively correlated with seasonal variations in surface (0-75 cm) volumetric soil water content (VSWC) while seasonal variations in Qe were not Given the differences in the seasonal patterns of and controls on Gc and Qe how can high rates of Fd and Qe be maintained during the dry season despite low Gc and why werenrsquot seasonal variations in Fd or Qe correlated with surface VSWC Unfortunately the ultimate answer to this question is unknown given the data described here however we feel that the following scenario is plausible First the high rates of Fd and Qe during the dry season must have been maintained be deep water reserves given the lack of available water in the soil surface (Figure 2b) Given the relatively shallow depth of the water table (Figure 2c) the trees undoubtedly had access to a stable water source during the dry season which would make Fd and Qe relatively insensishytive to seasonal variations in surface VSWC Furthermore Fd

during the dry-wet season transition (September-November) was highly correlated with LAI suggesting that canopy structural properties were more important in limiting Fd and Qe during this period than direct water limitations In turn trees with stable water reserves would likely maintain higher rates of Fd and Qe at a given Gc during the dry season because of relatively higher atmospheric demand for water vapor (Figures 3c and 4f) However the high evapshyorative demand coupled with possible limitations in hyshydraulic conductance [Meinzer et al 1993 Eamus 1999 Meir and Grace 2005] would lead to a decline in Y and gsmax (Figures 10 and 11) Given the observation that the forest was highly coupled with the atmosphere (Figure 5c)

especially during the dry season seasonal variations in Gc

would be highly correlated with seasonal variations in gsmax Thus differences in the seasonal patterns of Qe (and Fd) and Gc appear to reflect differences in the importance of water availability (rainfall soil moisture water potential) canopy structural properties (LAI) and meteorological conditions (D and Q) in limiting forest-atmosphere water vapor exchange

[40] Acknowledgments This research was supported in part by the National Science Foundation Division of Environmental Biology-Ecosysshytem Studies (DEB-0343964) and the NIH-NIGMS SCORE Program Grant (S06 GM 59833) Additional support was provided by California State University San Marcos (CSUSM) the Universidade Federal de Mato Grosso (UFMT) Conselho Nacional de Desenvolvimento Cientıfico e Tecnolo gico (CNPq) Northern Mato Grosso Forestry Trade Union (SINshyDUSMAD) the Coordenacao de Aperfeic oamento de Pessoal de Nıvel Superior (CAPES) NASA-LBA and the Brazilian Institute for Space Research (INPE)

References Ackerly D D W W Thomas C A C Ferreira and J R Pirani (1989) The forest-cerrado transition zone in southern Amazonia Results of the 1985 Projecto Flora Amazonica expedition to Mato Grosso Brittonia 41 113 ndash 128

Araujo A C et al (2002) Comparative measurements of carbon dioxide fluxes from two nearby towers in a central Amazonia rainforest The Manaus LBA site J Geophys Res 107(D20) 8090 doi101029 2001JD000676

Arris L L and P S Eagleson (1994) A water use model for locating the borealdeciduous forest ecotone in eastern North America Water Resour Res 30 1 ndash9

Aubinet M et al (2000) Estimates of the annual net carbon and water exchange of forests The EUROFLUX methodology Adv Ecol Res 30 113 ndash 175

Baldocchi D D B B Hicks and T P Meyers (1988) Measuring bioshysphere-atmosphere exchanges of biologically related gases with micro-meteorological methods Ecology 69 1331 ndash 1340

Baldocchi D D R J Luxmoore and J L Hatfield (1991) Discerning the forest from the trees An essay of scaling canopy stomatal conductance Agric For Met 54 197 ndash 226

Cabral O M R A L C McWilliam and J R Roberts (1996) In-canopy microclimate of Amazonian forest and estimates of transpiration pages 207 ndash 220 in J H C Gash C A Nobre J M Roberts and R L Victoria (Eds) Amazonian climate and deforestation J M Wiley amp Sons New York NY

Carswell F E et al (2002) Seasonality in CO2 and H2O flux at an eastern Amazonian rain forest J Geophys Res 107(D20) 8076 doi101029 2000JD000284

Cramer W A Bondeau S Schaphoff W Lucht B Smith and S Sitch (2005) Twenty-first century atmospheric change and deforestashytion Potential impacts on tropical forests pages 17ndash 30 in Y Malhi and O L Phillips (eds) Tropical Forests and Global Atmospheric Change Oxford University Press Oxford UK

Culf A D J L Esteves A de O Marques Filho and H R da Rocha (1996) Radiation temperature ad humidity over forest and pasture in Amazonia pages 175 ndash 192 in J H C Gash C A Nobre J M Roberts and R L Victoria (eds) Amazonian climate and deforestashytion J M Wiley and Sons New York NY USA

Dingman S L (1994) Physical Hydrology Prentice-Hall Inc Upper Saddle River NJ pp 575

Dynamax (1997) A Thermal Dissipation Sap Velocity Probe for Measureshyment of Sap Flow in Plants Dynamax Inc Huston TX USA pp 34

Eamus D (1999) Ecophysiological traits of deciduous and evergreen woody species in the seasonally dry tropics Trends Ecol Evol 14 11 ndash 16

Edwards D and B C Coull (1987) Autoregressive trend analysis An example using long-term ecological data Oikos 50 95 ndash 102

Falge E et al (2001a) Gap filling strategies for defensible annual sums of net ecosystem exchange Agric For Meteorol 107 43 ndash 69

Falge E et al (2001b) Gap filling strategies for long term energy flux data sets Agric For Meteorol 107 71 ndash 77

Giorgi F B Hewitson J Christensen M Hulme H von Storch P Whet-ton R Jones L Mearns and C Fu (2001) Regional climate informashy

12 of 14


tion-Evaluation and projections pages 583 ndash 638 in J T Houghton Y Ding D J Griggs M Nogour P F van der Linder X Dai K Maskell amp C A Johnson (eds) Climate Change 2001 The Scientific Basis Camshybridge University Press Inc New York NY

Goudriaan J (1988) The bare bones of leaf-angle distribution in radiation models for canopy photosynthesis and energy exchange Agric For Meteorol 43 155 ndash 169

Grace J (1992) Modelling energy flows and surface temperatures over forest and savanna pages 551 ndash 568 in P A Furley J Proctor and J A Ratter (Eds) Nature and Dynamics of Forest-Savanna Boundaries Chapman and Hall Inc New York NY USA

Grace J J Lloyd J McIntyre A Miranda P Meir H Miranda J Moncrieff J Massheder I Wright and J Gash (1995) Fluxes of carbon dioxide and water vapor over an undisturbed tropical forest in south-west Amazonia Global Change Biol 1 1ndash 12

Grace J Y Malhi J Lloyd J McIntyre A C Miranda P Meir and H S Miranda (1996) The use of eddy covariance to infer the net carbon dioxide uptake of Brazilian rain forest Global Change Biol 2 209 ndash 217

Granier A (1985) Une nouvelle methode pour la mesure du flux de seve brute dans le tronc des arbres Ann For Sci 42 81 ndash 88

Granier A (1987) Evaluation of transpiration in a Douglas fir stand by means of sap flow measurements Tree Phys 3 309 ndash 320

Granier A R Huc and S T Barigah (1996) Transpiration of natural rain forests and its dependence on climatic factors Agric For Meteorol 78 19 ndash 29

Granier A P Biron and D Lemoine (2000) Water balance transpiration and canopy conductance in two beech stands Agric For Meteorol 100 291 ndash 308

Harris P P C Huntingford P M Cox J H C Gash and Y Malhi (2004) Effect of soil moisture on canopy conductance of Amazonian rainforest Agric For Meteorol 122 215 ndash 227

Hintze J (2004) NCSS and PASS Number Cruncher Statistical Systems Kaysville UT USA wwwNCSScom

Hodnett M G L Pimentel da Silva H R da Rocha and R Cruz Senna (1995) Seasonal soil water storage changes beneath central Amazonian rainforest and pasture J Hydro 170 233 ndash 254

Hodnett M G M D Oyama J Tomasella A de and O Marques Filho (1996) Comparisons of long-term soil water storage behavior under pasture and forest in three areas of Amazonia pages 57ndash 78 in J H C Gash C A Nobre J M Roberts and R L Victoria (eds) Amazonian climate and deforestation J M Wiley amp Sons New York NY

Jarvis P G and K G McNaughton (1986) Stomatal control of transpirashytion Scaling up from leaf to region Adv Ecol Res 15 1 ndash 48

Laurance W F (2005) Forest-climate interactions in fragmented tropical landscapes pages 31 ndash 40 in Y Malhi and O L Phillips (Eds) Tropical Forests and Global Atmospheric Change Oxford University Press Oxford UK

Longman K A and J Jenik (1992) Forest-savanna boundaries General considerations pages 3 ndash 20 in P A Furley J Proctor and J A Ratter (eds) Nature and Dynamics of Forest-Savanna Boundaries Chapman and Hall Inc New York NY USA

Lorenzi H (2000) Avores Brasileiras Vol 1 Instituto Plantarum de Estudos da Flora Ltd Sao Paulo Brazil


Malhi Y and J Wright (2005) Late-twentieth-century patterns and trends in the climate of tropical forest regions pages 3 ndash 16 in Y Malhi and O L Phillips (eds) Tropical Forests and Global Atmospheric Change Oxford University Press Oxford UK

Malhi Y E Pegoraro A Nobre J Grace A Culf and R Clement (2002) Energy and water dynamics of a central Amazonian rain forest J Geoshyphys Res 107(D20) 8061 doi1010292001JD000623

Manzi O and S Planton (1996) Calibration of a GCM using ABRAshyCOS and ARME data and simulation of Amazonian deforestation Pages 505 ndash 530 in J H C Gash C A Nobre J M Roberts and R L Victoria (eds) Amazonian climate and deforestation J M Wiley amp Sons New York

McMillen R T (1988) An eddy correlation technique with extended applicability to non-simple terrain Boundary Layer Meteorol 43 231 ndash 245

McWilliam A-L C O M R Cabral B M Gomes J L Esteves and J M Roberts (1996) Forest and pasture leaf-gas exchange in southwest Amazonia pages 265 ndash 286 in J H C Gash C A Nobre J M Roberts and R L Victoria (eds) Amazon Deforestation and Climate John Wiley

amp Sons Inc New York NY USA Meinzer F C G Golstein N M Holbrook P Jackson and J Caveleir (1993) Stomatal and environmental control of transpiration in a lowland tropical forest tree Plant Cell Env 16 429 ndash 436

Meir P and J Grace (2005) The effects of drought on tropical forest ecosystems pages 75 ndash 86 in Y Malhi and O L Phillips (eds) Tropical Forests and Global Atmospheric Change Oxford University Press Oxshyford UK

Miranda A C H S Miranda J Lloyd J Grace R J Francey J A MacIntryre P Meir P Riggan R Lockwood and J Brass (1997) Fluxes of carbon water and energy over Brazilian cerrado An analysis using eddy covariance and stable isotopes Plant Cell Env 20 315 ndash 328

Monteith J (1981) Evaporation and surface temperature Q J R Meteorol Soc 107 1 ndash 27

Moran E F E Brondizio P Mausel and Y Wu (1994) Integrating Amazonian vegetation land-use and satellite data BioScience 44 329 ndash 338

Nepstad D C C R de Carvalho E A Davidson P H Jipp P A Lefebvre G H Negreiros E D da Silva T A Stone S E Trumbore and S Vieira (1994) The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures Nature 372 666 ndash 669

Nepstad D C et al (1999) Large-scale impoverishment of Amazonian forests by logging and fire Nature 398 505 ndash 508

Nepstad D C et al (2004) Amazon drought and its implications for fores t flammability and tree growth A basin-wide analysis Global Change Biol 10 704 ndash 717

Nobre C A P J Sellers and J Shulka (1991) Amazonian deforestation and regional climate change J Clim 4 957 ndash 988

OrsquoBrien J J S F Oberbauer and D B Clark (2004) Whole tree xylem sap flow responses to multiple environmental variables in a wet tropical forest Plant Cell Env 27 551 ndash 567

Poveda G A Jaramillo M M Gill N Quiceno and R I Mantilla (2001) Seasonality in ENSO-related precipitation river discharges soil moisture and vegetation index in Columbia Water Resour Res 37 2169 ndash 2178

Priante Filho N et al (2004) Comparison of the mass and energy exchange of a pasture and a mature transitio nal tropical forest of the southern Amazon Basin during a seasonal transition Global Change Biol 10 863 ndash 876

Ratter J A G P Askew R F Montgomery and D R Gifford (1978) Observations on the vegetation of northeastern Mato Grosso II Forests and soils of the Rio Suia-Missu area Proc R Soc Ser B 203 191 ndash 208

Roberts J O M R Cabral G Fisch L C B Molion C J Moore and W J Shuttleworth (1993) Transpiration from an Amazonian rainforest calculated from stomatal conductance measurements Agric For Meshyteorol 65 175 ndash 196

Rocha H R H C Freitas R Rosolem R I N Juarez R N Tannus M A Ligo O M R Cabral and M A F Silva Dias (2002) Measureshyments of CO2 exchange over a woodland savanna (Cerrado Sensu stricto) in southeast Brazil Biota Neotropica 2 1ndash 11

Rocha H R M L Goulden S D Miller M C Menton L D V O Pinto H C Freitas and A M S Figuera (2004) Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia Ecol Appl 14 S22 ndash S32

Sa T D A P C Costa and J M Roberts (1996) Forest and pasture conductances in Southern Para Amazonia pages 241 ndash 264 in J H C Gash C A Nobre J M Roberts and R L Victoria (eds) Amazon Deforestation and Climate John Wiley amp Sons Inc New York NY USA

Sanches L G Suli N Prinate-Filho G L Vourlitis and J S Nogueira (2005) I ndice de a rea foliar em floresta de transicao Amazonia Cerrado Revista Cie ncia e Natura 1 37 ndash 40

Scholander P F H T Hammel E D Bradstreet and E A Hemmingsen (1965) Sap pressure in vascular plants Science 148 339 ndash 346

Schuepp P H M Y Leclerc J I MacPherson and R L Desjardins (1990) Footprint prediction of scalar fluxes from analytical solutions of the diffusion equation Boundary Layer Meteorol 50 355 ndash 373

Shuttleworth W J (1988) Evaporation from Amazonian Rainforest Proc R Soc Ser B 233 321 ndash 346

Shuttleworth W J et al (1984a) Eddy correlation measurements of enshyergy partitioning for Amazonian forest Q J R Meteorol Soc 110 1143 ndash 1162

Shuttleworth W J et al (1984b) Observations of radiation exchange above and below Amazonian forest Q J R Meteorol Soc 110 1163 ndash 1169

13 of 14

----------------------------


Skole D L and C J Tucker (1993) Tropical deforestation and habitat fragmentation in the Amazon Satellite data from 1978 to 1988 Science 260 1905 ndash 1910

Soares-Filho B S D C Nepstad L M Curran G C Cerqueira R A Garcia C A Ramos E Voll A McDonald P Lefebvre and P Schlesinger (2006) Modelling conservation in the Amazon basin Nature 440 520 ndash 523

Souza J R S F M A Pinheiro R L C de Araujo H S Pinheiro and M G Hodnett (1996) Temperature and moisture profiles in soil beneath forest and pasture areas in eastern Amazonia pages 125 ndash 138 in J H C Gash C A Nobre J M Roberts and R L Victoria (eds) Amazonian climate and deforestation J M Wiley amp Sons New York NY

Vourlitis G L N Priante-Filho M M S Hayashi J de Sousa Nogueira F T Caseiro and J H Campelo Jr (2002) Seasonal variations in the evapotranspiration of a transitional tropical forest of Mato Grosso Brazil Water Resour Res 38(6) 1094 doi1010292000WR000122

Vourlitis G L N Priante-Filho M M S Hayashi J de Sousa Nogueira F T Caseiro F Raiter and J H Campelo Jr (2004) The role of seasonal variations in meteorology on the net CO2 exchange of a Brazishylian transitional tropical forest Ecol Appl 14 S89 ndash S100

Vourlitis G L J de S Nogueira N Priante-Filho W Hoeger F Raiter M S Biudes J C Arruda V B Capistrano J L B de Faria and F de Almeida Lobo (2005) The sensitivity of diel CO2 and H2O

vapor exchange of a tropical transitional forest to seasonal variation in meteorology and water availability Earth Interactions Volume 9 Paper 27

Webb E K G I Pearman and R Leuning (1980) Corrections of flux measurements for density effects due to heat and water vapor transfer Q J R Meteorol Soc 106 85ndash 100

Williams D G et al (2004) Evapotranspiration components determined by stable isotope sap flow and eddy covariance techniques Agric For Meteorol 125 241 ndash 258

Wright I R J H C Gash H R Da Rocha W J Shuttleworth C A Nobre G T Maitelli C A G P Zamparoni and P R A Carhaho (1992) Dry season micrometeorology of central Amazonian ranchland Q J R Meteorol Soc 118 1083 ndash 1099

C A Antunes Dias N L R de Andrade S R de Paulo J de Souza Nogueira and O B Pinto Jr Departamento de Fısica Universidade Federal de Mato Grosso Cuiaba Mato Grosso Brazil

F de Almeida Lobo Departamento de Solos e Engenharia Rural Universidade Federal de Mato Grosso Cuiaba Mato Grosso Brazil

K M Sendall and G L Vourlitis Biological Sciences Department California State University San Marcos CA 92096 USA (georgevcsusm edu)

14 of 14


Mato Grosso Brazil this transition lies between 9ndash 14degS latitude [Ratter et al 1978 Ackerly et al 1989] which also coincides with the lsquolsquoarc of deforestationrsquorsquo where rapid deforestation has taken place over the last three decades [Skole and Tucker 1993 Moran et al 1994 Nepstad et al 1999] Given the current lsquolsquobusiness as usualrsquorsquo land manageshyment scenario these forests are expected decline by 70 ndash 80 by the year 2050 [Soares-Filho et al 2006] Furthermore semi-deciduous forests of the SE Amazon Basin have experienced larger increases in temperature over the last 30 years than other regions within the Amazon Basin however trends in rainfall have been equivocal suggesting little change in the rainfall regime [Malhi and Wright 2005] The rapid deforestation [Skole and Tucker 1993 Nepstad et al 1999 2004] and climate change [Giorgi et al 2001 Malhi and Wright 2005] have the capacity to destabilize regional rainfall regimes surface water availability and surface-atmo sphere flux of water and energy [Nobre et al 1991 Wright et al 1992 Hodnett et al 1995 Culf et al 1996 Manzi and Planton 1996 Cramer et al 2005 Laurance 2005] highlighting the urgent need to understand energy balance and water cycling dynamics of these semi-deciduous ecotonal forests [4] Aside from modeling [Grace 1992] and short-term

field studies [Vourlitis et al 2002 2005 Priante Filho et al 2004] the surface-atmosphere exchange of water and energy of ecotonal forests has been poorly described To reduce uncertainty regarding tropical semi-deciduous forest energy and water flux measure ments of mass (H2O vapor) and energy exchange were combined with measure ments of sap flux density and maximum leaf conductance to characterize the seasonal patterns of and controls on Qe and Qh over the 2005-06 annual cycle

2 Methods 21 Site Description

[5] The study was conducted 50 km NE of Sinop Mato Grosso Brazil (11deg24750S 55deg19500W) in a 25 ndash 28 m tall intact mature terra firme tropical semi-deciduous forest 423 m above sea level Tree species at our study site are typical of semi-deciduous Amazonian forest [Ackerly et al 1989 Lorenzi 2000 2002] and include Protium sagotianum Marchland Dialium guianense (Aubl) Sandwith Hevea brasiliensis Mu ll Arg Brosimum lactescens (S Moore) CC Berg Cordia alliodora (Ruiz amp Pav) Oken Tovomita schomburgkii Planch amp Triana and Qualea paraensis Ducke There are approximately 80 species and 35 families of trees with a diameter 10 cm however nearly 50 of all individuals are in the families Burseraceae (P sagotianum) Clusiaceae (T schomburgkii) and Moraceae (B lactescens) Leaf area index (LAI) estimated from measureme nts of the extinction of photosynthetic photon flux density by the forest canopy [Goudriaan 1988] reaches a maximum of 50 m2m2 during the wet season (February) and a minimum of 25 m2m2 during the dry season (July) [Vourlitis et al 2004] The soil is a quartzarenic neosol characterized by a sandy texture ( 90 sand) which has high porosity and drains rapidly following rainfall events (ie within 4-7 days) [6] The 30-year mean annual temperature in the Sinop

area is 24degC with little seasonal variation and rainfall is

approximately 2000 mma with a 4 ndash 5 month dry season between May ndash September The seasonal climatology for the ecotonal semi-deciduo us forest is similar to rain forest and savan na howeve r the semi-decidu ous forest typica lly receives about 200 mm less rainfall per year than rain forest in northern Mato Grosso and eastern Rondonia and 500 mm more rainfall than savanna near Brasilia [Vourlitis et al 2002] Average air temperature is similar for semi-deciduo us and rain forest however savanna is typically 2 ndash 3degC cooler than the semi-deciduous forest

22 Micrometeorological Measurements

[7] Latent (Qe) and sensible heat flux (Qh) were quantishyfied using tower -based eddy covariance betwe en July 2005 ndash 2006 This micrometeorolo gical technique directly quantifies the surface-atmosphere exchange of mass and energy by measuring the turbulent transport of H2O vapor and heat [Baldocchi et al 1988] Eddy covariance sensors were mounted at a height of 42 m above ground level or 12ndash 14 m above the forest canopy Wind direction was typically out of the SSW and SE and analysis of the fetch or the upwind distance sampled by the eddy covariance system [Schuepp et al 1990] indicated that more than 90 of the flux originated within 1 km upwind of the tower [Vourlitis et al 2004] [8] The eddy covariance system utilized 3-dimensional

sonic anemometer-thermometer (CSAT-3 Campbell Scienshytific Inc Logan UT USA) and an open-path infrared gas analyzer (LI-7500 LI-COR Inc Lincoln NE USA) to measure the mean and fluctuating quantities of wind speed and temperature and H2O vapor respectively Both sensors sampled and outputted data at 10 Hz and were physically oriented into the direction of the mean wind at the upwind side of the tower to minimize the potential for flow distortion Raw (10 Hz) data and 30-min average fluxes of latent (Qe) and sensible heat flux (Qh) obtained from the eddy covariance array were stored and processed using a solid-state data logger (CR5000 Campbell Scientific Inc Logan UT USA) Average fluxes of Qe and Qh were obtained by calculating the covariance between the fluctuashytions in vertical wind speed and H2O vapor density and temperature respectively over a 30-min interval following a coordinate rotation of the wind vectors [McMillen 1988] Water vapor flux was corrected for the simultaneous flucshytuations in heat [Webb et al 1980] [9] Net radiation (Q) was measured above the canopy

(40 m above ground level) using a net radiometer (NR-LITE Kipp amp Zonen Bohemia NY USA) Soil heat flux (Qg) was measure d using heat flux transducers (n = 2) buried approxshyimately 2 cm into the surface litter layer (HFT-31 REBS Inc Seattle WA USA) Air temperature and atmospheric water vapor density were measured at the top of the tower (42 m above ground level) using a sonic anemometer and open-path gas analyzer respectively (described earlier) and the atmospheric vapor pressure deficit (D) was calculated as the difference between the saturation and actual vapor pressures derived from temperature and humidity measureshyments Micrometeorological data were averaged every 30 min from observations made every 60 s and stored using a solid-state data logger (CR5000 Campbell Scientific Inc Logan UT USA)

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