An empirical approach to retrieve monthly evapotranspiration over Amazonia
Robinson I. Negrón Juárez1, Rong Fu1, Ranga B. Myneni2, Robert E. Dickinson1, Sergio
Bernardes3, Huilin Gao1, Carlos Nobre4, Michael Goulden5, Steven C. Wofsy6
1
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, GA, USA.
E-mail: rjuarez@eas.gatech.edu, fu@eas.gatech.edu, robted@eas.gatech.edu,
huilin.gao@eas.gatech.edu
2
Department of Geography, Boston University, MA, USA.
E-mail: rmyneni@bu.edu
3
Department of Geography, University of Georgia, GA, USA.
E-mail: sbernard@uga.edu
4
Weather Forecast and Climate Studies Center (CPTEC), National Institute for Space
Research (INPE), Cachoeira Paulista, Sao Paulo, Brazil
E-mail: nobre@cptec.inpe.br
5
Earth System Science and Ecology and Evolutionary Biology, University of California,
Irvine, CA, USA.
E-mail: mgoulden@uci.edu
6
Division of Engineering and Applied Science/Department of Earth and Planetary
Science, Harvard University.
E-mail: swofsy@deas.harvard.edu
Intended for Submission to Journal of Geophysical Research-Atmosphere or
International Journal of Remote Sensing
Date: January XX, 2007
Author for correspondence
Robinson I. Negrón Juárez
School of Earth and Atmospheric Sciences
Georgia Institute of Technology
Ford ES&T Bldg., 311 Ferst Drive
Atlanta, GA 30332-0340
Phone: (404)894-1624
e-mail: rjuarez@eas.gatech.edu
1
Abstract.
The regional evapotranspiration (ET) over the Brazilian Amazonia remains uncertain
since in situ sites do not cover the entire domain, and its fetch is ~1km. Based upon an
improved physical understanding of what controls ET over the Amazonia rainforests
from analysis of recent in situ observations, this investigation developed an empirical
method to estimate ET over the Brazilian Legal Amazon. Satellite data used in this study
include the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imagining
Spectroradiometer (MODIS) and surface radiation budget from the International Satellite
Cloud Climatology Project (ISCCP) for the period 2000-2004. An empirical model was
validated by measurements at four sites located in the Brazilian states of Pará, Amazonas
and Rondônia. The observed and calculated values had the same mean and variance. Our
results show that the large-scale ET pattern is dominated by changes of surface solar
radiation and biome type. At seasonal scale, the regional ET peaks during austral spring
(SON). Possible decrease of ET due to deforestation was also observed.
1. INTRODUCTION
Evapotranspiration (ET) is a key component that links climate and terrestrial
ecosystem. Over the Amazon forest, ET contributes to about 50% of total precipitation as
calculated by water balance methods and eddy correlation systems [Salati, 1987;
Shuttleworth, 1988], however, these studies were limited to smaller areas of the basin and
it is not known if there are geographical variations of this rate. Observations provided by
the Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) [Gash et al.,
1996] and the more recent Large-Scale Biosphere-Atmosphere experiment in Amazonia
2
(LBA) [LBA, 1996; Avissar et al., 2002; Keller et al., 2004] have improved
understanding on the controls of ET at seasonal and interannual time scales. These field
studies have shown not only a higher ET in dry season than in wet season, but also a
higher ET over areas with less rainfall during dry season in eastern and central Amazonia
[e.g., Shuttleworth, 1988; Nepstad et al., 1994; Malhi et al., 2002; Sommer et al., 2002;
Souza Filho et al., 2005; Negrón Juárez et al., 2006]. Such a high rate of ET maintained
by the rainforest during the dry season plays a central role in determining the date of the
subsequent wet season onset [Li and Fu, 2004; Fu and Li, 2004]. A higher ET as a result
of forests responding to increased solar radiation can also mitigate the impact of
moderately dry anomalies on the surface climate condition [e.g., Negrón Juárez et al.,
2006]. Therefore, estimation of basin wide ET at seasonal to interannual scales is
essential in determining seasonal and interannual climate variability in the Amazonian
region.
Current techniques (e.g., the Eddy correlation technique) used to estimate ET give
only point measurements which probably represent ET within 1-2 km in radius as long as
the surface characteristics are the same. Due to heterogeneities in land surface and soil
characteristics, extrapolation of point data beyond that distance would be inaccurate
because of the dynamic and regional variability of ET.
Also, observational
measurements are not available in remote areas such as the interior western Amazon
forests, and numerical model estimates of ET [Potter et al., 2001] are limited by
subjective assumptions which are yet to be validated. Satellite data offer an alternative
for estimation of ET over large areas by complementing previous observed measurements
and numerical simulations of the ET. A common approach is to relate ET to remotely
3
sensed surface temperature [e.g., Jackson et al., 1977; Jackson, 1988; Hatfield, 1983;
Moran et al., 1989; Kustas, 1990] or solar irradiance [Jackson et al., 1983], and several
statistical/semi-empirical [e.g. Su et al., 2005; Loukas et al., 2005; Di Bella et al., 2000]
or physical methods, based on the Penman-Monteith equation forced by meteorological
variables and vegetation indexes [Monteith, 1981; Nishida et al., 2003; Nagler et al.,
2005]. While these approaches aim to provide the best possible estimate of ET globally,
their application to Amazon forests remains unknown. Although the temperature can be
used to estimate ET over some areas in the Amazon the relation between surface
temperature and ET varies in sign across the Amazonia. Thus, at central-eastern
Amazonia the ET is in phase with temperature [Hutyra et al., 2005; da Rocha et al.
[2004], but out of phase in eastern Amazonia [Sommer et al., 2002], and of opposite
phase during the wet season over southern Amazonia [Vourlitis et al., 2002]. These
observational results clearly suggest that a simple relationship between surface
temperature and ET does not exist at the basin scale.
Most of the flux tower observations over Amazonian forests during the last two
decades have suggested that ET is primarily controlled by surface radiation and
vegetation condition, consistent with that originally reported by Shuttlworth [1988].
Therefore, we propose an empirical approach for estimation of ET based on vegetation
condition inferred from EVI data [Justice et al., 1998; Huete et al., 2002], and surface net
radiation from the International Satellite Cloud Climatology Project (ISCCP) [Zhang et
al., 1995, 2004]. Compared to the Normalized Differential Vegetation Index (NDVI), the
EVI can better capture canopy structural variation, seasonal vegetation variation, land
cover variation, and biophysical variation for high biomass vegetation such as that in the
4
Amazonia [Gao et al., 2000; Huete et al., 2002]. It also performs well under the heavy
aerosol and biomass burning conditions [Miura et al., 1998]. The empirical model we
used is trained and validated using data collected at eight different upland forest sites
over the Brazilian Legal Amazon (BLA).
2. Methodology
2.1. Study area and model construction
The study area encompasses the nine states of the BLA (Figure 1) covering an
extension
of ~5x106 km2
(Brazilian
Institute of Geography and Statistics,
http://www.ibge.gov.br). Latent heat flux (E) data from eight upland sites (listed in
Table 1) from the LBA experiment were used for model construction and validation. Data
from K83 and RJA sites were used for training the empirical model, and data from K67
and K34 sites were used for model validation. The sites CRS, DCK, SIN, and BRG were
used for model comparison.
Observational data shows that E has a strong linear relationship with
measurements of net radiation (Rn). For example, Figure 2 shows the relationship
between daily E and Rn at sites K34, K83, and RJA. Latent heat flux represented ~70%
of Rn at K34 and K83, but only 40% at the RJA site, showing that for the Amazonia most
of Rn is used to sustain ET but at different rates from site to site. Over the dense
vegetation of the Amazonian rainforests, ET is primarily contributed by leaf transpiration
and evaporation of the intercepted water on canopy. Stomatal closure, hence transpiration
rate, is determined primarily by solar radiation at the leaf surface and the availability of
5
water to plants, although vapor pressure deficit and wind speed can also influence ET
[Shuttleworth, 1988; da Rocha et al., 2004, Negrón Juárez et al., 2006].
Based on Huete et al. [2002], we considered EVI as a driving factor that
characterizes the forest phenology. Consequently, our empirical model is based on the
assumption that ET over the Amazonian rainforests is primarily a function of net
radiation and EVI:
ET=f(Rn, EVI)
(1)
Different functions were tested independently at each site but the best fit between
observed and calculated ET was obtained when
ETsite (mm day 1 )  C1  C2  EVI C3  RnISCCP - C4
(2)
where EVI is the MODIS EVI data, RnISCCP (Wm-2) is the net radiation from the
ISCCP data (see below), and C1, C2, C3, and C4 are constants. For the ET model over
the BLA (ETgrl), constants in Equation 2 were adjusted until get simultaneously the
maximum determination coefficient (r2) between observed and modeled ET over K83 and
RJA. The error between observed and calculated ET was obtained as
 1 n Y(i)  Y' (i)



 100%    YY'
Error=   



Y(i)
n i
 Y 100%  



(3)
where Y and Y΄ are the observed and calculated ET values, respectively, n is the number
of elements in the series, and  is the standard deviation of errors.
2.2. MODIS EVI data
The MODIS Vegetation Indices products use surface reflectance from MODISTerra corrected for molecular scattering, ozone absorption, and aerosols [Vermote et al.,
6
2002; Miura et al., 2001]. The EVI is an optimized vegetation index developed to
enhance the vegetation signal with improved sensitivity in high biomass regions and
improved vegetation monitoring through a de-coupling of the canopy background signal
and a reduction in atmospheric influences [Justice et al., 1998]. The MODIS EVI is
calculated using the red, NIR, and blue reflectances (red , NIR and blue , respectively)
EVI=G(NIR-red)/( NIR+ared-bblue+L)
(4)
The coefficients adopted in the EVI algorithm are L=1, a=6, b=7.5, and G=2.5. a and b
are coefficients of the aerosol resistance term, which uses the blue band to correct for
aerosol influence in the red band, a concept that originated from the Atmosphere
Resistant Vegetation Index (ARVI) [Kaufman and Tanré, 1992]. The concept of the soil
correction, L, was derived from the soil-adjusted vegetation index (SAVI) [Huete, 1988].
Sixteen-day MODIS EVI images with resolution of 1km x 1km were downloaded
from http://modis-land.gsfc.nasa.gov/ from 2000 to 2005. Monthly data were composed
using the number of 16-day average images that overlap the calendar month weighted by
the number of actual days that overlap that month. For model construction and validation
we used a 3-pixel box centered in the tower location. For regional estimation, the 1km
resolution data was aggregated to 0.25°. The aggregation consists of calculating the
weighted average of all pixels from the 1 km resolution image that spatially overlaps the
output pixel (0.25°) and contributes to the final digital value of this pixel. Figure 3 shows
the EVI time series of monthly values at K67, K83, K34, and RJA sites at 1km and 0.25°
horizontal resolutions for the period 2000-2004. The T-student test (not shown) pointed
7
out that the average values were the same and the F-test (no shown) was satisfactory
suggesting that the spatial pattern at regional scale was maintained after aggregation.
2.3. ISCCP Rn data
Monthly Rn (Wm-2) at the surface was obtained from the upwelling and
downwelling shortwave (0.2-5.0 µm) and longwave (5.0-200 µm) radiative fluxes at the
surface from ISCCP. This global radiative flux profile data cover the time period from
July 1983 to December 2004, has a time step of three hours with horizontal resolution of
2.5°2.5°. Shortwave and longwave radiative flux profiles use data that specify the
properties of the earth’s atmosphere and surface. From the balance of full-sky radiative
fluxes at the land surface, we calculated the net radiation (RnISCCP). Full-sky fluxes are
weighted from fluxes calculated for 15 types of cloud conditions and the clear-sky fluxes
by their areas for each cell. Zhang et al. [1995, 2004] present a complete description of
this data set.
For model construction and validation we used the RnISCCP encompassing the
tower coordinates. For regional estimation of ET, the 2.5° resolution RnISCCP data was
interpolated to 0.25° using the Kriging interpolation method [Oliver and Webster, 1990].
Figure 4 shows that RnISCCP at 0.25°0.25° agrees well RnISCCP at 2.5°2.5°, but with
some offset respect to observed Rn, that would be easily counted in the calibration
equations. The F-test (an its probability) between observed Rn and RnISCCP at 0.25° at
sites K67, K34, K83, and RJA was 1.074 (0.839), 1.569 (0.392), 1.245 (0.559), and 2.667
(0.005), respectively. Excepted by the RJA site, RnISCCP at 0.25° had the same variance
respect to observations. A detailed analysis for observed Rn and RnISCCP at 0.25 at RJA
8
showed that the F-statistics for the period January 2000 to December 2001 was 1.9, and
its probability was 0.133; therefore observed and satellite series had the same variance.
However, for the period 2002 they had different variances that could be associated to bias
in satellite measurements or unreported bias in the in situ measurements.
3. Results
3.1. Model training and validation
Figure 5 shows the observed (ETobs) and calculated ET at K83, RJA, K67, and
K34 sites. The empirical formula trained for best fit at each specific site (ETsite)
generally provide close match of ETobs at seasonal and interannual scales, especially at
K83 and K67 sites. However, at RJA site, ETsite tends to overestimate annual maximum
ET. The correlation coefficients between ETsite and ETobs are 0.75 at K83 site, 0.73 at
K67 site, 0.8 at K34 site, and 0.32 at RJA site (Table 2). The apparent higher correlation
at K34 site is explained by the shorter time series.
A general empirical model (ETgrl) was obtained with C1=2.1, C2=0.052,
C3=1.85, C4=140. Although, some discrepancies are observed between ETgrl and
ETobs, the seasonal and interannual variations are in good agreement. The coefficient of
determination between ETgrl and ETobs is 0.74, 0.53, 0.39 and 0.31 at K83, K67, K34
and RJA, respectively. The F-test used to determine the variance of observed and
calculated values is shown in Table 2. The errors between ETsite (ETgrl) and ETobs at
sites K67, K83, K34, and RJA were 9%(17%), 12%(14%), 12%(25%), and
12%(20%), respectively. Using the RnISCCP and EVI at 0.25° spatial resolution, the
ETgrl error respect to ETobs at K67, K83, K34, and RJA were 18%, 19%, 16%, and
9
16%, respectively. This shows that the 0.25° resolution of satellite inputs would not
significantly diminish the quality of the calculated ET.
Figure 6 compares the seasonal mean of ETgrl at different spatial resolution
respect to ETobs. ETobs includes data used for model training as well as those reported
previously by other scientists at different sites (CRS, DCK, BRG, SIN). Seasonal ETobs
correspond to data available listed in Table 1, whereas the ETgrl seasonal averages
correspond to the period 2000-2004. At K34, K83, K67, and RJA, the averaged
differences at 1km of spatial resolution between ETobs and ETgrl were 8% during the
wet season and 10% during dry season, and at 0.25° resolution were similar, 8% and 9%
for wet and dry season, respectively. At CRS, DCK, BRG, and SIN, the average
discrepancies between in situ ET and satellite estimated ETgrl during the wet season
were 26% and 18% at 1 km and 0.25˚ resolution, respectively, and during the dry season
are 18% and 19%. The best agreement (~11% discrepancy) occurs at CRS in the central
equatorial Amazonia, near to the K34, and the worst at BRG (~30% discrepancy) near the
month of the Amazon River where climate is strongly influenced by wind coming from
the ocean.
Several factors can contribute to the discrepancies shown in Figure 6. For
example, the satellite inputs of the surface solar radiation and EVI may be different from
those at the flux towers, in part due to the lack of information on sub-scale structures
within the satellite footprints. The observed values also have significant uncertainty due
to instrument errors as well as the methods used for ET calculations. For example, at the
DCK site the ET values were obtained from a combination of observed and calculated
data using the Penman-Monteith; at the BRG site ET was calculated using both the
10
Bowen ration energy balance and the Penman-Monteith equation; at the SIN site the
reported ET values were obtained using the eddy correlation system and the PriestleyTaylor model.
3.2. Seasonal evapotranspiration
Seasonal ET and precipitation for the period 2000-2004 over BLA are shown in
Figure 7. For precipitation we use the Tropical Rainfall Measuring Mission (TRMM)
3B43 which use TRMM data merged with other satellite data and rain gauge available at
http://trmm.gsfc.nasa.gov. This dataset has proved to be consistent with observations
[Negrón Juárez et al., 2007] and has the advantage of its high spatial resolution. During
DJF the maximum values of ET (~3mm day -1, Figure 7a) appear in the north of the
Amazonas state, whereas precipitation was higher in southern BLA (>8mm day -1, Figure
7b). In MAM, ET is almost homogeneous over BLA with an average value of 2.6 mm
day-1, but some areas in Maranhão State exhibited values of 2.9 mm day-1. During this
period the precipitation is concentrated over the northern BLA with values higher than 6
mm day-1 except in the north of the Roraima state where was lower. In JJA a precipitation
gradient is observed from south (~1 mm day-1) to north (~ 7 mm day-1). Similar to
precipitation, the calculated ET exhibited a gradient with low values (2 mm day-1) in the
south and high values in the north, with the highest values (3 mm day-1) observed in the
north of the Pará state. In SON the ET had values ranging between 3 and 3.3 mm day-1
except in southern BLA where ranged between 2.2-2.5 mm day-1 and coincided with the
domain of Cerrado biome (see Figure 11). In the eastern Amazonia ET was higher than
3.1 mm day-1, but precipitation was lower than 3 mm day-1. This finding is consistent
11
with in situ observations that have proved the importance of root uptake of deep soil
moisture to keep the high ET rates [Nepstad et al. 1994; da Rocha et al. 2004; Oliveira et
al. 2005]. The increase of ET in eastern Amazonia from September to November
suggests an enhanced forest activity that was also observed by Huete et al. [2006].
Contrasting this forest enhancement, over the adjacent deforested areas, the ET had lower
value during dry season probably due to shallower rooting depth as also suggested by
Huete et al. [2006].
4. Discussion
4.1. Sensitivity of EVI to climate variability
Forest in Amazonia is subjected to severe droughts during El Niño events. Nepstad et al.
[2002] conducted an experiment to study the effects of these droughts in an east-central
Amazon forest at the Tapajos National Forest, in the Brazilian state of Para. At the
canopy level, the authors did no observe substantial drought stress of leaf canopy but a
thinning of the leaf canopy. Although the NDVI can be used to monitor the effects on
Amazon forest phenology during the El Niño events [Asner et al., 2000], it is sensitive to
chlorophyll [Gao et al., 2000] and is asymptotic at high leaf biomass conditions [e.g.
Justice et al., 1998; Gao et al., 2002]. On the contrary, the use of EVI seems to be more
appropriated since it is sensitive to canopy structure and also performs well even at
higher LAI values [Justice et al., 1998; Gao et al., 2002; Huete et al., 2002]. To
investigate the response of EVI to climate variability we selected two areas affected
differently during the 2005 drought event observed over Amazonia [Marengo et al.
2006]. Figure 8 shows the precipitation (TRMM) and EVI over A1(2°S-5°S,53°W-56°W)
12
and A2 (5°W-7.5°W,65°W-70°W) for year 2004 and 2005. The climatological
precipitation (1961-1990) is also showed and was obtained from the Global Precipitation
Climatoly Centre. The climatological precipitation at A1 1997 mm. This area encompass
the Tapajos National Forest where Nepstad et al. [2002] conducted their experiment.
During most of the 2004 year the precipitation was close to climatology, however in 2005
the precipitation was lower than climatology since April, coinciding with a decrease of
EVI (Figure 8a). If, as reported by Nespstad et al., the canopy experienced changes in its
structure during drought events, this result shows the capability of EVI to detect those
changes. However, data as litterfall and LAI are necessaries to be conclusive. On the
other hand, the effect of 2005 drought, although was more sever over southern Amazon,
appears to have had an opposite effect over A2, since the EVI increased during this year
respect to 2004 (Figure 8b). Although in 2005 the precipitation was about 400 mm less
than climatology (2578 mm), the decrease was not enough to produce stress on
vegetation. On the contrary, with sufficient water supply and more incoming solar
radiation a positive response of vegetation is expected. It has to be mentioned that in this
area the precipitation have decreased progressively since 2003, with values of 2694 mm,
2364 mm and 2197 mm in 2003, 2004 and 2005, respectively. If this reduction continues
the vegetation response is uncertain, since the west part of Amazon does not experience
regular dry season and, probably, this forest have not developed strategies to handle
droughts [Stokstad, 2005].
These results show that, even at different precipitation regime, the EVI is able to
detect the forest response to the climate variability. The capability of EVI to monitor the
seasonal variation of Amazon forest has been studied by Xiao et al. [2006].
13
4.2. Uncertainties of observed and calculated ET
As explained above some sites used different methodologies to obtain ET and
their associated errors have not been neither evaluated nor compared. Sites using the eddy
correlation system fail the energy balance. The energy balance is related to the sum of E
and sensible heat (SH) fluxes compared to the available energy Rn –G–S, where G is the
soil heat flux and S is the heat storage in canopy and biomass. The highest imbalance was
observed at the RJA site (28%) and the lowest at K83 (14%). At RJA the terrain
heterogeneity can promote mesoscale circulation with turbulent flow emerging at lower
frequencies [Kruijt et al., 2004] which are important for the eddy fluxes [Sakay et al.,
2001; Foken et al., 2006]. Although the imbalance of flux data used for model
construction are consistent with other flux tower measurements reported in the literature
using the eddy correlation technique [e.g. Wilson et al., 2002], it is difficult to quantify
the impact of such imbalance on the calculated ET. This is because it is unknown what
percentage of that imbalance is associated with E. Due to a clear underestimation of
turbulent fluxes or overestimation of the available energy Foken et al. [2006] have
recently suggested that the surface energy imbalance should not be used as an indicator of
accuracy.
Cloud cover is the most immediate problem in the land surface optical remote
sensing over Amazonia. Asner et al. [2001] have reported that the chance of acquiring
scenes with 30% or less cloud cover is minimal from December to May and unlikely
from October to November, but this chance increases from June to September. On an
annual basis the probability of obtaining scenes with 30% or less cloud cover in the BLA
is >90% below 5°S. The MODIS science team has established a quality assessment of its
14
data on a pixel by pixel basis. We used the VI Usefulness Index (UI) parameter
(http://edcdaac.usgs.gov/modis/moyd13_qa_v4.asp) to evaluate the quality of EVI
images. The values of the UI for a pixel are determined by several factors including
aerosol quantity, atmospheric correction conditions, cloud cover, shadow, and sun-targetviewing geometry. UI has 16 levels that vary from perfect quality (level 0) to low quality
(level 14). In this work the level Intermediate Quality (6th in the UI scale) was considered
the threshold between good and bad quality pixels. Although somewhat arbitrarily
selected, this threshold is more restrictive than the Average Quality (8th in the UI scale).
The 16 day 1x1 km2 usefulness quality data was used to calculate the percentage
of pixels with UI values higher than the Intermediate Quality threshold level. The results
were then rescaled to the 0.25°0.25° resolution (Figure 9). Central Pará had good
quality EVI in 30-40% of images, and this percentage was higher (50%) over west of
Maranhão, south of Amapá, some areas in Roraima, and west of Amazonas state.
Remaining areas were above the threshold for at least 60% of the cases with the highest
values observed in southern and western edges of the BLA. Our calibrated model worked
well at the K83 and K67 sites (central Pará), suggesting that problems due to
uncertainties were minimized. However, on the western edge of BLA the ET values
should be taken with caution since no observed ET data is available for comparison.
Uncertainties have been also reported in ISCCP-FD fluxes. The largest sources of
uncertainty are caused by sparse sampling of cloud variations, TOVS atmospheric
temperature, ISCCP surface temperatures, and water vertical profile [Zhang et al., 2004].
Zhang et al. reported that the overall uncertainties of these fluxes are at least 10-15 Wm-2
15
at surface. Although we observed that the uncertainty is as high as ~100Wm-2 it appears
to be invariable over the BLA (Figure 4).
4.3. Additional test of sensitivity
An independent dataset was processed to examine the sensitivity of the ET
results. The dataset is the microwave emissivity difference vegetation index (EDVI),
defined as EDVIV 
TbV19  TbV37
after Min and Lin [2006]. Here we used vertically
0.5(TbV19  TbV37 )
polarized brightness temperature at 19GHz and 37GHz ( TV19 , TV37 ) from the Advanced
Microwave Scanning Radiometer for the Earth Observing System (EOS) program
(AMSR-E), launched in May 2002. AMSR-E monitors important variables to weather
and climate, such as precipitation, oceanic water vapor, cloud water, near-surface wind
speed, sea surface temperature, soil moisture, snow cover, and sea ice parameters. The
EDVI values represent physical properties of vegetation such as vegetation water content
(VWC) of crown canopy. Over the heavily vegetated Amazon forest the remotely sensed
brightness temperature only reflects the VWC status of the thin top layer [Gao et al.,
2007]. Therefore, as shown by Min and Lin [2006], the calculated EDVI increases as
VWC increases.
Figure 10 shows the calculated ET and EDVI for the period 2002-2004 over K34
and RJA. Theses sites are located in the northern and southern section of the Amazon
basin respectively, regions with different precipitation seasonality and are differently
affected by the El Niño events [Foley et al., 2002]. Drought evens triggered by El Niño
have been observed in 2002/2003 and 2003/2004 (http://www.cpc.ncep.noaa.gov) and the
16
figure shows that vegetation was affected by those consecutive events, with strong signal
in 2003/2004, when ET decreases. Consistently, EDVI also showed a decrease of VWC
during this period. The decrease of canopy water content during drought stress has been
reported by Asner et al. [2005]. These results suggest a satisfactory performance of the
calculated ET.
4.4. ET rates over Amazon
The validation tests of our calculated ET allow us to be confident about their
performance. We determine the rate of ET respect to precipitation across the Amazon
forest for the study period as observed in Figure 11. At DCK, CRS, K83 and SIN the
observed values reported an average rate of 50%, and at RJA this rate was 45%. Our
result agrees with these measurements, but also shows a regional variability of this rate.
As observed in the figure the lowest rate (<40%) agreed with areas of maximum
precipitation (Figure 1 and Figure 7b), have 0-3 consecutives month with precipitation
<100 mm and lower daylight intensity [Sombroek, 2001]. More precipitation implies
more cloudiness, and less available radiation useful to evapotranspirate. Consequently,
these areas have the maximum plant-available soil water (PAW) and then less susceptible
to droughts as those observed during the El Niño event [Nepstad et al., 2004].
Rates with values >40% are observed in the southern, eastern and mid of the
basin. These areas are characterized by precipitation <2000 mm [Figure 1], 4-7
consecutives months with precipitation < 100 mm [Sombroek, 2001], low values of
maximum PAW [Nepstad et al., 2004]. The ENSO index correlates also strongly with
temperature anomalies in these areas [Malhi and Wright, 2004], therefore they are more
17
susceptible to drought event and fires that have been more recurrent during the last years.
The risk of fire is related to the severe deforestation observed especially in the
deforestation arc [see Figure 12].
4.5. Possible effects of deforestation on ET
Since the early 1970s the Amazon basin has been strongly deforested in the area
denominated arc of deforestation along the southern and eastern edges (Figure 12).
Consequences of this deforestation are loss of productivity, loss of biodiversity, changes
in the hydrological regime, and increased emission of greenhouse gases [Fearnside,
2005]. In the 1990s the deforestation rate was 17103 km2 year-1, increasing to 25103
km2 in 2002 and 2003, and by August 2004 the accumulated deforestation was ~14% of
the BLA (http://www.obt.inpe.br/prodes/). Causes of deforestation are many, but cattle
ranching is predominant (planted pasture represents 70% of the deforested area)
[Margulis, 2003]. After 2002 an increase in the deforestation rate was observed due to
international demand for soybeans (especially in the state of Mato Grosso) and beef
[Fearnside, 2005].
We focus our analysis in an area located in the Mato Grosso state (7.5-12.5°S,
52.5°-57.5°W) where intense deforestation have been observed during the last years. The
ET over deforested areas was ~2.6 mm day-1, while over adjacent forest was 3 mm day-1,
representing a reduction 13%. Even though comparable to the 17% of error (reported in
section 3.1) the EVI values were significantly different (not shown). This can only be
explained by the land use change since this area was dominated by the same biome
(Figure 11). While modeling studies consider complete deforestation to calculate changes
18
in the ET [e.g., Nobre et al., 1991; Costa and Foley, 2000; Valdoire and Royer, 2004] our
results suggest that the current deforestation in the Amazon has already produced a
decrease in ET. Changes of precipitation as a consequence of deforestation were also
reported in Roraima [Chagnon and Bras, 2005] and Rondonia [Negri et al., 2004] states.
This reduction of ET could have climatic implications as Fu and Li [2004] have
suggested that the ability of the forest to sustain high ET during the dry season is a key
driver for the transition from the dry to the wet season.
5. Conclusions
The empirical model developed based on satellite data to calculate ET agreed fairly well
with observations with a average error of 17%. The regional ET calculated proves to be
consistent with the biomass distributions of forest and cerrado in the Amazon basin. Low
quality of MODIS data was observed in the Pará state and west of Amazonas state. Over
the Pará state the model calculates agreed with observations; however, due to lack of
observational data for model validation our results over the west of Amazonas state
should be taken with caution. Our calculated ET also showed physiological characteristic
reported in the literature. Thus, high values of ET were observed during the dry season
(SON) and over northern Pará exceeding the precipitation, indicating that the root system
is taking water from deep soil. On the interannual variability the calculated ET was
consistent with microwave data monitoring the vegetation water content that let us to
infer respect to the drought effects over Amazon forest. The model may also be useful to
study changes in ET due to land surface changes; however, further studies need to be
carried out to explore its potential on this respect.
19
Acknowledgments
We acknowledge the LBA community for their extensive field measurements which
provide data focused on the understanding of the Amazon forest and for making them
available in the LBA Beija-Flor search data set (http://beija-flor.ornl.gov/lba). We thank
Ms. Susan Ryan for her valuable editorial assistance. We are also grateful to the
constructive and insightful comments made by the four anonymous reviewers. This work
was supported by the NASA Terrestrial Ecology Program for the Earth System Science
Research Using Data and Products from Terra, Aqua, and ACRIM Satellites.
REFERENCES
Araújo, A. C., A. D. Nobre, B. Kruijt, J. A. Elbers, R. Dallarosa, P. Stefani, C. von
Randow, A. O. Manzi, A. D. Culf, J. H. Gash, R. Valentini, and P. Kabat (2002),
Comparative measurements of carbon dioxide fluxes from two nearby towers in a
central Amazonian rainforest: The Manaus LBA site, Journal of Geophysical
Research-Atmosphere, 107(D20), 8090, doi: 10.1029/2001JD000676.
Asner, G. P., A. R. Townsend, and B. H. Braswell, (2000) Satellite observation of El
Niño effect on Amazon forest phenology and productivity, Geophysical Research
Letters, 27(7), 981-984.
Asner, G. P. (2001), Cloud cover in Landsat observations of Brazilian Amazon,
International Journal of Remote Sensing, 22(18), 3855-3862.
20
Asner, G. P., D. Nepstad, G. Cardinot, and D. Ray (2004), Drought stress and carbon
uptake in an Amazon forest measured with spaceborne imaging spectroscopy.
Proceedings of the National Academy of Sciences, 101(16), 6039-6044.
Avissar, R., P. L. S. Dias, M. A. F. S. Dias, and C. Nobre (2002), The Large-Scale
Biosphere-atmosphere Experiment in Amazonia (LBA): Insights and future
research needs, Journal of Geophysical Research-Atmospheres, 107(D20), 8086,
doi: 10.1029/2002JD002704.
Chagnon, F. J. F., and R. L. Bras (2005), Contemporary climate change in the Amazon,
Geophysical research letters, 32, L13703, doi: 10.1029/2005GL022722.
Costa, M. H., and J. A. Foley (2000), Combined effects of deforestation and doubled
atmospheric CO2 concentrations on the climate of Amazonia, Journal Of Climate,
13 (1), 18-34.
da Rocha, H. R., M. L. Goulden, S. D. Miller, M. C. Menton, L. D. V. O. Pinto, H. C. de
Freitas, and A. M. E. S. Figueira (2004), Seasonality of water and heat fluxes over
a tropical forest in eastern Amazonia, Ecological Applications, 14(4), S22-S32.
Di Bella C. M., C. M. Rebella, and J. M. Paruelo (2000), Evapotranspiration estimates
using NOAA AVHRR imagery in the Pampa region of Argentina, International
Journal of Remote Sensing, 21(4), 791-797.
Dickinson, R. E., G. L. Wang, X. B. Zeng, Q. R. Zeng (2003), How does the partitioning
of evapotranspiration and runoff between different processes affect the variability
and predictability of soil moisture and precipitation?, Advances in Atmospheric
Sciences, 20(3), 475-478.
21
D’Odorico P., and A. Porporato (2004), Preferential states in soil moisture and climate
dynamics, Proceedings of the National Academy of Sciences of the United States
of America, 101(24), 8848-8851.
Fearnside, P. M. (2005), Deforestation in Brazilian Amazonia: History, Rates and
Consequences, Conservation Biology, 19(3), 680-688.
Finnigan, J. J., R. Clement, Y. Malhi, R. Leuning, and H. A. Cleugh, (2003), A reevaluation of long-term flux measurements techniques. Part I: Averaging and
coordinate rotation, Boundary-Layer Meteorology, 107(1), 1-48.
Foken, T., F. Wimmer, M. Mauder, C. Thomas, and C. Liebethal (2006), Some aspects of
the energy balance closure problem, Atmospherics, Chemistry and Physics
Discussions, 6, 3381-3402.
Foley, J. A., A. Botta, M. T. Coe, and M. H. Costa (2002), El Niño–Southern oscillation
and the climate, ecosystems and rivers of Amazonia, Global Biogeochemical
Cycles, 16(4), 1132, doi:10.1029/2002GB001872.
Fu, R., and W. Li (2004), The influence of the land surface on the transition from dry to
wet season in Amazonia, Theoretical and Applied Climatology, 78(1-3), 97-110.
Gao, H., R. Fu, R. Negrón Juárez, and R. Dickinson, “Using passive microwave remote
sensing to monitor top canopy vegetation water content over the Amazon forest”,
in progress.
Gao, X., A. R. Huete, W. Ni, and T Miura (2000), Optical-biophysical relationships of
vegetation spectra without background contamination, Remote Sensing of the
Environment, 74, 609-620.
22
Gash, J. H. C., C. A. Nobre, J. M. Roberts, and R. L. Victoria (1996), An overview of
ABRACOS, in Amazonian Deforestation and Climate, edited by J. H. C. Gash, C.
A. Nobre, J. M. Roberts, and R. L. Victoria, pp. 1-14, John Wiley & Sons,
Chicheter.
Goulden, M. L., J. W. Munger, S. M. Fan, B. C. Daube, and S. C. Wofsy (1996),
Measurements of carbon sequestration by long-term eddy covariance: methods
and a critical evaluation of accuracy, Global Change Biology, 2, 169-182.
Goulden, M. L., S. D. Miller, H. R. da Rocha, M. C. Menton, H. C. de Freitas, A. M. E.
S. Figueira, and C. A. D. de Sousa (2004), Diel and seasonal patterns of tropical
forest CO2 exchange, Ecological Applications, 14(4), S42-S54.
Hatfield, J. L. (1983), Evapotranspiration obtained from remote sensing methods. In
Advances in irrigation, edited by D. E. Hilled, pp. 395-416, Academic Press, New
York.
Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira (2002),
Overview of the radiometric and biophysical performance of the MODIS
vegetation indices, Remote Sensing of the Environment, 83, 195-213.
Huete, A. R. (1988), A soil-adjusted vegetation index (SAVI), Remote Sensing of the
Environment, 25, 295-309.
Huete, A. R., K. Didan, Y. E. Shimabukuro, P. Ratana, S. R. Saleska, L. R. Hutyra, W.
Yang, R. Nemani, and R. Minen (2006), Amazon rainforest green-up with
sunlight
in
dry
season,
Geophysical
Research
Letters,
33,
L06405,
doi:10.1029/2005GL025583.
23
Hutyra, L. R., J. W. Munger, C. A. Nobre, S. R. Saleska, S. A. Vieira, and S. C. Wofsy,
(2005), Climatic variability and vegetation vulnerability in Amazonia,
Geophysical Research Letters, 32, L24712, doi: 10.1029/2005GL024981.
Jackson, R. D., R. J. Reginato, and S. B. Idso (1977), Wheat canopy temperature: a
practical tool for evaluating water requirements, Water Resources Research, 13,
651-656.
Jackson, R. D. (1988), Surface temperature and the surface energy balance. In Flow and
transport in the nature environment: Advances and application, edited by W. L.
Steffen and O. T. Dermard , pp. 384, Springer-Verlag, Berlin.
Jackson, R. D., J. L. Hatfield, R. J. Reginato, S. B. Idso, and P. J. Pinter Jr. (1983),
Estimation of daily evapotranspiration from one time-of-day- measurements,
Agricultural and Water Managements, 7,351-362.
Justice, C. O., E. Vermote, J. R. G. Townshend, R. Defries, D. P. Roy, D. Hall. V.
Salomonson, J. Privette, G. Riggs, A. Strahler, W. Lucht, R. Myneni, Y.
Knyazikhin, S. Running, R. Nemani, Z. Wan, A. Huete, W. van Leeuwen, R.
Wolfe, L. Giglio, J. Muller, P. Lewis, and M. Barnsley (1998). The Moderate
Resolution Imaging Spectroradiometer (MODIS): Land remote sensing for global
change research, IEEE Transactions on Geoscience and Remote Sensing, 36(4),
1228-1248.
Kaufman, Y. J., and Tanré, D. (1992), Atmospherically resistant vegetation index (ARVI)
for EOS-MODIS, IEEE Trans. Geosci. Remote Sensing, 30, 261-270.
Keller, M., A. Alencar, G. P. Asner, B. Braswell, M. Bustamante, E. Davidson, T.
Feldpausch, E. Fernandes, M. Goulden, P. Kabat, B. Kruijt, F. Luizao, S. Miller,
24
D. Markewitz, A. D. Nobre, C. A. Nobre, N. P. Filho, H. da Rocha, P. L. D. S.
Dias, C. von Randow, and G. Vourilitis (2004), Ecological research in the largescale biosphere-atmosphere experiment in Amazonia: Early results, Ecological
Applications, 14(4), S3-S16.
Kruijt, B., J. A. Elbers, C. Von Randow, A. C. Araujo, P. J. Oliveira, A. Culf, A. O.
Manzi, A. D. Nobre, P. Kabat, and E. J. Moors, 2004: The robustness of eddy
correlation fluxes for Amazon rain forest conditions. Ecological Applications,
14(4), S101-S113.
Kustas, W. P. (1990), Estimates of evapotranspiration with a one-and two-layer model of
heat transfer over partial canopy cover, Journal of Applied Meteorology, 29,704715.
LBA - The Large Scale Biosphere-Atmosphere Experiment in Amazonia (1996), Concise
Experimental Plan. Compiled by the LBA Science Planning Group. Document
available at CPTEC/INPE, Cachoeira Paulista, SP, Brazil.
Li, W., and R. Fu (2004), Transition of the large-scale atmospheric and land surface
conditions from the dry to the wet season over Amazonia as diagnosed by the
ECMWF Reanalysis, Journal of Climate, 17, 2637-2651.
Loukas, A., L. Vasiliades, C. Domenikiotis, and N. R. Dalezios (2005), Basin-wide actual
evapotranspiration estimation using NOAA/AVHRR satellite data, Physics and
Chemistry of the Earth, 30, 69-79.
Malhi, Y., E. Pegoraro, A. D. Nobre, M. G. P. Pereira, J. Grace, A. D. Culf, and R.
Clement (2002), Energy and water dynamics of a central Amazonian rain forest,
25
Journal
of
Geophysical
Research-Atmospheres,
107(D20),
8061,
10.1029/2001JD000623.
Malhi, Y., and J. Wright (2004), Spatial patterns and recent trends in the climate of
tropical rainforest regions, Philosophical Transactions of the Royal Society of
London B, 359, 311-329, doi: 10.1098/rstb.2003.1433
Marengo, J.A., C. A. Nobre, J. Tomasella, M. D. Oyama, G. S. de Oliveira, R. de
Oliveira, H. Camargo, and L. M. Alves, in press.. The drought of Amazonia in 2005.
Journal of Climate.
Margulis, S. (2003), Causes of deforestation of the Brazilian Amazon. World Bank
working paper no. 22.
Min, Q. L. and B. Lin (2006), Remote sensing of evapotranspiration and carbon uptake at
Harvard Forest, Remote Sensing of Environment, 100, 379-387.
Miura, T., A. R. Huete, W. J. D. van Leeuwen, and K. Didan (1998), Vegetation
detection through smoke-filled AVIRIS images: an assessment using MODIS
band passes, Journal of Geophysical Research, 103, 32,001-32,011.
Miura, T., A. R. Huete, H. Yoshioka, and B. N. Holben (2001), An error and sensitivity
analysis of atmospheric resistant vegetation indices derived from dark targetbased atmospheric correction, Remote Sensing of the Environment, 78, 284-298.
Monteith, J. L. (1981), Evaporation and Surface-Temperature, Quarterly Journal Of The
Royal Meteorological Society, 107(451), 1-27.
Monteith, J. L. (1988), Does transpiration limit the growth of vegetation or vice versa,
Journal of Hydrology, 100(1-3), 57-68.
26
Moran, M. S., R. D. Jackson, L. H. Raymond, L. W. Gay, and P. N. Slater (1989),
Mapping surface energy balance components by combining Landsat thematic
mapper and ground-based meteorological
data, Remote Sensing of the
Environment, 30, 77-87.
Nagler, P. L., J. Cleverly, E. Glenn, D. Lampkin, A. Huete, and Z. Wan (2005),
Predicting riparian evapotranspiration from MODIS vegetation indices and
meteorological data, Remote Sensing of the Environment, 94, 17-30.
Negri, A. J., R. E. Adler, L. Xu (2004): The impact of Amazonian deforestation on dry
season rainfall, Journal of Climate, 17(3), 1306-1319.
Negrón Juárez, R. I., M. G. Hodnett, R. Fu, M. L. Goulden, and C. von Randow (2006),
Control of dry season evapotranspiration over Amazonian forest as inferred from
observations at a Southern Amazon forest site, Journal of Climate (accepted).
Negrón Juárez, R. I., W. Li, R. Fu, K. Fernandes, and A. O. Cardoso (2007), Dataset
precipitation comparison over tropical land areas (in progress).
Nemani, R., M. White, P. Thornton, K. Nishida, S. Reddy, J. Jenkins, S. Running (2002),
Recent trends in hydrologic balance have enhanced the terrestrial carbon sink in
the United States, Geophysical Research Letters, 29(10), Art. No. 1468, doi:
10.1029/2002GL014867.
Nepstad, D. C., C. R. de Carvalho, E. A. Davison, P. H. Jipp, P. A Lefebvre, G. H.
Negreiros, E. D. da Silva, T. A. Stone, S. E. Trembore, and S. Vieira (1994), The
role of deep roots in the hydrological and carbon cycles of Amazonian forests and
pastures. Nature, 372(6507), 666-669.
27
Nepstad, D., P. Lefebvre, U. L. Da Silva, J. Tomasella, P. Schlesinger, L. Solorzano, P.
Moutinho, D. Ray, and J. G. Benito (2004), Amazon drought and its implications
for forest flammability and tree growth: a basin-wide analysis, 10, 704-717,
doi:10.1111/j.1529-8817.2003.00772.x
Nishida K., R. R. Nemani, S. W. Running, and J. M. Glassy (2003), An operational
remote sensing algorithm of land surface evaporation, Journal of Geophysical
Research, 108(D9), 4270, doi: 10.1029/2002JD002062.
Nobre, C. A., P. J. Sellers, and J. Shukla (1991), Amazon deforestation and regional
climate change, Journal of Climate, 4, 957-988.
Oliveira, R. S., T. E. Dawson, S. S. O. Burgess, and D. C. Nepstad (2005a), Hydraulic
redistribution in three Amazonian trees, Oecologia, 145(3), 354-363, doi:
10.1007/s00442-005-0108-2.
Oliveira, R. S., L. Bezerra, E. A. Davidson, F. Pinto, C. A. Klink, D. C. Nepstad, and A.
Moreira (2005b), Deep root function in soil water dynamics in cerrado savannas
of central Brazil, Functional Ecology, 19(4), 574-581.
Oliver, M. A., and R. Webster (1990), Kriging: a method of interpolation for
geographical information system, International Journal of Geographical
Information Systems, 4(3), 313-332.
Potter C., E. Davidson, D. Nepstad, and C. R. de Carvalho, 2001: Ecosystem modeling
and dynamic effects of deforestation on trace gas fluxes in Amazon tropical
forest. Forest Ecology and Management, 152, 97-117.
Priestley, C. H. B., and R. J. Taylor (1972), On the assessment of surface heat flux and
evaporation using large-scale parameters, Monthly Weather Review, 100, 81-92.
28
Sakay, R. K., D. R. Fitzjarrald, and K. Moore, 2001: Importance of low-frequency
contributions to eddy fluxes observed over rough surfaces. Journal of Applied
Meteorology, 40(12), 2178-2192.
Salati, E. (1987), The forest and the hydrological cycle, in The Geophysiology of
Amazonia. Vegetation and Climate Interactions, edited by R. E. Dickinson, pp.
273-296, John Wiley & Sons, New York.
Shuttleworth, W. J. (1988), Evaporation from Amazonian Rainforest, Proceedings of The
Royal Society of London Series B-Biological Sciences, 233 (1272), 321-346.
Silva Dias, P. L. S., and J. A. Marengo (1999), Atmospheric water (in Portuguese). Aguas
doces no Brasil, edited by C. Rebouças, B. Braga, and J. G. Tundisi, pp. 65-115.
Escrituras Editora, São Paulo.
Sombroek, W. (2001), Spatial and temporal patterns of Amazon rainfall, Ambio, 30(7),
388-396.
Sommer, R., M. Denich, and P. L. G. Vlek (2000), Carbon storage and root penetration in
deep soils under small-farmer land-use systems in the Eastern Amazon region,
Brazil, Plant Soil, 219, 231-241.
Sommer, R., T. D. D. Sa, K. Vielhauer, A. C. de Araujo, H. Folster, and P. L. G. Vlek
(2002), Transpiration and canopy conductance of secondary vegetation in the
eastern Amazon, Agricultural and Forest Meteorology, 112(2), 103-121.
Souza Filho, J. D., A. Ribeiro, M. H. Costa, and J. C. Cohen (2005), Control mechanisms
of the seasonal variation of transpiration in a northeast Amazonia tropical
rainforest (in Portuguese), Acta Amazonica, 35(2), 223-229.
29
Stokstad, E., (2005), Experimental drought predict grim future for rainforest, Science,
308,346-347.
Su H. B., M. F. McCabe, E. F. Word, Z. Su, and J. H. Prueger (2005), Modeling
Evapotraspiration during SMACEX: Comparing two approaches for local and
regional scale prediction, Journal of Hydrometeorology, 6(6), 910-922.
Valdoire, A., and J. F. Royer (2004), Tropical deforestation and Climate variability,
Climate Dynamics, 22, 857-874, doi: 10.1007/s00382-004-0423-z.
Vermote, E. F., N. El Saleous, and C. Justice (2002), Atmospheric correction of the
MODIS data in the visible to middle infrared: First results, Remote Sensing of the
Environment, 83(1-2), 97-111.
von Randow, C., A. O. Manzi, B. Kruijt, P. J. de Oliveira, F. B. Zanchi, R. L. Silva, M.
G. Hodnett, J. H. C. Gash, J. A. Elbers, M. J. Waterloo, F. L. Cardoso, and P.
Kabat (2004), Comparative measurements and seasonal variations in energy and
carbon exchange over forest and pasture in South West Amazonia. Theoretical
and Applied Climatology, 78(1-3), 5-26, doi: 10.1007/s00704-004-0041-z.
Vourlitis, G. L., N. Priante, M. M. S. Hayashi, J. D. Nogueira, F. T. Caseiro, and J. H.
Campelo (2002), Seasonal variations in the evapotranspiration of a transitional
tropical forest of Mato Grosso, Brazil, Water Resources Research, 38(6), 1094,
doi: 10.1029/2000WR000122
Williams, M, Y. Malhi, A. D. Nobre, E. B. Ratetter, J. Grace, and M. G. P. Pereira
(1998), Seasonal variation in a Brazilian rain forest: a modeling analysis, Plant,
Cell and Environment, 21, 953-968.
30
Wilson, K., A. Goldstein, E. Falge, M. Aubinet, D. Baldocchi, P. Berbigier, C. Bernhofer,
R. Ceulemans, H. Dolman, C. Field, A. Grelle, A. Ibrom, B. E. Law, A.
Kowalski, T. Meyers, J. Moncrieff, R. Monson, W. Oechel, J. Tenhunen, R.
Valentini, and S. Verma (2002), Energy balance closure at FLUXNET sites,
Agricultural and Forest Meteorology, 113(1-4), 223-243, doi: 10.1016/S01681923(02)00109-0.
Xiao, X., S. Hagen, Q. Zhang, M. Keller, and B. Moore III, (2006). Detecting leaf
phenology of seasonal moist tropical forest in South America with multi-temporal
MODIS images, Remote Sensing of the Environment, 103, 465-473.
Zhang, Y., W. B. Rossow, and A. A. Lacis (1995), Calculation of surface and top of
atmosphere radiative fluxes from physical quantities based on ISCCP data sets1.
Method and sensitivity to input data uncertainties, Journal of Geophysical
Research, 100, D1, 1149-1165.
Zhang, Y., W. B. Rossow, A. A. Lacis, V. Oinas, and M. I. Mishchenko (2004),
Calculation of radiative fluxes from the surface to top of atmosphere based on
ISCCP and other global data sets: Refinements of the radiative transfer model and
the
input
data.
Journal
of
Geophysical
Research,
109,
D19105,
doi:10.1029/2003JD004457.
31
Table 1. Sites used in this study.
Bragantina1
(Igarapé-Açu, Belém, Pará)
Cuieiras reserve
(Manaus, Amazonas)
Cuieiras reserve
(Manaus, Amazonas)
Ducke Reserve
(Manaus, Amazonas)
Tapajós National Forest
( Santarém, Pará)
Tapajós National Forest
(Santarém, Pará)
Biological Reserve of Jarú
(Jí-Paraná, Rondônia)
BRG
South
Latitude
(°)
1.1850
CRS
2.5894
60.1153
Jun-Aug
K34
2.6090
60.2093
Jun-Aug
DCK
2.9500
59.9500
Jun-Aug
K67
2.8853
54.9205
Jul-Dec
K83
3.0502
54.9280
Jul-Dec
RJA
10.0784
61.9337
Jun-Aug
Sinop5
(Sinop, Mato Grosso)
SIN
11.4125
55.3250
Jun-Sep
Sites
1
2
3
4
5
West
Longitude
(°)
47.5706
Sep-Dec
Dry
season
Source
(period of study)
Sommer et al. (2002)
(Apr 97 – Mar 98)
Malhi et al. (2002)
(Sep 95 – Aug 96)
Araújo et al. (2002)2
(Jul 99 – Sep 00)
Shuttleworth (1988)
(Sep 83 – Sep 85)
Hutyra et al. (2005)3
(Fev 00 – Dec04)
da Rocha et al. (2004)4
(Jul 00 – Dec 02)
von Randow et al. (2004)2
Negrón Juárez et al. (2006)2
(Jan 00 – Dec02)
Vourlitis et al. (2002)
(Aug 99 – Jul 00)
Secondary vegetation. Its height changed from h= 2.3 (Apr 97) to h= 3.5 m (Mar 98)
data available at http://beija-flor.ornl.gov/lba/
data available at ftp://ftp.as.harvard.edu/pub/tapajos/k67_flux/
data available at http://www.ess.uci.edu/%7Elba/
Transitional (ecotonal) tropical forest.
32
Table 2. Determination coefficient (r2) and F-test for the observed ET and the modeled
equation over each site (ETmodsite) and the general equation for all the sites
(ETmodgrl). F-statistic in italic means that observed and modeled values have
significantly different variances (the significance is lower than 0.05)
Sites
K67
K83
K34
RJA
r2
0.73
0.75
0.80
0.32
ETmodsite
F-test
1.61
1.33
1.26
3.12
r2
0.51
0.74
0.39
0.31
ETmodgrl
F-test
1.96
2.16
3.17
2.34
33
Figure 1. Study area and sites used. The background is the Climatological (1961-1990)
annual precipitation (mm) over the Brazilian Legal Amazon. For description of sites used
see Table 1. The states are: AC: Acre, AM: Amazonas, AP: Amapá, MA: Maranhão, MT:
Mato Grosso, PA: Pará, RO: Rondônia, RR: Roraima, TO: Tocantins
34
Figure 2. Relationship between net radiation (Rn, Wm-2) and the latent heat flux (E,
Wm-2) at sites K34, K83, and RJA.
35
Figure 3. Comparison of monthly EVI time series at 1km and 0.25° of horizontal
resolution at K67, K83, K34, and RJA sites for the period 2000-2004.
36
Figure 4. Comparison between observed net radiation (Rn obs, Wm-2) and calculated
from ISCCP data (RnISCCP) at 2.5° and 0.25° of horizontal resolution at K67, K83, K34,
and RJA sites.
37
Figure 5. Comparison between observed (ETobs, solid black line) and calculated values
of evapotranspiration using a calibrated model for each site (ETsite, gray line, and open
circles) and a general model (ETgrl, gray line, and full squares) based on K83 and RJA
sites for sites (a)K83, (b)RJA, (c)K67, (d)K34.
38
Figure 6. Observed (obs, black bar) and calculated values of evapotranspiration (ET) at
1km (1km, light gray bar) and 0.25° (0.25°, dark gray bar) of horizontal resolution during
(a) wet season and (b) dry season. The observed ET was obtained for periods described in
Table 1 and the calculated ET values correspond to the period 2000-2004 (except 2002).
Sites in italic indicate that the periods of observed ET values are out of the estimated
period.
39
Figure 7. Three-month average value (DJF, MAM, JJA, SON) and annual average value
(Annual) of (a) calculated ET (mm day-1) (b) precipitation (mm day-1), over the Brazilian
Legal Amazon. Values were calculated for the period 2000 to 2004. Precipitation
corresponds to the TRMM 3B43 data sets.
40
Figure 8. Monthly precipitation and EVI for years 2004 and 2005 over (a) A1 (2°S5°S,53°W-56°W) and (b) A2 (5°W-7.5°W,65°W-70°W). The monthly Climatological
precipitation is also showed.
41
Figure 9. Areas in the Brazilian Legal Amazon with recurrent problems of MODIS EVI
quality (see text for explanation).
42
Figure 10. Calculated ET for the period 2000-2004 and microwave emissivity difference
vegetation index (EDVI) for the period 2002-2004) over one site in the northern (K34)
and Southern (RJA) of the Amazon basin.
43
Figure 11. Percentage of evapotranspiration respect to precipitation over the Brazilian
Legal Amazon for the period 2000-2004.
44
Figure 12. Distribution of forest (1) and savanna (2) across the Brazilian Legal Amazon.
Area inside of solid line represents roughly areas with strong deforestation (source:
http://www.obt.inpe.br/prodes/)
45