Temporal Patterns of Energy Balance for a Brazilian ... · Temporal Patterns of Energy Balance for...

Hindawi Publishing CorporationInternational Journal of Atmospheric SciencesVolume 2013 Article ID 326010 9 pageshttpdxdoiorg1011552013326010

Research ArticleTemporal Patterns of Energy Balance for a Brazilian TropicalSavanna under Contrasting Seasonal Conditions

Thiago R Rodrigues1 Seacutergio R de Paulo1 Jonathan W Z Novais1 Leone F A Curado1

Joseacute S Nogueira1 Renan G de Oliveira1 Francisco de A Lobo2 and George L Vourlitis3

1 Instituto de Fısica Universidade Federal de Mato Grosso 78060-900 Cuiaba-MT Brazil2 Departamento de Agronomia e Medicina Veterinaria Universidade Federal de Mato Grosso 78060-900 Cuiaba-MT Brazil3 Department of Biological Sciences California State University San Marcos San Diego CA 92096 USA

Correspondence should be addressed toThiago R Rodrigues thiagorangelpgfaufmtbr

Received 5 February 2013 Revised 3 June 2013 Accepted 10 June 2013

Academic Editor Dimitris G Kaskaoutis

Copyright copy 2013 Thiago R Rodrigues et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The savanna of Central Brazil (locally known as cerrado) has a long history of land cover change due to human activity Thesechanges have led to the degradation of cerrado forests and woodlands leading to the expansion of grass-dominated cerrados andpastures Thus the aim of this study was to evaluate the temporal variation in energy flux in areas of degraded grass-dominatedcerrado (locally known as campo sujo) in Central BrazilThe amount of119877

119899partitioned intoH declined asmonthly rainfall increased

and reached a level of approximately 30 during the wet season while the amount of 119877119899partitioned into 119871

119890increased as monthly

rainfall increased and reached a level of approximately 60 during the wet season As a result H was significantly higher than 119871119890

during the dry season resulting in a Bowen ratio (120573=H119871119890) of 3-5 while Le was higher than H during the wet season resulting in

a 120573 asymp 1 These data indicate that the energy partitioning of grass-dominated cerrado is relatively more sensitive to water availabilitythan cerrado woodlands and forests and have important implications for local and regional energy balance

1 Introduction

Tropical savannas cover about 12 of the global land surface[1] and are characterized by high plant species diversity [2]In Brazil savanna (locally known as cerrado) covers about24 of the territory mostly in the central portion and is thedominant vegetation in areas where the dry season causesprolonged periods of plantwater stress [3ndash5]Thebiodiversityof cerrado is extremely high and is estimated to be 160000species including known plants animals and fungi [6]

Over the last few decades cerrado has been convertedto cattle pasture and more recently soybean and sugar caneagriculture [2 7] and has experienced deforestation ratesmuch higher than in the Amazon rainforest [8] Anothermajor threat to the remaining areas of cerrado is the decreaseof thewoody component due to the increase of anthropogenicfire frequency [9] converting the vegetation to a moreopen and shallow-rooted ecosystem These changes have

the potential to cause multiple changes in the structureand function of cerrado [7 10 11] however this biomehas received relatively little attention from researchers incomparison with tropical rainforests [12 13]

The change in land cover has the potential to changeenergy partitioning by affecting the seasonal pattern andmagnitude of radiation balance and albedo [12] and energypartitioning in the form of latent [14ndash16] sensible [17 18]and soil heat flux [19] which in turn will feedback on localand perhaps regional climate [20 21] For example tropicalforest conversion to pasture can cause a 15ndash20 kPa increasein vapor pressure deficit and a 5ndash10∘C increase in soil surfacetemperature relative to intact forest [22] Land cover changecan also lead to an increase in the duration of the dry seasoncause more rainfall to be partitioned into runoff affect thedevelopment of the nocturnal and convective boundary layerand destabilize regional rainfall regimes and surface wateravailability [12 20]

2 International Journal of Atmospheric Sciences

Given the potential for land cover change to alter surfaceenergy balance in cerrado we evaluated the seasonal andinterannual variations of energy partitioning in a degradedgrass-dominated cerrado (locally known as campo sujo) ofCentral Brazil We used Bowen ratio energy balance (BREB)methods over two consecutive years to characterize theseasonal and interannual variations in energy flux dynamicsWe hypothesized that campo sujo cerrado would exhibithigher rates of sensible and ground heat flux than latent heatflux especially during the dry season when surface wateravailability required for sustaining latent heat flux would beminimal Little is known about the seasonal and interannualvariations in energy balance for campo sujo cerrado andtesting these hypotheses is important for understanding howland degradation will affect energy balance

2 Materials and Methods

21 Site Description The experimental site was located inSanto Antonio de Leverger MT Brazil which is 15 km southof Cuiaba (15∘431015840S 56∘041015840W) The study site is within agrass-dominated cerrado that was degraded approximately35 years ago after the partial clearing of cerrado woodlandvegetation According to Koppen the climate of region ischaracterized as Aw tropical semihumid with dry wintersand wet summers Mean annual rainfall and temperature are1420mm and 265∘C respectively and rainfall is seasonalwith a dry season extending from May to September [11]The research area is on flat terrain at an elevation of 157mabove sea level The regional soil type is a rocky dystrophicred-yellow latosol locally known as a Solo ConcrecionarioDistrofico [23]

22 Data Collection A micrometeorological tower enabledthe collection of data on air temperature (119879

119886) relative

humidity (RH) wind speed (119906) precipitation (119875) soil tem-perature (119879

119904) soil heat flux (119866) net radiation (119877

119899) global

solar radiation (119877119892) and soil moisture (119902) 119879

119886and RH

were measured 5m and 18m above the ground level usingthermohygrometers (HMP45AC Vaisala Inc Woburn MAUSA) 119866 was measured using two heat flux plates (HFP01-L20 HuksefluxThermal Sensors BV Delft The Netherlands)installed 10 cm below the soil surface with one placed ina sandy soil type and the other placed in a laterite soiltype which were typical of the local soil 119877

119899and 119877

119892were

measured 5m aboveground using a net radiometer (NR-LITE-L25 Kipp amp Zonen Delft The Netherlands) and apyranometer (LI200X LI-COR Biosciences Inc LincolnNE USA) respectively Precipitation was measured using atipping-bucket rainfall gauge (TR-525M Texas ElectronicsInc Dallas TX USA) The sensors are connected to adatalogger (CR1000 Campbell Scientific Inc Logan UTUSA) that scanned each sensor every 30 seconds and storedaverage and in the case ofP total quantities every 30minutes

23 Data Processing Data were collected between May 2009and April 2011 Fluxes of latent (119871

119890) and sensible (119867) heat

were calculated over 30-minute intervals using Bowen ratio

and energy balance (BREB) techniques [24] following theguidelines and modifications described by Perez et al [25]Bowen ratio methods have been used for decades and whileother methods such as eddy covariance may be more directand amenable to analysis of measurement error there areobjective methods that are available for minimizing errorsassociated with resolving small gradients in vapor pressure ortemperature caused by poor instrument performance andoratmospheric conditions [25 26]

The balance of energy was calculated as

119877119899= 119866 + 119867 + 119871

119890 (1)

where 119877119899(J mminus2 sminus1) was measured by net radiometer and 119866

(J mminus2 sminus1) was the mean heat flux in the soil measured by thesoil heat flux plates installed in the sandy and laterite soils119867and 119871

119890were calculated as a function of the Bowen ratio (120573)

120573 =119867

119871119890

(2)

which in turn can be calculated as a function of the airtemperature (Δ119879) and vapor pressure (Δ119890) gradients and thepsychrometric constant [24]

120573 = 120574Δ119879

Δ119890 (3)

For (3) actual vapor pressure (119890)was calculated as a functionof saturation vapor pressure (119890

119904) and RH using (4) and (5)

respectively

119890119904= 2172 times 10

7

times 119890minus4157((119879minus273)minus3391)

(4)

119890 =119877119867 times 119890

119904

100(5)

while psychrometric constant (120574) was calculated as a func-tion of the specific heat at constant pressure (119862

119901=

1010 J kgminus1 Cminus1 according to [27]) the local atmosphericpressure (119901 = 103 kPa at the research site)

120574 =

119862119901times 119901

0622 times 119871 (6)

and the latent heat of vaporization (119871) which varies as a fun-ction of temperature [28]

119871 = 1919 times 106

times (119879 + 273

(119879 + 273) minus 3391)

2

(7)

With estimates of 120573 (3) 119871e can be calculated as

119871119890=(119877119899minus 119866)

(120573 + 1) (8)

and119867 can be calculated as the difference between 119877119899 119866 and

119871119890using (1) [25]The criteria for accepting data collected from the Bowen

ratio method were based on those described by Perez et al[25] Briefly the Bowen ratio method fails when (1) sensor

International Journal of Atmospheric Sciences 3

resolution is inadequate to resolve gradients in 119890 and 119879119886 (2)

stable atmospheric conditions such as during the dawn anddusk cause 120573 asymp minus1 and (3) conditions change abruptlyleading to errors in measurement [25 26] Using this filteringmethod physically realistic values of 120573 can be obtained in anobjective quantitative manner which limits the potential forbias and error in estimating energy balance terms [25 29]Gaps in estimates of 119867 and 119871

119890were filled by using linear

relationships between retained values of 119867 andor 119871119890and

measured values of 119877119899-119866

The percentage of available energy (119877119899) partitioned into

119871119890and 119867 was determined using linear regression where

diel (24 h) average 119867 or 119871119890(dependent variables) was

regressed against 119877119899over monthly intervals The slope of

these regressions indicates the relative partitioning of 119877119899into

119867 or 119871119890 Seasonal and annual differences between energy

balance terms were statistically analyzed using bootstraprandomization techniques where the mean and the 95confidence interval were calculated by randomly resamplingeach energy flux variable time series over 1000 iterations [30]

3 Results and Discussion

31 System Performance Approximately 63 of all possible 120573values were retained after filtering for inadequate resolutionstable atmospheric conditions and abrupt changes in mea-surement conditions [25] Independent measurements of 119871

119890

were obtained from eddy covariance in April 2011 to assessthe performance in the Bowen ratio energy balance estimatesUsing linear regression with the Bowen ratio estimates of119871119890as the dependent variable the mean (plusmn95 confidence

interval) intercept and slope were minus1354 plusmn 489Wm2 and096 plusmn 005 respectively (1198772 = 080 119899 = 1326 observations)These data indicate that estimates of 119871e derived from twoindependent measurement systems were comparable andprovide confidence in the Bowen ratio time series reportedhere

32 Seasonal and Interannual Variations inMicrometeorologySeasonal and interannual variations in micrometeorologywere large over the study period (Figure 1) For exampleprecipitation varied from 0mm in June 2010 to 380mm inMarch 2011 in general the months of JunendashAugust werethe driest and the months of JanuaryndashMarch were thewettest (Figure 1(a)) Both years had a pronounced dry season(defined as the number of months when precipitation waslt100mmmonth) however the dry season in 2010 (AprilndashDecember 9 months) was substantially longer than the dryseason in 2009 (AprilndashAugust 5 months) (Figure 1(a)) Thedry season in 2009 was about 1 month shorter than the long-term (30 year) average and 2010 dry season was about 4months longer than the long-term average [11 31] Normallythe rainy season commences in OctoberndashNovember in theCuiaba Basin [31] indicating an early transition to the rainyseason in 2009 and a late transition in 2010 (Figure 1(a))Total rainfall was 1415m in 2009-2010 (May 1ndash31 April) and1353mm in 2010-2011 even with large interannual differences

in dry season length These rainfall totals are similar to thelong-term average of 1420mm for the region [11 31]

Temporal trends in relative humidity were positively cor-related (119903 = 067 119875 lt 005) with trends in precipitation withthe highest values (70ndash80) during the peak of thewet seasonand the lowest values (ca 60 in 2009 and 45 in 2010)in August-September of the dry season (Figure 1(a)) Airtemperature trends were positively correlated with RH (119903 =065 119875 lt 005) however cross-correlation analysis indicatedthat RH lagged behind air temperature by approximatelytwo months (Figure 1(a)) Air temperature typically reacheda minimum in June and increased consistently at the endof the dry season (August-September) to a peak in the wetseasonThe increase in temperature toward the end of the dryseason coupled with corresponding increases in convectionand humidity serves as an important trigger in the transitionto the wet season [31]

Seasonal patterns in solar radiation (119877119892) were negatively

correlated with rainfall (119903 = minus47 119875 lt 005 Figure 1(b)) dueto frequent cloud cover during the wet season [31] However119877119899was negatively correlatedwith119877

119892(119903 = minus067) presumably

because low leaf area index (LAI) during the dry season[32] when 119877

119892was at a seasonal maximum causes a higher

proportion of 119877119892to be reflected [33]

33 Average Diel Patterns in Energy Partitioning Averagediel (24 h) patterns of 119877

119892and 119877

119899were consistent from

month to month with maximum values observed during themidday (1200 h) local time (Figure 2(a)) Diel peaks in 119877

119892

ranged from a maximum of 823 Jmminus2 sminus1 in April 2009 toa minimum of 568 Jmminus2 sminus1 in June 2009 while peaks in119877119899ranged from a maximum of 619 Jmminus2 sminus1 in April 2009

to a minimum of 407 Jmminus2 sminus1 in June 2009 (Figure 2(a))However peaks in average diel119877

119892were typically lower during

the wet season while peaks in average diel 119877119899were higher

in the wet season which is consistent with patterns observedin average monthly 119877

119899and 119877

119892described above (Figure 1(b))

These seasonal dynamics were likely due to variations incloud cover and LAI was described above [31ndash33]

Average diel trends in 119867 and 119871119890followed average diel

trends in radiation closely (Table 1) however seasonal vari-ations in energy fluxes were large (Figure 2(b)) For exam-ple coefficients of determination (1199032) for linear regressionsbetween 119877

119899and 119867 were typically gt094 while 1199032 values for

linear regressions between 119877119899and 119871

119890were typically gt087

expect during the peak of the dry season (August andorSeptember) when water limitation caused reductions in 119871

119890

(Table 1 Figure 2(b)) While diel variations in 119877119899controlled

the diel variations in 119871119890and 119867 (Table 1) the proportion

of energy partitioned into 119871119890or 119867 varied depending on

rainfall Substantially more119877119899was partitioned into 119871

119890during

the wet season while substantially more 119877119899was dissipated

by 119867 during the dry season (Table 1) Midday peaks in 119867exceeded peaks in 119871

119890 at times by as much as 2-fold during

the dry season but during the wet season the middaypeak in 119871

119890exceeded the peak in 119867 by the same amount

(Figure 2(b)) The relative difference in the amount of 119877119899

partitioned into 119871119890and119867 was found to be in part controlled


22

24

26

28

30

32

Relat

ive h

umid

ity (

)

40

50

60

70

80

90

Prec

ipita

tion

(mm

)

100

200

300

400

2009 2010 2011

Ta

RHP

Air

tem

pera

ture

(∘ C)

MayApr Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar MayApr MayAprJun Jul Aug Sep Oct Nov Dec Jan Feb Mar

(a)

22

24

26

28

30

32

Sola

r rad

iatio

n (M

J mminus2

dminus1)

Net

radi

atio

n (M

J mminus2

dminus1)

2009 2010 2011

Rg

Rn

13

12

11

10

9

8

7

6

34

20

18MayApr Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar MayApr MayAprJun Jul Aug Sep Oct Nov Dec Jan Feb Mar

(b)

Figure 1 (a) Average monthly air temperature (119879119886 closed symbols) relative humidity (RH open symbols) and total monthly precipitation

(P closed bars) (b) average monthly solar radiation (119877119892 closed symbols) and net radiation (119877

119899 open symbols) for the campo sujo cerrado

at Fazenda Miranda during the study period The shaded areas define the climatological dry season defined as the consecutive months whenprecipitation lt 100mmmonth

by monthly rainfall (Figure 3) For example the proportionof 119877119899partitioned into 119871

119890increased as rainfall increased up to

100mmmonth and then leveled off at approximately 050 athigher monthly rainfall rates (Figure 3(a)) Similarly the pro-portion of119877

119899partitioned into119867declined as rainfall increased

to approximately 100mmmonth and then stabilized at 030 athighermonthly levels of rainfall (Figure 3(b))These seasonaldynamics in energy partitioning are consistent with datareported from topical pastures grass-dominated savannaand semiarid temperate ecosystems [33ndash36] but are muchmore variable compared to cerrado woodlands and tropicalforests [11 14 37ndash40]

Diel variations in 119866 also followed average diel trends inradiation closely (Figure 2(b)) but as with H and 119871

119890 there

were large seasonal variations 119866 exhibited wider diel vari-ations during the dry season ranging from minus60 Jmminus2 sminus1 atnight to asmuch as 200 Jmminus2 sminus1 during the day while duringthe wet season 119866 ranged from approximately minus30 Jmminus2 sminus1 atnight to on average 100 Jmminus2 sminus1 during the day (Figure 2(b))Such large seasonal fluctuations in 119866 reflect the seasonal

variations in soil thermal conductivity which are affected byvariations in rainfall and soilmoisture and seasonal variationin vegetation coverage which influences exposure of soil to119877119899[19] In general soil thermal conductivity increases with

soil moisture [41] which should result in higher 119866 duringthe wet season however increased plant growth and coverduring the wet season cause shading of the soil surface whichcounteracts the increase in soil thermal conductivity

34 Seasonal Patterns in Energy Balance Temporal variationsin energy fluxes were large over daily seasonal and annualtime scales (Figure 4 Table 2) During both years119867 was sig-nificantly larger than 119871

119890during the dry season reflecting the

larger surface-to-air temperature gradient [31] and the lowerwater availability that are typical of the dry season in south-central Mato Grosso [14] Mean (plusmn95 confidence interval(CI)) values of119867were 481plusmn 037 and 600plusmn 028MJmminus2 dminus1during the 2009 and 2010 dry seasons respectively resultingin amean (plusmn95CI)120573 of 304plusmn081 in 2009 and 517plusmn100 in2010 (Table 2) These large 120573 values are comparable to those


800

600

400

200

0

Rg

orR

n(J

mminus2

sminus1)

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

2009 2010 2011

Rg

Rn

MayApr Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar MayApr AprJun Jul Aug Sep Oct Nov Dec Jan Feb Mar

(a)

HL

eor

G(J

mminus2

sminus1)

H

Le

G

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

400

300

200

100

0

2009 2010 2011


(b)

Figure 2 Mean monthly diel (24 hour) variation in (a) solar radiation (119877119892 closed symbols) and net radiation (119877

119899 open symbols) (b) latent

(119871119890 open symbols) sensible (119867 closed symbols) and ground (119866 crosses) heat fluxes for the campo sujo cerrado at Fazenda Miranda

during the study period The shaded areas define the climatological dry season defined as the consecutive months when precipitationlt100mmmonth

observed for grass-dominated cerrado [2 4 42] and semiaridtemperate ecosystems [34] but substantially higher than thoseobserved in cerrado woodlands and forests [11 14 43] Thesevariations reflect a decline in the 119867 as the density of woodyvegetation declines [2]

The significantly higher 119867 in 2010 presumably reflectedthe 4-month longer dry season experienced that year(Figure 4) However it is interesting to note that 119867 wasstatistically similar in the wet and dry seasons of 2009 butin 2010 119867 was significantly lower during the wet season(Table 2) The lower wet season 119867 in 2010 appeared tobe due to heavy rainfall that occurred during the 2010-2011 wet season For example after a long (9 months) dryseason approximately 1005mm of rain was recorded forJanuaryndashMarch 2011 (Figure 1) accounting for nearly 75of all of the rainfall for the 2010-2011 measurement year

In reality 119867 began to decline relative to 119871119890as early as

November 2010 when rainfall increased but was still belowthe 100mmmonth threshold for the dry season (Figure 1)however such a high amount of rainfall during the 2011dry season would act to increase surface water availabilityand hence energy partitioning to 119871

119890and decrease surface-air

temperature gradients that drive119867 [31]In contrast 119871

119890exhibited the largest and most consistent

seasonal and interannual variations that were coincidentwith seasonal and interannual variations in rainfall (Table 2Figure 4) The decline in dry season 119871

119890was presumably due

to declines in both transpiration and evaporation duringthe long dry season For example declines in surface wateravailability lead directly to a decline in surface evaporationand transpiration in shallow-rooted grasses [42] Similarlywhile cerrado trees are thought to be deeply rooted [9]


00

02

04

06

08

Precipitation (mmmonth)0 100 200 300 400

Regr

essio

n slo

peL

eve

rsus

Rn

m = 054 lowast (1 minus exp(minus0014 lowast x))

r2 = 067 P lt 00001

(a)


Regr

essio

n slo

peH

vers

usR

n

00

01

02

03

04

05

06

07

m = 026 + 033 lowast exp(minus0021 lowast x)

r2 = 071 P lt 00001

(b)

Figure 3 The proportion of net radiation (119877119899) portioned into (a) latent heat flux (119871

119890) and (b) sensible heat flux (119867) calculated as the slope

of the slope of the linear regression between 119877119899(independent variable) and 119867 or 119871

119890(dependent variables) as a function of total monthly

rainfall Linear regression statistics are from Table 1

Ener

gy fl

ux (M

J mminus2

dminus1)

orBo

wen

ratio

10

8

6

4

2

0

120573H

Le

G

2009 2010 2011


Figure 4 Averagemonthly values of latent (119871119890 open symbols dashed lines) sensible (119867 closed symbols solid lines) and ground (119866 inverted

triangles dotted lines) heat fluxes and the Bowen ratio (120573 cross symbols long-dashed lines) for the campo sujo cerrado at Fazenda Mirandaduring the study period The shaded areas define the climatological dry season defined as the consecutive months when precipitation lt100mmmonth

declines in root and stem hydraulic conductance with soildrying often lead to a concomitant decline in stomatalconductance transpiration and leaf area during the dryseason [44 45]

4 Conclusions

Energy fluxes were measured over a two-year period in aBrazilian savanna (campo sujo cerrado) using Bowen ratioenergy balance techniques Our data indicate that rainfall wasthe primary control on the partitioning of available energy(119877119899) into sensible (119867) latent (119871

119890) and ground (119866) heat fluxes

The amount of 119877119899partitioned into 119867 declined as monthly

rainfall increased and reached a level of approximately 30during the wet season while the amount of 119877

119899partitioned

into 119871119890increased as monthly rainfall increased and reached a

level of approximately 60 during the wet season As a result119867 was significantly higher than 119871

119890during the dry season

resulting in a Bowen ratio (120573) gt 1 while 119871119890was higher than

119867 during the wet season resulting in a 120573 asymp 1 Our dataare comparable to data collected from other grass-dominatedcerrado ecosystems but seasonal variations in energy fluxesare much higher in our system compared to tree-dominatedcerrado and tropical forest because of the importance ofwaterlimitation to grasses and evaporation Given that land cover


Table 1 Linear regression statistics formean diel latent (119871119890) and sensible (119867) fluxes (dependent variables) versus net radiation for eachmonth

during the 2009ndash2011 study period at Fazenda Miranda

Year Month 119871119890

119867

Slope Intercept (Jmminus2 sminus1) 1199032 Slope Intercept (Jmminus2 sminus1) 119903

2

2009

April 040 2106 100 029 241 099May 032 2358 099 031 519 099June 026 1919 099 035 632 098July 017 2520 098 034 646 099

August minus004 1674 052 059 1401 099September 021 3321 099 033 568 099October 029 2698 099 028 611 099November 042 2391 099 024 633 100December 051 1839 099 023 616 100

2010

January 069 1956 100 013 527 094February 052 1856 100 029 541 100March 055 1985 100 028 608 100April 032 1857 099 048 585 100May 029 2250 097 047 390 100June 022 1896 093 050 748 100July 017 1773 087 054 948 100

August 001 1114 002 066 1334 099September 004 1017 019 060 938 099October 024 1854 098 043 868 100November 044 2227 100 033 606 100December 046 2045 100 033 451 100

2011

January 051 2072 100 030 532 100February 051 2040 100 028 520 100March 056 1607 100 029 389 100April 045 1999 100 031 174 100

Table 2 Mean (+95 confidence interval) net radiation (119877119899) sensible heat flux (119867) latent heat flux (119871

119890) ground heat flux (119866) and the

Bowen ratio for the dry and wet seasons and annual cycle in 2009-2010 and 2010-2011 The number of days for each season and year is shownin parentheses Confidence intervals were calculated using bootstrap randomization techniques (119899 = 1000 iterations)

Variable 2009-2010 2010-2011Dry (107) Wet (231) Annual (338) Dry (229) Wet (126) Annual (355)

119877119899(MJmminus2 dminus1) 819 plusmn 055 1131 plusmn 056 1032 plusmn 044 882 plusmn 046 1101 plusmn 060 960 plusmn 039

119867 (MJmminus2 dminus1) 481 plusmn 037 463 plusmn 027 468 plusmn 021 600 plusmn 028 390 plusmn 050 525 plusmn 027



120573 304 plusmn 081 104 plusmn 017 167 plusmn 030 517 plusmn 100 096 plusmn 027 368 plusmn 071

and climate changes are expected to lead to an increase inthe dry season duration and a decrease in rainfall the highsensitivity energy partitioning to water availability in grass-dominated cerrado has important implications to local andregional energy balance

Acknowledgments

The research was supported by Universidade Federal deMatoGrosso (UFMT) Programa de Pos Graduacao em FısicaAmbiental (PPGFA) UFMT-Grupo de Ecofisiologia vegetal(GPEV) and Coordenacao de Aperfeicoamento de Pessoal

do Ensino Superior (CAPES) Special thanks are due to DrClovis Miranda and his family for allowing this work to beconducted at Fazenda Miranda

References

[1] R J Scholes and S R Archer ldquoTree-grass interactions inSavannasrdquo Annual Review of Ecology and Systematics vol 28pp 517ndash544 1997

[2] T W Giambelluca F G Scholz S J Bucci et al ldquoEvapo-transpiration and energy balance of Brazilian savannas withcontrasting tree densityrdquo Agricultural and Forest Meteorologyvol 149 no 8 pp 1365ndash1376 2009


[3] R J LascanoReview ofModels for Predicting SoilWater BalanceSoil Water Balance in the Sudano-Shaelian Zone IAHS Press1991

[4] J J San Jose N Nikonova and R Bracho ldquoComparison offactors affecting water transfer in a cultivated paleotropicalgrass (Brachiaria decumbens Stapf) field and a neotropicalsavanna during the dry season of theOrinoco lowlandsrdquo Journalof Applied Meteorology vol 37 no 5 pp 509ndash522 1998

[5] T R Rodrigues L F A Curado J W Z Novais et alldquoDistribuicao dos componentes do balanco de energia do Pan-tanal Mato-grossenserdquo Revista De Ciencias Agro-Ambientaisvol 9 no 2 pp 165ndash175 2011

[6] P SOliveira andR JMarquisTheCerrados of Brazil ColumbiaUniversity Press New York NY USA 2002

[7] C A Klink and A G Moreira Past and Current HumanOccupation and Land-Use The Cerrados of Brazil Ecology andNatural History of a Neotropical Savanna Columbia UniversityPress New York NY USA 2002

[8] C Mueller ldquoExpansion andmodernization of agriculture in thecerradomdashthe case of soybeans in Brazilrsquos center-westrdquo WorkingPaper 306 Department of Economics University of BrasiliaBrasilia Brazil 2003

[9] R S Oliveira L Bezerra E A Davidson et al ldquoDeep rootfunction in soil water dynamics in cerrado savannas of centralBrazilrdquo Functional Ecology vol 19 no 4 pp 574ndash581 2005

[10] J A Ratter J F Ribeiro and S Bridgewater ldquoThe Braziliancerrado vegetation and threats to its biodiversityrdquo Annals ofBotany vol 80 no 3 pp 223ndash230 1997

[11] G L Vourlitis and H R da Rocha ldquoFlux dynamics in thecerrado and cerrado-forest transition of Brazilrdquo in EcosystemFunction in Global Savannas Measurement and Modeling atLandscape To Global Scales M J Hill and N P Hanan Edspp 97ndash116 CRC Boca Raton Fla USA 2011

[12] C Von Randow A O Manzi B Kruijt et al ldquoComparativemeasurements and seasonal variations in energy and carbonexchange over forest and pasture in South West AmazoniardquoTheoretical and Applied Climatology vol 78 no 1ndash3 pp 5ndash262004

[13] M Zeri and L D A Sa ldquoThe impact of data gaps and qualitycontrol filtering on the balances of energy and carbon for aSouthwest Amazon forestrdquoAgricultural and Forest Meteorologyvol 150 no 12 pp 1543ndash1552 2010

[14] G L Vourlitis J de Souza Nogueira F de Almeida Loboet al ldquoEnergy balance and canopy conductance of a tropicalsemi-deciduous forest of the southern Amazon Basinrdquo WaterResources Research vol 44 no 3 Article IDW03412 2008

[15] M A Minor ldquoSurface energy balance and 24-h evapotranspi-ration on an agricultural landscape with SRF willow in centralNew Yorkrdquo Biomass and Bioenergy vol 33 no 12 pp 1710ndash17182009

[16] S Chen J Chen G Lin et al ldquoEnergy balance and partitionin Inner Mongolia steppe ecosystems with different land usetypesrdquo Agricultural and Forest Meteorology vol 149 no 11 pp1800ndash1809 2009

[17] R A Memon D Y C Leung and L Chunho ldquoA review onthe generation determination and mitigation of Urban HeatIslandrdquo Journal of Environmental Science vol 20 pp 120ndash1282008

[18] J L Schedlbauer S F Oberbauer G Starr and K L JimenezldquoControls on sensible heat and latent energy fluxes from a short-hydroperiod Florida Everglades marshrdquo Journal of Hydrologyvol 411 no 3-4 pp 331ndash341 2011

[19] B G Heusinkveld A F G Jacobs A A M Holtslag and S MBerkowicz ldquoSurface energy balance closure in an arid regionrole of soil heat fluxrdquo Agricultural and Forest Meteorology vol122 no 1-2 pp 21ndash37 2004

[20] M H Costa and G F Pires ldquoEffects of Amazon and CentralBrazil deforestation scenarios on the duration of the dry seasonin the arc of deforestationrdquo International Journal of Climatologyvol 30 no 13 pp 1970ndash1979 2010

[21] S K Kharol D G Kaskaoutis K V S Badarinath A RSharma and R P Singh ldquoInfluence of land useland cover(LULC) changes on atmospheric dynamics over the arid regionof Rajasthan state Indiardquo Journal of Arid Environments vol 88pp 90ndash101 2013

[22] A D Culf J L Esteves A O Marques Filho and H R daRocha ldquoRadiation temperature and humidity over forest andpasture in Amazoniardquo inAmazonian Climate andDeforestationJ H CGash C ANobre JM Roberts andR L Victoria Edspp 175ndash192 J M Wiley and Sons New York NY USA 1996

[23] Radambrasil Levantamentos dos Recursos NaturaisMinisterio das Minas de Energia Secretaria Geral ProjetoRADAMBRASIL Folha SD 21 Cuiaba Rio de Janeiro Brazil1982

[24] I S Bowen ldquoThe ratio of heat losses by conduction and byevaporation from any water surfacerdquo Physical Review vol 27no 6 pp 779ndash787 1926

[25] P J Perez F Castellvi M Ibanez and J I Rosell ldquoAssessmentof reliability of Bowen ratio method for partitioning fluxesrdquoAgricultural and Forest Meteorology vol 97 no 3 pp 141ndash1501999

[26] J Z Drexler R L Snyder D Spano and U Kyaw Tha PawldquoA review of models and micrometeorological methods usedto estimate wetland evapotranspirationrdquoHydrological Processesvol 18 no 11 pp 2071ndash2101 2004

[27] J L Monteith and M Unsworth Principles of EnvironmentalPhysics Arnold London UK 1990

[28] R G Allen L S Pereira D Raes and M Smith ldquoEvapotran-spiracion del Cultivordquo in Guıas Para la Determinacion de losRequerimientos de Agua de los Cultivos p 298 Organizacionde las Naciones Unidas para la Agricultura y La Alimentacion(FAO) 2006

[29] P J Perez F Castellvi and A Martınez-Cob ldquoA simple modelfor estimating the Bowen ratio from climatic factors for deter-mining latent and sensible heat fluxrdquo Agricultural and ForestMeteorology vol 148 no 1 pp 25ndash37 2008

[30] B Efron and R Tibshirani An Introduction to the BootstrapChapman amp Hall New York NY USA 1993

[31] L A T Machado H Laurent N Dessay and I MirandaldquoSeasonal and diurnal variability of convection over the Ama-zonia a comparison of different vegetation types and large scaleforcingrdquo Theoretical and Applied Climatology vol 78 no 1ndash3pp 61ndash77 2004

[32] P Ratana A R Huete and L Ferreira ldquoAnalysis of cer-rado physiognomies and conversion in the MODIS seasonal-temporal domainrdquo Earth Interactions vol 9 no 3 2005

[33] M S Biudes ldquoBalanco de energia em area de vegetacaomonodominante deCambara e pastagemnonorte doPantanalrdquoTese (doutorado)mdashUniversidade Federal de Mato Grosso Fac-uldade de Agronomia e Medicina Veterinaria Pos-graduacaoem Agricultura Tropical 2008

[34] W Eugster W R Rouse R A Pielke et al ldquoLand-atmosphereenergy exchange in Arctic tundra and boreal forest available


data and feedbacks to climaterdquo Global Change Biology vol 6no 1 pp 84ndash115 2000

[35] N Priante-Filho G L Vourlitis M M S Hayashi et alldquoComparison of the mass and energy exchange of a pasture anda mature transitional tropical forest of the southern AmazonBasin during a seasonal transitionrdquo Global Change Biology vol10 no 5 pp 863ndash876 2004

[36] M H Costa A Botta and J A Cardille ldquoEffects of large-scale changes in land cover on the discharge of the TocantinsRiver Southeastern Amazoniardquo Journal of Hydrology vol 283pp 206ndash217 2003

[37] A C Miranda H S Miranda J Lloyd et al ldquoFluxes of carbonwater and energy over Brazilian cerrado an analysis using eddycovariance and stable isotopesrdquo Plant Cell and Environmentvol 20 no 3 pp 315ndash328 1997

[38] Y Malhi E Pegoraro A D Nobre et al ldquoEnergy and waterdynamics of a central Amazonian rain forestrdquo Journal ofGeophysical Research D vol 107 no 20 p 8061 2002

[39] H R da RochaM L Goulden S DMiller et al ldquoSeasonality ofwater andheat fluxes over a tropical forest in easternAmazoniardquoEcological Applications vol 14 no 4 pp S22ndashS32 2004

[40] H R Rocha A O Manzi O M Cabral et al ldquoPatterns ofwater and heat flux across a biome gradient from tropicalforest to savanna in Brazilrdquo Journal of Geophysical Research-Biogeosciences vol 114 no 1 2009

[41] DHillel ldquoThermal properties and processesrdquo inEncyclopedia ofSoils in the Environment D Hillel C Rosenzweig D PowlsonK Scow M Singer and D Sparks Eds pp 156ndash163 AcademicPress San Diego Calif USA 2005

[42] A J B Santos G T D A Silva H S Miranda A C Mirandaand J Lloyd ldquoEffects of fire on surface carbon energy andwater vapour fluxes over campo sujo savanna in central BrazilrdquoFunctional Ecology vol 17 no 6 pp 711ndash719 2003

[43] H R Rocha H C Freitas R Rosolem et al ldquoMeasurementsof CO

2exchange over a woodland savanna (Cerrado Sensu

stricto) in southeast Brazilrdquo Biota Neotropica vol 2 pp 1ndash112002

[44] S J Bucci F G Scholz G Goldstein et al ldquoControls on standtranspiration and soil water utilization along a tree densitygradient in a Neotropical savannardquo Agricultural and ForestMeteorology vol 148 no 6-7 pp 839ndash849 2008

[45] H J Dalmagro F A Lobo G L Vourlitis et al ldquoPhotosyntheticparameters for two invasive tree species of the Brazilian Pan-tanal in response to seasonal floodingrdquo Photosynthetica vol 51pp 281ndash294 2013

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2013

Geological ResearchJournal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawi Publishing Corporation httpwwwhindawicom Volume 2013

The Scientific World Journal

OceanographyHindawi Publishing Corporationhttpwwwhindawicom Volume 2013

International Journal of

ISRN Oceanography

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2013


MineralogyInternational Journal of


Paleontology Journal

Geochemistry


Journal of

ISRN Geology



Petroleum EngineeringJournal of

ISRN Atmospheric Sciences


ISRN Geophysics


EarthquakesJournal of


Volume 2013

ISRN Paleontology


Meteorology


Advances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013


Geophysics


Atmospheric SciencesInternational Journal of

ScientificaHindawi Publishing Corporationhttpwwwhindawicom Volume 2013

ISRN Meteorology


Mining


Journal of


Given the potential for land cover change to alter surfaceenergy balance in cerrado we evaluated the seasonal andinterannual variations of energy partitioning in a degradedgrass-dominated cerrado (locally known as campo sujo) ofCentral Brazil We used Bowen ratio energy balance (BREB)methods over two consecutive years to characterize theseasonal and interannual variations in energy flux dynamicsWe hypothesized that campo sujo cerrado would exhibithigher rates of sensible and ground heat flux than latent heatflux especially during the dry season when surface wateravailability required for sustaining latent heat flux would beminimal Little is known about the seasonal and interannualvariations in energy balance for campo sujo cerrado andtesting these hypotheses is important for understanding howland degradation will affect energy balance

2 Materials and Methods

21 Site Description The experimental site was located inSanto Antonio de Leverger MT Brazil which is 15 km southof Cuiaba (15∘431015840S 56∘041015840W) The study site is within agrass-dominated cerrado that was degraded approximately35 years ago after the partial clearing of cerrado woodlandvegetation According to Koppen the climate of region ischaracterized as Aw tropical semihumid with dry wintersand wet summers Mean annual rainfall and temperature are1420mm and 265∘C respectively and rainfall is seasonalwith a dry season extending from May to September [11]The research area is on flat terrain at an elevation of 157mabove sea level The regional soil type is a rocky dystrophicred-yellow latosol locally known as a Solo ConcrecionarioDistrofico [23]

22 Data Collection A micrometeorological tower enabledthe collection of data on air temperature (119879

119886) relative

humidity (RH) wind speed (119906) precipitation (119875) soil tem-perature (119879

119904) soil heat flux (119866) net radiation (119877

119899) global

solar radiation (119877119892) and soil moisture (119902) 119879

119886and RH

were measured 5m and 18m above the ground level usingthermohygrometers (HMP45AC Vaisala Inc Woburn MAUSA) 119866 was measured using two heat flux plates (HFP01-L20 HuksefluxThermal Sensors BV Delft The Netherlands)installed 10 cm below the soil surface with one placed ina sandy soil type and the other placed in a laterite soiltype which were typical of the local soil 119877

119899and 119877

119892were

measured 5m aboveground using a net radiometer (NR-LITE-L25 Kipp amp Zonen Delft The Netherlands) and apyranometer (LI200X LI-COR Biosciences Inc LincolnNE USA) respectively Precipitation was measured using atipping-bucket rainfall gauge (TR-525M Texas ElectronicsInc Dallas TX USA) The sensors are connected to adatalogger (CR1000 Campbell Scientific Inc Logan UTUSA) that scanned each sensor every 30 seconds and storedaverage and in the case ofP total quantities every 30minutes

23 Data Processing Data were collected between May 2009and April 2011 Fluxes of latent (119871

119890) and sensible (119867) heat

were calculated over 30-minute intervals using Bowen ratio

and energy balance (BREB) techniques [24] following theguidelines and modifications described by Perez et al [25]Bowen ratio methods have been used for decades and whileother methods such as eddy covariance may be more directand amenable to analysis of measurement error there areobjective methods that are available for minimizing errorsassociated with resolving small gradients in vapor pressure ortemperature caused by poor instrument performance andoratmospheric conditions [25 26]

The balance of energy was calculated as

119877119899= 119866 + 119867 + 119871

119890 (1)

where 119877119899(J mminus2 sminus1) was measured by net radiometer and 119866

(J mminus2 sminus1) was the mean heat flux in the soil measured by thesoil heat flux plates installed in the sandy and laterite soils119867and 119871

119890were calculated as a function of the Bowen ratio (120573)

120573 =119867

119871119890

(2)

which in turn can be calculated as a function of the airtemperature (Δ119879) and vapor pressure (Δ119890) gradients and thepsychrometric constant [24]

120573 = 120574Δ119879

Δ119890 (3)

For (3) actual vapor pressure (119890)was calculated as a functionof saturation vapor pressure (119890

119904) and RH using (4) and (5)

respectively

119890119904= 2172 times 10

7

times 119890minus4157((119879minus273)minus3391)

(4)

119890 =119877119867 times 119890

119904

100(5)

while psychrometric constant (120574) was calculated as a func-tion of the specific heat at constant pressure (119862

119901=

1010 J kgminus1 Cminus1 according to [27]) the local atmosphericpressure (119901 = 103 kPa at the research site)

120574 =

119862119901times 119901

0622 times 119871 (6)

and the latent heat of vaporization (119871) which varies as a fun-ction of temperature [28]

119871 = 1919 times 106

times (119879 + 273

(119879 + 273) minus 3391)

2

(7)

With estimates of 120573 (3) 119871e can be calculated as

119871119890=(119877119899minus 119866)

(120573 + 1) (8)

and119867 can be calculated as the difference between 119877119899 119866 and

119871119890using (1) [25]The criteria for accepting data collected from the Bowen

ratio method were based on those described by Perez et al[25] Briefly the Bowen ratio method fails when (1) sensor
















119890













119892and 119877



119892






119899and 119877









119890





119890during









22

24

26

28

30

32

Relat

ive h

umid

ity (

)

40

50

60

70

80

90

Prec

ipita

tion

(mm

)

100

200

300

400

2009 2010 2011

Ta

RHP

Air

tem

pera

ture

(∘ C)


(a)

22

24

26

28

30

32

Sola

r rad

iatio

n (M

J mminus2

dminus1)

Net

radi

atio

n (M

J mminus2

dminus1)

2009 2010 2011

Rg

Rn

13

12

11

10

9

8

7

6

34

20


(b)











119890 there








800

600

400

200

0

Rg

orR

n(J

mminus2

sminus1)

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

2009 2010 2011

Rg

Rn


(a)

HL

eor

G(J

mminus2

sminus1)

H

Le

G

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

400

300

200

100

0

2009 2010 2011


(b)
















00

02

04

06

08


Regr

essio

n slo

peL

eve

rsus

Rn


r2 = 067 P lt 00001

(a)


Regr

essio

n slo

peH

vers

usR

n

00

01

02

03

04

05

06

07


r2 = 071 P lt 00001

(b)






Ener

gy fl

ux (M

J mminus2

dminus1)

orBo

wen

ratio

10

8

6

4

2

0

120573H

Le

G

2009 2010 2011





4 Conclusions





119899partitioned









Year Month 119871119890

119867


2

2009



2010



2011












Acknowledgments



References





















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of
















119890













119892and 119877



119892






119899and 119877









119890





119890during









22

24

26

28

30

32

Relat

ive h

umid

ity (

)

40

50

60

70

80

90

Prec

ipita

tion

(mm

)

100

200

300

400

2009 2010 2011

Ta

RHP

Air

tem

pera

ture

(∘ C)


(a)

22

24

26

28

30

32

Sola

r rad

iatio

n (M

J mminus2

dminus1)

Net

radi

atio

n (M

J mminus2

dminus1)

2009 2010 2011

Rg

Rn

13

12

11

10

9

8

7

6

34

20


(b)











119890 there








800

600

400

200

0

Rg

orR

n(J

mminus2

sminus1)

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

2009 2010 2011

Rg

Rn


(a)

HL

eor

G(J

mminus2

sminus1)

H

Le

G

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

400

300

200

100

0

2009 2010 2011


(b)
















00

02

04

06

08


Regr

essio

n slo

peL

eve

rsus

Rn


r2 = 067 P lt 00001

(a)


Regr

essio

n slo

peH

vers

usR

n

00

01

02

03

04

05

06

07


r2 = 071 P lt 00001

(b)






Ener

gy fl

ux (M

J mminus2

dminus1)

orBo

wen

ratio

10

8

6

4

2

0

120573H

Le

G

2009 2010 2011





4 Conclusions





119899partitioned









Year Month 119871119890

119867


2

2009



2010



2011












Acknowledgments



References





















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of


22

24

26

28

30

32

Relat

ive h

umid

ity (

)

40

50

60

70

80

90

Prec

ipita

tion

(mm

)

100

200

300

400

2009 2010 2011

Ta

RHP

Air

tem

pera

ture

(∘ C)


(a)

22

24

26

28

30

32

Sola

r rad

iatio

n (M

J mminus2

dminus1)

Net

radi

atio

n (M

J mminus2

dminus1)

2009 2010 2011

Rg

Rn

13

12

11

10

9

8

7

6

34

20


(b)











119890 there








800

600

400

200

0

Rg

orR

n(J

mminus2

sminus1)

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

2009 2010 2011

Rg

Rn


(a)

HL

eor

G(J

mminus2

sminus1)

H

Le

G

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

400

300

200

100

0

2009 2010 2011


(b)
















00

02

04

06

08


Regr

essio

n slo

peL

eve

rsus

Rn


r2 = 067 P lt 00001

(a)


Regr

essio

n slo

peH

vers

usR

n

00

01

02

03

04

05

06

07


r2 = 071 P lt 00001

(b)






Ener

gy fl

ux (M

J mminus2

dminus1)

orBo

wen

ratio

10

8

6

4

2

0

120573H

Le

G

2009 2010 2011





4 Conclusions





119899partitioned









Year Month 119871119890

119867


2

2009



2010



2011












Acknowledgments



References





















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of


800

600

400

200

0

Rg

orR

n(J

mminus2

sminus1)

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

2009 2010 2011

Rg

Rn


(a)

HL

eor

G(J

mminus2

sminus1)

H

Le

G

0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0

400

300

200

100

0

2009 2010 2011


(b)
















00

02

04

06

08


Regr

essio

n slo

peL

eve

rsus

Rn


r2 = 067 P lt 00001

(a)


Regr

essio

n slo

peH

vers

usR

n

00

01

02

03

04

05

06

07


r2 = 071 P lt 00001

(b)






Ener

gy fl

ux (M

J mminus2

dminus1)

orBo

wen

ratio

10

8

6

4

2

0

120573H

Le

G

2009 2010 2011





4 Conclusions





119899partitioned









Year Month 119871119890

119867


2

2009



2010



2011












Acknowledgments



References





















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of


00

02

04

06

08


Regr

essio

n slo

peL

eve

rsus

Rn


r2 = 067 P lt 00001

(a)


Regr

essio

n slo

peH

vers

usR

n

00

01

02

03

04

05

06

07


r2 = 071 P lt 00001

(b)






Ener

gy fl

ux (M

J mminus2

dminus1)

orBo

wen

ratio

10

8

6

4

2

0

120573H

Le

G

2009 2010 2011





4 Conclusions





119899partitioned









Year Month 119871119890

119867


2

2009



2010



2011












Acknowledgments



References





















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of




Year Month 119871119890

119867


2

2009



2010



2011












Acknowledgments



References





















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of



















































Volume 2013






ISRN Oceanography






Geochemistry


Journal of

ISRN Geology






ISRN Geophysics




Volume 2013

ISRN Paleontology


Meteorology


Advances in



Geophysics




ISRN Meteorology


Mining


Journal of

Temporal Patterns of Energy Balance for a Brazilian ... · Temporal Patterns of Energy Balance for...

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Transcript of Temporal Patterns of Energy Balance for a Brazilian ... · Temporal Patterns of Energy Balance for...