Research Article A Study of Fitting a Swamp Meadow...

Research ArticleA Study of Fitting a Swamp MeadowEcosystem Evapotranspiration to a Model Based onthe Penman-Monteith Equation

Feng Li12 and Bin Wang2

1Management Station of Economic Forestry of Qingyang 1 West Qingzhou Road Qingyang 745000 China2College of Life Science and Agriculture and Forestry Qiqihar University 23 Wenhua Road Qiqihar 161006 China

Correspondence should be addressed to Bin Wang wbin03sinacom

Received 17 January 2015 Accepted 26 February 2015

Academic Editor Wenshan Guo

Copyright copy 2015 F Li and B Wang This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

To accurately estimate themagnitude and seasonal dynamics of evapotranspiration (ET) over an important a swampmeadow in theFenghuoshan permafrost region we employed the Food and Agriculture Organization- (FAO-) Penman-Monteith (P-M) modelThemodel was also used to investigate changes in the crop coefficient (119896

119888) which was calculated as the ratio of the measured actual

ET (ET119886from the eddy covariance (EC) system) to the reference ET (ET

0from the P-M model) The results indicated a reference

ET of 900mmyear from the swamp meadow ecosystem which was significantly higher than the actual ET (426mmyear) Thereference ET peaked from April to July while the actual ET was primarily in growing season The value of 119896

119888exhibited significant

seasonal variations within the range 03ndash10 with a mean 119896119888of 055 during the growing seasonThe daily 119896

119888showed a linear increase

with 119877119899and 119879

119886and a linear decrease with the VPD With respect to the biotic factors the biomass exhibited a significant positive

correlation with 119896119888 Thus a daily 119896

119888model is developed as a function of the VPD 119877

119899 119879119886 and biomass

1 Introduction

The hydrologic balance of terrestrial ecosystems is an impor-tant determinant of ecosystem structure function and pro-ductivity [1] Evapotranspiration (ET) which is the secondlargest water flux in the terrestrial hydrologic cycle playsan important role in the maintenance of the water andenergy balance of the ground surface [2] In addition ETprocesses are closely related to vegetation conditions the eco-physiological processes of plants soil environments andmicrometeorological characteristics Thus ET is a pivotalwater exchange process in the soil-plant-atmosphere contin-uum (SPAC)Therefore accurate ET estimation is significantnot only to the regulation and management of hydrologiccycles in ecosystems but also to scientific decisions regardinglocal ecological construction and production activities inagriculture and animal husbandry [3ndash5]

Domestic and international scholars have investigatedland surface ET for more than 200 years and have developeda number of fitting models and observational methods for

ecosystem evapotranspiration [6ndash9] Currently the eddycovariance (EC) system is the most extensively used andsophisticated micrometeorological approach Ecosystem ETmay be continuously monitored with the EC system withoutdamage to the soil or vegetation The Penman equationwhich utilizes conventional meteorological data is the mostinfluential fitting equation for predicting ET This equationwas proposed in 1948 when Penman proposed a formula forcalculating ET from a water surface which considers radiantenergy air saturation deficit wind speed and other ET-influencing factors Based on prior research Monteith andUnsworth modified the Penman equation and proposed thePenman-Monteith (P-M)model which is a canopy ETmodelthat has been extensively applied [7] In 1990s the Foodand Agriculture Organization (FAO) of the United Nationsamended the P-Mmodel for the estimation of actual ET fromfarmland and grassland and recommended this modifiedmodel as a standard method for calculating ET [10]

In the P-M model recommended by the FAO (the FAO-P-M model) a crop coefficient (119896

119888) has been introduced to

Hindawi Publishing CorporationJournal of ChemistryVolume 2015 Article ID 315708 8 pageshttpdxdoiorg1011552015315708

2 Journal of Chemistry

correct reference ET values and to accurately estimate actualET values in specific ecosystems Studies have reported that119896119888is related to various biotic factors such as crop type and

growth stage [5 11] For homogeneous vegetation whichconsists of plants in a particular growth stage 119896

119888may be

regarded as a constant For example in the FAO-56 method119896119888values are assigned based on a meadowrsquos growth stage

thus 119896119888values for the initial mid and late season growth

stages are 04 105 and 085 respectively [10] Recent studieshave demonstrated that 119896

119888exhibits specific variations and

is affected by radiant energy moisture levels and otherenvironmental factors [12 13]The 119896

119888value is the key to using

the FAO-P-Mmodel to accurately estimate the actual ETof anecosystemThus the study of the patterns and characteristicsof changes in 119896

119888in natural meadowland can improve the

accuracy and simplicity of the calculation of the ecologicalwater demand of meadowlands and provide a theoreticalfoundation for the grazing production of these lands

The Sanjiangyuan Region (ie the source of the YangtzeYellow and Mekong rivers well known as the ldquowater towerof Asiardquo) is located in the hinterlands of the Qinghai-TibetPlateau which plays a pivotal role in the global hydrologiccycle and the global water balance Swamp meadow eco-system which is one of the most extensively distributed typesof vegetation in the Sanjiangyuan Region covers an areaof approximately 12763 km2 [14] The swamp meadow is alsoa unique natural landscape and one of the most impor-tant grassland resources for grazing [15] Few studies haveexplored the ET characteristics of meadow ecosystems [16]as a result studies on applicable models for accurately andconveniently evaluating ET in the swampmeadow ecosystemalso remain scarce

This study aimed to achieve the following objectives(1) use the FAO-P-M model and eddy covariance systemto explore the dynamics of the actual ET and reference ETchanges in the swamp meadow ecosystem of the Sanjiang-yuan Region and (2) derive a suitable 119896

119888-based model of the

swamp meadow ecosystem by determining how 119896119888values

vary with changes in meadow climate and vegetation andby establishing the relationships between 119896

119888and the factors

(including both the environmental and biotic factors)

2 Material and Methods

21 Study Site The Fenghuoshan permafrost region of theSanjiangyuan Region (92∘501015840ndash93∘31015840 E and 34∘401015840ndash34∘481015840N)located in the upper watershed of the Zuomo Xikong Rivera tributary of the Yangtze River with typical swamp meadowecosystem was chosen as the system of the study The regioncovers a total area of 12763 km2 at elevations ranging from4680 to 5360m belonging to an arid climate area andwithout glacial or multiyear snowpack The mean annual(1973 to 2005) air temperature the highest air temperaturethe lowest air temperature precipitation evaporation andrelative humidity are minus52∘C 232∘C minus377∘C 2909mm13169mm and 57 respectively The mean annual groundtemperature ranges from minus15∘C to 40∘C and themain frozensoil depth ranges from 50m to 120m The permafrost table

varies between 08 and 25m depth The average annualduration of sunshine is 24627 h and the total radiation (119877

119904)

received per year ranges from 6000 to 7000MJmminus2 Theswamp meadow features high vegetation cover composed ofshort and densely distributed plants

The vegetation primarily consists of meadow speciessuch as Stipa aliena Kobresia tibetica Festuca sp Carex atro-fusca Leontopodium leontopetaloides and other alpine plantsThe meadow land which is primarily used for meadow(January to May and September to December) serves asan important grazing resource in the plateau region Theaboveground biomass of the swamp meadow increases inearly May and peaks in August with a multiyear averagebiomass of approximately 350 gm2 The study area receives ayearly precipitation of approximately 600mmThis precipita-tion is primarily concentrated between May and Septemberand changes in the soil water content (SWC) are subject toprecipitation-related effects

22 Measurements

221 Flux Measurements EC observation system was placedin the center of the study area This area features flat terrainwhich enables unobscured observation and provides a suffi-ciently large ldquofetchrdquo to satisfy the required physical conditionsfor eddy and meteorological observations ET was measuredusing a H

2OCO

2infrared gas analyzer (Li-7500 Li-Cor

USA) and a three-dimensional sonic anemometer (CSAT3CSI USA) with a sensor placed 22m above the groundA data logger (CR5000 Campbell Inc) was employed tocontinuously record water vapor flux data and output averagevalues at 15min intervals The actual ET (ET

119886) was measured

by the EC system in this study

222 Meteorological Measurements Environmental vari-ables were continuously measured at this site Air temper-ature (119879

119886) humidity and actual vapor pressures were mea-

sured at 110 cm and 220 cm (HMP45C CSI) soil temperaturewas measured at 0 5 10 20 30 and 50 cm depths (ther-mocouple) solar radiation (119877

119904) and net radiation (119877

119899) were

measured at 150 cm (CNR-1 Kipp and Zonen Netherlands)a horizontal wind-speed sensor (014A and 034A-L CSI) wasattached at 110 cm and 220 cm to measure horizontal windspeeds precipitation was measured at 70 cm (TE525MMCSI) SWC was measured at 5 20 and 50 cm depths (TDRsoil moisture sensor CS615 CSI) and the soil heat flux wasset at a depth of 2 cm (HFT-3 CSI)The signals were sampledat 10Hz and 15minmean data were logged by the data logger(CR23X CSI)

223 Observations of Vegetation Data Aboveground bio-mass in the study area was measured semimonthly duringthe growing season A harvesting method was adopted toobtain these measurements In particular five 05m times 05mquadrants were randomly selected within each quadrantplants were harvested by cutting the plants at ground leveland loading the plants into sampling bags Each sample wasnumbered rapidly transported to a laboratory and dried at

Journal of Chemistry 3

65∘C until a constant weight was obtained for each sampleThese weights were subsequently converted into gm2

23 The FAO-P-MModel

231 The Calculation of Reference ET The reference cropcomprises a well-managed short grass of uniform height (8ndash15 cm) that completely covers the ground and grows lushlyacross open ground without experiencing water stress Thereference crop ET refers to the ET of the reference crop underthese conditions [10] In this study the standardized FAO-P-M model was used to calculate the actual ET in the swampmeadow ecosystem This model includes two componentsmdashreference ET and 119896

119888mdashas indicated in the following equation

ETP M = 119896119888ET0 (1)

In this equation ETP M is the actual ET of an ecosystem inthe FAO-P-Mmodel 119896

119888is the crop coefficient and ET

0is the

reference ET The reference surface consists of well-wateredgrass with a height of 12 cm and a fixed surface resistance of70 sm The applicable equation is expressed as

ET0=0408Δ (119877

119899minus 119866) + 120574 (900 (119879 + 273)) 119906 (119890

119904minus 119890119886)

Δ + 120574 (1 + 034119906)

(2)

where Δ is the slope of the saturation vapor pressure curve atthe examined temperature119877

119899is the net radiation119866 is the soil

heat flux 120574 is the psychrometric constant 119879 is the mean dailytemperature at a height of 2m 119906 is the wind speed at a heightof 2m and 119890

119904and 119890119886are the saturation vapor pressure and

actual vapor pressure respectively Δ and 120574 are calculated as

Δ =4098 sdot 06108 exp [1727119879 (119879 + 2373)]

(119879 + 2373)2

120574 =119888119901sdot 119875

0622120582

120582 = 2501 minus (2361 times 10minus3

) sdot 119879

(3)

where 119888119901is the specific heat at constant pressure which has

a fixed value of 1013 times 10minus3MJkg∘C 119875 is the atmosphericpressure 0662 is the ratio of the molecular weight of watervapor to the molecular weight of dry air and 120582 is the latentheat of vaporization which represents the energy per unitvolume of water required to convert water into steam underambient temperature and pressure conditions The studymeadow area is located on a plateau zone at an averageelevation of 3250m Using (4) the atmospheric pressure (119875)can be calculated from this average elevation (119867 = 3250m)

119875 = 1013 minus 01093119867 (4)

232 The Crop Coefficient (119896119888) The crop coefficient 119896

119888was

obtained from the ratio of the reference ET in 2004 to theET obtained from EC system observations in 2004 and fittedwith environmental factors to obtain an empirical equation

which was eventually validated by 2005 data In the FAO-56 approach the growth of meadow plants was divided intodifferent growth stages [10] The study area of the currentinvestigation features an alpine climate in this area plantgrowth typically begins in late April and the growing seasonextends fromMay to September

24 Evaluation Methods In this study the slope linear cor-relation coefficient (1198772) relative root-mean-squared error(RRMSE) index of agreement (IA) and coefficient of deter-mination (CD) were utilized to examine the extent of thedifferences between the observed and calculated values of ETand to statistically analyze the accuracy of the adopted fittingapproach from multiple perspectives The slope and 1198772 werecalculated by Origin 80 RRMSE IA and CD values werecomputed as [13 17]

RRMSE = radicsum119899

119894=1(119874119894minus 119864119894)2

119899sdot1

119874

IA = 1 minussum119899

119894=1(119864119894minus 119874119894)2

sum119899

119894=1(10038161003816100381610038161003816119864119894minus 11987410038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 11987410038161003816100381610038161003816)2

CD =sum119899

119894=1(119874119894minus 119874)2

sum119899

119894=1(119864119894minus 119874)2

(5)

where 119874119894indicates the 119894th observed value 119864

119894represents the

119894th simulated or estimated value 119899 represents the numberof samples and 119874 is the mean observed value RRMSE is ameasure of the relative magnitude of residuals smaller valuesof RRMSE indicate better model calculation results The IAcan be used to evaluate the correlation between the observedvalues and the simulated values the closer the IA is to 1 themore closely the fitted values match the observed values TheCD can be used to measure the dispersion of the simulatedvalues from the mean observed values a CD greater than 08indicates satisfactory simulation results [18]

3 Results and Discussion

31 Seasonal Variations in Reference ET (ET0) and ActualET As indicated in Figure 1 the reference ET derived fromthe P-M model was significantly higher than the actual ETmeasured by the EC system The reference ET (ET

0) refers

to the reference capacity of moisture diffusion from theecosystem to the atmosphere under specific environmentalconditions thus ET

0represents the maximum ET The

annual reference ET of the examined swamp meadow was9002mm which was significantly higher than the actualET of 4258mm These parameters also exhibited significantseasonal variations ET

0began to significantly increase in

March and attained peak levels from April to July howeverit began to decline in August The total ET

0during the

growing season (from May to September) was 4871mmwhich represented 54 of the annual cumulative ET

0 Con-

versely seasonal variations in actual ET (ET119886) in the swamp

meadow lagged behind variations in reference ET The ET119886


0

20

40

60

80

100

120

140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

ET0

and

ETa

(mm

)

aETET0

Figure 1 Annual variations in reference ET (ET0) and actual ET

(ET119886)

was concentrated in the growing season between May andSeptember the ET

119886was 3371mm which represented 79 of

the annual cumulative ET119886

According to its definition the reference ET (ET0) was

only affected by climatic parameters [10] As shown in (2)the net radiation (119877

119899) vapor pressure deficit (VPD 119890

119904minus 119890119886)

air temperature (119879119886) and wind speed (119906) determined the

reference ET The annual variation of these environmentalfactors was shown in Figure 2 As the maximum of 119877

119899 VPD

and 119906 occurred during April to June (Figure 2) ET0reached

its maximum value at a similar period (March to June)However with the canopy development in growing seasonthe effect of biotic factors will exert increasing influence onthe actual ET Therefore with the ET

119886rapidly increasing in

growing season the difference between ET0and ET

119886became

smaller (Figure 1)Moreover the reference ET in growing season (4871mm)

was lower than the value of precipitation (5797mm) Theratio of ET

0to 119875 was 84 during growing season This result

was similar to a nonirrigated pasture [19] however it wasmuch lower than some grassland ecosystems [9 20] In thesegrasslands the reference ET was significantly higher than theprecipitation in growing season

Comparing to these grasslands [9 20] the lower ET0

may be caused by the unique climate in this plateau Thenet radiation (119877

119899) in the Qinghai-Tibetan Plateau was much

lower than that for lowland grasslands [21] despite the highincident solar radiation (119877

119904) due to the fact that the net long-

wave radiation in alpine region is much higher than that forlowland regions [22] In this swamp meadow the 119877

119899was

much lower than the 119877119904 and the average 119877

119899and 119877

119904val-

ues were 77 and 166mol(m2 d) respectively (Figure 2(a))The low energy available for water evaporation may limitthe reference ET to some extent Moreover VPD and 119879

119886

were meteorological variables for control of water vapourexchange between the atmosphere and vegetation [23] Gu etal [24] indicated that low VPD and 119879

119886due to the frequent

precipitation and the altitude were characteristics of theclimate in the swamp meadow in Northeast Qinghai-TibetanPlateau Figure 2(b) showed the variation of VPD and 119879

119886in

this study site The maximum values of daily mean VPD and119879119886were only 15 kPa and 145∘C which were greatly lower

thanmany other grasslands with themaximumVPD rangingfromabout 2 to 5 kPa andwith themaximum119879

119886ranging from

about 20 to 30∘C [25ndash27] The low VPD and 119879119886might imply

the weak driving power and are considered as the factors torestrict the reference ET in growing season

32 The Seasonal Variation in 119896119888 Due to the difference

between reference ET and actual ET the correct determina-tion of 119896

119888 which is the ratio of daily ET to ET

0 is important

for the accurate estimation of the actual ET In this study119896119888exhibited a gradual rise from mid-April to mid-June and

remained at a high level in July and August (Figure 3) Thevalue of 119896

119888rapidly declined in September and was less than

02 by the end of October During the growing season whichextended from May to September 119896

119888exhibited a mean value

of 055 and fluctuated within the range of 030ndash092 Thisrange is consistent with the range of 119896

119888values reported in

FAO-56 (030ndash105) [10] but is significantly higher than therange of 119896

119888values observed for a typical steppe (032ndash068)

[28] and temperate desert steppe (002ndash050) [13] of InnerMongolia These results indicated that the 119896

119888for the swamp

meadow features more adequate moisture and better plantgrowth conditions than arid and semiarid steppe ecosystems

33 Factors That Impact 119896119888

331 The Effect of Environmental Factors on 119896119888 Previous

studies have focused on 119896119888values related to local climate

conditions [12 13] In this meadow the Pearson product-moment correlation coefficients for the relationships between119896119888and its main environmental factors were computed on a

daily scale (Table 1) This computation showed that the VPDand 119877

119899were critical for controlling 119896

119888 119879119886also showed a

significant relation to 119896119888 whereas 119906 and SWC at a depth of

5 cm were not significant at the 95 confidence levelThe responses of 119896

119888to changes in VPD 119877

119899 and 119879

119886are

illustrated in Figure 4 To reduce or offset the errors associ-ated with the 119896

119888values VPD 119877

119899 and 119879

119886were grouped into

bins with the following criteria 02 kPa for VPD 1 MJm2 dfor 119877119899 and 2∘C for 119879

119886 119896119888increased linearly in response to an

increase in 119877119899and 119879

119886and in response to a decrease in VPD

(Figure 4) L Zhou andG S Zhou [29] also obtained a similarresult from a study of a reed marsh However our resultwas different from a study in a temperate desert steppe [13]which demonstrated that the soil water content (SWC) wasthe most important factor for determining 119896

119888 In this study

the root-layer (0ndash10 cm) SWC fluctuated within the range02ndash06m3m3 (Figure 1(c)) which was much better than theSWC at desert steppe in Inner Mongolia (005ndash015m3m3)The higher soil moist in this swamp meadow might weakenthe SWC impact on 119896

119888

Daily 119896119888for the swamp meadow can be expressed by

an empirical equation from the correlation and regressionanalyses of the relationships between 119896

119888and its statistically

significant environmental variables

119896119888= 0597 minus 0801VPD + 0026119877

119899+ 0040119879

119886 (6)


0

10

20

30

Rn

andRs

(MJ(

m2

d))

0 60 120 180 240 300 360DOY

Rn

Rs

(a)

0 60 120 180 240 300 360DOY

20

10

0

minus10

minus20

minus30

Ta

Ta

(∘C)

VPD

VPD

(kPa

)

2

15

1

05

0

(b)

0 60 120 180 240 300 360DOY

0

10

20

30

40

50 06

04

02

0

P

SWC

P(m

m)

SWC

(m3m

3)

(c)

0 60 120 180 240 300 360DOY

3

25

2

15

1

u(m

s)

(d)

Figure 2 Temporal variations of (a) net radiation (119877119899) and solar radiation (119877

119904) (b) daily mean air temperature (119879

119886) and vapor pressure deficit

(VPD) (c) daily precipitation (119875) and soil water content at 5 cm depth (SWC) and (d) wind speed (119906) in the swamp meadow during 2004

0

02

04

06

08

1

MonthMay JulJunApr SepAug Oct

kc

Figure 3 Seasonal variation in the 119896119888

332 The Effects of Biotic Factors on 119896119888 In addition to envi-

ronmental factors the plant species composition growthconditions and other biotic factors can significantly affect119896119888[3 30 31] Biomass directly reflects the growth status of

a biological community In this study the sampled plantsbegan to grow at the end of April which caused an increase inthe biomass that primarily began in early May The biomassrapidly increased during June and August and attained apeak in late August (with a maximum value of 3531 gm2 in

the study year) After August the biomass rapidly declinedas plants gradually died (Figure 5(a)) The linear regressionanalysis of the relationship between 119896

119888and the biomass

demonstrated a significant positive correlation between 119896119888

and the biomass however the significance of this correlationchanged during different growth stages As indicated inFigure 5(b) the biomass increased and 119896

119888rapidly increased

from 045 to 093 fromMay to middle August After biomassattained its peak 119896

119888began to decline and decreased to 039 in

late October while no significant decrease in 119896119888was observed

from September to October because 119896119888only decreased from

054 to 039 during this periodThe following linear equations describe how 119896

119888increases

with biomass

MayndashMiddle August 119896119888= 00019 times biomass + 04521

Middle AugustndashOct 119896119888= 00008 times biomass + 02514

(7)

Considering both biotic and environmental factors dailyactual ET can be calculated as follows

MayndashMiddle August

ET = (0597 minus 0801VPD + 0026119877119899+ 0040119879

119886)

sdot (00019 biomass + 04521) times ET0

(8)


Table 1 Pearson product-moment correlation coefficients for the relationships between daily 119896119888and daily average values for environmental

variables SWC at a depth of 5 cm atmospheric VPD wind speed (119906) 119877119899 and 119879

119886over the swamp meadow on a daily basis for days without

rain during the 2004 growing season (119899 = 106)

119896119888

SWC VPD 119906 119877119899

119879119886

119896119888

1000SWC minus0168 1000VPD minus0509lowastlowast minus0324lowast 1000119906 minus0175 0339lowast minus0114 1000119877119899

0442lowastlowast minus0440lowastlowast 0164 minus0309lowast 1000119879119886

0402lowast minus0590lowastlowast 0400lowast minus0518lowastlowast 0429lowastlowast 1000lowastCorrelation coefficient at 005 significant levellowastlowastCorrelation coefficient at 001 significant level

0

02

04

06

08

1

12

02 04 06 08 1 12 14 16VPD (kPa)

kc

(a)

7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Rn (MJ(m2 d))

(b)

1 3 5 7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Ta (∘C)

(c)

Figure 4 Linear response of 119896119888to (a) VPD (b)119877

119899 and (c)119879

119886The daily 119896

119888data were averaged with a bin width of 02 kPa for VPD 1MJ(m2 d)

for 119877119899 and 2∘C for 119879

119886 respectively Bars indicate mean plusmn SD

Middle AugustndashOctET = (0597 minus 0801VPD + 0026119877

119899+ 0040119879

119886)


(9)

34 Test for Modeling Evapotranspiration Equation (9) wasvalidated using theECdata collected during the 2005 growingseason The model performed well during the growingseasonThe simulated daily ET (ETmod) followed the seasonaltrend of the EC measured ET (ET

119886) (Figure 6) The slope

(=095) of the regression line between the measured and

simulated ET passes through the origin close to 1 with 1198772 =076 a RRMSE of 023mmd and an IA of 092

In this study the abiotic and biotic factors enabled abetter estimation of evapotranspiration In Table 2 the slope1198772 IA and CD of ETmod1 are greater and the RRMES is

smaller than those of ETmod2 These results indicated thatrelative to the fittingmethod that accounts formeteorologicalvariables the fitting method that comprehensively considersmeteorological factors and vegetation conditions can be usedto derive simulated ET results that are closer to the actualobserved ET results (Table 2)


Biom

ass (

gm

2)

Month

0

100

200

300

400

May JulJun SepAug Oct

(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)

Figure 5 (a) The seasonal variation of biomass in this swamp meadow ecosystem in 2004 and (b) the response of 119896119888to biomass The 119896

119888was

averaged with a bin width of 50 gm2 for biomass Bars indicate mean plusmn SD

0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod

Figure 6 Seasonal variation in themeasured and simulated ET (ET119886

and ETmod) for days without rain in 2005 using (9)

Table 2 The validation statistics of the model simulated by themeteorological factors versus the model simulated by the meteoro-logical factors and biotic factors

Slope 1198772 RRMSE IA CD

ETmod1 089 068 028 087 076ETmod2 095 076 023 093 087ETmod1 is simulated by themeteorological factors (VPD119877

119899 and119879

119886) ETmod2

is simulated by these meteorological factors and biomass

4 Conclusions

The FAO-Penman-Monteith model was used to simulate ETover the swamp meadow on the Sanjiangyuan Region Thereference ET was estimated by the FAO-P-M model andshowed difference from the actual ET measured by the ECsystem The crop coefficients (119896

119888) were used to estimate ET

119886

The average value of 119896119888during growing season was 055

(ranging from 030 to 092) VPD 119877119899 and 119879

119886explained

the majority of the daily variation in 119896119888 which showed a

linear increase with an increase in 119877119899and 119879

119886and a linear

decrease with an increase in VPD We also considered theimpact of biomass on 119896

119888 which showed a significant positive

effect on 119896119888 A daily empirical 119896

119888model driven by VPD

119877119899 119879119886 and biomass was developed to estimate daily actual

ET using the FAO-56 119896119888approach and the P-M model for

reference ET This ET model was validated against 2005growing season data for this meadow and demonstrated asuitable and consistent performance between the simulatedand measured ET

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This study was supported by the National Natural Sci-ence Foundation of China (31070433) and the Japan-ChinaResearch Cooperative Program (2010DFA31290)

References

[1] R Burman and L O Pochop ldquoEvaporation evapotranspirationand climatic datardquoDevelopment in Atmospheric Science vol 22pp 1ndash5 1994

[2] T Oki and S Kanae ldquoGlobal hydrological cycles and worldwater resourcesrdquo Science vol 313 no 5790 pp 1068ndash1072 2006

[3] N K Tyagi D K Sharma and S K Luthra ldquoDetermination ofevapotranspiration and crop coefficients of rice and sunflowerwith lysimeterrdquo Agricultural Water Management vol 45 no 1pp 41ndash54 2000

[4] L B Gong C-Y Xu D L Chen S Halldin and Y D ChenldquoSensitivity of the Penman-Monteith reference evapotranspira-tion to key climatic variables in the Changjiang (Yangtze River)basinrdquo Journal of Hydrology vol 329 no 3-4 pp 620ndash629 2006

[5] D S Torriani P Calanca S Schmid M Beniston and JFuhrer ldquoPotential effects of changes in mean climate and cli-mate variability on the yield of winter and spring crops in Switz-erlandrdquo Climate Research vol 34 no 1 pp 59ndash69 2007

[6] C H B Priestley and R J Taylor ldquoOn the assessment ofsurface heat flux and evaporation using large-scale parametersrdquoMonthly Weather Review vol 100 no 2 pp 81ndash92 1972


[7] J L Monteith andM H Unsworth Principles of EnvironmentalPhysics Chapman and Hall New York NY USA 2nd edition1990

[8] A B Frank ldquoEvapotranspiration fromnorthern semiarid grass-landsrdquo Agronomy Journal vol 95 no 6 pp 1504ndash1509 2003

[9] S-G Li C-T Lai G Lee et al ldquoEvapotranspiration from a wettemperate grassland and its sensitivity to micro-environmentalvariablesrdquo Hydrological Processes vol 19 no 2 pp 517ndash5322005

[10] R G Allen L S Pereira D Raes and M Smith ldquoCrop evap-otranspiration guidelines for computing crop water require-mentsrdquo FAO Irrigation and Drainage Paper 56 FAO RomeItaly 1998

[11] L EWilliams and J E Ayars ldquoGrapevinewater use and the cropcoefficient are linear functions of the shaded area measuredbeneath the canopyrdquo Agricultural and Forest Meteorology vol132 no 3-4 pp 201ndash211 2005

[12] J G Lockwood ldquoIs potential evapotranspiration and its rela-tionship with actual evapotranspiration sensitive to elevatedatmospheric CO

2levelsrdquo Climatic Change vol 41 no 2 pp

193ndash212 1999[13] F L Yang and G S Zhou ldquoCharacteristics and modeling of

evapotranspiration over a temperate desert steppe in InnerMongolia Chinardquo Journal of Hydrology vol 396 no 1-2 pp139ndash147 2011

[14] D Zheng Q S Zhang and S H Wu Mountain Geoecologyand Sustainable Development of the Tibetan Plateau KluwerAcademic Dordrecht The Netherlands 2000

[15] X F Cui and H F Graf ldquoRecent land cover changes on theTibetan Plateau a reviewrdquo Climatic Change vol 94 no 1-2 pp47ndash61 2009

[16] J Li S Jiang B Wang et al ldquoEvapotranspiration and its energyexchange in alpine meadow ecosystem on the Qinghai-TibetanPlateaurdquo Journal of Integrative Agriculture vol 12 no 8 pp1396ndash1401 2013

[17] S Alexandris and P Kerkides ldquoNew empirical formula forhourly estimations of reference evapotranspirationrdquo Agricul-tural Water Management vol 60 no 3 pp 157ndash180 2003

[18] J Cai Y Liu T W Lei and L S Pereira ldquoEstimating referenceevapotranspiration with the FAO Penman-Monteith equationusing daily weather forecast messagesrdquo Agricultural and ForestMeteorology vol 145 no 1-2 pp 22ndash35 2007

[19] D M Sumner and J M Jacobs ldquoUtility of Penman-MonteithPriestley-Taylor reference evapotranspiration and pan evapo-rationmethods to estimate pasture evapotranspirationrdquo Journalof Hydrology vol 308 no 1ndash4 pp 81ndash104 2005

[20] L A Wever L B Flanagan and P J Carlson ldquoSeasonal andinterannual variation in evapotranspiration energy balance andsurface conductance in a northern temperate grasslandrdquo Agri-cultural and Forest Meteorology vol 112 no 1 pp 31ndash49 2002

[21] A Hammerle A Haslwanter U Tappeiner A Cernusca andG Wohlfahrt ldquoLeaf area controls on energy partitioning of atemperate mountain grasslandrdquo Biogeosciences vol 5 no 2 pp421ndash431 2008

[22] XC Zhang SGuXQ Zhao et al ldquoRadiation partitioning andits relation to environmental factors above a meadow ecosys-tem on the Qinghai-Tibetan Plateaurdquo Journal of GeophysicalResearch Atmospheres vol 115 no 10 Article ID D10106 2010

[23] D Baldocchi and T Meyers ldquoOn using eco-physiologicalmicrometeorological and biogeochemical theory to evaluatecarbondioxide water vapor and trace gas fluxes over vegetation

a perspectiverdquo Agricultural and Forest Meteorology vol 90 no1-2 pp 1ndash25 1998

[24] S Gu Y H Tang X Y Cui et al ldquoCharacterizing evapotran-spiration over a meadow ecosystem on the Qinghai-TibetanPlateaurdquo Journal of Geophysical Research Atmospheres vol 113no D8 2008

[25] E Kellner ldquoSurface energy fluxes and control of evapotranspi-ration from a Swedish Sphagnummirerdquo Agricultural and ForestMeteorology vol 110 no 2 pp 101ndash123 2001

[26] Y Hao Y Wang X Huang et al ldquoSeasonal and interannualvariation in water vapor and energy exchange over a typicalsteppe in InnerMongolia ChinardquoAgricultural and Forest Mete-orology vol 146 no 1-2 pp 57ndash69 2007

[27] G Dong J Guo J Chen et al ldquoEffects of spring drought oncarbon sequestration evapotranspiration and water use effi-ciency in the songnen meadow steppe in northeast ChinardquoEcohydrology vol 4 no 2 pp 211ndash224 2011

[28] H XMiao S P Chen J Q Chen et al ldquoCultivation and grazingaltered evapotranspiration and dynamics in Inner MongoliasteppesrdquoAgricultural and Forest Meteorology vol 149 no 11 pp1810ndash1819 2009

[29] L Zhou and G S Zhou ldquoMeasurement and modelling ofevapotranspiration over a reed (Phragmites australis) marsh inNortheast Chinardquo Journal of Hydrology vol 372 no 1ndash4 pp 41ndash47 2009

[30] L EWilliams C J Phene DW Grimes and T J Trout ldquoWateruse of mature Thompson Seedless grapevines in CaliforniardquoIrrigation Science vol 22 no 1 pp 11ndash18 2003

[31] G A de Medeiros F B Arruda and E Sakai ldquoCrop coefficientfor irrigated beans derived using three reference evaporationmethodsrdquo Agricultural and Forest Meteorology vol 135 no 1ndash4 pp 135ndash143 2005

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Inorganic ChemistryInternational Journal of

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

International Journal ofPhotoenergy


Carbohydrate Chemistry

International Journal of


Journal of

Chemistry


Advances in

Physical Chemistry

Hindawi Publishing Corporationhttpwwwhindawicom

Analytical Methods in Chemistry

Journal of

Volume 2014

Bioinorganic Chemistry and ApplicationsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

SpectroscopyInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Medicinal ChemistryInternational Journal of


Chromatography Research International


Applied ChemistryJournal of



Theoretical ChemistryJournal of


Journal of

Spectroscopy

Analytical ChemistryInternational Journal of


Journal of


Quantum Chemistry


Organic Chemistry International

ElectrochemistryInternational Journal of



CatalystsJournal of


correct reference ET values and to accurately estimate actualET values in specific ecosystems Studies have reported that119896119888is related to various biotic factors such as crop type and

growth stage [5 11] For homogeneous vegetation whichconsists of plants in a particular growth stage 119896

119888may be

regarded as a constant For example in the FAO-56 method119896119888values are assigned based on a meadowrsquos growth stage

thus 119896119888values for the initial mid and late season growth

stages are 04 105 and 085 respectively [10] Recent studieshave demonstrated that 119896

119888exhibits specific variations and

is affected by radiant energy moisture levels and otherenvironmental factors [12 13]The 119896

119888value is the key to using

the FAO-P-Mmodel to accurately estimate the actual ETof anecosystemThus the study of the patterns and characteristicsof changes in 119896

119888in natural meadowland can improve the

accuracy and simplicity of the calculation of the ecologicalwater demand of meadowlands and provide a theoreticalfoundation for the grazing production of these lands

The Sanjiangyuan Region (ie the source of the YangtzeYellow and Mekong rivers well known as the ldquowater towerof Asiardquo) is located in the hinterlands of the Qinghai-TibetPlateau which plays a pivotal role in the global hydrologiccycle and the global water balance Swamp meadow eco-system which is one of the most extensively distributed typesof vegetation in the Sanjiangyuan Region covers an areaof approximately 12763 km2 [14] The swamp meadow is alsoa unique natural landscape and one of the most impor-tant grassland resources for grazing [15] Few studies haveexplored the ET characteristics of meadow ecosystems [16]as a result studies on applicable models for accurately andconveniently evaluating ET in the swampmeadow ecosystemalso remain scarce

This study aimed to achieve the following objectives(1) use the FAO-P-M model and eddy covariance systemto explore the dynamics of the actual ET and reference ETchanges in the swamp meadow ecosystem of the Sanjiang-yuan Region and (2) derive a suitable 119896

119888-based model of the

swamp meadow ecosystem by determining how 119896119888values

vary with changes in meadow climate and vegetation andby establishing the relationships between 119896

119888and the factors

(including both the environmental and biotic factors)

2 Material and Methods

21 Study Site The Fenghuoshan permafrost region of theSanjiangyuan Region (92∘501015840ndash93∘31015840 E and 34∘401015840ndash34∘481015840N)located in the upper watershed of the Zuomo Xikong Rivera tributary of the Yangtze River with typical swamp meadowecosystem was chosen as the system of the study The regioncovers a total area of 12763 km2 at elevations ranging from4680 to 5360m belonging to an arid climate area andwithout glacial or multiyear snowpack The mean annual(1973 to 2005) air temperature the highest air temperaturethe lowest air temperature precipitation evaporation andrelative humidity are minus52∘C 232∘C minus377∘C 2909mm13169mm and 57 respectively The mean annual groundtemperature ranges from minus15∘C to 40∘C and themain frozensoil depth ranges from 50m to 120m The permafrost table

varies between 08 and 25m depth The average annualduration of sunshine is 24627 h and the total radiation (119877

119904)

received per year ranges from 6000 to 7000MJmminus2 Theswamp meadow features high vegetation cover composed ofshort and densely distributed plants

The vegetation primarily consists of meadow speciessuch as Stipa aliena Kobresia tibetica Festuca sp Carex atro-fusca Leontopodium leontopetaloides and other alpine plantsThe meadow land which is primarily used for meadow(January to May and September to December) serves asan important grazing resource in the plateau region Theaboveground biomass of the swamp meadow increases inearly May and peaks in August with a multiyear averagebiomass of approximately 350 gm2 The study area receives ayearly precipitation of approximately 600mmThis precipita-tion is primarily concentrated between May and Septemberand changes in the soil water content (SWC) are subject toprecipitation-related effects

22 Measurements

221 Flux Measurements EC observation system was placedin the center of the study area This area features flat terrainwhich enables unobscured observation and provides a suffi-ciently large ldquofetchrdquo to satisfy the required physical conditionsfor eddy and meteorological observations ET was measuredusing a H

2OCO

2infrared gas analyzer (Li-7500 Li-Cor

USA) and a three-dimensional sonic anemometer (CSAT3CSI USA) with a sensor placed 22m above the groundA data logger (CR5000 Campbell Inc) was employed tocontinuously record water vapor flux data and output averagevalues at 15min intervals The actual ET (ET

119886) was measured

by the EC system in this study

222 Meteorological Measurements Environmental vari-ables were continuously measured at this site Air temper-ature (119879

119886) humidity and actual vapor pressures were mea-

sured at 110 cm and 220 cm (HMP45C CSI) soil temperaturewas measured at 0 5 10 20 30 and 50 cm depths (ther-mocouple) solar radiation (119877

119904) and net radiation (119877

119899) were

measured at 150 cm (CNR-1 Kipp and Zonen Netherlands)a horizontal wind-speed sensor (014A and 034A-L CSI) wasattached at 110 cm and 220 cm to measure horizontal windspeeds precipitation was measured at 70 cm (TE525MMCSI) SWC was measured at 5 20 and 50 cm depths (TDRsoil moisture sensor CS615 CSI) and the soil heat flux wasset at a depth of 2 cm (HFT-3 CSI)The signals were sampledat 10Hz and 15minmean data were logged by the data logger(CR23X CSI)

223 Observations of Vegetation Data Aboveground bio-mass in the study area was measured semimonthly duringthe growing season A harvesting method was adopted toobtain these measurements In particular five 05m times 05mquadrants were randomly selected within each quadrantplants were harvested by cutting the plants at ground leveland loading the plants into sampling bags Each sample wasnumbered rapidly transported to a laboratory and dried at



23 The FAO-P-MModel



ETP M = 119896119888ET0 (1)



0is the


ET0=0408Δ (119877

119899minus 119866) + 120574 (900 (119879 + 273)) 119906 (119890

119904minus 119890119886)

Δ + 120574 (1 + 034119906)

(2)






Δ =4098 sdot 06108 exp [1727119879 (119879 + 2373)]

(119879 + 2373)2

120574 =119888119901sdot 119875

0622120582


) sdot 119879

(3)



119875 = 1013 minus 01093119867 (4)


119888was





119894=1(119874119894minus 119864119894)2

119899sdot1

119874


119894=1(119864119894minus 119874119894)2

sum119899

119894=1(10038161003816100381610038161003816119864119894minus 11987410038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 11987410038161003816100381610038161003816)2

CD =sum119899

119894=1(119874119894minus 119874)2

sum119899

119894=1(119864119894minus 119874)2

(5)






0) refers






0during the


0 Con-




0

20

40

60

80

100

120

140


ET0

and

ETa

(mm

)

aETET0


(ET119886)







119904minus 119890119886)



119899 VPD





119886became











119899was


119899and 119877

119904val-


119886




119886in



119886ranging from






0 is important











119888for the swamp








119888 119879119886also showed a




119899 and 119879

119886are


119888values VPD 119877

119899 and 119879




increase in 119877119899and 119879





119888





119896119888= 0597 minus 0801VPD + 0026119877

119899+ 0040119879

119886 (6)


0

10

20

30

Rn

andRs

(MJ(

m2

d))

0 60 120 180 240 300 360DOY

Rn

Rs

(a)

0 60 120 180 240 300 360DOY

20

10

0

minus10

minus20

minus30

Ta

Ta

(∘C)

VPD

VPD

(kPa

)

2

15

1

05

0

(b)

0 60 120 180 240 300 360DOY

0

10

20

30

40

50 06

04

02

0

P

SWC

P(m

m)

SWC

(m3m

3)

(c)

0 60 120 180 240 300 360DOY

3

25

2

15

1

u(m

s)

(d)





0

02

04

06

08

1


kc















119888increases

with biomass



(7)



ET = (0597 minus 0801VPD + 0026119877119899+ 0040119879

119886)


(8)






119896119888

SWC VPD 119906 119877119899

119879119886

119896119888




0

02

04

06

08

1

12

02 04 06 08 1 12 14 16VPD (kPa)

kc

(a)

7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Rn (MJ(m2 d))

(b)

1 3 5 7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Ta (∘C)

(c)


119899 and (c)119879

119886The daily 119896


for 119877119899 and 2∘C for 119879



119899+ 0040119879

119886)


(9)








Biom

ass (

gm

2)

Month

0

100

200

300

400


(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)


119888was


0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod






119899 and119879

119886) ETmod2


4 Conclusions



119886



119886explained



119886and a linear










Acknowledgments


References













































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of



23 The FAO-P-MModel



ETP M = 119896119888ET0 (1)



0is the


ET0=0408Δ (119877

119899minus 119866) + 120574 (900 (119879 + 273)) 119906 (119890

119904minus 119890119886)

Δ + 120574 (1 + 034119906)

(2)






Δ =4098 sdot 06108 exp [1727119879 (119879 + 2373)]

(119879 + 2373)2

120574 =119888119901sdot 119875

0622120582


) sdot 119879

(3)



119875 = 1013 minus 01093119867 (4)


119888was





119894=1(119874119894minus 119864119894)2

119899sdot1

119874


119894=1(119864119894minus 119874119894)2

sum119899

119894=1(10038161003816100381610038161003816119864119894minus 11987410038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 11987410038161003816100381610038161003816)2

CD =sum119899

119894=1(119874119894minus 119874)2

sum119899

119894=1(119864119894minus 119874)2

(5)






0) refers






0during the


0 Con-




0

20

40

60

80

100

120

140


ET0

and

ETa

(mm

)

aETET0


(ET119886)







119904minus 119890119886)



119899 VPD





119886became











119899was


119899and 119877

119904val-


119886




119886in



119886ranging from






0 is important











119888for the swamp








119888 119879119886also showed a




119899 and 119879

119886are


119888values VPD 119877

119899 and 119879




increase in 119877119899and 119879





119888





119896119888= 0597 minus 0801VPD + 0026119877

119899+ 0040119879

119886 (6)


0

10

20

30

Rn

andRs

(MJ(

m2

d))

0 60 120 180 240 300 360DOY

Rn

Rs

(a)

0 60 120 180 240 300 360DOY

20

10

0

minus10

minus20

minus30

Ta

Ta

(∘C)

VPD

VPD

(kPa

)

2

15

1

05

0

(b)

0 60 120 180 240 300 360DOY

0

10

20

30

40

50 06

04

02

0

P

SWC

P(m

m)

SWC

(m3m

3)

(c)

0 60 120 180 240 300 360DOY

3

25

2

15

1

u(m

s)

(d)





0

02

04

06

08

1


kc















119888increases

with biomass



(7)



ET = (0597 minus 0801VPD + 0026119877119899+ 0040119879

119886)


(8)






119896119888

SWC VPD 119906 119877119899

119879119886

119896119888




0

02

04

06

08

1

12

02 04 06 08 1 12 14 16VPD (kPa)

kc

(a)

7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Rn (MJ(m2 d))

(b)

1 3 5 7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Ta (∘C)

(c)


119899 and (c)119879

119886The daily 119896


for 119877119899 and 2∘C for 119879



119899+ 0040119879

119886)


(9)








Biom

ass (

gm

2)

Month

0

100

200

300

400


(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)


119888was


0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod






119899 and119879

119886) ETmod2


4 Conclusions



119886



119886explained



119886and a linear










Acknowledgments


References













































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


0

20

40

60

80

100

120

140


ET0

and

ETa

(mm

)

aETET0


(ET119886)







119904minus 119890119886)



119899 VPD





119886became











119899was


119899and 119877

119904val-


119886




119886in



119886ranging from






0 is important











119888for the swamp








119888 119879119886also showed a




119899 and 119879

119886are


119888values VPD 119877

119899 and 119879




increase in 119877119899and 119879





119888





119896119888= 0597 minus 0801VPD + 0026119877

119899+ 0040119879

119886 (6)


0

10

20

30

Rn

andRs

(MJ(

m2

d))

0 60 120 180 240 300 360DOY

Rn

Rs

(a)

0 60 120 180 240 300 360DOY

20

10

0

minus10

minus20

minus30

Ta

Ta

(∘C)

VPD

VPD

(kPa

)

2

15

1

05

0

(b)

0 60 120 180 240 300 360DOY

0

10

20

30

40

50 06

04

02

0

P

SWC

P(m

m)

SWC

(m3m

3)

(c)

0 60 120 180 240 300 360DOY

3

25

2

15

1

u(m

s)

(d)





0

02

04

06

08

1


kc















119888increases

with biomass



(7)



ET = (0597 minus 0801VPD + 0026119877119899+ 0040119879

119886)


(8)






119896119888

SWC VPD 119906 119877119899

119879119886

119896119888




0

02

04

06

08

1

12

02 04 06 08 1 12 14 16VPD (kPa)

kc

(a)

7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Rn (MJ(m2 d))

(b)

1 3 5 7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Ta (∘C)

(c)


119899 and (c)119879

119886The daily 119896


for 119877119899 and 2∘C for 119879



119899+ 0040119879

119886)


(9)








Biom

ass (

gm

2)

Month

0

100

200

300

400


(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)


119888was


0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod






119899 and119879

119886) ETmod2


4 Conclusions



119886



119886explained



119886and a linear










Acknowledgments


References













































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


0

10

20

30

Rn

andRs

(MJ(

m2

d))

0 60 120 180 240 300 360DOY

Rn

Rs

(a)

0 60 120 180 240 300 360DOY

20

10

0

minus10

minus20

minus30

Ta

Ta

(∘C)

VPD

VPD

(kPa

)

2

15

1

05

0

(b)

0 60 120 180 240 300 360DOY

0

10

20

30

40

50 06

04

02

0

P

SWC

P(m

m)

SWC

(m3m

3)

(c)

0 60 120 180 240 300 360DOY

3

25

2

15

1

u(m

s)

(d)





0

02

04

06

08

1


kc















119888increases

with biomass



(7)



ET = (0597 minus 0801VPD + 0026119877119899+ 0040119879

119886)


(8)






119896119888

SWC VPD 119906 119877119899

119879119886

119896119888




0

02

04

06

08

1

12

02 04 06 08 1 12 14 16VPD (kPa)

kc

(a)

7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Rn (MJ(m2 d))

(b)

1 3 5 7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Ta (∘C)

(c)


119899 and (c)119879

119886The daily 119896


for 119877119899 and 2∘C for 119879



119899+ 0040119879

119886)


(9)








Biom

ass (

gm

2)

Month

0

100

200

300

400


(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)


119888was


0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod






119899 and119879

119886) ETmod2


4 Conclusions



119886



119886explained



119886and a linear










Acknowledgments


References













































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of






119896119888

SWC VPD 119906 119877119899

119879119886

119896119888




0

02

04

06

08

1

12

02 04 06 08 1 12 14 16VPD (kPa)

kc

(a)

7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Rn (MJ(m2 d))

(b)

1 3 5 7 9 11 13 15 17 190

02

04

06

08

1

12

kc

Ta (∘C)

(c)


119899 and (c)119879

119886The daily 119896


for 119877119899 and 2∘C for 119879



119899+ 0040119879

119886)


(9)








Biom

ass (

gm

2)

Month

0

100

200

300

400


(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)


119888was


0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod






119899 and119879

119886) ETmod2


4 Conclusions



119886



119886explained



119886and a linear










Acknowledgments


References













































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of


Biom

ass (

gm

2)

Month

0

100

200

300

400


(a)

Biomass (gm2)

kc

03

05

07

09

11

0 100 200 300 400

(b)


119888was


0

1

2

3

4

5

6

DOY152 182 213 243 273121

aET

ETm

odan

d ET

a(m

m)

ETmod






119899 and119879

119886) ETmod2


4 Conclusions



119886



119886explained



119886and a linear










Acknowledgments


References













































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of







































Journal of

Chemistry


Advances in

Physical Chemistry



Journal of

Volume 2014














Journal of

Spectroscopy



Journal of


Quantum Chemistry






CatalystsJournal of

Research Article A Study of Fitting a Swamp Meadow...

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