Dynamics of Dew in a Cold Desert-Shrub Ecosystem and Its Abiotic Controls

15 Pages·2016·1.79 MB·English

atmosphere
Article
Dynamics of Dew in a Cold Desert-Shrub Ecosystem
and Its Abiotic Controls
XiaonanGuo1,2,TianshanZha1,2,*,XinJia1,2,3,BinWu1,2,WeiFeng1,2,JingXie1,2,
JinnanGong3,YuqingZhang1,2andHeliPeltola3
1 YanchiResearchStation,SchoolofSoilandWaterConservation,BeijingForestryUniversity,
Beijing100083,China
2 KeyLaboratoryofSoilandWaterConservationandDesertificationCombating,BeijingForestryUniversity,
MinistryofEducation,Beijing100083,China;[email protected](X.G.);[email protected](X.J.);
[email protected](B.W.);[email protected](W.F.);[email protected](J.X.);[email protected](Y.Z.)
3 FacultyofScienceandForestry,SchoolofForestSciences,UniversityofEasternFinland,
JoensuuFI-80101,Finland;jinnan.gong@uef.fi(J.G.);heli.peltola@uef.fi(H.P.)
* Correspondence:[email protected];Tel.:[phone];Fax:[phone]
AcademicEditor:RobertW.Talbot
Received:18December2015;Accepted:13February2016;Published:25February2016
Abstract: Thetemporaldynamicsofdewformationincolddesert-shrubecosystemsarestillpoorly
understood. WeexamineddewanditsabioticcontrolsinashrublandinnorthwesternChinawith
continuouseddy-covariancemeasurementsoflatentheatfluxesgatheredoverthegrowing-seasonof
2012. Thedewamountwaslargerinmid-summerthaninspringandautumn,butthedewduration
wasshorterinsummer(from~10:00p.m. to~6:30a.m.) thaninspringandautumn(from~8:30p.m.
to~7:30a.m.).Dewoccurredon85%(166days)ofgrowing-seasondays,withmonthlymeansranging
from0.09to0.16mmday´1. DewwasdominantlyandpositivelycontrolledbyRelativeHumidity
(RH),whichexplained89%ofitsvariation. Soilheatflux(G),airtemperature(T ),windspeed(W ),
a s
SoilWaterContent(SWC)andVaporPressureDeficit(VPD)alsoinfluenceddewformation. Themost
favorableconditionsfordewformationwereatT <17˝CandRH>75%. ThePenman–Monteith
a
equationpredictedactualdewreasonablywell.Thepredictedgrowing-seasondewamount(21.3mm)
wasequivalentto7.2%and8.9%ofcorrespondingrainfallandevapotranspiration,respectively. Itis
suggestedthatdewcouldbeastableandcontinuoussourceofwaterthathelpsdesertplantssurvive
duringwarmsummers.
Keywords: dew;eddycovariance;abioticcontrols;Penman–Monteithequation
1. Introduction
Dewoccurswhenmoistaircondensesonasubstrate[1,2],asitssurfacetemperaturefallsbelow
the dew point temperature. Dew can originate from three separate sources: air (dewfall), plant
(guttation),andsoil(dewrise)[3]. Sufficientmoistureintheairandintensiveradiativecoolingofthe
surfacearetwobasicrequirementsfortheformationofdew[4]. Relativehumiditydirectlyaffects
atmosphericvisibilityandtheformationofclouds,fog,anddew[3,5,6]. Inaddition,theformation
of dew is also indirectly affected by cloudiness [7], wind speeds, which may dampen the effect
of radiative cooling by mechanically mixing the lower nocturnal boundary layer [1,8], soil water
content[9],andthesoilheatflux[6]. Likewise,soiltexture[10],soilcrusts[9],plantcharacteristics
such as leaf area index [11], soil salinity [12], and elevation [13] all play a role in influencing the
productionofdew. However,therehavebeenrelativelyfewstudiesontheabioticcontrolsofdewin
colddesert-shrubecosystems.
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Dewcanbeastableandcontinuoussourceofwatertoplantsthroughfoliaruptakeofmoisture,
aswellasconservingsoilwaterbyreducingtranspirationratesinearlymorninghours[14,15]. Dew
iscrucialtomaintainingthewaterbalanceinsomeecosystemsandcanefficientlyreducewaterloss
causedbysoilevaporation[3,6,16]. Thisisparticularlythecasefordesertecosystems[17,18],where
rainfallislimitedandsoilmoistureisoftenlow.
Despiteitsecologicalimportance,thedynamicsofdewincolddesertecosystemsarestillpoorly
understood,mainlyduetothelackoflong-termmeasurements. Dryland(aridandsemi-arid)accounts
for53%ofChina’sterritory. Shrublanddominatesthisareabylargeandisanimportantecosystem[19].
Understandingthedynamicsofdewofdesertecosystemanditsenvironmentalcontrolis,therefore,
crucialtosustainablymanagingdesertecosystems.
Manyattemptshavebeenmadetoquantifydewindesertenvironments,usingfieldmeasurements
andmodels.Bothhavedrawbacks.Fieldmethodsbasedoncondensingsurfaces[1],absorbentmaterial
orclothplates[20],Duvdevanigauges[21]ordewharvesting[22]areusedtoquantifydewamount.
Otherfieldmethodsbasedonsoilorairmoisturesensors[23–25],leafWetnessSensors(WS)[17,26]
or electricalimpedance grids [27–29] are used toquantify the dew duration. Microlysimetersand
Hiltner balances or electronic balances can be used for estimating both the duration and amount
ofdew[30,31]. Mostofthesefield-basedmethodsarelaborintensiveandthuslimitedintheiruse
to acquire long-term continuous data. Furthermore, many devices either under- or over-estimate
dewamount, becausetheirreactivesurfacesarecomposedofmaterialswhichresponddifferently
thanplantcanopies. Theoreticalmethodstoestimatedewcanbebroadlyclassifiedasempiricalor
analytical [22]. Empirical models are based on simple relationships with meteorological variables
and are easy to use but can be highly site specific [32]. Conversely, analytical models based upon
physicalprinciples,suchastheprocess-basedPenman–Monteithequation[26,30],thecloud-based
energymodel[33],andasimpleanalyticalformulabasedonafewmeteorologicaldata[34],canbe
appliedindifferentenvironmentsbutaremorecomplexandoftenrequiredatathatarenotreadily
available. ThePenman–Monteithequationhastheadvantageofconsideringboththephysiological
andphysicalregulatorsofdew.
TheEddy-Covariancemethod(EC)hasbeenappliedintheexaminationofdewdynamicsinmany
differentecosystems[6,26,35].TheECmethodmeasuresthelatentheatflux(LE)betweenthebiosphere
andtheatmosphereatthefieldscale[36–38],allowingforcontinuouslong-termmeasurements[6,26].
However,ECmeasurementsunderestimatethefluxduringnighttimeperiodswithlowturbulence[39],
andmaythusunderestimatedew.
Using the EC technique, we measured LE and the corresponding atmospheric water vapor
contentinacolddesert-shrubecosystemdominatedbyArtemisiaordosicaduringthegrowing-season
of2012(6Aprilto18October). Ourprimaryobjectiveswere: (1)tounderstandthedynamicsofdew
(duration, and amount); (2) to explore its abiotic controlling mechanisms; and (3) to evaluate the
Penman–Monteith(P–M)equationforpredictingdew.
2. Experiments
2.1. SiteDescription
Measurementsweremadeina10-year-oldshrublandattheYanchiResearchStation(37˝42131”N,
107˝13145”E, 1530 m above sea level (a.s.l.)), Ningxia, Northwest China. The research site is
locatedatthesouthernedgeoftheMuUsdesertandischaracterizedbyamid-temperatesemiarid
continental monsoon climate. The site is dominated by a mixture of deciduous shrub species
includingArtemisiaordosica,Hedysarummongolicum,Hedysarumscopariumandsparselydistributed
Agropyroncristatum. Thecanopyheightwasabout1.4mandthemaximumLeafAreaIndex(LAI)was
1.20m2¨m´2duringthestudyperiod.
Mean annual temperature (1954–2004) is 8.1 ˝C in the research area and the frost-free season
periodis165days.Meanannualprecipitationis292mm,62%ofwhichfallsfromJulytoSeptember[40].
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Meanannualpanevaporationis2024mm. Thesoilissandyandthebulkdensityintheupper10cmis
1.54˘0.08g¨cm´3(mean˘StandardDeviation(SD),n=16,wherenisthenumberofsoilsamples
collectedusingasoilcorer).
2.2. Measurements
ThesensibleheatfluxH (W¨m´2)andlatentheatfluxLE(W¨m´2)weremeasuredusingthe
Eddy-Covariancemethod(EC).Themeasurementsystemconsistedofa3-Dultrasonicanemometer
(CSAT3,CampbellScientificInc.,Logan,UT,USA)andafastresponseinfraredgasanalyzer(LI-7200,
LI-CORInc.,Lincoln,NE,USA).TheECinstrumentsweremountedataheightof6.2monascaffold
towerandorientedintheprevailingwinddirection(northwest). Adatalogger(LI-7550,LI-CORInc.,
USA)wasusedtostore10Hzdata. Thefieldsitewasflatanduniformforover250minalldirections
fromthefluxtower. FootprintanalysisusingtheFluxSourceAreaModel(FASM)showedthat>90%
ofthefluxesoriginatedfromwithin200mofthetower[41].
DaytimeandnighttimewasdistinguishedbyPARatthethresholdof5µmol¨m´2W¨s´1[42–44].
TherateofnighttimedewwasobtaineddirectlyfromtheECmeasurementsofnegativeLEatnight
(PAR<5µmol¨m´2¨s´1). DaytimedewwasderivedfromthenegativeLEstartingfromdawnor
sunrise(i.e.,PARě5µmol¨m´2¨s´1). ThedewrateE(mm¨period´1)wasthenestimatedas:
E“ LE{L (1)
whereListhelatentheatofvaporization(2450J¨kg´1). PositiveLEduringnighttimeanddaytime
(PARě5µmol¨m´2¨s´1)wasconsideredasevapotranspiration(ET).
IncidentphotosyntheticallyactiveradiationPAR(µmol¨m´2¨s´1)wasmeasuredusingaquantum
sensor(PAR-LITE,KippandZonen,Delft,theNetherlands). NetradiationR (W¨m´2)wasmeasured
n
using a four-component radiometer (CNR-4, Kipp and Zonen, the Netherlands). Air temperature
T (˝C)andrelativehumidityRH(%)weremeasuredwithathermohydrometer(HMP155A,Vaisala,
a
Finland). SurfacetemperatureT (˝C)wasmeasuredwithaninfraredradiometer(SI-111,Campbell
s
Scientific, Inc. Logan, UT, USA). Wind speed W (m¨s´1) was measured using a wind speed and
s
directionsensor(034B,MetOneInstrumentsInc.,GrantsPass,OR,USA).Allthesemeteorological
sensorsweremountedonthetowerat6maboveground. SoiltemperatureT (˝C)andsoilwater
10cm
content SWC (m3¨m´3) at 10 cm depth were monitored adjacent to the tower using ECH2O-5TE
sensors(DecagonDevices,USA).SoilheatfluxG(W¨m´2)wascomputedasthesumofthefluxes
measuredat10cmdepthbyfivesoilheatplates(HFP01,HuksefluxThermalSensors,theNetherlands)
andtherateofchangeinheatstorageabovetheheatfluxplates. Theheatfluxplateswereinstalledat
adistanceof5mfromthefluxtower.
Rainfall measurements started from 15 May 2012. The measurements were done with a
manual rain bucket before 22 July, and thereafter with a tipping bucket rain gauges (TE525WS,
Campbell Scientific Inc., Logan, UT, USA) at distances of about 50 m around the tower. All
micrometeorologicalvariableswererecordedevery30-minbydataloggers(CR200XforPPT,CR3000
forallothers,CampbellScientificInc.,USA)from6Aprilto18October,2012. LAIwasmeasuredby
destructivesamplingatroughlyweeklyintervalswithina100mˆ100mplotcenteredontheflux
tower[44].
2.3. Post-ProcessingofData
Half-hourlyfluxesthatsufferedfrominaccuraciescausedbybadweatherorinstrumentfailure
were excluded. In evaluating the P–M equation, LE values were excluded during calm nights
(frictionvelocityu* <0.30m¨s´1,wheretheu* thresholdwasestimatedfollowingtheChinaFLUX
standardmethod[45]). Gapsoflessthan2hinallvariableswerefilledbylinearinterpolation. Longer
gaps in LE were filled using the P–M equation, adjusted to match the EC measurements. Longer
gaps in meteorological variables were filled by the Mean Diurnal Variation method (MDV) [36].
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ToassesstheaccuracyofLE,theEnergyBalanceRatio(EBR)methodwasused. Nighttimeenergy
balanceclosurewasanalyzedforthestudyperiodbasedontheregressionbetweenthemeasured
availableenergy(R ´G)andthesumoftheECturbulentheatfluxes(LE+H),wherenighttimewas
n
delineatedusingPAR(i.e.,asperiodwithPAR<5µmol¨m´2¨s´1). ThenighttimeECmeasurements
showedanacceptableenergy-closurefractionofabout67%(slope=0.67,R2=0.91),whichfallsinthe
rangeofthevaluesof0.55to0.99reportedforFLUXNET[46],Ourclosurefractionof0.67indicates
reasonabledataqualitybutsuggestsourestimatesofdewamountmaybeunderestimatedbyasmuch
as33%. However,becausetheactualvalueoftheclosurefractionisuncertain,wedidnotapplyan
energy-closureadjustmenttothemeasurementsofdewamount,exceptintheevaluationofthedew
estimates from the P–M equation. All data processing procedures were performed within Matlab
(Version7.5.0,TheMathWorks,Natick,MA,USA).
2.4. CalculationofBulkParameters
The dew point temperature was calculated using the Magnus–Tetens approximation
method[47]as:
T “b¨γpT ,RHq{ra´γpT ,RHqs
d a a (2)
γpT ,RHq“ a¨T {pb`T q`lnpRH{100q
a a a
where T is dew point temperature (˝C) and parameters a (17.27) and b (237.7) are constants.
d
DailyminimumT wasobtainedfromthe30-minT series. Dewdaysweredefinedinthisworkas
d d
dayswhendailyminimumT waslessthandailyminimumT .
s d
TheP–MestimateofLEwascomputedas:
C
0.408¨∆pR ´Gq`γ¨ n ¨W pe0´e q
n T `273 s s a
λE“ s (3)
∆`γ¨p1`C W q
d s
where∆(kPa¨K´1)istheslopeofthesaturationvaporpressureversusairtemperaturecurve(Equation(6)),
γisthepsychrometricconstant(0.055kPa¨˝C´1),C andC arereferenceparameterswhichvaryby
n d
planttype,e ˝(kPa)isthesaturatedvaporpressureatsurfacetemperatureande (kPa)istheambient
s a
vaporpressureatthereferenceheightof6mabovetheground. Inourstudy,C wassetto66forboth
n
daytimeandnighttimeperiod[48],andC wassetto0.25duringthedayand1.7atnight.
d
ThesaturationandambientvaporpressureswerecalculatedwiththeTetensformula[49]:
ˆ ˙
b¨T
e0 “ a¨exp s (4)
s T `c
s
ˆ ˙
b¨T
a
e “ RH¨a¨exp (5)
a
T `c
a
wherea=0.611kPa,b=17.502(dimensionless),andc=240.97K.Thevalueof∆maybecalculatedas:
b¨c¨e0
∆“ s (6)
pc`T q2
s
2.5. DataAnalyses
Dayswithrainwerescreenedfromtheanalysis.One-wayanalysisofvariance(ANOVA)wasused
totestfordifferencesamongmonthsinthetimeofdewcommencementandcessation. Dailyvalues
wereconsideredasreplicateswithineachmonth(testgroups). Pathanalysiswasusedtoanalyzethe
comprehensiveeffectsofabioticfactorsondewformation. Nonlinearregressionwasconductedbased
on daily non-gapfilled data to analyze the relationship between dew and different environmental
factors. Theregressionanalyseswerelimitedtothemid-growingseason(June–August)toexclude
theconfoundingeffectsofplantphenology. Thedewvalueswerebin-averagedforeachabioticfactor,
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with a minimum of five data points required for a valid bin. Threshold values for some fits were
identifiedwhenthefittedrelationshipwasasymptotic. Regressionanalysiswasalsousedtoexamine
therelationshipbetweenECmeasurementsandP–Mequationestimatesofdewamount. Regression
significancewasevaluatedusingtheFstatisticatasignificancelevelof0.05. Root-Mean-SquareError
(RMSE)andBiasError(BE)(estimatedminusmeasuredvalues)wereusedtoevaluatethegoodnessof
fitoftheP–Mequation. AllthestatisticalanalyseswereperformedusingtheSPSS20.0programfor
windows(SPSS20.0Inc.,Chicago,IL,USA).
3. Results
3.1. SeasonalChangesinEnvironmentalVariables
Environmentalvariablesobservedfrom6Juneto18October(196days)showedclearseasonal
patternsbothforday-andnighttimevalues(Figure1). Diurnalvaluesofairtemperature(T ),surface
a
temperature(T ),windspeed(W ),soilheatflux(G)andvaporpressuredeficit(VPD)wereallhigher
s s
thanthevaluesatnight,butoppositetoRH.ThemeanT atnightrangedfrom1.94˝Con12April
a
to 21.35 ˝C on 29 July (Figure 1a). The mean T at night had a minimum of ´2.36 ˝C on 12 April
s
and a maximum of 20.7 ˝C on 29 July (Figure 1b). The mean RH at night was 64.3 ˘ 18.8%, with
117 days being greater than 60% and 90 days greater than 70% (Figure 1c). The mean W at night
s
hadamaximumof5.17m¨s´1andaminimumof0.59m¨s´1,averaging2.18˘0.93m¨s´1overthe
growingseason(Figure1d). Ofallthenightswithdew,thenumberofnightswithW <3.0m¨s´1
s
were 150, accounting for 90% of the nights with dew. The number of nights with W < 1.0 m¨s´1
s
accountedfor6.6%ofdewnights. AccumulatedGatnightreachedamaximumof84.0W¨m´2 on
1 Juneandaminimumof´273.2W¨m´2on1September. Eighty-threepercentofnightshadGvalues
ď0duringtheobservationperiod(Figure1e). Totalrainfallwas296mmoverthegrowingseason.
Therewerethreebigrainfalleventsofě20mm¨day´1overthegrowingseason,withthelargestbeing
50mm¨day´1on27June(Figure1f). SoilWaterContent(SWC)ata10cmdepthfollowedthepattern
ofrainfallandtherewasnosignificantdifferencebetweenmeansduringthedayandatnightduring
themeasurementperiod(Figure1f). ThenightlymeanVPDreachedamaximumof1.29kPaon13June
andtheminimumofVPDis0.06kPaon4May,averaging0.55˘0.29kPaovertheseason(Figure1g).
Figure 1. Daytime and nighttime means for: (a) air temperature (Ta) at 6 m above the ground;
(b)surfacetemperature(Ts);(c)RelativeHumidity(RH);(d)windspeed(Ws)at6mabovetheground;
dailysumsfor(e)soilheatflux(G)attheupper10cmofsoiland(f)rainfall;daytimeandnighttime
meansfor(f)SoilWaterContent(SWC)intheupper10cmofthesoiland(g)VaporPressureDeficit
(VPD)from6Aprilto18October.
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3.2. SeasonalChangesinDew
Dew amounts varied over the growing season (Figure 2), with higher values in mid-summer
(June–August)andlowervaluesinspringandautumn. Themaximumdailydewamountwas0.16
mmon21September,andtheminimumwas2.4ˆ10´3mmon13May,withameandewamountof
0.049˘0.04mmovertheobservationalperiod. Themaximummonthlymeandailydewamountwas
0.08mminJulyandtheminimumwas0.03mminMay. Themaximummonthlymeandaytimedew
amountwas0.04mminJulyandtheminimumwas0.004mminMay.
Figure2. (a)Dailydewamountfrom6Aprilto18Octoberand(b)monthlymeansofdailytotal
(night- and daytime) and day-time values of dew. Rainy days were excluded and the data are
non-gapfilledvalues.
Thetotalnumberofdewdayswas166,whichaccountedfor85%ofthegrowingseason. Mean
daily dew duration varied by month (Figure 3). Daytime dew duration was longest in July, and
shortestinAprilandMay,mostvariableinJuly(from0.5to7h),andleastvariableinMay(0.5to2h)
(Figure3a). NighttimedurationwaslongestinOctober,mostvariableinAprilandAugust(from1to
9.5h),andleastvariableinOctober(3.5to10h)(Figure3b). Theaveragedurationfordaytimeand
nighttimewere1.3h¨day´1and6.2h¨day´1,respectively.
Figure3.Monthlymean,maximum,andminimumvaluesof(a)daytimedewduration(b)nighttime
dewdurationfrom6Aprilto18October.
Inautumn(SeptemberandOctober),dewcommencementwasearliestandcessationwaslatest
(Table1). Inthesummer,dewcommencedlaterandceasedearlier. Significantinter-monthvariationin
meandailycommencementtimeswereobserved(P=0.000,ANOVA).However,therewerenoclear
inter-monthdifferencesindewcessation(P=0.108,ANOVA).
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Table1.Monthlydewestimates,including:meanstartandendtimes(rain-freedaysonly),meandaily
dewamount(afterfillingofgaps),monthlytotal(afterfillingofgaps),monthlyevapotranspiration
(ET),rainfall,ratioofdewtorainfall,andratioofwaterinput(dew+rainfall)toET,foradesert-shrub
ecosysteminYanchi,Ningxia.
EndTime Daily Monthly Dew: Dew+
StartTime(HH: Rainfall
Month (HH:MM Amount Amount ET(mm) Rainfall Rainfall:
MMp.m) (mm)
a.m) (mm¨day´1) (mm) (%) ET(%)
April 8:40 7:10 0.16 3.9 80 – – 5
May 9:20 6:30 0.11 3.1 75 22 14 33
June 10:40 6:30 0.09 2.1 67 83 2 127
July 9:50 6:40 0.12 2.7 42 63 4 156
August 9:30 6:55 0.16 4.3 63 42 10 74
September 8:40 7:25 0.11 2.8 55 72 4 136
October 8:20 7:30 0.14 3.0 32 15 15 54
Total – – – 21.3 238 296 7 133
3.3. ControlofDew
Pathanalysisoftheeffectofmeteorologicalfactorsondewamount(Figure4)revealedthestrong
positiveeffectofRH,T ,amoderatenegativeeffectofSWC,aweakpositiveeffectofVPD,andweak
a
negativeeffectsofGandW . Amongthesefactors,theinfluenceofRHwasstronglydominant.
s
Figure4.Finalpathdiagramsfortheeffectsofsoilheatflux(G),SoilWaterContent(SWC)at10cm
depth,airtemperature(Ta),RelativeHumidity(RH)andwindspeed(Ws)andVaporPressureDeficit
(VPD)ondewamountduringthepeak-growingseasonof2012.Standardizedcoefficientsareshown
foreacharrow.
Nonlinear regressions of daily dew amount against environmental variables during the
peak-growingseasonareshowninFigure5. DewincreasedwithincreasingT whenT ď17˝C,and
a a
thenremainedconstantatT >17˝C.DewstayedconstantandrelativelylowwhenRHď75%,and
a
thenincreasedsharplywhenRH>75%. TheindependentvariablesT andRHindividuallyexplained
a
54% and 89%, respectively, of the variations in daily dew amount (Figure 5a,b). The independent
variablesW ,G,SWCandVPDindividuallyexplained36%,68%,56%and77%ofthevariationsin
s
dew, respectively (Figure 5c–f). In general, dew decreased with increasing W , SWC, VPD and G
s
(whichisvector,sothatnegativevaluesmeansupwardsoilheatfluxandpositivemeansdownward).
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Figure5. Dailydewamountasafunctionofnighttimemean: (a)airtemperature(Ta);(b)Relative
Humidity(RH);(c)windspeed(Ws);(d)SoilHeatFlux(G);(e)SoilWaterContent(SWC);and(f)Vapor
PressureDeficit(VPD).Tominimizetheconfoundingeffectsofphenology,theanalysiswaslimited
todatafromthemid-growingseason(June–August).Dailydatapointswerebinnedwithincrements
of1˝CforTa,5%forRH,0.3m¨s´1forWs,50W¨m´2forG,0.004m3¨m´3forSWCand0.1kPafor
VPDwithaminimumoffiveobservationsperbin.Thebarsindicatestandarderrors.
There was a significant positive relationship between daily rainfall amount and dew amount
onthefirstdayafterrainfall(R2 =0.81,p<0.05,Figure6). Tofurtherexaminetheeffectsofrainfall
eventsondew,weexamineddewduringsixconsecutivedays(dayofyear(DOY)178–183),including
tworainfalldays(DOY179–180), thedayprior(178)andthethreedaysafter(181–183)(Figure7).
Onthetwodayswithrain,allrain(61mm)fellduringdaytimehours. TheSWCincreasedsharply
during the rainy day DOY 179, and then decreased slowly afterwards. The RH increased sharply
onDOY179, andthenremainedalmostsaturatedduringtheday DOY180and181. T remained
a
relativelylowduringthetworainydaysandincreased,showingaclearday-nightdifferenceonthe
dayDOY181–183. Dewwasmarkedlyincreasedonthefirstday(DOY181)followingraincompared
tothedaybeforerain.
Figure6.Relationshipbetweendewondrydaysfollowingdayswithrain,anddailyrainfall,during
mid-growingseason(June–August).
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Figure 7. Half-hourly measurements over six consecutive days including periods before, during
andafteramajorrainfallevent:(a)dewamountandSoilWaterContent(SWC)at10cmdepthand
(b)RelativeHumidity(RH)andairtemperature(Ta).Theverticaldotlinesin(a)and(b)separateeach
day.Theshadowareasin(a)and(b)indicaterainydays.Rainfallwas50mmondayofyear(DOY)179
and11mmonDOY180.Onthetwodayswithrain(DOY179–180),norainfellatnight.
3.4. ContributionofDewtotheWaterBalance
Energy-closure-adjusted values of LE were used to evaluate the P–M equation (Figure 8).
The closure adjustment was applied by dividing the LE values by the observed closure fraction
of0.67. TheP–Mequationestimatesofdewamountweremorecloselycorrelatedwiththemeasured
dewoverdailythanhourlytimescales(Figure8a,b;hourlyR2 =0.37,P<0.05anddailyR2 =0.73,
P<0.05). Thefittedlineswerereasonablycloseto1:1linebothatthehourlyanddailyscale,witha
slopeof1.11atthehourlyscaleand1.21atthedailyscale. TheRMSEandBEweresmall,withvalues
of1.9ˆ10´4and3.7ˆ10´3mmathourlyscale,and5.4ˆ10´3and1.80ˆ10´3mmatthedailyscale,
respectively. ResidualsshowedthattheP–Mequationwasabletoestimatedewreasonablywellat
bothtimescales(Figure8c,d).
Figure8.LinearregressionsofdewamountsmeasuredbyECandestimatedbythePenman–Monteith
equationat(a)hourlyand(b)dailytimescales.Theregressionresidualsat(c)hourlyscaleand(d)daily
scale.Thedottedlinesin(a)and(b)are1:1lines.
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Afterapplyingtherelationshipbetweenestimatedandmeasureddewtofillgapsindailydew,
andconductingasimilardaytimeanalysistofillgapsinday-andnighttimeET(Figure9a),thevalues
weresummedoverthestudyperiod. Thetotaldewwas21.3mm,whichaccountsfor8.9%ofseasonal
ET(238mm),and7.2%ofcorrespondingrainfall(296mm)overthesameperiod. Atamonthlytime
scale, the ratio of dew to rainfall varied significantly from 2% in June to 15% in October (Table 1).
Theratioofinputstooutputs(dewplusrainfalltoET)variedgreatlyfrom5%inAprilto156%in
Julydemand(Table1). ThemonthlycontributionofdewtoETwasgreaterduringJuly–Octoberthan
April–June(Figure9b).
Figure 9. (a) Daily dew amount and evapotranspiration (ET) and (b) the ratio (%) of monthly
dew to evapotranspiration from 6 April to 18 October. The data were gap-filled using the
Penman–Monteithequation.
4. Discussion
4.1. MeasurementsIssuesandUncertainties
ECmeasurementsofLEaresubjecttomoderateuncertainty,andthemeasurementofnighttime
fluxisparticularlychallenging[26,50],TheECtechniquefailsduringperiodsoflowwindspeedat
nightandroutinelyundermeasuresofLEathigherwindspeeds,withenergy-closurefractionsofless
thanone[46]. Tocompensatefortheseproblems,wehaveexcludedcalmnightsfromtheanalysis
usingaveryconservativeu˚thresholdof0.30m¨s´1. Wehavealsoevaluatedenergy-balanceclosure
atnightandfoundaclosurefraction(i.e.,(H+LE)/(R ´G))of0.67,whichiscomparabletoother
n
studies. However,thisfractionwasnotusedtoadjustthedewamountbecauseitissubjecttohigh
uncertainty. Althoughthereismoderateuncertaintyinthegrowingseasontotaldewamountrelated
totheuncertaintyinLE,theprocessrelationshipsfromthisstudy,suchasshowninFigures4and5are
robustandarenotaffectedbytheuncertaintiesintheenergy-closureadjustments.
The meteorological sensors in this study were mounted at 6 m height, which is higher than
thestandardheightformeteorologicalmeasurementsandhigherthanpreviousstudies. Theheight
differencebetweenthisstudyandpreviousstudiesmayaccountforthelowerRHthreshold(75%)for
dewinthisstudy,becauseofthesteepgradientsinRHthatexistnearthesurfaceatnight. Fortunately,
thehighersensorheightinthisstudydoesnotinvalidatethefunctionalrelationshipsin,e.g.,Figure5,
butitmayalterthevaluesofthefittedparameters. Thedewestimatesfromeddy-covariancereflected
thedynamicsofdewwellandcanbeusedtounderstanditscontrollingmechanisms.
Inaddition,theP–MequationwasshowntofittheECdewmeasurementsreasonablywellwhen
au*thresholdof0.30m¨s´1andanenergyclosureadjustmentof1/0.67wereused. Thissuggeststhat
theP-Mequationissuitabletoestimatedewinthestudyarea.
4.2. TheFormationCharacteristicsofDew
Our study site is in the south edge of the Mu Us desert (also called the Mu Us sandland,
1200–1600ma.s.l.), which lies in a critical geographic transition zone between arid and semiarid
climates,andbetweenagriculturalandpastorallanduses. Comparedtothedesertsinitswest(suchas