Feb 2, 2000 - red surfaces, and pasture is visible as pink, white and light blue surfaces. ... fluxes were calculated and stored on a laptop computer as 30-min.
WATER RESOURCES RESEARCH, VOL. 38, NO. 0, 10.1029/2000WR000122, 2002
Seasonal variations in the evapotranspiration of a transitional tropical forest of Mato Grosso, Brazil George L. Vourlitis,1 Nicolau Priante Filho,2 Mauro M. S. Hayashi,3 Jose´ de S. Nogueira,2 Fernando T. Caseiro,3 and Jose´ Holanda Campelo Jr.3 Received 30 November 2000; revised 24 September 2001; accepted 25 September 2001; published XX Month 2002.
[1] The seasonal pattern of evapotranspiration (expressed as latent heat flux Qe) for a 28- to 30-mtall tropical transitional (ecotonal) forest was quantified over an annual cycle using eddy covariance measurement and micrometeorological estimation techniques. The study was conducted near the city of Sinop, in northern Mato Grosso, Brazil, which is located within the ecotone of tropical rain forest and savanna (cerrado). Although the majority of net radiation (Q*) was consumed by Qe (50 – 90%), seasonal variations in Qe were large and positively correlated with precipitation. Total daily Qe for the dry season (June –August) was on average 6.0 MJ m2 d1, while daily Qe for the transition (October – November and April –May) and wet (December – March) season periods were 7.5 and 10.0 MJ m2 d1, respectively. The seasonal variation in midday (0900 –1500 LT) surface conductance (gs) was also positively correlated with precipitation. Analysis of the ‘‘decoupling factor’’ () indicated that the forest was strongly coupled to the atmosphere ( = 0.1 –0.3) over the dry season and transition periods, suggesting that Qe was under relatively strong stomatal control. Although rainfall during the study period was above the longterm (30-year) average, our results indicate that the seasonal dynamics of Qe for the tropical transitional forest were more comparable to tropical savanna than to rain forest.
1. Introduction [2] Tropical forests and woodlands (savanna) exchange vast amounts of water and energy with the atmosphere and are thought to be important in controlling local and regional climate [Avissar, 1991; Nobre et al., 1991; Grace, 1992]. However, rapid deforestation of the Brazilian Amazon [Skole and Tucker, 1993; Moran et al., 1994] has the capacity to destabilize the surface-atmosphere flux of water and energy [Nobre et al., 1991; Wright et al., 1992; Bastable et al., 1993; Polcher and Laval, 1994; Xue et al., 1996]. Research conducted during the Amazon Region Micrometeorological Experiment (ARME) and Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) assessed the effects of deforestation on mass and energy exchange by quantifying the micrometeorology [Shuttleworth et al., 1984a; Wright et al., 1992; Bastable et al., 1993; Culf et al., 1996], water balance [Shuttleworth et al., 1984b; Shuttleworth, 1988; Grace et al., 1995; Hodnett et al., 1995; Miranda et al., 1997], and physiology [Dolman et al., 1991; Sa et al., 1996] of rain forest, savanna, and cattle pasture. Data from these studies form the basis for the parameterization, calibration, and validation of general circulation (GCM) and mesoscale models used to assess the effects of deforestation on tropical ecosystem energy exchange. [3] Although ARME and ABRACOS generated substantial information on tropical rain forest and savanna micrometeorology and energy exchange, the tropical transitional (ecotonal) forest that separates these two regionally important ecosystems has largely 1 Biological Sciences Department, California State University, San Marcos, California, USA. 2 Departamento de Fı´sica, Universidade Federal de Mato Grosso, Cuiaba´, Mato Grosso, Brazil. 3 Departamento de Fı´sica, Nu´cleo de Technologia em Armazenagem, Universidade Federal de Mato Grosso, Cuiaba´, Mato Grosso, Brazil.
Copyright 2002 by the American Geophysical Union. 0043-1397/02/2000WR000122$09.00
been ignored. Aside from modeling studies [Grace, 1992], little is known about the surface-atmosphere exchange of water and energy in the transition zone. However, transitional forests are spatially extensive in the Amazon Basin [Murca Pires, 1978; Ratter, 1992] and were subject to rapid rates of deforestation over the last 2 decades [Skole and Tucker, 1993; Moran et al., 1994]. [4] To reduce uncertainty regarding tropical transitional forest energy and water cycling, measurements of mass (H2O vapor) and energy were initiated in August 1999 as part of the NASA/INPE Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) [Cerri et al., 1995]. Here we describe variations in evapotranspiration (expressed as latent heat flux, Qe) over the dry (June – September), wet (December – March), and transition (October – November and April – May) hydrologic periods and propose mechanisms for the temporal variations in the surfaceatmosphere exchange of H2O vapor.
2. Methods 2.1.
Site Description
[5] The study was conducted near the city of Sinop, Mato Grosso, Brazil (1124.750S, 5519.500W), in an area of extensive mature, intact forest, selectively logged forest, and pasture (Figure 1). The measurements were conducted in intact, mature forest with a relatively continuous, 28- to 30-m-tall canopy (Figure 2). The terrain is generally flat within approximately 5 km of the tower, and the fetch (the upwind distance sampled by the eddy covariance sensor array) is dominated by a relatively continuous canopy of intact mature forest for at least 1.5 km in all directions. [6] Soils of the study site are largely sandy, nutrient-poor (dystrophic) ultisols [Ratter, 1992], which are common to the Amazon Basin [Moraes et al., 1995]. These soils have high porosity and drain rapidly following rainfall events (i.e., within 4 – 7 days) (P. Girard et al., unpublished data, 2001). The vegetation consists of evergreen trees that are characteristic of transitional forest in Mato Grosso [Ratter, 1992], such as Qualea sp.,
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Figure 1. Map of Brazil and false-color image of the study area located 50 km N of the city of Sinop, Mato Grosso, Brazil (1124.750:5519.50W). The location of the eddy covariance tower and study site is visible on the false-color image as a filled white circle. Mature, intact forest is visible as dark red surfaces, secondary forest is visible as light red surfaces, and pasture is visible as pink, white and light blue surfaces. Note the logging roads and ‘‘patios’’ (light blue trails and clearings, respectively) associated with selective logging. The shaded area on the map displays the approximate boundaries of the Brazilian Legal Amazon. The false-color image is produced from IKONOS imagery courtesy of the University of New Hampshire.
Vochysia sp., Ocotea sp., and Mezilaurus itauba [Vourlitis et al., 2001], and diversity is high so there are no obvious dominant tree species. Overstory trees are 28 – 30 m, and although the canopy is relatively closed and continuous, understory trees (1 – 5 m tall) are widespread especially in gaps formed by fallen and/or dead overstory trees. Leaf area index (LAI), estimated from measurements of the extinction of photosynthetic photon flux density by the forest canopy [Goudriaan, 1988], reaches a maximum of 4.5 – 5 during the wet season (January) and a minimum of 4 – 4.5 during the dry season in July (G. Vourlitis et al., The seasonal variations in net CO2 exchange and canopy structural properties of a transitional
tropical forest of Northern Mato Grosso, Brazil, submitted to Ecological Applications, 2001, hereinafter referred to as submitted manuscript, 2001). The decline in LAI during the transition from the wet to the dry season corresponds with an increase in leaf and stem litter production (G. Vourlitis et al., submitted manuscript, 2001). The transitional forest LAI contrasts with a maximum LAI for tropical rain forest of 5 – 6 [Malhi et al., 1999] and 1 for savanna [Miranda et al., 1997]. [7] The 30-year mean annual temperature in the Sinop area is 24C with little seasonal variation, and rainfall is approximately 2000 mm yr1, with a 4-month dry season between June and
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of mass and energy by measuring the turbulent transport of H2O vapor and heat [Baldocchi et al., 1988; Verma, 1990]. Eddy covariance sensors were mounted at a height of 42 m above ground level or 12 – 14 m above the forest canopy (Figure 2). The eddy covariance system utilized a three-dimensional sonic anemometer-thermometer (SWS-211/3K, Applied Technologies, Inc., Boulder, Colorado) and an open-path infrared gas analyzer (NOAA-ATDD, Oak Ridge, Tennessee) to measure the mean and fluctuating quantities of wind speed and temperature and H2O vapor, respectively. Both sensors sampled and output 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 from the tower. The H2O vapor channel of the gas analyzer was calibrated every 2 – 4 weeks using a portable dewpoint generator (LI-610, LI-COR, Inc., Lincoln, Nebraska). [9] Raw H2O vapor fluctuations were output as mean voltages and converted to densities by multiplying by the requisite calibration constant [Leuning and Moncrieff, 1990], and H2O vapor and sensible heat fluxes were computed following a coordinate rotation of the wind vectors [McMillen, 1986, 1988]. Fast response (10-Hz) fluxes were calculated and stored on a laptop computer as 30-min averages using a 200-s running mean and digital recursive filtering technique. Water vapor flux was corrected for the simultaneous fluctuations in heat [Webb et al., 1980]. 2.3.
Figure 2. Schematic of the eddy covariance/micrometeorology measurement system used to measure the latent heat flux (Qe) of the transitional tropical forest near Sinop, Mato Grosso. Shown are the locations of the vertical wet-bult (Tw) and dry-bulb (Td) temperature measurements, net radiometer (Q*), tipping-bucket rainfall gauge (PPT), and ground heat flux transducers (Qg). The eddy covariance array, consisting of the open-path infrared gas analyzer for measuring the mean and fluctuating quantities of water vapor (H2OEC), and the three-dimensional sonic anemometer for measuring the mean and fluctuating quantities of virtual temperature (Tv) and wind speed (u, v, and w), was mounted at a height of 42 m above ground level. Canopy height is 28 – 30 m. September (Table 1). The seasonal climatology for the transitional forest is similar to rain forest and savanna; however, transitional forest typically receives about 200 mm less rainfall per year than rain forest and 500 mm more rainfall than savanna (Table 1). Average air temperature is similar for transitional and rain forests; however, savanna is typically 2 – 3C cooler than transitional forest (Table 1). 2.2. Eddy Covariance Instrumentation and Data Treatment [8] Latent heat flux (Qe) and sensible heat flux (Qh) were quantified using tower-based eddy covariance [Baldocchi et al., 1988; Verma, 1990; Vourlitis et al., 2001]. This micrometeorological technique directly quantifies the surface-atmosphere exchange
Micrometeorological Measurements and Data Treatment
[10] Net radiation (Q*) was measured above the canopy (40 m above ground level; Figure 2) using a ventilated net radiometer (Q*7.1, REBS, Inc., Seattle, Washington). Soil heat flux was measured using heat flux transducers (n = 2) buried approximately 2 cm into the surface litter layer (HFT-3.1, REBS, Inc., Seattle, Washington). Air temperature and vapor pressure were measured at the top of the tower (40 m above ground level; Figure 2) using a relative humidity sensor (HMP-35, Vaisala, Inc., Helsinki, Finland). After approximately 1 March 2000, the vertical vapor pressure profile was also measured using wet- and dry-bulb psychrometers mounted at heights of 1, 4, 12, 20, 28, and 40 m above ground level (Figure 2). Vapor pressure deficit of the atmosphere (VPD) at the top of the tower was calculated as the difference between saturation vapor pressure (es) and actual vapor pressure (ea) from temperature and humidity data collected by the relative humidity sensor and/or the wet- and dry-bulb psychrometer. Precipitation was measured at the top of the tower using a tipping-bucket rainfall gauge (2501, Sierra-Misco, Inc., Berkeley, California). Micrometeorological data were averaged over 30-min intervals from observations made every 60 s and stored using a solid-state data logger (CR-10, Campbell Scientific, Inc., Ogden, Utah). 2.4.
Statistical Analysis and Derived Quantities
[11] Diurnal Q* and Qe were summarized as diurnal averages calculated over seasonal intervals. Diurnal averages were calculated by averaging each 30-min energy flux density measurement for a particular time (e.g., 0900 – 0930 LT) over the dry (June – September), transition (October – November and April – May), or wet (December – March) season periods. This averaging process was conducted to allow the utilization of all data collected and to provide an indication of how the diurnal trend in energy flux density varied, on average, during the different hydrological periods. Daily total values of Qe, Q*, and precipitation were summed over a daily (24-hour) period. [12] Total daily Qe and the evaporative fraction (Qe/Q*) were bootstrapped over monthly, intraseasonal (2-month), and seasonal intervals to estimate the random variance (95% confidence inter-
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Table 1. Monthly Summary of the Average Daily Air Temperature and Total Precipitation During the Study Period and for Tropical Transitional Forest (TF), Rain Forest (RF), and Savanna (S)a Average Daily Air Temperature, C
Total Monthly Precipitation, mm
Aug. Sept. Oct. Nov. Dec. Jan. Feb. March April May June July Annual
99 – 00
RF
TF
S
99 – 00
RF
TF
S
0 71 195 235 139 704 394 153 204 0 0 0 2095
38 120 190 230 322 312 306 266 251 132 49 27 2243
10 71 189 267 365 320 347 266 139 45 15 3 2037
12 52 172 238 249 241 215 189 124 39 9 12 1552
23.9 25.3 25.4 24.4 24.6 24.6 24.6 25.0 24.9 24.5 24.5 22.8 24.6
26.1 26.3 26.2 26.1 25.5 25.5 25.5 25.5 25.6 25.4 24.9 25.0 25.6
23.2 24.6 25.6 24.9 24.5 24.5 24.3 24.7 24.8 23.9 22.7 21.9 24.1
21.2 22.5 22.1 21.7 21.5 21.6 21.8 22.0 21.4 20.2 19.1 19.1 21.2
Data for the study period are for 15 August 1999 to 31 July 2000 (99 – 00), while data for TF are 30-year averages from Vera, Mato Grosso located approximately 30 km southeast of Sinop (N. Priante-Filho et al., unpublished data, 2001). Data for S are 27-year averages from Brasilia, Districto Federal [Miranda et al., 1997], and data for RF are 30-year averages from Porto Velho, Rondonia (N. Priante-Filho et al., unpublished data, 2001).
val) about the mean [Efron and Tibshirani, 1993]. The bootstrap calculated a confidence interval by (1) constructing 4000 bootstrapped sample data series by randomly sampling (with replacement) the observed average daily Qe and Qe/Q* time series, (2) calculating an average Qe or Qe/Q* from each constructed sample data series, and (3) calculating the grand mean ±95% CI from the distribution of averages calculated from the bootstrapped sample data series [Efron and Tibshirani, 1993]. [13] Least squares linear regression was used to assess the degree of energy balance closure and the performance of the eddy covariance flux system [McMillen, 1986, 1988]. For each day, the sum of the instantaneous (i.e., 30-min average) sensible and latent heat flux (Qh + Qe) measured from the eddy covariance system (n = 48) was regressed against the difference between the instantaneous net radiation and ground heat flux (Q* Qg) measured from the micrometeorological sensors (n = 48) [Vourlitis et al., 2001]. This yielded a slope and intercept quantifying the degree of energy balance closure for each day of the measurement campaign. A slope of 1 and an intercept of 0 J m2 s1 indicates complete closure of the energy balance and excellent performance of the measurement system [McMillen, 1986, 1988]. Results indicate that the slope of the regression was on average (±1 standard deviation) 0.92 ± 0.03 and the intercept was 11.03 ± 14.67 J m2 s1 (n = 72 individual regressions) [Vourlitis et al., 2001], which are well within the range reported for other forest measurement systems [e.g., Goulden et al., 1996]. These data indicate satisfactory performance of the eddy covariance measurement system. [14] Power system failure limited the amount of data collected from the eddy covariance system. However, the micrometeorological system used a separate power system and was operational more frequently (75% of the total possible time) than the eddy covariance system (26% of the total possible time). Therefore data collected from the more consistent micrometeorological measurements, along with the direct measurements of Qe from the eddy covariance system, were used to estimate Qe for the measurement period. Instantaneous (30-min average) rates of Qe were calculate from micrometeorological data using the Priestley and Taylor [1972] expression, Qe ¼ aðs=s þ gÞ Q* Qg ; ð1Þ
curve at the appropriate temperature, g is the psychrometric constant, and Q* and Qg are the net radiation and ground heat flux measured from the micrometeorological sensors. The advantage of the Priestley-Taylor relationship is that few data (Q*, Qg, and temperature) are required to estimate the Qe of saturated surfaces [Priestley and Taylor, 1972]. However, the theoretical basis of a is unclear, and a can vary substantially depending on canopy roughness and surface water content [Shuttleworth et al., 1984a; Xu and Singh, 2000]. [15] Given a known Qe, such as that measured from the eddy covariance system, a can be estimated by rearranging equation (1) [Priestley and Taylor, 1972], where a ¼ Qe ðs=s þ gÞ Q* Qg : ð2Þ
where a is the Priestley-Taylor coefficient (usually taken as 1.26), s is the slope of the saturation vapor pressure versus temperature
[17] Temperature and precipitation during the study period (15 August 1999 to 31 July 2000) were similar to the long-term
With (2), monthly values of a were estimated for the annual measurement period using least squares linear regression with instantaneous (30-min average) Qe measured from the eddy covariance system as the dependent variable and instantaneous (s/ s + g)(Q* Qg) measured from the micrometeorological data as the independent variable. The calibrated values of a were interpolated between August 1999 and July 2000 using a polynomial function to provide an estimate of a for the measurement period (Figure 3). [16] Average midday (0900 – 1500 LT) surface conductance (gs) was estimated by inversion of the Penman-Monteith equation [Baldocchi et al., 1991], with Qe measured from the eddy covariance array, and Q*, Qg, and vapor pressure measured from the micrometeorological instruments. Aerodynamic conductance (ga) was calculated as [u/(u*)2]1 corrected for atmospheric stability [Grace et al., 1995], where u is wind speed measured from the triaxial sonic anemometer and u* is frictional velocity calculated from eddy covariance measurements of momentum flux [Baldocchi et al., 1991]. The average midday (0900 – 1500 LT) ‘‘decoupling factor’’ () [Jarvis and McNaughton, 1986] was calculated to assess the relative importance of physiological and meteorological limitations to canopy water vapor exchange.
3. Results 3.1. Correspondence Between the Long-Term Average and Observed Meteorology
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period, compared with a long-term average temperature of 24.1C (Table 1). 3.2.
Temporal Dynamics of the Calibrated Priestley-Taylor A
[18] Estimates of a calculated from the eddy covariance measurements of Qe (equation (2)) ranged between 0.57 during the dry season (August) and 1.07 during the wet season in February (Table 2). The maximum value of a (1.07) was well below the value of 1.26 for saturated surfaces suggested by Priestley and Taylor [1972] but is similar to the value estimated by Viswanadham et al. [1991] (1.03) for Amazonian rain forest. Although data were limited, the seasonal variation in estimated monthly a was highly correlated with total monthly rainfall (a = 0.54 + 0.0012 rainfall; r2 = 0.84; p < 0.01; n = 7). The close correspondence between a and precipitation is expected given that a relates evapotranspiration to surface features such as the amount of available water [Priestley and Taylor, 1972]. These
Figure 3. Estimates of the calibrated Priestley-Taylor alpha (a) calculated from equation (2) (circles) using least squares linear regression between Qe measured from eddy covariance (dependent variable) and (s/s + g)(Q*Qg) derived from micrometeorological data (independent variable). The curve depicts interpolated values of a estimated using a polynomial function that was fit to the data using nonlinear, least squares regression. (30-year) average; however, approximately 25% of the micrometeorological data were missing due to system failure. Total annual rainfall during the study period was 2095 mm compared with the long-term average of 2037 mm (Table 1), but with missing precipitation data, it is likely that the study period was substantially wetter than the long-term average. However, the seasonal distribution of rainfall during the study was identical to the long-term average. For example, most of the rainfall recorded during the study period (66%) occurred during the wet season (December – March), while wet season rainfall historically accounts for 65% of the total annual rainfall (Table 1). Similarly, rainfall recorded during the dry season (June – September) and transition periods (October – November and April – May) accounted for 4 and 30%, respectively, of annual rainfall, which is similar to the long-term seasonal distribution (Table 1). Air temperature was on average 24.6C during the study
Table 2. Estimates of the Priestley-Taylor Alpha (a) Calculated as the Slope of the Least Squares Linear Regression With Latent Heat Flux Measured From the Eddy Covariance System (Qe,) as the _ Dependent Variable and (s/s + g)(Q* Qg) as the Independent Variablea nb Aug. 1999 Sept. 1999 Oct. 1999 Nov. 1999 Feb. 2000 April 2000 July 2000 a
700 561 1174 653 96 142 394
a (±1 SD) 0.57 0.58 0.65 0.74 1.07 0.84 0.59
± ± ± ± ± ± ±
0.01 0.01 0.01 0.01 0.06 0.03 0.01
See equation (2). Here n is the number of 30-min observations used in the linear regression. b
r2 0.86 0.77 0.83 0.88 0.77 0.82 0.86
Figure 4. (a) Latent heat flux (Qe) measured from eddy covariance (Qe(EC); independent variable) versus Qe estimated by the calibrated Priestley-Taylor expression (equations (1) and (2) (Qe(PT); dependent variable). (b) Sensible heat flux (Qh) measured from eddy covariance (Qh(EC); independent variable) versus Qh estimated by the calibrated Priestley-Taylor expression (Qh(PT); dependent variable). The solid line depicts the best fit line estimated from least squares, linear regression, and the dashed line depicts the 1:1 relationship. The equations correspond to the slope, intercept, and coefficient of determination (r 2) of the regression. Data are 30-min. averages (n = 3729 observations).
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Figure 5. (a – c) Average diurnal trend in net radiation (Q*, solid curves), latent heat flux measured by eddy covariance (Qe(EC), solid circles), and latent heat flux estimated from the calibrated Priestley-Taylor expression (equations (1) and (2)) (Qe(PT); open circles). (d – f ) Average diurnal trend in air temperature (solid curves) and vapor pressure deficit (VPD, open diamonds) measured at the top of the tower (40 m above ground level). Data represent means ±1 standard deviation calculated over the dry season (June – September, Figures 5a and 5d), transition (October – November and April – May, Figures 5b and 5e), and the wet season hydrologic periods (December – March, Figures 4c and 4f ).
data suggest that seasonal (and possibly interannual) variations in a can be estimated from relatively simple data such as total monthly rainfall. 3.3. Correspondence Between Eddy Covariance and Priestley-Taylor Qe [19] Instantaneous (30-min average) estimates of latent heat flux (Qe) calculated from the calibrated Priestley-Taylor (PT) expression (equations (1) and (2)) were similar to those measured
directly from eddy covariance (EC) (Figure 4a). Although there was substantial variability about the 1:1 line and few actual measured data, the slope of the regression was 0.91 and the intercept was 1.7 J m2 s1 (Figure 4a), indicating that on average, Qe(PT) underestimated Qe(EC) by
