Variations in evapotranspiration and climate for an Amazonian semi-deciduous forest over ...

16 Pages·2015·1.16 MB·English

Variations in evapotranspiration and
climate for an Amazonian semi-deciduous
forest over seasonal, annual, and El Niño
cycles
George L. Vourlitis, José de Souza
Nogueira, Francisco de Almeida Lobo &
Osvaldo Borges Pinto
International Journal of
Biometeorology

ISSN 0020-7128
Volume 59
Number 2

Int J Biometeorol (2015) 59:217-230
DOI 10.1007/s00484-014-0837-1
1 23
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1 23
Author's personal copy
IntJBiometeorol(2015)59:217–230
DOI10.1007/s00484-014-0837-1
ORIGINALPAPER
Variations in evapotranspiration and climate for an Amazonian
semi-deciduous forest over seasonal, annual, and El Niño cycles
GeorgeL.Vourlitis&JosédeSouzaNogueira&
FranciscodeAlmeidaLobo&OsvaldoBorgesPintoJr.
Received:29October2013/Revised:9April2014/Accepted:9April2014/Publishedonline:16May2014
#ISB2014
Abstract Tropical forests exchange large amounts of water witha long-term, regionalwarming and drying trend. These
andenergywiththeatmosphereandareimportantincontrol- resultssuggestthatdroughtandwarminginducedbyElNiño
ling regional and global climate; however, climate and and/orclimatechangecausedeclinesinEforsemi-deciduous
evaportranspiration(E)varysignificantlyacrossmultipletime forestsofthesoutheastAmazonBasin.
scales. To better understand temporal patterns in E and cli-
mate,wemeasuredtheenergybalanceandmeteorologyofa Keywords Climatechange .Brazil .Deforestation .
semi-deciduous forest in the rainforest-savanna ecotone of Ecotone .Energybalance .MatoGrosso .Transitionaltropical
northernMato Grosso, Brazil,overa 7-yearperiodand ana- forest
lyzedregionalclimatepatternsovera16-yearperiod.Spectral
analysisrevealedthatEandlocalclimateexhibitedconsistent
cyclesoverannual,seasonal,andweeklytimescales.Annual Introduction
andseasonalcycleswerealsoapparentintheregionalmonth-
lyrainfallandhumiditytimeseries,andacycleontheorderof Amazoniantropicalforestsexchangelargeamountsofwater
3–5.5yearswasalsoapparentintheregionalairtemperature andenergywiththeatmosphereandareimportantincontrol-
timeseries,whichiscoincidentwiththeaveragereturninter- ling regional and global climate (Meir and Grace 2005).
valofElNiño.AnnualratesofEweresignificantlyaffected However, spatial and temporal variation in rainfall and dry
by the 2002 El Niño. Prior to this event, annual E was on season duration can profoundly affect energy exchange dy-
average1,011mm/yearandaccountedfor52%oftheannual namics by affecting the partitioning of net radiation into
rainfall,whileafter,annualEwas931mm/yearandaccounted sensible and latent heat exchange (Vourlitis et al. 2002,
for 42% ofthe annualrainfall. Ourdataalsosuggest thatE 2005, 2008; Malhi et al. 2002; da Rocha et al. 2009;
declined significantly over the 7-year study period while air Lathuilliereetal.2012;Rodriguesetal.2013,2014).Forests
temperature significantly increased, which was coincident ofthenorthernandcentralAmazonBasinoftenexhibitlittle
seasonalvariationinevapotranspiration(E)becauseofsmall
variationinrainfalland/orseasonaltrade-offsbetweenavail-
Electronicsupplementarymaterial Theonlineversionofthisarticle
(doi:10.1007/s00484-014-0837-1)containssupplementarymaterial, ableenergyandevaporativedemand,whilesavannaandsemi-
whichisavailabletoauthorizedusers. deciduous forest of the rainforest-savanna ecotone often ex-
G.L.Vourlitis(*) perience larger seasonal variation in E because of higher
BiologicalSciencesDepartment,CaliforniaStateUniversity, variability inrainfall and a morepronounced dry season (da
SanMarcos,CA,USA Rocha et al. 2009; Vourlitis and da Rocha 2011; Rodrigues
e-mail:[email protected]
etal.2014).
:
While seasonal variation in E has been relatively well
J.deSouzaNogueira O.B.PintoJr.
DepartamentodeFísica,UniversidadeFederaldeMatoGrosso, described, interanual variations are less well known. High
Cuiabá,MatoGrosso,Brazil rainfall years also have high cloud cover (Vourlitis et al.
2011); thus, cooler-wetter years may exhibit similar rates of
F.deAlmeidaLobo
E compared to warmer-drier years. However, warming and
DepartamentodeSoloseEngenhariaRural,
UniversidadeFederaldeMatoGrosso,Cuiabá,MatoGrosso,Brazil dryingthatoccurwithElNiño(Potteretal.2004;Malhiand
Author's personal copy
218 IntJBiometeorol(2015)59:217–230
Wright2005)cancauseanincreaseintreemortality(Phillips (Fig.1).Meanannualtemperatureis24°Cwithlittleseasonal
et al. 2005) that can alter C and H O cycling dynamics for variation,andrainfallisonaverage2,000mm/yearwitha4–
2
months–years after the event (Saleska et al. 2003; Meir and 5-monthdryseasoninMay–September(Vourlitisetal.2005).
Grace 2005; Qian et al. 2008; Phillips et al. 2010). Tree species at our study site are typical of semi-deciduous
Superimposedonthenaturalclimaticvariationistheclimate Amazonianforest(Ackerlyetal.1989;Lorenzi2000,2002)
changeresulting fromgreenhousegas emissions and/orland and include Protium sagotianum Marchland, Dialium
coverchange,whichisexpectedtocausea3–5°Cincreasein guianense (Aubl.) Sandwith, Hevea brasiliensis Müll. Arg.,
temperaturefortheAmazonBasinbytheendofthetwenty- Brosimumlactescens(S.Moore)C.C.Berg,Cordiaalliodora
first century (Cramer et al. 2005; Costa and Pires 2010). (Ruiz&Pav.)Oken,TovomitaschomburgkiiPlanch&Triana,
Warmingand dryingmay beespecially pronouncedinareas and Qualea paraensis Ducke. There are approximately 80
thatexperiencewidespreaddeforestationbecauseofchanges species and 35 families of trees with a diameter ≥10 cm;
inalbedo,declinesinsurfaceroughness,increasesinconvec- however,nearly50%ofallindividualsareintheBurseraceae
tion and runoff, and declines in soil water storage (Hodnett (P.sagotianum),Clusiaceae(T.schomburgkii),andMoraceae
etal.1995;BaidyaRoyandAvissar2002;Costaetal.2003; (B. lactescens) families. Leaf area index (LAI) reaches a
HaslerandAvissar2007;CostaandPires2010). maximumof5.0m2/m2duringthewetseasonandaminimum
Given the potential importance of climate variation on of2.5m2/m2duringthedryseason(Sanchesetal.2008a).The
tropical forest E, the goal of this research was to quantify soilisaquartzarenicneosolcomposedof≈90%sand,which
seasonal and interannual variations in E and climate for a hashighporosityanddrainswithin4–7daysofrainfallevents
tropical semi-deciduous forest located in the rainforest- (Vourlitisetal.2008).
savanna ecotone of northern Mato Grosso, Brazil. This area
islikelytobeespeciallysensitivetoclimatevariationgivenits Fieldmeasurements
transitionalnature(ArrisandEagleson1994).Here,wereport
temporal variations in weekly averaged E and local climate Twodifferenteddycovariancesystemswereusedtomeasure
observed over a 7-year period and use well-known, but sel- E over the January 2000–2007 study period. Both systems
domused,spectralanalysestoquantifypotentiallyimportant weremountedinthedirectionofthemeanwindataheightof
patterns and cycles in these time series. The study period 42mabovegroundlevel,or12–14mabovetheforestcanopy.
encompassed a relatively intense El Niño event in 2002 The first system (January 2000–March 2005) consisted of a
(Chen et al. 2010) and a significant drought event in 2005 three-dimensional sonic anemometer-thermometer (SWS-
(Marengo et al. 2008), allowing the characterization of E 211/3K,AppliedTechnologiesInc.,Boulder,CO,USA)and
before and after these climate perturbations. In addition to an open-path infrared gas analyzer (NOAA-ATDD, Oak
the7-yeartimeseriesforEandlocalclimate,monthlyclimate Ridge,TN,USA)tomeasurethemeanandfluctuatingquan-
datawascompiledfortheperiodbetween1989and2005for titiesofwindspeedandtemperatureandH Ovapor,respec-
2
severallocaleswithin,oradjacentto,theforest-savannatran- tively (Vourlitis et al. 2001, 2002, 2004, 2005; Priante et al.
sitionofnorthernMatoGrossotoassesslonger-term,regional 2004). Raw H O vapor fluctuations were outputted asmean
2
cyclesandtrendsinclimate.Giventhatover35%ofthesemi- voltages and converted to densities by multiplying by the
deciduous forest of the forest-savanna ecotone of northern requisitecalibrationconstant(LeuningandMoncrieff1990),
MatoGrossohasbeenconvertedtopastureoragricultureover andlatent(Q )and sensibleheat(Q )fluxeswerecomputed
e h
thelast30years(Soares-Filhoetal.2006),theseclimatedata followingacoordinaterotationofthewindvectors(McMillen
provideanopportunitytoanalyzeifchangesinclimateforthis 1988). Fast response (10 Hz) fluxes were calculated and
climaticallysensitiveecotonehavechangedinamannerthat storedonalaptopcomputeras30-minaveragesusinga200-
isconsistentwithwhatisexpectedfromlandcoverchange. s running mean and digital recursive filtering technique. Al-
though longer time constants have been suggested to mini-
mize errors in the estimation of low-frequency turbulent
Materialandmethods fluxes(Rannik and Vesala 1999),analysisofraw data using
200-and 800-s timeconstants suggeststhatthe potential for
Sitedescription underestimating of low-frequency turbulent flux was small
(Ferreira2004).
Tower-basededdycovariancewas usedtomeasure instanta- Thesecondeddycovariancesystem(March2005–January
neous (30 min average) rates of evapotranspiration (E) be- 2007) utilized a three-dimensional sonic anemometer-
tweenJanuary2000and2007.Measurementsweremadeina thermometer(CSAT-3,CampbellScientific,Inc.,Logan,UT,
25–28-m-tall intact, mature terra firme tropical semi- USA) and an open-path infrared gas analyzer (LI-7500, LI-
deciduous forest located 50 km NE of Sinop Mato Grosso, COR, Inc. Lincoln, NE, USA) to measure the mean and
Brazil (11° 24.75′ S: 55° 19.50′ W), 423 m above sea level fluctuating quantities of wind speed and temperature and
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IntJBiometeorol(2015)59:217–230 219
H O vapor, respectively (Vourlitis et al. 2008, 2011). Raw frommicrometeorologicaldatausingthePriestleyandTaylor
2
(10 Hz) data and 30-min average fluxes of Q and Q were (1972)equation,
e h
storedandprocessedusingasolid-statedatalogger(CR5000, (cid:2) . (cid:3)(cid:2) (cid:3)
CampbellScientific,Inc., Logan, UT, USA).Averagefluxes Q ¼α s sþγ Q*−Q ; ð1Þ
e g
ofQ and Q were calculatedas the covariance between the
e h
fluctuationsinverticalwindspeedandH Ovapordensityand where α=the Priestley–Taylor coefficient, s=slope of the
2
temperature,respectively,overa30-minintervalfollowinga saturation vapor pressure vs. temperature curve at a given
coordinaterotationofthewindvectors. temperature, γ=psychrometric constant, and Q* and Q are
g
Sensorsforbothsystemssampledandoutputdataat10Hz. thenetradiationandgroundheatflux,respectively,measured
The H O vapor channel of the gas analyzers used for both from the meteorological sensors. The advantage of Eq. 1 is
2
systemswascalibratedapproximatelymonthlyusingaporta- that Q is calculated using variables that are known to be
e
bledew-pointgenerator(LI-610,LI-COR,Inc.Lincoln,NE, important for driving spatial and temporal variations in Q
e
USA). Water vapor flux for both measurement systems was (i.e.,availableenergyandairandsurfacehumidity),andfew
correctedforthesimultaneousfluctuationsinheat(Webbetal. dataarerequiredtoestimatetheQ .However,thetheoretical
e
1980).Unfortunately,bothsystemswerenotoperationalatthe basisofαisunclear,andαcanvarysubstantiallydepending
same time, so cross-comparison of the eddy covariance sys- oncanopyroughnessandsurfacewatercontent(XuandSingh
temswasnotpossible. 2000).Givena knownQ ,suchasthatmeasuredfromeddy
e
Net radiation was measured above the canopy (40 m covariance, estimates of α calibrated to local conditions of
above ground level) using a net radiometer (Q*7, canopyroughnessandsurfacemoisturecanbeestimatedfora
REBS, Inc., Bellevue, WA, USA or NR-LITE, Kipp given timeperiodbyrearranging Eq. 1 (Viswanadham etal.
and Zonen, Bohemia, NY, USA). Soil heat flux was 1991;Vourlitisetal.2002),
measured using heat flux transducers (n=2) buried ap- .(cid:2)(cid:2) . (cid:3)(cid:2) (cid:3)(cid:3)
proximately 2 cm into the surface litter layer (HFT-3.1, α¼Q s sþγ Q*−Q ð2Þ
e g
REBS, Inc., Bellevue, WA, USA). Air temperature and
atmospheric water vapor density were measured at the Usinglinearregressionwiththeoriginforcedthroughzero,
top of the tower (42 m above ground level) using a calibrated values of α were estimated for each month using
thermohygrometer (HMP-35, Vaisala, Inc., Helsinki, Eq.2wheneddycovariancemeasurementsofQ wereavail-
e
Finland). Precipitation was measured at the top of the able,withthe30-minaverageQ measuredfromeddycovari-
e
tower using a tipping-bucket rainfall gauge (TE-525; anceasthedependentvariableandthe30-minaverage(s/s+
Texas Electronics, Inc., Dallas, TX, USA), and data gaps γ)(Q*−Q ) measured from the micrometeorological sensors
g
werefilledwithdataobtainedfromamanualraingaugethat as the independent variable (see for example, Fig. 2).
was read daily by personnel from the Fazenda Maracai When no eddy covariance data were available, values of
located 5 km E of the study site. These data were highly α were interpolated for months before and after eddy
correlated to data collected on-site (Vourlitis et al. 2008). covariance data were available, while longer gaps were
Micrometeorological data were averaged every 30 min filled using the mean variation approach (Falge et al.
from observations made every 60 s and stored on a 2001) where values of α were estimated as the mean
datalogger (CR5000, Campbell Scientific, Inc., Logan, value for a given month.
UT,USA). Using calibrated values of α, daily estimates of E were
calculated using Eq. 1, where E=Q /λ, and λ is the
e
Gap-fillingofE latent heat of vaporization. Daily estimates of E were
averaged over weekly intervals, providing estimates of
Powersystemand/orsensorfailurelimitedtheamountofdata E for 77 % of all possible weeks. Missing values of E,
collected from the eddy covariance system, and over the 7- which occurred when there were gaps in meteorological
yearstudyperiod,theeddycovariancesystemwasoperational data, were estimated using auto-regressive, integrated
only 53 % of the time. However, the meteorological system moving average (ARIMA) models, which were fit to the E
used a separate power system and was operational more timeseriesusinganiterativeBox–Jenkinsapproach(Edwards
frequently than the eddy covariance system, and over the 7- andCoull1987).
yearstudyperiod,themeteorologicalsystemwasoperational
for77%ofthetime.Therefore,datacollectedfromthemore Erroranalysisandestimation
consistentmeteorologicalmeasurements,alongwiththedirect
measurements of E from the eddy covariance system, were Errors associated with the calibrated estimates of E (Eq. 1)
usedtoestimateEforthemeasurementperiod.Instantaneous werecalculatedassumingbothrandomandsystematicsources
(30-minaverage)ratesoflatentheatflux(Q )werecalculated of error (Moncrieff et al. 1996). Random errors in E were
e
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220 IntJBiometeorol(2015)59:217–230
estimated as a function of the ±95 % confidence interval Long-termtrendsinregionalclimate
(±95 % CI) of α calculated by linear regression (Eq. 2).
Systematic errors in E were estimated by assuming that all Climate data collected over a 16-year period from 1989 to
oftheerrorassociatedwithenergybalanceclosurewasdueto 2005consistingoftotalmonthlyrainfallandaveragemonthly
asystematicerrorintheunder-measurementofEfromeddy humidityandairtemperaturewereobtainedfromtheNational
covariance. This assumption is reasonable given that E is Climatic Data Center (www.NCDC.gov) for 7 sites located
measured by two different sensors (i.e., sonic anemometer within or adjacent to the 9–14° S transition zone of the
andanopen-pathgasanalyzer)thathavedifferencesinsensi- southern Amazon Basin (Fig. 1; Table 1). Unfortunately,
tivity and stability, are spatially separated, and may not be longer time series were not available for these sites because
perfectlysynchronized(Burba2013);however,werecognize ofanincreaseinthefrequencyofdatagapsbeforeand after
that errors associated with energy balance closure may stem this 16-year period. Data gaps were filled by calculating the
fromavarietyofsourcesincludingerrorsinthemeasurement composite average of a given variable across all sites and
ofavailableenergyandsensibleheatflux(Wilsonetal.2002). regressingthetimeseriesofagivensiteagainstthecomposite
Our eddy covariance systems have typically exhibited an average. An inverse-distanceweighting function (Isaaks and
energy balance closure of 78–92 % (Vourlitis et al. 2001, Srivastava1989)wasusedtoestimatethemonthlyclimatol-
2002, 2004, 2005, 2008, 2011; Priante et al. 2004). Using ogyfortheSinopstudysitefrom1989to2005.
estimates of energy balance closure for each month, a
new value of monthly α was recalculated (Eq. 2) after Statisticalanalysis
increasing the value of E to achieve perfect energy
balance closure. The new value of α was then used to Powerspectralanalysiswasusedtoquantifythetimescalesof
recalculate a daily value of E (Eq. 1) to reflect a variance and elucidate the potential for cycles in the E and
systematicunder-measurementofE. Thedifferencebetween climatedata(PlattandDenman1975;Baldocchietal.2001).
the estimates of E assuming full and normal energy balance Following Baldocchi et al. (2001), Fourier transformation
closurewastakenasthesystematicerrorinE.Thetotalerror techniques were used to decompose the time series into a
associatedwiththeweeklyestimateofEwasthencalculated series of frequencies, and peaks in spectral density are ob-
asthesumoftherandomandsystematicerrors.Onaverage, servedasafunctionoffrequencyorperiod.Timeserieswere
systematic errors accounted for 51 % of the total estimated differenced prior to Fourier transformation by subtracting
errorinE. each observation by the mean of the time series. The power
Fig.1 Mapofthestudyregion
withthestateofMatoGrosso
(center)andtheSinop,Mato
Grossostudysite(blackcircle).
Alsoshownarethelocationand
elevationofsitesusedto
reconstructthelong-term
(16years)climate(graycircles)
fortherainforest-savanna
transitionzoneneartheSinop
studysite
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IntJBiometeorol(2015)59:217–230 221
Table1 Characteristicsofthesitesusedtoreconstructthelong-term(1989–2005)regionalchangesinclimatefortheforest-transitionzoneoftheSE
AmazonBasin
WMOID Stationname Latitude(degS) Longitude(degW) Elevation(m) DistancetoSinop(km) Direction(deg) Weighting
82861 ConceiçãodoAraguaia 8°15′ 49°17′ 160 690 61 0.10
83361 Cuiabá 15°33′ 56°07′ 170 472 198 0.14
83064 PortoNacional 10°43′ 48°25′ 253 690 82 0.10
82825 PortoVelho 8°46′ 63°55′ 85 1,054 295 0.06
83235 Taguatinga 12°16′ 46°26′ 660 910 96 0.07
83264 Vera 12°12′ 56°30′ 450 162 213 0.41
83208 Vilhena 12°44′ 60°08′ 652 563 258 0.12
DataincludetheWorldMeteorologicalOfficeidentification,stationname,latitudeandlongitude,elevation,distanceanddirectionfromtheSinop,Mato
Grossofieldsite,andtheweightingusedtogeneratetheaverageclimatetrendforSinop,MatoGrosso.Theweightingschemewascalculatedasan
inverse-distanceweightingthatcalculatedtheratiooftheinversedistanceofagivensitedividedbythesumoftheinversedistancesofallsites
spectral densities were normalized by their respective vari- season (Fig. 3). Values ranged from a maximum of 1.32 in
ances,sowhenplottedonalog–logscale,theareaunderthe May 2002, which exceeds the value of 1.26 for saturated
curveequaledunity(Baldocchietal.2001). surfaces (Priestley and Taylor 1972), to a minimum of 0.41
Temporal trendsinE and local and regionalclimate vari- in September 2002 (Fig. 3; Supplementary data Table S1).
ables(airtemperature,vaporpressure,rainfall,anddryseason Overall, α was on average (±95 % CI) 0.75±0.05, and the
duration) were assessed using ordinary linear regression coefficient of determination of the linear regression (r2)
(Fig.2).Atemporaltrendwasdeterminedtobesignificantly between the eddy covariance measurements of latent
different from zero if the mean (±95 % CI) slope did not heat flux (Q ) and the meteorological variables (Eq. 2) was
e
encompasszero. 0.84(range=0.62–0.92;TableS1).
Average weekly rates of E generally increased during the
wet season in January and February and reached a peak in
Results March–April; however, week-to-week and interannual vari-
ability was high (Fig. 4a). After the March–April peak, E
Seasonaltrendsinα,E,andmicroclimate declinedduringthedryseasonintoSeptemberandincreased
onlyslightlyintoDecembereventhoughtherewasadequate
There was a clear seasonal pattern in the Priestley–Taylor rainfall(Fig.4a,c).TheseasonalpatternofEwassimilarto
coefficient(α)whereαwashighestinMayattheendofthe theseasonalpatterninQ*(Fig.4b)withtheexceptionthatQ*
wet season and lowest in September at the end of the dry exhibitedsubstantiallylessvariabilitythanEduringtheMay–
Fig.2 Anexampleofthelinear
April 2005
regressionmethodusedto
800
calculatethecalibratedPriestley–
Taylorcoefficientαfromeddy
covariancemeasurementsof
latentheatflux(Q)and 600
e
micrometeorologicaldata
consistingoftemperature,net )
2
m
radiation(Q*),andgroundheat W/ 400
flux(Q)forApril2005.The
g (
slopeoftheregression e
Q
correspondstoαwiththe
regressionforcedthroughthe 200
origin(seeEq.2).Alsoshownis
thecoefficientofdetermination
(r2)andthenumberof 0
instantaneous(30-minaverage)
values(n)usedtoestimateα
a = 0.72; r2 = 0.82; n = 921
-200
-200 0 200 400 600 800
[(s/s+γ)(Q*-Q )] (W/m2)
g
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222 IntJBiometeorol(2015)59:217–230
Fig.3 Averagemonthlyvalues 1.4
ofthecalibratedPriestley–Taylor
coefficientαcalculatedfrom
eddycovariancemeasurementsof 1.2
latentheatflux(Q)and
e
micrometeorologicalvariables
a
(Eq.2).Thesolidlinedisplaysthe or 1.0
interpolatedvaluesusedwhen yl
a
eddycovariancemeasurementsof T
Qewerenotavailable ey- 0.8
stl
e
Pri
d 0.6
e
at
br
ali 0.4 1999
C 2000
2001
2002
0.2
2003
2005
2006
0.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Septemberdryseason,andQ*increasedmorethanEduring weeks experiencing up to 300 mm of rainfall and others
the October–December dry–wet season transition. Weekly experiencingnomeasureablerainfall,butduringthedrysea-
variations in rainfall revealed a distinct wet season between son, there were extended periods when there was no
OctoberandAprilandadistinctdryseasonbetweenMayand measureablerainfall.Rapidincreasesanddeclinesinrainfall
September (Fig. 4c). During the wet season, week-to-week were apparent during the dry–wet (September–October) and
and interannual variations in rainfall were large, with some wet–dry (April–May) season transitions, respectively.
35 0 30 60 90 120150 180210240 270300330360 220 0 30 60 90 120150180210240270300330360 350 0 30 60 90 120150180210240270300330360
a b c
200
30 300
180
k) 25 160 ek)250
m/wee 20 2W/m)112400 mm/we200
E (m 1105 R (net18000 ainfall (110500
R
60
5 50
40
0 20 0
J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D
28 0 30 60 90 120150 180210240 270300330360 100 0 30 60 90 120150 180210240270300330360
d e
C) 26 %) 90
oure ( 24 dity ( 80 22000001
at mi 2002
Air temper 22 elative hu 70 2222000000003456
20 R 60
18 50
J F M A M J J A S O N D J F M A M J J A S O N D
Fig.4 aAverageweeklyevapotranspiration,bnetradiation(Q*),ctotalweeklyrainfall,daverageweeklyairtemperature,and(e)relativehumidityasa
functionofdayoftheyear(upperx-axis)andmonth(lowerx-axis)fortheJanuary2000–2007studyperiod
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IntJBiometeorol(2015)59:217–230 223
Seasonal variations in air temperature (Fig. 4d) and relative withtheaverage(±95%CI)warmingontheorderof0.001±
humidity(Fig.4e)exhibitedrelativelylessweek-to-weekand 0.0009°C/week,or0.052°C/year(Fig.5c).
interannual variations compared with other meteorological Power spectral densities for E (Fig. 6a), air temperature
variables, and on average, temperature and humidity were (Fig.6b),andrainfall(Fig.6c)exhibitedpeaksover52-week
higherduringthewetseasonandlowerduringthedryseason. cycles, indicating substantial annual variation in all of these
variables.Thetemperatureandrainfalltimeseriesexhibiteda
cycleontheorderofahalfyear(20,26weeks),whichreflects
Interannualtrendsinα,E,andmicroclimate the periodicity associated with peaks in the wet and dry
seasons (Fig. 4c, d). All three time series exhibited peaks in
Weekly estimates of E for the 7-year study period varied spectraldensityover10–17-weekcycles,whichwerecoinci-
substantially over time with a mean of 18.5 mm/week dent with seasonal climate peaks and transitions, and more
(2.6 mm/day) and a range of 3.4–30.7 mm/week (0.5– rapidcyclesoccurredover4–5-(Fig.6a,c)and2-weektime
4.4 mm/day) (Fig. 5a; Supplementary data Table S2). Large scales (Fig. 6b), which were coincident with time scales of
temporalvariationsinEnotwithstanding,ourdataindicatea frontalactivity.
small but statistically significant decline in E over time Annualvariationsinrainfallwerelargeandvariedbetween
(Fig. 5a), and when calculated over an annual time scale 1,861 mm in 2002 and 2,645 mm in 2003 (Table 2). These
(Table 2), E declined by on average (±95 % CI) 17.6± years corresponded to an El Niño (2002)–La Niña (2003)
12.0 mm/year (n=7 years; r2=0.74; p<0.01) over the study cycle, which brought lower (2002) and higher than average
period. The declining trend in E was not coincident with (2003) rainfall to this portion of the Amazon Basin (Chen
declines in local rainfall or net radiation, as temporal trends et al. 2010), but total annual E was remarkably similar (i.e.,
inthesetimeserieswerenotsignificantlydifferentfromzero 1,008and942mm,respectively)betweentheseyearsdespite
(Fig. 5; Table 2). However, the decline in E was coincident the nearly 800-mm difference in annual rainfall. However,
withasignificantincreaseinaverageweeklyairtemperature, priortoElNiño(2000–2002),Ewas onaverage 1,011 mm/
40 a
k) 30
e
e
w 20
m/
m
E ( 10
0
-0.007 + 0.006 mm/week
300 b 0.006 + 0.052 mm/week
250
k)
ee 200
w
m/ 150
m
P ( 100
50
0
c
C) 28
o
e ( 26
ur
at 24
er
p
m 22
e
Air t 20
0.001 + 0.0009 oC/week
18
Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan Jul Jan
2000 2001 2002 2003 2004 2005 2006
Fig.5 aAverageweeklyevapotranspiration,btotalweeklyprecipita- evapotranspiration(a),thestandarddeviationofmeanweeklytempera-
tion,andcaverageweeklyairtemperaturefortheJanuary2000–2007 ture(c),andthemean(±95%CI)temporaltrendcalculatedbyordinary
study period. Also shown is the total error associated with estimating linearregression
Author's personal copy
224 IntJBiometeorol(2015)59:217–230
Table2 Totalannualprecipita-
tionandevapotranspiration(E), Year Rainfall Dryseason Temperature RH(%) Rnet(W/m2) E(mm) E/P
averageannualtemperature,rela- (mm) (weeks) (°C)
tivehumidity(RH),netradiation
(R ),theratioofevapotranspira- 2000 1,999 26.0 24.6 84.5 137.1 1,009 0.50
net
tiontoprecipitation(E/P),anddry 2001 2,006 25.0 24.5 80.9 135.5 1,018 0.51
seasondurationfromJanuary
2002 1,861 31.0 25.0 78.3 131.9 1,008 0.54
2000to2007.Alsoshownarethe
2003 2,645 23.0 24.9 78.8 128.8 942 0.36
grandmean±1sd
2004 2,155 28.0 24.8 81.2 126.1 934 0.43
2005 2,253 28.0 25.2 72.2 134.8 908 0.40
2006 2,040 22.0 24.7 79.3 136.3 942 0.46
Average 2,137 26.1 24.8 79.3 132.9 965 0.46
sd 256 3.1 0.3 3.8 4.1 44 0.07
Fig.6 Normalizedpowerspectra a
ofaverageweekly
evapotranspiration(a),air 0.1 52 w
temperature(b),andtotalweekly
rainfall(c)fortheJanuary2000– 17 w
2007studyperiod.Numbers 10 w
abovepeaksinspectraldensity 0.01 4-5 w
correspondtotheperiod(weeks/
14 w
cycle)associatedwitheach
spectralpeak.Thepowerspectral
densitieswerenormalizedbytheir 0.001
respectivevariancessowhen
plottedonalog–logscale,the
areaunderthecurveequaledunity
0.0001
b
0.1 26 w
52 w
17 w
2 w
10 w
n) 0.01 3-6 w
S(
n
14 w
0.001
0.0001
c
52 w
0.1
14 w
0.01 8 w 4-5 w
20 w
0.001
0.0001
0.01 0.1 1
Frequency (Cycles/week)