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Article

Simulation of Forest Evapotranspiration UsingTime-Series Parameterization of the Surface EnergyBalance System (SEBS) over the Qilian Mountains

Xin Tian 12 Christiaan van der Tol 2 Zhongbo Su 2 Zengyuan Li 1 Erxue Chen 1 Xin Li 3Min Yan 1 Xuelong Chen 2 Xufeng Wang 3 Xiaoduo Pan 3 Feilong Ling 4 Chunmei Li 1Wenwu Fan 14 and Longhui Li 5

Received 26 July 2015 Accepted 17 November 2015 Published 26 November 2015Academic Editors George Petropoulos Randolph Wynne and Prasad S Thenkabail

1 Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing 100091 Chinatianxincafaccn (XT) wfyanminsinacn (MY) cm_lisinacom (CL) fanwenwu09aliyuncom (WF)

2 Faculty of Geo-Information Science and Earth Observation University of Twente Enschede 7500AAThe Netherlands cvandertolutwentenl (CT) zsuutwentenl (ZS) xchenutwentenl (XC)

3 Cold and Arid Regions Environmental and Engineering Research Institute Chinese Academy of SciencesLanzhou 730000 China lixinlzbaccn (XL) wangxufenglzbaccn (XW) panxiaoduolzbaccn (XP)

4 Key Laboratory of Spatial Data Mining amp Information Sharing of Ministry Education Fuzhou UniversityFuzhou 350002 China lflfzueducn

5 School of Life Sciences University of Technology Sydney Sydney 2007 Australia lilhchngmailcom Correspondence lizycafaccn (ZL) chenerxcafaccn (EC) TelFax +86-10-6288-9164 (EC)

Abstract We propose a long-term parameterization scheme for two critical parameters zero-planedisplacement height (d) and aerodynamic roughness length (z0m) that we further use in the SurfaceEnergy Balance System (SEBS) A sensitivity analysis of SEBS indicated that these two parameterslargely impact the estimated sensible heat and latent heat fluxes First we calibrated regressionrelationships between measured forest vertical parameters (Loreyrsquos height and the frontal area index(FAI)) and forest aboveground biomass (AGB) Next we derived the interannual Loreyrsquos heightand FAI values from our calibrated regression models and corresponding forest AGB dynamicsthat were converted from interannual carbon fluxes as simulated from two incorporated ecologicalmodels and a 2009 forest basis map These dynamic forest vertical parameters combined withrefined eight-day Global LAnd Surface Satellite (GLASS) LAI products were applied to estimatethe eight-day d z0m and thus the heat roughness length (z0h) The obtained d z0m and z0h werethen used as forcing for the SEBS model in order to simulate long-term forest evapotranspiration(ET) from 2000 to 2012 within the Qilian Mountains (QMs) As compared with MODIS MOD16products at the eddy covariance (EC) site ET estimates from the SEBS agreed much better with ECmeasurements (R2 = 080 and RMSE = 021 mmumldayacute1)

Keywords roughness length forest vertical parameters Surface Energy Balance System (SEBS)evapotranspiration remote sensing

1 Introduction

In addition to the terrestrial carbon cycle the hydrological cycle is also critical for thefunctionality and sustainability of terrestrial ecosystems [1] As the principle component ofterrestrial hydrology evapotranspiration (ET) which is composed of evaporation and transpirationis affected by both biophysical and environmental processes at the interface of soils vegetationand the atmosphere thereby linking hydrological energy and carbon processes [2ndash4] GloballyET returns approximately 60 of land-based precipitation to the atmosphere [56] and consumes

Remote Sens 2015 7 15822ndash15843 doi103390rs71215806 wwwmdpicomjournalremotesensing

Remote Sens 2015 7 15822ndash15843

more than 50 of the solar radiation absorbed by the landrsquos surface [7] Within the atmosphericsurface layer ET affects environmental conditions through the turbulent exchange of momentumheat and moisture [8ndash10] In return environmental variation controls diverse physical andphysiological processes within terrestrial ecosystems and finally alters the mass and energyexchange of land-atmosphere interactions leading to direct climate impacts [311ndash13] Consideringthe importance of ET the characterization of spatiotemporal variability is critical for betterunderstanding interactions between the land and the atmosphere the sustainable management ofwater resources and the response of terrestrial ecosystems under climate change [14ndash18]

As one of the most important parts of the terrestrial ecosystem global forests account forapproximately 45 of total terrestrial ET [5] Therefore forest ET is considered to be an importantindicator of water availability in natural ecosystems as well as for human needs [19] The routinemonitoring of ET from forests in the headwaters of basins is particularly critical because theseheadwaters often provide water to ecosystems downstream Knowledge of the dynamics of theheadwaters is therefore often relevant for the sustainable development of an entire basin The HeiheRiver Basin (HRB) located in the cold and arid environment of northwest China is entirely dependenton headwaters Water conflicts in this area are severe and information regarding forest processes isurgently needed

In situ measurements using weighing lysimeters Bowen ratios sap flow meters eddy covariance(EC) and scintillometers are considered reliable for quantifying ET at individual sites or for smallfootprints [42021] However these types of measurements are unsuitable at large scales Analternative approach is to use remote sensing information for characterizing spatiotemporal landsurface information over large areas With the incorporation of meteorological and other auxiliarydata remote sensing-based methods can be used to map various large-scale patterns of ET in aglobally consistent and economically feasible manner by linking surface parameters and energybalance with ET [22ndash25] In general land surface parameters retrieved using remote sensing datasuch as the Normalized Difference Vegetation Index (NDVI) the Leaf Area Index (LAI) Albedotemperature and emissivity have been used to drive ET models [1626]

The following four types of remote sensing-based ET models having been developed in recentdecades (1) surface energy balance models [2327ndash29] (2) empirical statistical models [30] (3)physical models [3132] and (4) water balance models [3334] Although they have been applied overa number of study areas uncertainties related to the disadvantage of these models and their inputsand parameterization and scaling schemes still exist [435ndash38] As far as remote sensing techniquesare concerned the availability of measurements and estimations of land surface and atmosphericparameters have increased over time and the improvement of model performance through theintegration of various techniques and data sources has received renewed global interest

As one of the most widely used and validated ET models the Surface Energy Balance System(SEBS) developed by Su [23] estimates turbulent heat fluxes by means of Moni-Obukhov Similarity(MOS) theory [3940] Several important parameters are a part of the MOS and SEBS includingzero-plane displacement (d) roughness height (z0m) and heat roughness height (z0h) The scalarz0h can be calculated from z0m using the kBacute1 model that provides excess resistance for heattransportation For d and z0m experimental methods based on measurements of turbulent fluxescan provide estimates but are only locally valid and cannot be scaled to larger areas Alternativelyremotely sensed methods have been developed for these parameters using the functions of vegetationstructural parameters (ie LAI [41] frontal area index (FAI) [42] both LAI and FAI [43] stand density(SD) (treesumlhaacute1) and stem-branch-leaf distributions [44]) For the kBacute1 model some studies haveimproved parameterization [384546] However few have focused on forests

Our previous study [47] demonstrated the advantages and disadvantages of four notable modelsfor retrieving d and z0m values in forests located within the Qilian Mountains (QMs) the headwaterarea of the HRB A key parameter needed for d and z0m estimates is forest height Other vegetationstructure parameters further affect the ratios of d or z0m over forest height (dh or z0mh) In

15823


combination with forest measurements remote sensing is capable of providing reliable instantaneousretrievals of forest height that can be used to parameterize d and z0m models for those time intervalswhen forest structural parameters (height LAI FAI etc) and thus dh or z0mh are assumed to beunchanged For long-term parameterization LAI can easily be obtained from moderate resolutionremote sensing data (ie MODIS) over a fine time resolution However time-series retrieval forother parameters is a problem especially for young and immature forests Normally the structure(including the height and FAI) of young and immature forests changes markedly over a few yearsIf constant structural information is used to parameterize d z0m and z0h uncertainties will bepropagated into ET due to the sensitivity of ET to parameters such as these in models such asthe SEBS

Despite having the ability to estimate surface turbulent heat fluxes within various ecosystemsuncertainties in the SEBS arise that result from parameterizations of land surface input data(vegetation height z0m LAI land surface temperature (LST) etc) and from inherent errors inmeteorological parameters A few studies [3848ndash50] have conducted sensitivity analyses for theSEBS However more investigations over heterogeneous forests (for example forests located in coldand arid areas as in our study area) are necessary

The objectives of the present study are therefore (1) to investigate the effects of major inputvariables (including d and z0m) and their changes over a 13 year long period on sensible heat flux asestimated with SEBS and (2) to obtain a 13 year time series of forest ET in the headwater of the HRBBelow after discussing our sensitivity data we propose a time-series parameterization scheme for dz0m and z0h using a combination of forest AGB dynamics and remote sensing data Specifically theuse of regression relationships for forest vertical parameters (the Loreyrsquos height [51] and FAI) versusforest AGB was established based on measurements Thus interannual vertical parameters from 2000to 2012 were connected using the corresponding interannual forest AGB dynamics AGB dynamicswere obtained by incorporating simulations derived from two ecological models (the MODIS MOD17(MOD_17) GPP and the Biome-BioGeochemical Cycles (Biome-BGC) models) using the 2009 forestbasis map generated using Landsat Thematic Mapper 5 (TM) data [52] Using Global LAnd SurfaceSatellite (GLASS) LAI products vertical parameters were applied in order to parameterize d z0mand then z0h using the outperformed roughness model determined from our previous study [47]and the kBacute1 model Afterward original MODIS products (NDVI and land LST) refined GLASSAlbedo products and downscaled weather research and forecasting (WRF) estimates were appliedfor driving the SEBS over the mountainous forests using the forest dynamics parameterizationscheme Based on this procedure estimates were obtained for forest ET over the QMs from 2000to 2012 Finally two-year (2010ndash2011) comparisons were performed amongst MODIS MOD16 ETproducts SEBS ET outputs and EC measurements to investigate the applicability and feasibility ofthe SEBS over heterogeneous forests

2 Study Area and Observations

The headwater QMs of the HRB (97˝241Endash102˝101E and 37˝411Nndash42˝421N) were selected as thestudy area because it is ecologically and hydrologically important for agricultural irrigation withinthe middle reaches (Hexi Corridor) and maintains ecological viability within the lower reaches(northern Alxa Highland) Geographically the landscapes are diverse and include glaciers frozensoils alpine meadows and the forests of the QMs irrigated crops within the Hexi Corridor andriparian ecosystems deserts and Gobi within the Alax Highlands (Figure 1) The land use mapin Figure 1 was first generated using TM data acquired in 2009 [53] The map was upscaled to1 km in order to maintain the same resolution as the driving data (ie the MODIS products andthe downscaled WRF estimates) for the ET model

Spanning an area of 10400 km2 and with altitudes from 1500 to 6000 m above sea level thevegetation types of the QMs vary from high to low altitudes in the following sequence alpinemeadows subalpine meadows and shrubbery forests and drying shrubbery and desert steppes

15824


Forests in the area are largely composed of Picea crassifolia mixed with a fairly small fraction of Sabinaprzewalsk that only survives on shady slopes (from altitudes of 2500ndash3300 m) and that occupies 297of the total area

Lying along the northeastern margin of the Tibetan Plateau the QMs have a temperatecontinental mountainous climate with a warm and humid summer and a cold and dry winter Meanannual precipitation and mean annual temperature display a decreasing trend from the southeastto the northwest Mean annual precipitation (with approximately 90 occurring during Mayand October) decreases from approximately 600 mm to less than 200 mm The mean annual airtemperature is ~6 degrees (Celsius) at low altitudes and ~acute10 degrees (Celsius) at high altitudes

To improve the observability understanding and predictability of eco-hydrological processeson the catchmental scale the Water Allied Telemetry Experimental Research (WATER) campaignthat spanned from 2007 to 2011 conducted multi-scale and simultaneous airborne space-borne andground-based remotely sensed experiments within the HRB [5455] A network of automatic weatherstations (AWS) surface meteorological towers and EC stations were established by WATER Onlyobservations from the mountainous forest site (Guantan 100˝151E 38˝321N 2835 m) were used in thisstudy The forest EC system contained a three-dimensional sonic anemometer (CSAT-3 CampbellInc Logan UT USA) a CO2 and H2O gas analyzer (LI-7500 LI-COR Inc Lincoln NE USA) a heatflux plate (HFP01 Campbell Inc USA) a four-component radiometer (CM3 and CG3 CampbellInc USA) a temperature and relative humidity probe (HMP45C Vaisala Inc Helsinki Finland) awind speed sensor (014A and 034B Met One Instruments Inc Grants Pass OR USA) and a datalogger (CR5000 Campbell Inc USA)

Data quality control processes were applied to the raw 10 Hz EC data in order to obtainhalf-hourly flux data [56] Processing steps included despiking coordinate rotation a time lagcorrection a frequency response correction a WPL correction and gap filling [56ndash58] The gap-fillingprocess was based on two methods (1) a Look Up Table (LUT) when only EC data were missingand (2) Mean Diumal Variations (MDV) when both EC and AWS data were missing Due tolow data quality and data gaps measurements obtained from 2008ndash2009 were used for this studyMeasurements obtained from 2010ndash2011 at the mountainous forest site were used to compare ETsobtained from the SEBS

Two forest inventory surveys were conducted from June to August during 2007 and 2008 Thefirst survey was conducted in 16 permanent forest plots (25 m ˆ 25 m) and in 69 rectangular forestplots that had two sizes (20 m ˆ 20 m and 25 m ˆ 25 m) The second survey was conducted in58 circular forest plots (with diameters ranging from 10 to 28 m) Obtained measurements includedLAI tree height (m) first branch height (FBH) the diameter at breast height (DBH) (cm) etc Atotal of 133 forest plots measurements were used for this study The 133 plots included a variety ofdifferent terrains (slope aspect altitude) and stand (age crown coverage AGB level) situations thatrepresented local ecological gradients According to inventory records Picea crassifolia comprised9939 of the total measured trees (8667 trees) Therefore the Picea crassifolia allometric equations ofWang et al [59] were used for our analyses

The major input variables for SEBS include meteorological data (air temperature wind speedair and vapor pressures and downwelling shortwave and long-wave radiation) and land surfaceparameters (LAI LST emissivity Albedo and NDVI) Meteorological forcing data for the HRBdownscaled from estimates of the WRF model based on the meteorological model (MicroMet) [60]were used in this study WRF estimates were validated at 15 surface meteorological stationsmaintained by the China Meteorological Administration (CMA) and seven intensive observationstations setup by the WATER project Based on past experiments the data have been provento be reliable [6162] Land surface parameters were derived from MODIS data InnovativeMODIS products including LAI and Albedo were obtained from GLASS [6364] Other parametersincluding NDVI and LST were downloaded from the National Aeronautics and Space Administration(NASA) [65] The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

15825


Global Digital Elevation Map (GDEM) version-2 was downloaded from the Japan AerospaceExploration Agency (JAXA) [66] To perform multiple comparisons amongst the SEBS EC andMODIS ET estimates 1 km eight-day MODIS MOD16 ET (MOD16-ET) products obtained from 2000to 2012 were downloaded from the Numerical Terradynamic Simulation Group (NTSG) [67] of theUniversity of Montana

Remote Sens 2015 7 6

the National Aeronautics and Space Administration (NASA) [65] The Advanced Spaceborne Thermal

Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) version-2 was

downloaded from the Japan Aerospace Exploration Agency (JAXA) [66] To perform multiple

comparisons amongst the SEBS EC and MODIS ET estimates 1 km eight-day MODIS MOD16 ET

(MOD16-ET) products obtained from 2000 to 2012 were downloaded from the Numerical Terradynamic

Simulation Group (NTSG) [67] of the University of Montana

Figure 1 The location and sub-reaches of the Heihe River Basin (HRB)

3 Methodology

31 The Surface Energy Balance System (SEBS)

The SEBS is an advanced single source model developed by Su [23] for estimating turbulent heat

fluxes based on energy balance (EB) and employs an evaporative fraction (ETF) for estimating actual

ET (AET) by accounting for water limiting (wet-limit and dry-limit) cases (wetness and dryness)

Briefly the SEBS consists of the functions of land surface parameters derived from remote sensing data

a model of dynamic roughness lengths with a reference to heat transfer and the determination of ETF

Abiding by the theories of EB bulk atmospheric similarity (BAS) [68] and MOS [39] based on a

combination of physical land surface parameters obtained from remote sensing data and meteorological

forcing data the SEBS has been proven to be a reliable remote sensing-based ET model in numerous


3 Methodology


The SEBS is an advanced single source model developed by Su [23] for estimating turbulent heatfluxes based on energy balance (EB) and employs an evaporative fraction (ETF) for estimating actualET (AET) by accounting for water limiting (wet-limit and dry-limit) cases (wetness and dryness)Briefly the SEBS consists of the functions of land surface parameters derived from remote sensingdata a model of dynamic roughness lengths with a reference to heat transfer and the determinationof ETF Abiding by the theories of EB bulk atmospheric similarity (BAS) [68] and MOS [39] basedon a combination of physical land surface parameters obtained from remote sensing data andmeteorological forcing data the SEBS has been proven to be a reliable remote sensing-based ET modelin numerous studies conducted over multiple ecosystems and under various climate and landscapeconditions [69ndash74] However few studies have focused on forests [75ndash77] especially those located incold and arid regions

15826


A concise description of the SEBS algorithm is provided below Details can be found in Su [23]Basically the surface energy balance is expressed as follows

Rn ldquo G0 ` H ` λE (1)

where G0 is the soil heat flux Rn is the net radiation H is the sensible heat flux and λE is the latentheat flux (λ is the latent heat of vaporization (Jumlgacute1) and E is the AET) Surface available energy(H + λE) is generally used to partition the energy exchange for heat and water vapor The unit forEquation (1) is watts per square meter (Wumlmacute2)

To derive sensible and latent heat fluxes the theory of BAS (for the mixed layer of theatmosphere) or MOS (for the atmospheric surface layer (ASL)) was employed In most cases MOSrelationships for profiles of mean wind speed u (muml sacute1) and mean temperature θ0 acute θa wereapplied within the SEBS and the integral form was written as follows [39]

u ldquou˚k

bdquo

lnpzacute dz0m

q acuteΨmpzacute d

Lq `Ψmp

z0m

Lq

(2)

θ0 acute θa ldquoH

ku˚ρcp

bdquo

lnpzacute dz0h

q acuteΨhpzacute d

Lq `Ψhp

z0hLq

(3)

where u˚ is the friction velocity (muml sacute1) calculated as (τ0ρ)12 τ0 is the surface shear stress(kgumlmuml sacute2) ρ is the air density (kgumlmacute3) k = 041 is von Karmanrsquos constant z is the height ofthe measurement d is the zero displacement height (m) z0m is the aerodynamic roughness lengthfor momentum transfer θ0 is the potential temperature (K) at the surface θa is the potential airtemperature (K) at z cp is the specific heat capacity of air at constant pressure (Jumlkgacute1umlKacute1) z0h isthe roughness height for heat transfer (m) and Ψm and Ψh are the stability correction functions formomentum and heat transfer L is the Obukhov length (m)

Obviously both the zero-plane displacement height (d) and the roughness lengths (z0m and z0h)are critical parameters for determinations of momentum and heat transfer between the land surfaceand the atmosphere The SEBS parameterizes roughness height based on a semi-empirical modelusing the following equations

β ldquo C1 acute C2exp pacuteC3Cd ˆ LAIq (4)

nec ldquopCd ˆ LAIq

2β2 (5)

dhldquo 1acute

12nec

r1acute exppacute2necqs (6)

z0m

hldquo r1acute

dhsrexpp

acutekβqs (7)

where β is the ratio of friction velocity to wind speed at canopy height C1 C2 and C3 are constantsrelated to the bulk surface drag coefficient Cd is the foliage drag coefficient and nec is the wind speedprofile extinction coefficient within the canopy

The roughness model utilizes a proportional relationship between canopy height and roughnessheight [78] Obviously canopy height is also a critical parameter by which z0m and d0 are directlyprescribed In the SEBS if canopy height information is not available it can be also derived using anempirical equation as follows

h ldquo hmin `NDVIveg acute NDVIsoil

NDVIveg ` NDVIsoilˆ phmax acute hminq (8)

15827


where hmin and hmax are the minimal and maximal canopy heights respectively NDVImax andNDVImin are the NDVI values of fully vegetated and bare soil

The kBacute1 model proposed by Su [23] was used in this study and it links z0m and z0h as follows

z0h ldquoz0m

exppkBacute1q(9)

where kBacute1 is a scalar heat transfer coefficient called the inverse Stanton numberThe energy balance of a limiting case at dryness and wetness are then used to estimate the

relative evaporative fraction (Λr) as follows

Λr ldquo 1acuteH acute Hwet

Hdry acute Hwet(10)

where Hwet is H at wetness and Hdry is dryness Both were estimated as described in Su [23]The evaporative fraction (Λ) is calculated as follows

Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)

where λEwet is λE at wetnessFinally λE is determined by the following equation

λE ldquo Λˆ pRn acute G0q (12)

In practice the remote sensing data used in the SEBS represents instantaneous information forthe land surface The evaporative coefficient is assumed to be constant throughout the day andfinally daily evapotranspiration is expressed as follows

Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ

ldquo 864ˆ 107 ˆΛˆRn acute G0

λρ(13)

where Edaily is the daily ET (mmumldacute1) and Rn and G0 are the mean daily net radiation (Wmacute2) andmean daily soil heat fluxes that can be assumed to be negligible for the entire day

In brief the SEBS generally requires two sets of driven data remotely sensed land surfaceparameters (for example the surface Albedo Emissivity temperature LAI NDVI and roughnesslengths) and meteorological data (for example air temperature wind speed air and vapor pressuresand downwelling shortwave and long-wave radiations)

32 Roughness Length Models

Based on the aforementioned importance of d z0m and z0h the parameterization scheme shouldfirst optimize d and z0m through the use of the roughness model and z0h through use of the kBacute1

model In a previous study the process was validated using EC measurements in which the modelof Schaudt and Dickinson [43] (SD00) indicated superiority for estimating z0m but was compromisedfor d [47] Since ET estimates from SEBS are more sensitive to z0m and since z0m directly impacts thez0h value the SD00 model was employed for parameterizing d and z0m Three parameters includingforest height LAI and FAI are required for the SD00 model The SD00 model is expressed as follows

fz ldquo 03299Lp15 ` 21713 f or Lp ă 08775 (14)

fz ldquo 16771exppacute01717Lpq ` 10 f or Lp ě 08775 (15)

Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828


fd ldquo 10acute 03991exppacute01779Lpq (17)

where Lp is the mean plant LAI Lb is background LAI fb is the fraction of the canopy coverageand fb is the fraction of understory vegetation (fv = fc + fb) Multiplying fz on the right hand side ofEquation (19) or Equation (20) yields roughness length as a function of both LAI and FAI The dh isquantified by multiplying Equation (17) by Equation (18)

Here it is worth mentioning that only understory mosses live on the forest floor and that theLAI derived from MODIS was used as a surrogate for Lp in Equations (14)ndash(17)

The estimation for the normalized displacement height dh and the roughness length z0mhrelated to FAI (λ f ) is as follows

dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m

hldquo a2exppacuteb2λc2

f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo

10acute exppacuteb3λc3f qı

` f2 pλ ą 0152q (20)

where a1 = 150 a2 = 586 b2 = 109 c2 = 112 d2 = 133 a3 = 00537 b3 = 109 c3 = 0874 d3 = 0510f 2 = 000368 and z00h = 000086 λ f is the FAI calculated from the frontal area A f By assuming thatthe frontal area of the stem is much smaller than the frontal area of the crown [43] for each individualneedle tree A f is simplified as follows

A f ldquo12

hc ˚wc (21)

where hc is the height of the crown (ie tree height-FBH) and wc is the crown width The λ f is thencalculated from the total A f divided by the total area of the plot

33 The SEBS Model Sensitivity

To compare model sensitivities and their dependencies on inputs a sensitivity analysis wasperformed for all input maps (land surface temperature NDVI LAI and Albedo) and meteorologicalinputs (air temperature air pressure wind speed humidity and incoming shortwave radiation)

The sensitivity (Sj) of the model to an input parameter (j) can be expressed as

Sj pH˘q ldquoH˘ acute Hr

Hr(22)

Sj is calculated for a positive or for a negative deviation of an input (j) within the SEBS modelH+ and Hacute correspond to the sensible heat fluxes simulated by the SEBS model when driven bythe positive and negative deviation of the j respectively Hr is the result predicted by the referencevariable r

The major input maps (z0m NDVI LAI Albedo and LST) and the meteorological inputs (airtemperature (Ta) air pressure (P) wind speed (u) humidity (Hh) and incoming shortwave radiation(Rs)) were considered in this analysis

With the exception of LST and air temperature the averages of additional inputs duringthe growing seasons of 2010 and 2011 at the EC site were used as reference data for r whiler˘25 (˘25 ˆ r) were used as the testing variables respectively For LST and air temperature weused ˘5 K as the deviation because the 25 deviation exceeded the physical limits The ranges ofthe above values can represent the majority of the local land surface and meteorological conditions atthe site

15829


34 Long-Term Parameterization for the SEBS

For dynamic parameterizations of d z0m and thus z0h the long-term (from 2000 to 2012)eight-day GLASS LAI was directly inputted into the SD00 model No remote sensing productsfor forest height and FAI are currently available although these products can be retrieved from acombination of multi-temporal remote sensing data and corresponding forest measurements overshort time intervals (at least several year intervals ie 3ndash5 years) However due to the limitedaccessibility of forest areas comprehensive forest measurements over the QMs at such short timeintervals are impractical

To derive long-term forest vertical parameters (the forest Loreyrsquos height and the FAI) anincorporation of interannual forest AGB dynamics based on the synergistic simulations of twoecological models (the MOD_17 and Biome-BGC models) using multi-parameter remote sensingdata was employed Regression relationships were first established between forest AGB and theseparameters that were derived based on field survey measurements As a result of these two regressionmodels (Loreyrsquos height versus AGB and FAI versus AGB) the interannual forest Loreyrsquos height andthe FAI were then regressed using simulated interannual forest AGB dynamics Interannual forestAGB dynamics were derived from a combination of interannual forest AGB increments originallyconverted from forest net primary productivity (NPP) and simulated using the Biome-BGC calibratedby the MOD_17 model using the forest AGB baseline retrieved from TM data from 2009 [52]

4 Results


Table 1 provides the sensitivities of H in regards to the SEBSrsquos inputs Low sensitivities(|Sj(H˘)| lt 10) were found for changes in d NDVI LAI Albedo Hh and Rs High sensitivities(|Sj(H˘)| gt 10) were found for z0m LST Ta P and u In general the results indicate thatthe variation of H largely depends on meteorological parameters at the reference height and z0mExceptions to this finding include both H calculations outside of the limiting cases and the wet-limitand dry-limit sensible heat flux and both are determined from net radiation and soil heat flux Hereit is worth mentioning that in the SD00 model the prescribed forest height (in this case the Loreyrsquosheight) is as sensitive as the d and z0m

Table 1 The sensitivities of sensible heat flux (H) using different SEBS input parameters as a fractionof their reference values

Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -

NDVI 062 acute074 - -LAI 186 acute048 - -

Albedo acute074 012 - -LST - - 7867 acute6578Ta - - acute2353 1939P 1682 acute1893 - -u 1225 acute1025 - -

Hh 053 048 - -Rs 667 acute735 - -

42 Parameterization of the SEBS

A polynomial was fitted for forest Loreyrsquos height (YL) versus forest AGB (X) based on both DBHand tree height measurements from 119 plots (out of the total 133 plots) The FAI (YFAI) versus forestAGB was based on tree height and the FBH and crown width measurements from 70 plots (Figure 2)

15830


For the Loreyrsquos height (YL) the fitting regression was as follows

YL ldquo 1055ˆ X0544 (23)

where R2 = 052 and RMSE = 235 meters (m)For the FAI (YFAI) the fitting regression was as follows

YFAI ldquo 0133ˆ X0539 (24)

with R2 = 036 and RMSE = 035 (scalar)The interannual forest Loreyrsquos height and the FAI parameters were derived from

Equations (23)ndash(24) and the corresponding forest AGB dynamics were estimated using theBiome-BGC model calibrated using the MOD_17 model Through the use of Equations (14)ndash(21) andthe eight-day GLASS LAI products the corresponding eight-day d and z0m were derived using theSD00 model (the statistics for the interannual forest Loreyrsquos height d and z0m are shown in Table 2)The retrievals were further applied in order to estimate eight-day z0h values using the kBacute1 model


the eight-day GLASS LAI products the corresponding eight-day d and z0m were derived using the SD00

model (the statistics for the interannual forest Loreyrsquos height d and z0m are shown in Table 2) The

retrievals were further applied in order to estimate eight-day z0h values using the kBminus1 model

Figure 2 The fitting regression of forest Loreyrsquos height (a) and the frontal area index (FAI)

(b) against the forest aboveground biomass (AGB) based on measurements

Table 2 Statistics for the interannual forest Loreyrsquos heights (hL) (unit meter) d (dR value

range dM mean value) (unit meter) and z0m (z0mR value range z0mM mean value) (unit

meter) from 2000 to 2012 at the EC site

Year hL dR dM z0mR z0mM

2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614

652ndash762 728ndash1051 748ndash1110 769ndash1139 786ndash1141 804ndash1109 812ndash1205 825ndash1235 838ndash1249 844ndash1255 779ndash918

865ndash1281 880ndash1314

684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090

132ndash146 072ndash148 065ndash149 067ndash151 073ndash153 066ndash155 068ndash155 066ndash157 068ndash158 069ndash158 071ndash161 071ndash160 069ndash162

136 119 113 116 120 113 112 114 116 121 148 122 117

43 A comparison of ET Estimates between EC Measurements the SEBS Simulations and MODIS

MOD16 Products

By incorporating original eight-day MODIS products (NDVI LST) eight-day GLASS Albedo

products and downscaled WRF estimates (air temperature wind speed air and vapor pressure and

downwelling shortwave and long-wave radiations) the parameterized SEBS was employed for

simulating eight-day ET from 2000 to 2012 Comparisons amongst the eight-day average ET estimates

(a total of 86 samples) during 2010 and 2011 from EC SEBS and MODIS products are provided

in Figures 3 and 4

Figure 2 The fitting regression of forest Loreyrsquos height (a) and the frontal area index (FAI) (b) againstthe forest aboveground biomass (AGB) based on measurements

Table 2 Statistics for the interannual forest Loreyrsquos heights (hL) (unit meter) d (dR value range dMmean value) (unit meter) and z0m (z0mR value range z0mM mean value) (unit meter) from 2000to 2012 at the EC site


2000 1339 652ndash762 684 132ndash146 1362001 1362 728ndash1051 850 072ndash148 1192002 1396 748ndash1110 907 065ndash149 1132003 1430 769ndash1139 925 067ndash151 1162004 1458 786ndash1141 936 073ndash153 1202005 1487 804ndash1109 992 066ndash155 1132006 1502 812ndash1205 1006 068ndash155 1122007 1524 825ndash1235 1022 066ndash157 1142008 1544 838ndash1249 1032 068ndash158 1162009 1555 844ndash1255 1019 069ndash158 1212010 1572 779ndash918 822 071ndash161 1482011 1590 865ndash1281 1044 071ndash160 1222012 1614 880ndash1314 1090 069ndash162 117

15831


43 A comparison of ET Estimates between EC Measurements the SEBS Simulationsand MODIS MOD16 Products

By incorporating original eight-day MODIS products (NDVI LST) eight-day GLASS Albedoproducts and downscaled WRF estimates (air temperature wind speed air and vapor pressureand downwelling shortwave and long-wave radiations) the parameterized SEBS was employed forsimulating eight-day ET from 2000 to 2012 Comparisons amongst the eight-day average ET estimates(a total of 86 samples) during 2010 and 2011 from EC SEBS and MODIS products are provided inFigures 3 and 4Remote Sens 2015 7 13

Figure 3 A comparison of two-year eight-day average evapotranspiration (ETs) betweenthe

Surface Energy Balance System (SEBS) simulations and eddy covariance (EC) measurements

As compared to the two-year EC measurements the parameterized SEBS generated a reliable

eight-day average ET with R2 = 080 and RMSE = 021 mmmiddotdayminus1 The MOD16-ET biased the

simulations with R2 = 035 and RMSE = 076 mmmiddotdayminus1 The SEBS slightly underestimated ET during

the forest growing seasons (summer and autumn) during 2010 but overestimated ET for the growing

seasons during 2011 As is clear from the results the MOD16-ET significantly underestimated ET for

the growing seasons but significantly overestimated ET during the spring seasons when the SEBS

matched EC measurements

Figure 4 A plot of the eight-day average SEBS simulated ET versus EC measurements

44 Interannual ET Simulations Using the SEBS

Interannual ET dynamics (unit mmmiddotyearminus1) estimated by the SEBS model over the QM forest from

2000 to 2012 are provided in Figure 5 Due to diverse environmental conditions (various meteorological

Figure 3 A comparison of two-year eight-day average evapotranspiration (ETs) betweenthe SurfaceEnergy Balance System (SEBS) simulations and eddy covariance (EC) measurements

As compared to the two-year EC measurements the parameterized SEBS generated a reliableeight-day average ET with R2 = 080 and RMSE = 021 mmumldayacute1 The MOD16-ET biased thesimulations with R2 = 035 and RMSE = 076 mmumldayacute1 The SEBS slightly underestimated ET duringthe forest growing seasons (summer and autumn) during 2010 but overestimated ET for the growingseasons during 2011 As is clear from the results the MOD16-ET significantly underestimated ETfor the growing seasons but significantly overestimated ET during the spring seasons when the SEBSmatched EC measurements
















15832



Interannual ET dynamics (unit mmumlyearacute1) estimated by the SEBS model over the QM forestfrom 2000 to 2012 are provided in Figure 5 Due to diverse environmental conditions (variousmeteorological and forest stand conditions) during the simulation period the figure shows theregional distribution of interannual forest ET over a wide range In all of the maps due to the coldand arid conditions of the QM forest the estimated forest ET is generally lower than 400 mmumlyearacute1In general forest ETs located in the northern portion are lower than those in the southern portion


and forest stand conditions) during the simulation period the figure shows the regional distribution of

interannual forest ET over a wide range In all of the maps due to the cold and arid conditions of the

QM forest the estimated forest ET is generally lower than 400 mmmiddotyearminus1 In general forest ETs located

in the northern portion are lower than those in the southern portion

Figure 5 Cont

Figure 5 Cont

15833

Remote Sens 2015 7 15822ndash15843Remote Sens 2015 7 15

Figure 5 The interannual dynamics of forest ET simulated by the SEBS over the QMs from

2000 to 2012

Table 3 The statistics for interannual simulated forest ETs and precipitation (unit

mmmiddotyearminus1) downscaled from WRF estimates over the QMs from 2000 to 2012

Year ETMean ETMin ETMax ETsd ETSum P ETMeanP()

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811

Figure 5 The interannual dynamics of forest ET simulated by the SEBS over the QMs from 2000to 2012

Table 3 The statistics for interannual simulated forest ETs and precipitation (unit mmuml yearacute1)downscaled from WRF estimates over the QMs from 2000 to 2012


2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834


Correspondingly the interannual statistics of ET (including average minimum maximumstand deviation and total amount) precipitation (downscaled from the WRF result) andETprecipitation in the forested areas are provided in Table 3 In total the statistics calculatedfrom the 780 forest pixels (1 km resolution) also represented the diversity of the estimates Thetable indicates that the highest annual average of forest ET (317 mmumlyearacute1) occurred during 2003and the lowest (201 mmumlyearacute1) occurred during 2009 Interestingly the highest ratio of annualETprecipitation was also determined during 2003 while the lowest was found for 2009 The ratioin 2003 was higher than one implying that forest growth during 2003 was largely supported bysoil moisture

5 Discussion

The parameters d and z0m have been used in ET models that include MOS theory such as theSEBS model Although our sensitivity analysis indicated that errors within the SEBS led to estimatedH values with deviations (˘ 25) of z0m that were smaller than those for LST and air temperature a50 deviation in the parameterization of z0m as well as forest height can generate errors of more than40 for the H estimation The result is consistent with those presented by van der Kwast et al [50]Zhou et al [75] and Ma et al [79] In particular according to the study of Zhou et al [71] the annualmaximum effect of the roughness length dynamic on sensible heat flux was 3380 and 1811 forthe Qianyanzhou (with a 11ndash12 meter high artificial needle forest) and Changbai Mountains (with a26 meter high natural mixed forest) experimental stations respectively

If canopy height information is not available for z0m parameterization within the SEBS it canbe retrieved using the empirical equation that involves NDVI values (NDVIs of fully vegetated andbare soil) as well as maximum and minimum canopy heights Some sensitivity analyses [3849]for maximum height have indicated the importance of this parameter through replacement withmeasured values This type of empirical method may be suitable for low canopies with shortheight ranges However due to the saturation of spectral signals the NDVI in forests cannotrepresent canopy height variations so canopy heights especially for long-term heterogeneous forestparameterization will generally be underestimated

Additional studies [434447] have indicated that vegetation vertical parameters (ie the heightand FAI) are critical for estimates of d and z0m obtained via remote sensing-based roughnessmodels [42ndash44] However few studies have focused on the long term parameterization scheme ofd and z0m while considering the variation of vertical vegetation information over time Vegetationheight is commonly derived from vegetation indexes (ie NDVI and LAI) using optical remotesensing data that may be suitable for low vegetation (for example pastures crops shrubs etc) [8081]or that can be derived from active remote sensing (ie Synthetic Aperture Radar (SAR) and LightDetection and Ranging (LiDAR)) data sensitive to forest vertical structure [8283] Neverthelessthese methods are not possible for growing forests On the one hand vegetation indexes in the QMsrepresent a mixture of the top canopy and the understory in forest gaps On the other hand the localextremely complicated terrain condition hinders SAR data applicability and expensive single-passairborne LiDAR data obtained by WATER can only derive vegetation height over a certain period andfor a certain area Therefore at present remote sensing data cannot provide reliable forest verticaldynamic information for this study area

In an earlier study [47] we reported that the SD00 model outperformed other aerodynamicroughness models Here dynamic forest vertical information for height and the FAI were obtainedfrom field calibrated regression models and interannual forest AGB variations in order to drive theSD model Instead of the arithmetical mean height the Loreyrsquos height was derived due to the fact thatit is a better expression for forest stand status as well as for d and z0m [4447] Although the R2 values(052 and 036) of both fittings were not high the RMSEs (235 m and 035 were relatively low Basedon the high heterogeneity of local forests under various stand conditions (topography tree densityheight FBH age AGB level overlapping canopies etc) regression relationships (forest Loreyrsquos

15835


height versus forest AGB and FAI versus forest AGB) established on the basis of forest measurementswere reasonable However due to natural disturbance as well as the self-adaption in forests Loreyrsquosheight and the FAI are apt to defy the regressions For example as a result of self-thinning adaptionmatured or over-matured forests would definitely change SD (treesumlhaacute1) so that ecosystem balanceis maintained causing an increase in forest height and a decrease in forest AGB As understoodforest AGB levels should obey a parabolic relationship between forest AGB and forest age In thisstudy regression models based on measurements without forest age information would be biasedbecause in practice different forest heights exist in stands that have a similar forest AGB level butvarious stand densities Additional studies should take forest stand age and forest SD into accountin their analyses Alternatively continuous national forest inventories using a five-year interval andupcoming active spaceborne remote sensing (ie the Chinese high-resolution satellite constellationwith SAR and LiDAR sensors on board) with the ability to derive multi-temporal forest verticalparameters could also be helpful for calibrating bias for local forest vertical information

According to the statistics of the dynamics of interannual forest ET and meteorological dataobtained from the downscaled WRF local forest ET is the most sensitive to a vapor pressuredeficit (VPD) then temperature precipitation and downwelling short wave radiation (DSR) VPDtemperature and precipitation yielded negative correlation to forest ET while DSR yielded a positivecorrelation For example for the 13 years analyzed the highest forest ET occurred during 2003the year with the second lowest VPD the low temperature the least amount of precipitation anda sufficient DSR while the lowest forest ET was determined for 2009 that had the second highestVPD and temperature moderate precipitation and a low DSR As shown in Table 3 the ratio of ETto precipitation during 2003 was higher than one implying that during this driest year soil moisturecontributes the most positive ldquogreen waterrdquo (the water mainly used by the ecosystem itself [84]) tothe forest ecosystem

In forested areas with altitudes from 2600 to 3300 m surface soil with a field water capacityof more than 50 [85] is largely covered by moss and litter with a very high porosity and a lowbulk density resulting in a high evapotranspiration rate and hydraulic conductivity Thus the Piceacrassifolia forest has the greatest potential water retention capacity [86] The water storage capacityof the soil in this area was determined to be 3914 and 3738 higher within the Picea crassifoliaforest than in Sabina przewalsk forest and shrubs respectively at a similar soil depth [87] Thehigher soil water content may result in a higher plant transpiration rate as indicated in the studyof Chang et al [85] who stated that the relationship between sap flow and soil moisture content wasa logistic function

Overall the performance of the SEBS in this study is promising and the results are comparableto those of other studies [4988ndash90] For example based on the input data retrieved from multiplesensors onboard NASA Earth Observing Satellites (EOS) and the AVHRR sensor as well asmodel-derived surface pressure and winds from NASArsquos Global Modeling and Assimilation Officedaily global ETs were estimated by Vinukollu et al [88] using the SEBS the Penman-Monteith (P-M)and the Priestley-Taylor (P-T) models for the years 2003ndash2006 Evaluations using an inferred ETestimate based on a long-term water balance residual of precipitation minus runoff for large riverbasins and on a global scale yielded a good correlation (the mean correlationmdashthe Kendallrsquos τ from072 to 079) The study of Vinukollu et al [89] that reported long-term global ET estimations for1984ndash2007 using the SEBS P-M and P-T models indicated a downward trend for global ET after1998 In our study we also found a similar downward trend within the study area since 2000 dueto a warmer and wetter climate Based on MODIS products the results presented here are similar tothose reported by Byun et al [49] who applied the SEBS within a forest site located in the middle of theKorean peninsula with R2 = 080 (for this case) versus 088 and with RMSE = 021 mmumldayacute1 (for thiscase) versus 022 mmumldayacute1 Ma et al [91] incorporated the TM using diverse compositions of forcingand in situ meteorological data in order to estimate daily ETs using the SEBS over the ColeamballyIrrigation Area located in the southwestern portion of New South Wales Australia Their results

15836


yielded a higher R2 (up to 095) than we report here that can be ascribed to high-resolution remotelysensed data and other high-resolution and accurate inputs for homogenous irrigated croplandsHowever the RMSE of their results was up to 074 mmumldayacute1 larger than those we report here Theresult can be explained by understanding that due to abundant irrigation daily ETs over croplandsare much larger than daily forest ETs for the QMs In a similar manner when applying Chinesesatellite HJ-1images (with a 30 m visible 150m near-infrared and 300 m thermal infrared bands)the same WRF estimates as those used in this study the SEBS generated comparable results forirrigated cropland sites located within the middle reaches of the HRB [92] (with R2 = 079 andRMSE = 058 mmumldayacute1 at the vegetable site R2 = 081 and RMSE = 129 mmumldayacute1 at the maize siteand R2 = 086 and RMSE = 125 mmumldayacute1 at the orchard site) However due to the heterogeneity ofthe vegetation the performance of the SEBS was reduced (with R2 = 053 and RMSE = 067 mmumldayacute1)at the wetland site

In general the SEBS model could perform well in areas with low vegetation or bare soil [38]Nevertheless the SEBS also showed inferiority in forested areas especially for heterogeneousforests [9394] As Weligepolage et al [95] indicated correction terms that adjust the effects ofthe roughness sub-layer could contribute to improve the SEBSrsquos performance in areas containingcomplex vegetation structural scenarios [72] Further improvement of ET can be obtained using moreaccurate meteorological inputs although in our study meteorological inputs have been verified to bereliable Some improvements of wind speed and downwelling long-wave radiation estimates are stillpossible [62] As indicated by the HiWATER team other improvements for long-term MODIS LSTinputs can also be expected [96] Using unreliable meteorological inputs and MODIS LST productsfor the SEBS would result in deterioration for the agreement of both H and λE and thus ET and ECmeasurements [9798]

The SEBS model has been proven to be reliable for multi-scale ET estimations However only afew studies have focused on short-term simulations within forests [6570] Due to complicated heattransfer mechanisms within forests and their surroundings the SEBS cannot consistently capture theET process For cold and arid forests with high heterogeneity such as the ones analyzed in this studyclimate and environmental factors were found to enhance the complexity of the ET process Forexample melting snow within the canopy and underground possibly has a large impact on the ETprocess Some sharply increasing daily ET values were determined for EC measurements during thelate spring and early summer indicating that this is the case (Figure 3) Further improvements shouldbe expected if soil moisture simulations and snow melt monitoring modules are embedded into theSEBS algorithm [99] Moreover the appropriate parameterization of z0h is also critical for improvingthe behavior of the SEBS [3889] Such additions could make the SEBS more applicable for multiplebiomes and various climate and environmental conditions

6 Conclusions

For this study we conducted a sensitivity analysis The z0m as well as forest heightturned out to be one of the most important factors for parameterizing the SEBS whose 50 deviationscould generate errors of more than 40 for the SEBS derived sensible heat fluxes As the foreststructural dynamic information (height FBH crown width and LAI) largely affect the temporalchanges in z0m it is necessary to apply the multi-disciplinary techniques to develop the effectiveparameterization of z0m

Here we propose a time-series parameterization scheme (for the years 2000 to 2012) for twocritical parameters (d and z0m) using MOS theory which is the basic mechanism for the SEBS Wecalibrated two parameters (the forest height and FAI) of the SD00 model against forest measurementsusing regression models The two regression equations were linked to interannual forest AGBdynamics derived from 2009 forest AGB basis maps and interannual forest AGB increments obtainedfrom the synergistic simulations of two ecological models By combining GLASS LAI products forestheight and FAI dynamics d and z0m were derived using the SD00 model Afterward d and z0m

15837


were further employed in order to derive z0h using the kBacute1 model These three parameters wereused to parameterize the SEBS from 2000 to 2012 With the incorporation of original MODIS products(NDVI and LST) GLASS LAI Albedo products and downscaled WRF estimates the parameterizedSEBS was used to simulate eight-day forest ET over the QMs As compared to MODIS MOD16 ETproducts (eight-day average) at the site (the nearest pixel) the SEBS estimates (eight-day average)were much better and agreed well with EC measurements with the exception of the growing seasonsfor which the SEBS underestimated forest ET during 2010 and overestimated ET during 2011

The dynamic parameterization scheme employed in this study is more realistic than thatobtained by assuming ldquostaticrdquo conditions based on constant values for d z0m and z0h The spatialdistribution of forest ET estimates provides valuable information for the sustainable management offorest and water resources which are extremely important to the QMs and the entire HRB river basinRunoff generated from mountainous regions is recognized as the main water source for the HRBThe local forests are important for flood control runoff regulation and soil erosion control andthus contribute to hydrological service at the basin scale The interannual forest ETs as estimatedhere showed a downward trend that was largely caused by a local climate trend of warm drynessto warm wetness [100] Moreover the current forest belt may shift upward in response to warmwetness and the grassland (or shrubland) above an elevation of 3300 m may convert to a forest onshady slopes [86] Rigorous local forest protection measures including the expansion of grazingprohibition areas and the conversion of grasslands to secondary Picea crassifolia forests that havebeen implemented within the Qilian Mountains since the 1980s could have caused decreased runoffbut may also have expanded the water retention capacity of this mountain ecosystem

Acknowledgments This work was supported by the National Basic Research Program of China (973 Program)under grant 2013CB733404 Fundamental Research Funds for the Central Non-profit Research Institution ofIFRIT CAF under grant IFRIT201302 and the National Natural Science Foundation under grant 41101379Portions of the ground measurements used for this work were obtained from Heihe Watershed Allied TelemetryExperimental Research (HiWATER) We also thank the joint team for providing measurement support

Author Contributions Xin Tian is the principal author of this manuscript He wrote the majority of themanuscript and contributed to all phases of the investigation Christiaan van der Tol Zhongbo Su Zengyuan LiErxue Chen Xin Li and Longhui Li contributed to the work during field logistics field design the selection andinterpretation of methods Min Yan Xuelong Chen Xufeng Wang Xiaoduo Pan Feilong Ling Chunmei Li andWenwu Fan contributed to the data processing and some portions of the written manuscript

Conflicts of Interest The authors declare no conflict of interest

References

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2 Fisher JB Tu KP Baldocchi DD Global estimates of the land-atmosphere water flux based on monthlyAVHRR and ISLSCP-II data validated at 16 FLUXNET sites Remote Sens Environ 2008 112 901ndash919[CrossRef]

3 Jung M Reichstein M Ciais P Seneviratne SI Sheffield J Goulden ML Bonan G Cescatti AChen J Jeu RD et al Recent decline in the global land evapotranspiration trend due to limited moisturesupply Nature 2010 467 951ndash954 [CrossRef] [PubMed]

4 Liu Y Zhou Y Ju W Chen J Wang S He H Wang H Guan D Zhao F Li Y et alEvapotranspiration and water yield over Chinarsquos landmass from 2000 to 2010 Hydrol Earth Syst Sci2013 17 4957ndash4980 [CrossRef]

5 Oki T Kanae S Global hydrological cycles and world water resources Science 2006 313 1068ndash1072[CrossRef] [PubMed]

6 Trenberth KE Smith L Qian T Dai A Fasullo J Estimates of the global water budget and its annualcycle using observational and model data J Hydrometeorol 2007 8 758ndash769 [CrossRef]

7 Trenberth KE Fasullo JT Kiehl J Earthrsquos global energy budget Am Meteorol Soc 2009 90 311ndash323[CrossRef]

15838


8 Molders N Rabe A Numerical investigations on the influence of subgrid-scale surface heterogeneity onevapotranspiration and cloud processes J Appl Meteorol 1996 35 782ndash795 [CrossRef]

9 Koster RD Dirmeyer PA Guo ZC Bonan G Chan E Cox P Gordon CT Kanae S Kowalczyk ELawrence D et al Regions of strong coupling between soil moisture and precipitation Science 2004 3051138ndash1140 [CrossRef] [PubMed]

10 Spracklen DV Arnold SR Taylor CM Observations of increased tropical rainfall preceded by airpassage over forests Nature 2012 489 282ndash285 [CrossRef] [PubMed]

11 Githui F Selle B Thayalakumaran T Recharge estimation using remotely sensed evapotranspiration inan irrigated catchment in southeast Australia Hydrol Process 2012 26 1379ndash1389 [CrossRef]

12 Campos GEP Moran MS Huete A Zhang YG Bresloff C Huxman TE Eamus D Bosch DDBuda AR Gunter SA et al Ecosystem resilience despite large-scale altered hydroclimatic conditionsNature 2013 494 349ndash352 [CrossRef] [PubMed]

13 Keenan TF Hollinger DY Bohrer G Dragoni D Munger JW Schmid HP Richardson AD Increasein forest water-use efficiency as atmospheric carbon dioxide concentrations rise Nature 2013 499 324ndash327[CrossRef] [PubMed]

14 Wang SS Yang Y Trishchenko AP Barr AG Black TA McCaughey H Modelling the response ofcanopy stomatal conductance to humidity J Hydrometeorol 2009 10 521ndash532 [CrossRef]

15 Wang S Yang Y Luo Y Rivera A Spatial and seasonal variations in evapotranspiration over Canadarsquoslandmass Hydrol Earth Syst Sci 2013 17 3561ndash3575 [CrossRef]

16 Yuan WP Liu SG Yu GR Bonnefond JM Chen JQ Davis K Desai AR Goldstein AHGianelle D Rossi F et al Global estimates of evapotranspiration and gross primary production basedon MODIS and global meteorology data Remote Sens Environ 2010 114 1416ndash1431 [CrossRef]

17 Fisher JB Whittaker RJ Malhi Y ET come home Potential evapotranspiration in geographical ecologyGlob Ecol Biogeogr 2011 20 1ndash18 [CrossRef]

18 Zeng ZZ Piao SL Lin X Yin GD Peng SS Ciais P Myneni RB Global evapotranspiration overthe past three decades Estimation based on the water balance equation combined with empirical modelsEnviron Res Lett 2012 [CrossRef]

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20 Li ZL Tang RL Wan ZM Bi YY Zhou CH Tang BH Yan GJ Zhang CY A review of currentmethodologies for regional evapotranspiration estimation from remotely sensed data Sensors 2009 93801ndash3853 [CrossRef] [PubMed]

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23 Su Z The surface energy balance system (SEBS) for estimation of turbulent heat fluxes Hydrol Earth SystSci 2002 6 85ndash99 [CrossRef]

24 Ju WM Gao P Wang J Zhou YL Zhang XH Combining an ecological model with remote sensingand GIS techniques to monitor soil water content of croplands with a monsoon climate Agric Water Manag2010 97 1221ndash1231 [CrossRef]

25 Yang YT Shang SH Jiang L Remote sensing temporal and spatial patterns of evapotranspiration andthe responses to water management in a large irrigation district of North China Agric For Meteorol 2012164 112ndash122 [CrossRef]

26 Ryu Y Baldocchi DD Kobayashi H van Ingen C Li J Black TA Beringer J van Gorsel EKnohl A Law BE et al Integration of MODIS land and atmosphere products with a coupled-processmodel to estimate gross primary productivity and evapotranspiration from 1 km to global scales GlobBiogeochem Cycles 2011 [CrossRef]

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28 Miralles DG Holmes TRH de Jeu RAM Gash JH Meesters AGCA Dolman AJ Globalland-surface evaporation estimated from satellite-based observations Hydrol Earth Syst Sci 2011 15453ndash469 [CrossRef]

15839


29 Anderson RG Jin YF Goulden ML Assessing regional evapotranspiration and water balance across aMediterranean montane climate gradient Agric For Meteorol 2012 166 10ndash22 [CrossRef]

30 Wang KC Liang SL An improved method for estimating global evapotranspiration based on satellitedetermination of surface net radiation vegetation index temperature and soil moisture J Hydrometeorol2008 9 712ndash727 [CrossRef]

31 Mu Q Heinsch FA Zhao M Running SW Development of a global evapotranspiration algorithmbased on MODIS and global meteorology data Remote Sens Environ 2007 111 519ndash536 [CrossRef]

32 Zhang K Kimball JS Nemani RR Running SW A continuous satellite-derived global record of landsurface evapotranspiration from 1983 to 2006 Water Resour Res 2010 [CrossRef]

33 Rodell M McWilliams EB Famiglietti JS Beaudoing HK Nigro J Estimating evapotranspirationusing an observation based terrestrial water budget Hydrol Process 2011 25 4082ndash4092 [CrossRef]

34 Sahoo AK Pan M Troy TJ Vinukollu RK Sheffield J Wood EF Reconciling the global terrestrialwater budget using satellite remote sensing Remote Sens Environ 2011 115 1850ndash1865 [CrossRef]

35 Su ZB Zhang T Ma YM Jia L Wen J Energy and water cycle over the Tibetan Plateau Surfaceenergy balance and turbulent heat fluxes Adv Earth Sci 2006 21 1224ndash1236

36 Fernaacutendez-Prieto D van Oevelen P Su Z Wagner W Advances in Earth observation for water cyclescience Hydrol Earth Syst Sci 2012 16 543ndash549 [CrossRef]

37 Jia ZZ Liu SM Xu ZW Chen YY Zhu MJ Validation of remotely sensed evapotranspiration overthe Hai River Basin China J Geophys Res 2012 [CrossRef]

38 Chen XL Su ZB Ma YM Yang K Wen J Zhang Y An improvement of roughness heightparameterization of the Surface Energy Balance System (SEBS) over the Tibetan Plateau J Appl MeteorolClimatol 2013 52 607ndash622 [CrossRef]

39 Monin AS Obukhov AM Basic laws of turbulent mixing in the ground layer of the atmosphere TrudyGeofiz Instit Akad Nauk SSSR 1954 24 163ndash187

40 Foken T 50 years of the Monin-Obukhov similarity theory Bound Layer Meteorol 2006 119 431ndash447[CrossRef]

41 Choudhury BJ Monteith JL A four-layer model for the heat budget of homogeneous land surfaces Q JR Meteorol Soc 1988 114 373ndash398 [CrossRef]

42 Raupach MR Simplified expressions for vegetation roughness length and zero-plane displacement as afunction of canopy height and area index Bound-Layer Meteorol 1994 71 211ndash216 [CrossRef]

43 Schaudt KJ Dickinson RE An approach to deriving roughness length and zero-plane displacementheight from satellite data prototyped with BOREAS data Agric For Meteorol 2000 104 143ndash155[CrossRef]

44 Nakai T Sumida A Daikoku K Matsumoto K van der Molen MK Kodama Y Kononov AVMaximov TC Dolman AJ Yabuki H et al Parameterisation of aerodynamic roughness over borealcool- and warm-temperate forests Agric For Meteorol 2008 148 1916ndash1925 [CrossRef]

45 Su Z Schmugge T Kustas WP Massman WJ An evaluation of two models for estimation of theroughness height for heat transfer between the land surface and the atmosphere J Appl Meteorol 2001 401933ndash1951 [CrossRef]

46 Yang K Koike T Fujii H Tamagawa K Hirose N Improvement of surface flux parametrizations witha turbulence related length Q J R Meteorol Soc 2002 128 2073ndash2087 [CrossRef]

47 Tian X Li ZY van der Tol C Su Z Li X He QS Bao YF Chen EX Li LH Estimating zero-planedisplacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data RemoteSens Environ 2011 115 2330ndash2341 [CrossRef]

48 Gibson L Muumlnch Z Engelbrecht J Particular uncertainties encountered inusing a pre-packaged SEBSmodel to derive evapotranspiration in a heteroge-neous study area in South Africa Hydrol Earth Syst Sci2011 15 295ndash310 [CrossRef]

49 Byun K Liaqat UW Choi M Dual-model approaches for evapotranspiration analyses over homo- andheterogeneous land surface conditions Agric For Meteorol 2014 197 169ndash187 [CrossRef]

50 Van der Kwast J Timmermans W Gieske A Su ZB Oliso A Jia L Elbers J Karssenberg Dde Jong S Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery withflux-measurements at the SPARC 2004 site (Barrax Spain) Hydrol Earth Syst Sci 2009 13 1337ndash1347[CrossRef]

15840


51 Lorey T Die mittlereBestandeshohe Allg Forst J Ztg 1878 54 149ndash15552 Tian X Yan M van der Tol C Li ZY Su ZB Chen EX Li X Li LH Wang XF Pan XD

et al Modeling of forest above-ground biomass dynamics using multi-sourced remote sensing data andincorporated models Remote Sens Environ 2015 in resubmission

53 Tian X Li ZY Su ZB Chen EX van der Tol C Li X Guo Y Li LH Ling FL Estimating montaneforest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data Int JRemote Sens 2014 35 7339ndash7362 [CrossRef]

54 Li X Li XW Li ZY Ma MG Wang J Xiao Q Liu Q Che T Chen EX Yan GJ et al Watershedallied telemetry experimental research J Geophys Res 2009 [CrossRef]

55 Li X Li XW Roth K Menenti M Wagner W Preface ldquoObserving and modeling the catchment scalewater cyclerdquo Hydrol Earth Syst Sci 2011 15 597ndash601 [CrossRef]

56 Wang WZ Xu ZW Liu SM Li X Ma MG Wang JM The Characteristics of Heat and Water VaporFluxes over Different Surfaces in the Heihe River Basin Adv Earth Sci 2009 7 714ndash723 (In Chinese)

57 Zhang ZH Wang WZ Ma MG Wu YR Xu ZW The processing methods of eddy covariance fluxdata and products in ldquoWATERrdquo Test Remote Sens Technol Appl 2010 25 788ndash796 (In Chinese)

58 Wang XF Ma MG Song Y Tan JL Wang HB Coupling of a biogeochemical model with asimultaneous heat and water model and its evaluation at an alpine meadow site Environ Earth Sci 2014[CrossRef]

59 Wang JY Ju KJ Fu HE Chang XX He HY Study on biomass of water conservation forest on northslope of Qilian mountains J Fujian Coll For 1998 18 319ndash323 (In Chinese)

60 Liston GE Elder K A meteorological distribution system for high-resolution terrestrial modeling(MicroMet) J Hydrometeorol 2006 7 217ndash234 [CrossRef]

61 Pan XD Li X Validation of WRF model on simulating forcing data for Heihe River Basin Sci Cold AridReg 2011 3 344ndash357

62 Pan XD Li X Shi XK Han XJ Luo LH Wang LX Dynamic downscaling of near-surface airtemperature at the basin scale using WRFmdashA case study in the Heihe River Basin China Front EarthSci 2012 6 314ndash323 [CrossRef]

63 Liang SL Zhao X Liu SH Yuan WP Cheng X Xiao ZQ Zhang XT Liu Q Cheng J Tang HRet al A long-term global Land surface satellite (GLASS) data-set for environmental studies Int J DigitEarth 2013 6 5ndash33 [CrossRef]

64 Generation amp Application of Global Products of Essential Land Variables Available onlinehttpglass-productbnueducn (accessed on 26 July 2015)

65 Level 1 and Atomosphere Archive and Distribution System Available onlinehttpladswebnascomnasagov (accessed on 26 July 2015)

66 ASTER Global Digital Elevation Model (GDEM) Available online httpwwwgdemasterersdacorjp(accessed on 26 July 2015)

67 Numerical Terrdynamic Simulation Group Available online httpwwwntsgumteduprojectmod16(accessed on 26 July 2015)

68 Brutsaert W Aspect of bulk atmospheric boundary layer similarity under free-convective conditions RevGeohys 1999 37 439ndash451 [CrossRef]

69 Su HB McCabe MF Wood EF Su Z Prueger JH Modeling evapotranspiration during SMACEXComparing two approaches for local- and regional-scale prediction J Hydrometeorol 2005 6 910ndash922[CrossRef]

70 Pan M Wood EF Wojcik R McCabe MF Estimation of regional terrestrial water cycle usingmulti-sensor remote sensing observations and data assimilation Remote Sens Environ 2008 1121282ndash1294 [CrossRef]

71 Sobrino JA Gomez M Jimenez-Munoz C Olioso A Application of a simple algorithm to estimate dailyevapotranspiration from NOAA-AVHRR images for the Iberian Peninsula Remote Sens Environ 2007 110139ndash148 [CrossRef]

72 Chirouze J Boulet G Jarlan L Fieuzal R Rodriguez JC Ezzahar J Raki SE Bigeard G Merlin OGaratuza-Payan J et al Intercomparison of four remote-sensing-based energy balance methods to retrievesurface evapotranspiration and water stress of irrigated fields in semi-arid climate Hydrol Earth Syst Sci2014 18 1165ndash1188 [CrossRef]

15841


73 Ma WQ Ma YM Shikawa H Evaluation of the SEBS for upscaling the evapotranspiration based onin-situ observations over the Tibetan Plateau Atmos Res 2014 138 91ndash97 [CrossRef]

74 Pardo N Sanchez ML Timmermans J Su ZB Perez IA Garcia MA SEBS validation in a Spanishrotating crop Agric For Meteorol 2014 195 132ndash142 [CrossRef]

75 Zhou YL Sun XM Zhu ZL Zhang RH Tian J Liu YF Guan DX Yuan GF Surface roughnesslength dynamic over several different surfaces and its effects on modeling fluxes Sci China Ser D-EarthSci 2006 49 262ndash272 [CrossRef]

76 Ershadi A McCabe MF Evans JP Chaney NW Wood EF Multi-site evaluation of terrestrialevaporation models using FLUXNET data Agric For Meteorol 2014 187 46ndash61 [CrossRef]

77 Zhuo G La B Pubu C Luo B Study on daily surface evapotranspiration with SEBS in TibetAutonomous Region J Geogr Sci 2014 24 113ndash128 [CrossRef]

78 Massman W An analytical one-dimensional model of momentum transferby vegetation of arbitrarystructure Bound-Layer Meteorol 1997 83 407ndash421 [CrossRef]

79 Ma WQ Ma YM Hu ZY Su ZB Wang JM Ishikawa H Estimating surface fluxes over middle andupper streams of the Heihe River Basin with ASTER imagery Hydrol Earth Syst Sci 2011 15 1403ndash1413[CrossRef]

80 Ramsey EW Jensen JR Remote sensing of mangrove wetlands Relating canopy spectra to site-specificdata Photogramm Eng Remote Sens 1996 62 939ndash948

81 Papadavid G Fasoula D Hadjimitsis M Perdikou PS Hadjimitsis DG Image based remote sensingmethod for modeling black-eyed beans (Vignaunguiculata) Leaf Area Index (LAI) and Crop Height (CH)over Cyprus Cent Eur J Geosci 2013 5 1ndash11

82 Kovacs JM Lu XX Flores-Verdugo F Zhang C de Santiago FF Jiao X Applications of ALOSPALSAR for monitoring biophysical parameters of a degraded black mangrove (Avicenniagerminans)forest ISPRS J Photogramm Remote Sens 2013 82 102ndash111 [CrossRef]

83 Lefsky MA Cohen WB Acker SA Parker GG Spies TA Harding D Lidar remote sensing of thecanopy structure and biophysical properties of Douglas-fir western hemlock forests Remote Sens Environ1999 70 339ndash361 [CrossRef]

84 Falkenmark M Rockstroumlm J The new blue and green water paradigm Breaking new ground for waterresources planning and management J Water Resour Plan Manag 2006 132 129ndash132 [CrossRef]

85 Change XX Zhao WZ He ZB Radial pattern of sap flow and response to microclimate and soilmoisture in Qinghai spruce (Piceacrassifolia) in the upper Heihe River Basin of arid northwestern ChinaAgric For Meteorol 2014 187 14ndash21 [CrossRef]

86 Sun FX Lu YH Wang JL Hu J Fu BJ Soil moisture dynamics of typical ecosystems in response toprecipitation A monitoring-based analysis of hydrological service in the Qilian Mountains Cetena 2015129 63ndash75 [CrossRef]

87 Wang C Zhao CY Xu ZL Wang Y Peng HH Effect of vegetation on soil water retention and storagein a semi-arid alpine forest catchment J Arid Land 2013 5 207ndash219 [CrossRef]

88 Vinukollu RK Wood EF Ferguson CR Fisher JB Global estimates of evapotranspiration for climatestudies using multi-sensor remote sensing data Evaluation of three process-based approaches RemoteSens Environ 2011 115 801ndash823 [CrossRef]

89 Vinukollu RK Meynadier R Sheffield J Wood EF Multi-model multi-sensor estimates of globalevapotranspiration Climatology uncertainties and trends Hydrol Process 2011 25 3993ndash4010 [CrossRef]

90 Luo XP Wang KL Jiang H Sun J Zhu QL Estimation of land surface evapotranspiration over theHeihe River basin based on the revised three-temperature model Hydrol Process 2011 26 1263ndash1269[CrossRef]

91 Ma WQ Hafeez M Rabbani U Ishikawa H Ma YM Retrieved actual ET using SEBS model fromLandsat-5 TM data for irrigation area of Australia Atmos Environ 2012 59 408ndash414 [CrossRef]

92 Li ZS Jia L Hu GC Lu J Zhang JX Chen QT Wang K Estimation of growing season daily ET inthe middle stream and downstream areas of the Heihe River Basin Using HJ-1 data IEEE Geosci RemoteSens Lett 2015 12 948ndash952 [CrossRef]

93 Timmermans J Su ZB van der Tol C Verhoef A Verhoef W Quantifying the uncertainty in estimatesof surface-atmosphere fluxes through jointevaluation of the SEBS and SCOPE models Hydrol Earth SystSci 2013 17 1561ndash1573 [CrossRef]

15842


94 Timmermans J van der Tol C Verhoef A Verhoef W Su ZB van Helvoirt M Wang L Quantifyingthe uncertainty in estimates of surface-atmosphere fluxes through joint evaluation of the SEBS and SCOPEmodels Hydrol Earth Syst Sci 2011 8 2861ndash2893 [CrossRef]

95 Weligepolage K Gieske ASM van der Tol C Timmermans J Su Z Effect of sub-layer corrections onthe roughness parameterization of a Douglas fir forest Agric For Meteorol 2012 162 115ndash126 [CrossRef]

96 Zhou J Zhang X Zhan WF Zhang HL Land surface temperature retrieval from MODIS data byintegrating regression models and the genetic algorithm in an arid region Remote Sens 2014 6 5344ndash5367[CrossRef]

97 Su H Wood EF Mccabe MF Su Z Evaluation of remotely sensed evapotranspiration over the CEOPEOP-1 reference sites J Meteorol Soc Jpn 2007 85 439ndash459 [CrossRef]

98 Tang RL Li ZL Jia YY Li CR Sun XM Kustas WP Anderson MC An intercomparison of threeremote sensing-based energy balance models using Large Aperture Scintillometer measurements over awheat-corn production region Remote Sens Environ 2011 115 3187ndash3202 [CrossRef]

99 Goumlkmen M Vekerdy Z Verhoef A Verhoef W Batelaan O van der Tol C Integration of soil moisturein SEBS for improving evapotranspiration estimation under water stress conditions Remote Sens Environ2012 121 261ndash274 [CrossRef]

100 Shi YF Shen YP Li DL Zhang GW Ding YJ Hu RJ Kang ES Discussion on the present climatechange from warm-dry to warm-wet in Northwest China Quat Sci 2003 23 152ndash164

copy 2015 by the authors licensee MDPI Basel Switzerland This article is an openaccess article distributed under the terms and conditions of the Creative Commons byAttribution (CC-BY) license (httpcreativecommonsorglicensesby40)

15843

Introduction

Study Area and Observations

Methodology

The Surface Energy Balance System (SEBS)

Roughness Length Models

The SEBS Model Sensitivity

Long-Term Parameterization for the SEBS

Results


Parameterization of the SEBS

A comparison of ET Estimates between EC Measurements the SEBS Simulations and MODIS MOD16 Products

Interannual ET Simulations Using the SEBS

Discussion

Conclusions


more than 50 of the solar radiation absorbed by the landrsquos surface [7] Within the atmosphericsurface layer ET affects environmental conditions through the turbulent exchange of momentumheat and moisture [8ndash10] In return environmental variation controls diverse physical andphysiological processes within terrestrial ecosystems and finally alters the mass and energyexchange of land-atmosphere interactions leading to direct climate impacts [311ndash13] Consideringthe importance of ET the characterization of spatiotemporal variability is critical for betterunderstanding interactions between the land and the atmosphere the sustainable management ofwater resources and the response of terrestrial ecosystems under climate change [14ndash18]

As one of the most important parts of the terrestrial ecosystem global forests account forapproximately 45 of total terrestrial ET [5] Therefore forest ET is considered to be an importantindicator of water availability in natural ecosystems as well as for human needs [19] The routinemonitoring of ET from forests in the headwaters of basins is particularly critical because theseheadwaters often provide water to ecosystems downstream Knowledge of the dynamics of theheadwaters is therefore often relevant for the sustainable development of an entire basin The HeiheRiver Basin (HRB) located in the cold and arid environment of northwest China is entirely dependenton headwaters Water conflicts in this area are severe and information regarding forest processes isurgently needed

In situ measurements using weighing lysimeters Bowen ratios sap flow meters eddy covariance(EC) and scintillometers are considered reliable for quantifying ET at individual sites or for smallfootprints [42021] However these types of measurements are unsuitable at large scales Analternative approach is to use remote sensing information for characterizing spatiotemporal landsurface information over large areas With the incorporation of meteorological and other auxiliarydata remote sensing-based methods can be used to map various large-scale patterns of ET in aglobally consistent and economically feasible manner by linking surface parameters and energybalance with ET [22ndash25] In general land surface parameters retrieved using remote sensing datasuch as the Normalized Difference Vegetation Index (NDVI) the Leaf Area Index (LAI) Albedotemperature and emissivity have been used to drive ET models [1626]

The following four types of remote sensing-based ET models having been developed in recentdecades (1) surface energy balance models [2327ndash29] (2) empirical statistical models [30] (3)physical models [3132] and (4) water balance models [3334] Although they have been applied overa number of study areas uncertainties related to the disadvantage of these models and their inputsand parameterization and scaling schemes still exist [435ndash38] As far as remote sensing techniquesare concerned the availability of measurements and estimations of land surface and atmosphericparameters have increased over time and the improvement of model performance through theintegration of various techniques and data sources has received renewed global interest

As one of the most widely used and validated ET models the Surface Energy Balance System(SEBS) developed by Su [23] estimates turbulent heat fluxes by means of Moni-Obukhov Similarity(MOS) theory [3940] Several important parameters are a part of the MOS and SEBS includingzero-plane displacement (d) roughness height (z0m) and heat roughness height (z0h) The scalarz0h can be calculated from z0m using the kBacute1 model that provides excess resistance for heattransportation For d and z0m experimental methods based on measurements of turbulent fluxescan provide estimates but are only locally valid and cannot be scaled to larger areas Alternativelyremotely sensed methods have been developed for these parameters using the functions of vegetationstructural parameters (ie LAI [41] frontal area index (FAI) [42] both LAI and FAI [43] stand density(SD) (treesumlhaacute1) and stem-branch-leaf distributions [44]) For the kBacute1 model some studies haveimproved parameterization [384546] However few have focused on forests

Our previous study [47] demonstrated the advantages and disadvantages of four notable modelsfor retrieving d and z0m values in forests located within the Qilian Mountains (QMs) the headwaterarea of the HRB A key parameter needed for d and z0m estimates is forest height Other vegetationstructure parameters further affect the ratios of d or z0m over forest height (dh or z0mh) In

15823








15824








15825











3 Methodology











3 Methodology



15826






u ldquou˚k

bdquo

lnpzacute dz0m

q acuteΨmpzacute d

Lq `Ψmp

z0m

Lq

(2)


ku˚ρcp

bdquo

lnpzacute dz0h

q acuteΨhpzacute d

Lq `Ψhp

z0hLq

(3)





2β2 (5)

dhldquo 1acute

12nec


z0m

hldquo r1acute

dhsrexpp

acutekβqs (7)





15827




z0h ldquoz0m

exppkBacute1q(9)




Hdry acute Hwet(10)


Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)




Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ


λρ(13)








Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions








15824








15825











3 Methodology











3 Methodology



15826






u ldquou˚k

bdquo

lnpzacute dz0m

q acuteΨmpzacute d

Lq `Ψmp

z0m

Lq

(2)


ku˚ρcp

bdquo

lnpzacute dz0h

q acuteΨhpzacute d

Lq `Ψhp

z0hLq

(3)





2β2 (5)

dhldquo 1acute

12nec


z0m

hldquo r1acute

dhsrexpp

acutekβqs (7)





15827




z0h ldquoz0m

exppkBacute1q(9)




Hdry acute Hwet(10)


Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)




Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ


λρ(13)








Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions








15825











3 Methodology











3 Methodology



15826






u ldquou˚k

bdquo

lnpzacute dz0m

q acuteΨmpzacute d

Lq `Ψmp

z0m

Lq

(2)


ku˚ρcp

bdquo

lnpzacute dz0h

q acuteΨhpzacute d

Lq `Ψhp

z0hLq

(3)





2β2 (5)

dhldquo 1acute

12nec


z0m

hldquo r1acute

dhsrexpp

acutekβqs (7)





15827




z0h ldquoz0m

exppkBacute1q(9)




Hdry acute Hwet(10)


Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)




Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ


λρ(13)








Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions











3 Methodology











3 Methodology



15826






u ldquou˚k

bdquo

lnpzacute dz0m

q acuteΨmpzacute d

Lq `Ψmp

z0m

Lq

(2)


ku˚ρcp

bdquo

lnpzacute dz0h

q acuteΨhpzacute d

Lq `Ψhp

z0hLq

(3)





2β2 (5)

dhldquo 1acute

12nec


z0m

hldquo r1acute

dhsrexpp

acutekβqs (7)





15827




z0h ldquoz0m

exppkBacute1q(9)




Hdry acute Hwet(10)


Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)




Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ


λρ(13)








Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions






u ldquou˚k

bdquo

lnpzacute dz0m

q acuteΨmpzacute d

Lq `Ψmp

z0m

Lq

(2)


ku˚ρcp

bdquo

lnpzacute dz0h

q acuteΨhpzacute d

Lq `Ψhp

z0hLq

(3)





2β2 (5)

dhldquo 1acute

12nec


z0m

hldquo r1acute

dhsrexpp

acutekβqs (7)





15827




z0h ldquoz0m

exppkBacute1q(9)




Hdry acute Hwet(10)


Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)




Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ


λρ(13)








Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions




z0h ldquoz0m

exppkBacute1q(9)




Hdry acute Hwet(10)


Λ ldquoλE

Rn acute G0ldquo

Λr ˆ λEwet

Rn acute G0(11)




Edaily ldquo

24yuml

hldquo0

bdquo

ΛˆpRn acute G0q

λρ


λρ(13)








Lp ldquofv

fcLAI acute

fbfc

Lb (16)

15828






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions






dhldquo 10acute

10acute exppacuteb

a1λ f qb

a1λ f

(18)

z0m


f qλd2f `

z00

hpλ ď 0152q (19)

z0m

hldquo

a3

λd3f

rdquo


` f2 pλ ą 0152q (20)


A f ldquo12

hc ˚wc (21)






Hr(22)




15829





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions





4 Results




Inputs Sj(H+25)()

Sj(Hacute25)()

Sj(H+5K)()

Sj(Hacute5K)()

d 001 001 - -z0m 2132 acute1993 - -



Hh 053 048 - -Rs 667 acute735 - -



15830



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions



YL ldquo 1055ˆ X0544 (23)















2000

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

1339 1362 1396 1430 1458 1487 1502 1524 1544 1555 1572 1590 1614



684 850 907 925 936 992

1006 1022 1032 1019 822

1044 1090


136 119 113 116 120 113 112 114 116 121 148 122 117


MOD16 Products






in Figures 3 and 4





15831


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions


































15832









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions









Figure 5 Cont

Figure 5 Cont

15833



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions



2000 to 2012




2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

244

281

299

317

271

290

248

277

255

201

222

236

290

136

165

166

194

158

165

143

165

136

124

131

137

175

1016

1008

1025

985

1075

949

1242

1150

1051

882

969

1024

1015

80

76

79

77

82

62

97

80

82

69

75

79

75

190426

219356

233380

247049

211567

225890

193126

216346

198537

156904

173053

184386

225943

454

344

453

279

386

454

509

671

481

512

482

550

499

5374

8177

6602

11359

7021

6377

4865

4134

5297

3933

4605

4299

5811




2000 244 136 1016 80 190426 454 53742001 281 165 1008 76 219356 344 81772002 299 166 1025 79 233380 453 66022003 317 194 985 77 247049 279 113592004 271 158 1075 82 211567 386 70212005 290 165 949 62 225890 454 63772006 248 143 1242 97 193126 509 48652007 277 165 1150 80 216346 671 41342008 255 136 1051 82 198537 481 52972009 201 124 882 69 156904 512 39332010 222 131 969 75 173053 482 46052011 236 137 1024 79 184386 550 42992012 290 175 1015 75 225943 499 5811

15834



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions



5 Discussion





15835






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions






15836





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions





6 Conclusions



15837







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions







References








15838























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions























15839
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions
























15840
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions
























15841























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions























15842










15843

Introduction


Methodology





Results





Discussion

Conclusions










15843

Introduction


Methodology





Results





Discussion

Conclusions

opus at uts: home - simulation of forest evapotranspiration … · 2020-03-14 · article...

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