Evapotranspiration comparisons between eddy covariance...

ECOHYDROLOGYEcohydrol. (2014)Published online in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/eco.1586

Evapotranspiration comparisons between eddy covariancemeasurements and meteorological and remote-sensing-based

models in disturbed ponderosa pine forests

Wonsook Ha,1 Thomas E. Kolb,2,3 Abraham E. Springer,1* Sabina Dore,4 Frances C. O’Donnell,1

Rodolfo Martinez Morales,5 Sharon Masek Lopez1 and George W. Koch5,61 School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ, USA

2 School of Forestry, Northern Arizona University, Flagstaff, AZ, USA3 Merriam-Powell Center for Environmental Research, Northern Arizona University, Flagstaff, AZ, USA

4 Department of Environmental Science, Policy, and Management, University of California at Berkeley, Berkeley, CA, USA5 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA

6 Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA

*CEn860E-m

Co

ABSTRACT

Evapotranspiration (ET) comprises a major portion of the water budget in forests, yet few studies have measured or estimated ETin semi-arid, high-elevation ponderosa pine forests of the south-western USA or have investigated the capacity of models topredict ET in disturbed forests. We measured actual ET with the eddy covariance (eddy) method over 4 years in three ponderosapine forests near Flagstaff, Arizona, that differ in disturbance history (undisturbed control, wildfire burned, and restorationthinning) and compared these measurements (415–510mmyear�1 on average) with actual ET estimated from five meteorologicalmodels [Penman–Monteith (P-M), P-M with dynamic control of stomatal resistance (P-M-d), Priestley–Taylor (P-T),McNaughton–Black (M-B), and Shuttleworth–Wallace (S-W)] and from the Moderate Resolution Imaging Spectroradiometer(MODIS) ET product. The meteorological models with constant stomatal resistance (P-M, M-B, and S-W) provided the mostaccurate estimates of annual eddy ET (average percent differences ranged between 11 and �14%), but their accuracy variedacross sites. The P-M-d consistently underpredicted ET at all sites. The more simplistic P-T model performed well at the controlsite (18% overprediction) but strongly overpredicted annual eddy ET at the restoration sites (92%) and underpredicted at the firesite (�26%). The MODIS ET underpredicted annual eddy ET at all sites by at least 51% primarily because of underestimation ofleaf area index. Overall, we conclude that with accurate parameterization, micrometeorological models can predict ET within30% in forests of the south-western USA and that remote sensing-based ET estimates need to be improved through use of higherresolution products. Copyright © 2014 John Wiley & Sons, Ltd.

KEY WORDS evapotranspiration; latent heat; eddy covariance; forest ecosystems; ponderosa pine; Moderate Resolution ImagingSpectroradiometer (MODIS)

Received 19 May 2014; Revised 16 September 2014; Accepted 20 November 2014

INTRODUCTION

Forests occur over approximately 31% of the land surfaceof the Earth (FAO, 2012) and are important regulators ofterrestrial water balance (Arora, 2002; Ueyama et al.,2010). Evapotranspiration (ET) is the largest flux of annualprecipitation from most forests except in cool and wetclimate zones. For example, ET has been reported to useapproximately 70% of annual precipitation in a loblollypine (Pinus taeda) plantation in the south-eastern USA(Sun et al., 2002), more than 85% in a Canadian black

orrespondence to: Abraham E. Springer, School of Earth Sciences andvironmental Sustainability, Northern Arizona University, Flagstaff, AZ11, USA.ail: [email protected]

pyright © 2014 John Wiley & Sons, Ltd.

spruce (Picea mariana) forest (Arain et al., 2003), andmore than 85% in a ponderosa pine (Pinus ponderosa)forest in Arizona (Dore et al., 2012). Consequently, themagnitude and seasonality of forest ET are importantregulators of water resources available to humans andecosystems.The question of how forest management affects ET is

long standing (e.g. Bosch and Hewlett, 1982) but is still notadequately answered for many forest types. This questionis increasingly relevant to current restoration projects insemi-arid forests that use tree thinning to reduce the risk ofwildfire (e.g. Covington et al., 1997; Agee and Skinner,2005; McIver et al., 2013). Studies in other forest typesreport that reforestation generally increases ET (Bosch andHewlett, 1982; Trabucco et al., 2008), whereas deforesta-tion decreases ET (Nobre et al., 1991; Bala et al., 2007;

W. HA et al.

Costa et al., 2010; Krishnaswamy et al., 2012; Lathuillièreet al., 2012; Bright et al., 2013). Impacts on ET of moresubtle changes in forest cover produced by tree thinninghave been investigated at only a few sites (Moreaux et al.,2011; Dore et al., 2012).More information on impacts to ET of forest restoration

thinning and of intense wildfire, which often occurs indense semi-arid forests in the absence of thinning (e.g.Finney et al., 2005), is needed for upland forest landscapes.These forests are critical for supplying water to downslopeecosystems and human settlements (e.g. Troendle, 1983)and are the targets of major new management initiatives.Landscape-scale forest restoration treatments (e.g. Coving-ton et al., 1997) are planned for 1.5 million ha of semi-arid,dense ponderosa pine forests in upland watersheds ofArizona (USDA Forest Service, 2012). Because the impactof vegetation manipulation on ET is highly variable insemi-arid regions (Bosch and Hewlett, 1982; Stednick,1996; Brown et al., 2005; Huxman et al., 2005), betterunderstanding of the coupled land management andhydrological response processes is needed.A major challenge to understanding impacts of forest

management actions on ET arises from the difficulty inestimating ET accurately over large areas. Forest ET can bemeasured by numerous methods, such as site waterbalance, lysimeters, sap flow, Bowen ratio, and plantchambers (Jackson et al., 2000; Moncrieff et al., 2000), butthe eddy covariance (eddy) approach is considered to beaccurate (Wilson et al., 2001; Baldocchi and Ryu, 2011;Barr et al., 2012). Despite advances in ET measurement bythe eddy approach, accurate estimation of annual ET inforests using this approach remains challenging over broadlandscapes because of the difficulty of establishing andmaintaining eddy systems in remote locations and theinfluence of complex topography that can prevent adequatemeasurement of energy balance closure (Baldocchi et al.,1988; Foken, 2008; Reba et al., 2009). To overcome thesechallenges, models that predict ET from site and climatedata have been used, and ET predictions from these modelshave been compared with eddy ET for coniferous forests inseveral studies (e.g. Federer et al., 1996; Cienciala et al.,1998; Sun et al., 2002; Fisher et al., 2005).Comparisons of modelled and measured ET have

generally used only short time series, have neitherincluded multiple years nor included recently disturbedforests (e.g. Fisher et al., 2005) nor focused on forestswith complex climatic influences on the seasonality of ET,such as is the case in the south-western USA, where thebimodal precipitation regime of winter snow and late-summer rainfall is punctuated by a distinct dry season, andinter-annual variability in precipitation is pronounced(Sheppard et al., 2002). Modelling ET is known to bechallenging in seasonally water-stressed forests becausemost models use energy availability as the primary control

Copyright © 2014 John Wiley & Sons, Ltd.

on ET (Law et al., 2000; Gordon and Famiglietti, 2004;Morales et al., 2005). Estimation of ET from remotelysensed spectral data, such as the Moderate ResolutionImaging Spectroradiometer (MODIS) satellite (Yuan et al.,2010; Mu et al., 2011; Goulden et al., 2012), is anotherapproach that has potential application in investigations offorest water balance, but more site-specific comparisons toeddy ET are needed to assess accuracy. Although thespatial resolution of MODIS ET products (1 km2) oftendoes not match the flux tower footprint coverage (Gouldenet al., 2012), MODIS provides spatially and temporallycontinuous data of land surface and atmosphere interac-tions (Wan, 2008).The objective of this study was to evaluate the accuracy

of ET predictions from five meteorological models and theMODIS ET product over 4 years at three sites in theponderosa pine forest region of northern Arizona that havedifferent types of recent disturbance. We compare ETpredictions from these models with ET measured directlyat each site by the eddy covariance approach (Dore et al.,2012). The sites consist of (1) a dense, unmanaged forest,(2) a similar forest treated with restoration thinning, and(3) a former forest that was converted to grassland byintense wildfire. The meteorological models that we usedhave shown potential for accurate ET prediction (e.g.Fisher et al., 2005; Morales et al., 2005), yet they have notbeen adequately evaluated for the ponderosa pine region ofthe south-western USA where landscape-scale forestrestoration treatments are being implemented and intensewildfires are common. The meteorological models that weinvestigated are Penman–Monteith (P-M), P-M withdynamic stomatal resistance (P-M-d), Priestley–Taylor(P-T), McNaughton–Black (M-B), and Shuttleworth–Wallace (S-W).

MATERIAL AND METHODS

Study sites

We used three study sites located within the ponderosa-pine-dominated forest region of northern Arizona thatdiffer in disturbance and for which ET was measured withthe eddy covariance method over several years by Doreet al. (2008, 2010, 2012). The three sites (control, fire, andrestoration) were located less than 35 km apart nearFlagstaff, AZ, USA. Site characteristics were described indetail by Dore et al. (2010). In brief, the control site was aponderosa pine stand located in the Northern ArizonaUniversity Centennial Forest (35°5′20.5″N, 111°45′43.33″W, elevation 2180m a.s.l.) that was excluded fromharvesting, thinning, and fire over the last century. Thecontrol site had an average leaf area index (LAI; projectedarea) of 2.3m2m�2, basal area of 30m2ha�1, and treedensity of 853 trees ha�1 (Dore et al., 2010). The fire site

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EVAPOTRANSPIRATION COMPARISONS IN PONDEROSA PINE FORESTS

was part of a 10 500-ha area in the Coconino NationalForest (35°26′43.43″N, 111°46′18.64″W, elevation2270ma.s.l.) burned by an intense fire in 1996. The firekilled all trees in the stand, which prior to the fire had treedensity and basal area similar to the control site (Doreet al., 2010). No ponderosa pine was regenerated in thissite. During the measurement period, more than a decadeafter the fire, vegetation on the fire site consisted of grasses,forbs, and shrubs, with average ground cover of 40%vegetation, 50% bare soil, and 10% snags and logs(Montes-Helu et al., 2009). The restoration site was aponderosa pine stand located in the Centennial Forest (35°8′33.48″N, 111°43′38.37″W, 2155ma.s.l.) about 6 kmfrom the control site that was treated with fuel-reductionthinning in 2006, several months before the measurementsreported in this study. To reduce tree density and fire riskand to restore pre-settlement forest structure, approximately90 ha of the restoration site was thinned in September 2006.The treatment focused on removal of small-diameter treesand reduced tree density 70%, basal area 35%, tree LAI40%, and stand LAI (including understory) 30%. Canopyheight was approximately 18m at the control andrestoration sites and approximately 0.3m at the fire site(Dore et al., 2010). Climatic and edaphic conditions at thethree sites were similar because of their close proximity.The region is characterized by cold winters and dry springswith precipitation as snow in the winter and rain in latesummer.

Eddy covariance measurements

Fluxes of water vapour, sensible and latent heats (H and LE),net solar radiation (Rn), and soil heat flux (G)weremeasuredcontinuously with the eddy covariance (eddy) methodbetween 2007 and 2010 at each site using identicalinstruments and sensors. The eddy instruments and methodsare described in detail by Dore et al. (2008, 2010, 2012).Instrumentation included a sonic anemometer (CSAT3,Campbell Scientific, Logan, UT, USA) for measurement ofwind velocity; a closed-path CO2/H2O infrared gas analyser(LI-COR Li-7000, Lincoln, NE, USA) for measurement ofwater vapour density; incoming and outgoing short-waveand long-wave radiometers (CNR1, Kipp and Zonen, Delft,the Netherlands) for measurement of global and netradiation; a photosynthetic photon flux density (PPFD)sensor (BF3 Delta-T devices, Cambridge, UK); a reflectedPPFD sensor (LI-COR Li-190); precipitation sensors(5.4103.20.041, Thies Clima, Göttingen, Germany; TR-525-R3 gauge, Texas Instruments, USA); a weathertransmitter (WXT510, Vaisala, Helsinki, Finland) formeasurement of atmospheric pressure, air humidity, andair temperature; volumetric water content sensors (model615, Campbell Scientific, USA); soil temperature probes(model 107, Campbell Scientific, USA); and soil heat flux


plates (Hukseflux HFP01SC, Delft, the Netherlands). Theeddy instruments were mounted on towers 23m aboveground surface at the control and restoration sites and 2.5mabove ground surface at the fire site (Dore et al., 2008). Soiltemperature and water content were measured at depths of 2,10, 20, and 50 cm, and soil heat flux was measured at a depthof 8 cm. All data were collected and stored by CR-1000 andCR-10X dataloggers (Campbell Scientific, USA) andaveraged over 30-min intervals. Methods for processingand gap filling of the eddy data are described in Dore et al.(2008, 2010). Energy balance closure is a comparison ofavailable energy (Rn�G) and turbulent heat fluxes (LE+H)and is necessary for long-term flux measurements(Baldocchi et al., 1996). We calculated energy balanceclosure of the eddy data for all 4 years of our study(2007–2010) for daily gap-filled data following Aubinetet al. (2000).

Meteorological models

We used five previously developed meteorological modelsthat have been shown to be useful for predicting ET ofvegetated sites (Federer et al., 1996; Fisher et al., 2005).All of these models predict potential ET (PET), which wescaled to actual ET (AET) using a soil moisture limitationfactor (Fisher et al., 2005), except for the P-M-d model,which directly predicts AET. The models, which differ ininput data and complexity, are briefly described in thesucceeding texts. Model input parameters introducedin this section are listed in Table A in the SupportingInformation.

P-M model. The P-M model (Monteith, 1965) improvedthe earlier Penman model (Penman, 1948) by includingeffects of canopy stomatal resistance (rs) and above-canopyaerodynamic resistance (ra) on PET. The P-M equation is

λΕ ¼ Δ� Rn � Gð Þ þ ρ�Cρ� es�eð Þra

Δþϒ � 1þ rsra

� � (1)

where λE is PET as evaporative LE flux (Wm2), Δ is theslope of saturated vapour pressure and air temperature (hPa°C�1), Rn is the net solar radiation flux (Wm�2), G is thesoil heat flux (Wm�2), ρ is the air density of 1.23 kgm�3,Cρ is the specific heat of air, which is equal toapproximately 1006 J kg�1 °C�1, es is the saturation vapourpressure (kPa), e is the vapour pressure (kPa), ϒ is thepsychrometric constant (hPa °C�1), rs is the bulk stomatalresistance of the canopy (sm�1; 416.67 for control andrestoration sites and 210.0 for the fire site), and ra is theaerodynamic resistance above the canopy (sm�1; 6.0 forcontrol and restoration sites and 30.0 for the fire site). Dataused are attached in the Supporting Information. The termes� e is known as the vapour pressure deficit (kPa).

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W. HA et al.

P-M model with dynamic stomatal resistance. This modelenhanced the P-M model by including environmentalcontrols on canopy resistance, which is the inverse ofcanopy conductance ðgsÞ. These controls for our study weretaken from Stewart’s (1988) investigation of a pine forestnear Thetford Forest, Norfolk, UK, that quantifiedrelationships between gs and four environmental variables,incident short-wave solar radiation (Kin), specific humiditydeficit (Δρv), air temperature (Ta), and soil moisture deficit(Δθ), as

gs ¼ f s �LAI �g�leaf � f K Kinð Þ � f ρ Δρvð Þ � f T Tað Þ � f θ Δθð Þ(2)

where gs is the canopy conductance (m s�1), fs is the shelterfactor that represents the degree of sheltered portions of theleaves from the sun and wind, LAI is the LAI, g�leaf is themaximum value of leaf conductance (m s�1; 5.3 × 10�3 forcontrol and restoration sites and 8.0 × 10�3 for the fire site),and fk, fρ, fT, and fθ are functions of environmental variablesexplained previously (Stewart, 1988; Dingman, 2002). Weused site-specific fs, LAI, and input environmental data tocalculate gs in Equation 2 for each time period. The fsvalues were 0.65 for the control site, 0.85 for therestoration site, and 1.0 for the fire site. We calculatedatmospheric resistance (ra) for Equation 1 followingDingman (2002):

1ra

¼ ga ¼u

6:25 � ln zm� zdz0

� �h i2 (3)

where ga is the atmospheric conductance (m s�1), u is thewind speed (m s�1), zm is the height of wind speedmeasurement at the top of the eddy tower, which isapproximately 2.5m above the vegetation canopy (m), zd isthe zero-plane displacement (m), and z0 is the roughnessheight (m).

P-T model. The Priestley and Taylor model (1972)predicts PET based primarily on variation in net radiationwithout formal use of wind velocity, stomatal or atmo-spheric resistance, and vapour pressure data. The P-T ETmodel is

λE ¼ α�Δ� Rn � G

Δ þ ϒ

� �(4)

where α is the unitless ‘P-T coefficient’, which adjusts PETfor differences in water availability, advection, andaerodynamic resistance among surface and vegetationtypes (Flint and Childs, 1991). The correct α value for agiven vegetation type is not known with certainty. Theconstant α value of 1.26 suggested by Priestley and Taylor(1972) for saturated surfaces is known to be inaccurate fordrier sites where dynamic values based on soil water


content (SWC) and often less than 1.0 are recommended(Flint and Childs, 1991; Fisher et al., 2005). We calculateddaily α at each site with the Flint and Childs (1991)approach using a regression equation developed by Fisheret al. (2005) for a ponderosa pine forest in California:α=0.84·SWC+0.72, where SWC equals soil volumetricwater content in the rooting zone. Soil volumetric watercontent was measured every 30min at each flux towerlocation (Dore et al., 2012) and averaged over depths of 2,10, 20, and 50 cm to calculate α. We investigatedcorrespondence between ET predicted with the P-T modeland eddy ET for three temporal averaging approaches forthe calculation of α: daily averages, monthly averages, andseasonal averages (January–March, April–June, July–September, and October–December) for each year and sitecombination. We found that all approaches producedsimilar results based on root-mean-square error (RMSE),coefficient of determination (R2), and mean bias error(MBE); thus, we used average seasonal α values over all4 years in our final predictions of AET with the P-T model.Seasonal average α ranged between 0.91 (October–December 2009) and 1.08 (January–March 2008) at thecontrol site, 0.90 (October–December 2009) and 1.06(January–March 2008) at the fire site, and 1.08 (October–December 2009) and 1.24 (January–March 2008 and 2009)at the restoration site. Specific α values used in the final P-T model predictions are shown in Table B in theSupporting Information. While others (Flint and Childs,1991; Fisher et al., 2005) have suggested that adjustment ofα based on SWC is adequate for converting predictionsfrom the P-T model from PET to AET, we further adjustedpredictions of ET from the P-T model by a scaling factorthat expressed SWC as a fraction of SWC at field capacity(section on Conversion of PET to AET) to obtain finalvalues of AET. This second adjustment produced AETestimates from the P-T model that were closer to AETmeasured by eddy covariance, likely because the modifi-cation of α based on SWC alone does not adequatelyconsider SWC at field capacity.

M-B model. McNaughton and Black (1973) derived thefollowing equation that assumes that stomatal resistance ofthe canopy, rs, is generally much larger than atmosphericresistance, ra. The equation starts from the P-M equationand assumes that ra is close to zero:

λE ¼ ρ �Cp� es � eð Þϒ � rs (5)

These assumptions are made because well-documentedwind profile data often are unavailable or inaccurate, whichcould introduce error to the calculation of λE. The modelimplies that vapour pressure deficit controls diurnalchanges in ET over a small range of rs (McNaughton and

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Black, 1973). The model is theoretically valid only forforest environments (Federer et al., 1996).

S-W model. Shuttleworth and Wallace (1985) improvedprevious models, which were limited to a closed, stable,and uniform canopy, by accounting for soil evaporation ina model for sparse crops that separates soil evaporation(λEs) from transpiration from the plant canopy (λEc). Theyused a Monteith-type equation with horizontal averages ofenergy fluxes and resistance terms defined for heteroge-neous land cover. Total evaporation (λE) is modelled as

λE ¼ λEs þ λEc ¼ CsPMs þ CcPMc (6)

where Cs and Cc are partitioning coefficients, and PMs

and PMc are P-M-like terms to describe evaporation fromthe soil substrate and a closed canopy, respectively. ThePMs term includes surface resistance at the soil ( rss ),aerodynamic resistance leaving the soil surface beforeincorporation into the mean canopy flow (rsa), and transferresistance between the mean canopy flow and the screenheight (ra). The PMc term accounts for stomatalresistance of the vegetation (rs) and bulk boundary layerresistance of the vegetative elements in the canopy (rca )and ra. The partitioning coefficients and resistance termsare parameterized for a sparse canopy based on LAI, avegetation-type-specific extinction coefficient, windspeed, and parameters describing the aerodynamiccharacteristics and diffusivity of the canopy and soil.Full equations for these terms are given by Shuttleworthand Wallace (1985).

Conversion of PET to AET. PET data obtained from themeteorological models were adjusted to AET prior tocomparison with ET data estimated by eddy covarianceexcept for the P-M-d model, which estimates AET directlywithout the need for further adjustment. Based on themodification of the Saxton et al. (1986) method describedin Fisher et al. (2005), AET was calculated as PETmultiplied by a scaling parameter equal to the averageSWC over four depths (2, 10, 20, and 50 cm) divided byfield capacity. Although ponderosa pine can have deeproots in deep soils (approximately 150 cm; Vickers et al.,2012), we averaged SWC over depths and assumed thisaveraged value to represent SWC in the rooting depthbecause our results were not sensitive to specific SWCdepth (data not shown) and because measurements of SWCat depths greater than 50 cm were not possible at our sitesbecause of shallow bedrock. When SWC was greater thanfield capacity, AET was equal to PET. Site-specific valuesof field capacity, equal to volumetric SWC of 0.391, 0.395,and 0.452m3m�3, at the control, fire, and restoration sites,respectively, were calculated based on soil texture (Doreet al., 2010) following Saxton et al. (1986).


MODIS-based remote sensing model

Forest canopy reflectance in the red and near-infrared(NIR) regions can be related to indicators of foreststructure, providing a functional basis for LAI estimation.Because of global coverage of imagery by the Terra andAqua MODIS at 1-km resolution and 8-day frequency,estimates of green vegetation LAI and the fraction ofphotosynthetically active radiation absorbed by vegetation(FPAR) have been produced on a continuous basis sincethe year 2000. This information is used to estimate ETregionally and globally (Nagler et al., 2005; Allen et al.,2007; Cleugh et al., 2007; Mu et al., 2007; Jung et al.,2010; Yuan et al., 2010; Mu et al., 2011).The MOD16 remote-sensing-based ET algorithm pre-

dicts ET globally at 86% accuracy when compared witheddy measurements of ET over many sites in theAmeriFlux network (Mu et al., 2011). Building onprevious algorithms, it uses a physically based P-Mapproach driven by MODIS-derived vegetation data. ETis calculated as the sum of daytime and night-timecomponents using vapour pressure deficit and minimumtemperature to control stomatal resistance. Stomatalresistance is scaled up to the canopy level using LAI tocalculate canopy resistance for plant transpiration. Thealgorithm also models soil heat flux and separatesevaporation from a wet canopy and transpiration from adry canopy. Actual soil evaporation is calculated from apotential evaporation value. The required data inputs to theMOD16 algorithm are (1) Collection 4 MODIS global landcover maps (MOD12Q1; Friedl et al., 2002), (2) Collection5 MODIS FPAR/LAI layers (MOD15A2; Myneni et al.,2002), and (3) Collection 5 MODIS albedo products(MCD43B2 and MCD43B3; Lucht et al., 2000). Dailymeteorological data inputs are from National Aeronauticsand Space Administration’s (NASA’s) Modern-Era Retro-spective Analysis for Research and Applications GlobalModeling and Assimilation Office (Goddard Earth Observ-ing System Model, version 5). The global ET rasterdatasets obtained from MOD16 are now available from theUniversity of Montana (ftp://ftp.ntsg.umt.edu/pub/MODIS/NTSG_Products/MOD16/MOD16A3.105_MERRAGMAO/Geotiff/) and NASA through the Oak Ridge NationalLaboratory (ORNL) Distributed Active Archive Centerweb data portal (http://daac.ornl.gov/MODIS/modis.html).The available MODIS products have pixel resolution of1 km2 and cover 109 million km2 of vegetated land areasglobally at 8-day, monthly, and annual intervals since2000. We obtained 1-km2 8-day MODIS AET productsbetween 2007 and 2009 for each study site from the ORNLwebsite to calculate monthly and annual ET (mmmonth�1

and mmyear�1). Annual ET was obtained by summing all8-day AET per year, while monthly ET was obtained byadding 8-day values that better corresponded to the month

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http://daac.ornl.gov/MODIS/modis.html

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of each year. Eight-day LAI data were also downloadedfrom the same website and were averaged per year forcomparisons with measured LAI at three sites. The control,fire, and restoration flux tower locations were overlaid toMODIS product layers using ArcGIS to select the locationof pixels and to extract ET and LAI pixel values thatrepresented the land cover conditions of tower measure-ments at each site. Data were extracted for only one pixelper site because the eddy tower footprint approximatelymatches the 1-km2pixel size of the MODIS data. Forcontrol and fire sites, the south-east pixel from the towerlocation was selected to represent eddy tower footprint. Forthe restoration site, the north-east pixel was selected. Theselected pixels had vegetation most similar to vegetation ofthe measurement footprint of each eddy instrument towerbased on visual inspection of recent Google Earth images(Figure A in the Supporting Information).

ET comparisons

We ran each meteorological model using both daily (24 h)and average monthly environmental data for each site. Theenvironmental data for each site were obtained from theeddy tower instruments and associated soil measurements(Dore et al., 2012). We evaluated fit between model-predicted AET and eddy ET at daily and monthly scalesusing RMSE, R2, and MBE. Patterns of results forpredictions based on monthly data were similar to resultsbased on daily data, and RMSE, R2, and MBE showedbetter fit of model-predicted values to eddy ET forpredictions based on monthly data. Thus, we do notpresent predictions based on daily data for brevity. Inaddition, we converted monthly data of eddy ET and AETpredicted by each of the five meteorological models fromLE to linear amount of water (mmmonth�1) and thensummed over months to obtain annual values for each yearand site, which were compared with annual ET derivedfrom the MODIS product. Data analysis was performed inR (R Core Team, 2013).

RESULTS

Energy balance closure

The fraction of energy balance closure for the eddymeasurements, defined as (H +LE)/(Rn�G) (Wilsonet al., 2001; Amiro, 2009; Barr et al., 2012), averagedover all years was 0.95, 1.12, and 0.78 for the control, fire,and restoration sites, respectively. Monthly values of LE+H were strongly correlated (R2>0.9) with Rn�G for allyears at each site (Figure 1). The slope of the linearregression between LE+H and Rn�G was 1.02, 1.10,and 0.95 at the control, fire, and restoration sites,respectively.


AET from model simulation versus ET from eddycovariance

Monthly AET. For monthly data pooled over all years(Table I), the model with the highest R2 in comparisonswith eddy ET was the P-T model at the control andrestoration sites and the P-M-d model at the fire site. RMSEwas lowest at the control site for the P-T model and at fireand restoration sites for the P-M model. At the control site,the P-T and P-M-d models had similar strong predictiveperformance (R2 = 0.84, 0.81; RMSE=13.2, 18.5; respec-tively). At the fire site, all models had similar predictiveperformance, with R2 ranging between 0.54 and 0.64 andRMSE ranging between 14.7 and 26.2 among models. Atthe restoration site, the P-T and P-M-d models had the bestpredictive performance based on R2 (0.81 and 0.78,respectively), whereas the P-M and M-B models had thelowest RMSE (13.6 and 13.9, respectively).Comparisons of MBE for monthly data (Table II)

showed that the P-M, M-B, and S-W models consistentlyoverpredicted monthly eddy ET at fire and restoration sites(0.62 to 13.77mmmonth�1), whereas the P-M-d modelconsistently underpredicted at all sites (�22.15 to�13.25mmmonth�1). The P-T model overpredicted atthe control and restoration sites and underpredicted at thefire site. The two models producing the closest predictionsof monthly eddy ET based on the lowest MBE were P-Tand S-W at the control site, P-M and S-W at the fire site,and P-M and M-B at the restoration site (Table II).All models simulated similar seasonal variation in

monthly AET, which was characterized by the highestAET in the summer and the lowest AET in the winter(Figure 2). At the control site, the P-M, M-B, and S-Wmodels consistently underpredicted monthly eddy ETduring all periods, the P-M-d model underpredicted eddyET in late winter and spring, and the P-T modeloverpredicted eddy ET in late winter and spring (Figure 2).At the fire site, consistent overprediction by the P-M, M-B,and S-W models in the spring and underprediction by theP-M-d model also occurred. Predictions by the P-T modelat the fire site fluctuated between small overpredictions andunderpredictions. At the restoration site, the P-T modeloverpredicted eddy ET the most, followed by S-W, P-M,and M-B models. The P-M-d model consistentlyunderpredicted eddy ET at the restoration site (Table I).MODIS AET also consistently underpredicted eddy ET atall sites.Scatter plots of monthly AET data show how model

prediction accuracy varied over the range of eddy ET. Atthe control site (Figure 3), underprediction of eddy ET bythe P-M, M-B, and S-W models increased nonlinearly aseddy ET increased, the P-M-d model consistentlyunderpredicted over the range of eddy ET, and the P-Tmodel tended to overpredict intermediate values of eddy

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Table I. Comparisons of root-mean-square error (RMSE) and coefficient of determination (R2) among five ET models from three(control, fire, and restoration) sites between 2007 and 2010 for monthly data.

Site RMSE (mmmonth�1) R2

Models P-M P-M-d P-T M-B S-W P-M P-M-d P-T M-B S-WControl 22.25 18.50 13.19 23.18 15.49 0.67 0.81 0.84 0.64 0.67Fire 14.73 26.18 17.45 17.26 17.77 0.62 0.64 0.54 0.60 0.59Restoration 13.58 17.21 42.35 13.88 20.97 0.65 0.78 0.81 0.63 0.65

Models included Penman–Monteith (P-M), P-M with dynamic stomatal resistance (P-M-d), Priestley–Taylor (P-T), McNaughton–Black (M-B), andShuttleworth–Wallace (S-W). Numbers in bold indicate the best model for each site.

Figure 1. Eddy covariance energy balance closure, shown by the relationship between LE +H versus Rn�G, for monthly data at (a) control, (b) fire, and(c) restoration sites between 2007 and 2010. LE = latent heat; H = sensible heat; Rn = net radiation; G = soil heat flux. Red dashed lines are linear

regressions (control, y = 1.02x� 6.29; fire, y = 1.10x� 10.53; restoration, y = 0.95x� 15.70). The solid line indicates a 1:1 line.

Table II. Comparisons of mean bias error (MBE) among five ETmodels from three (control, fire, and restoration) sites between

2007 and 2010 for monthly data.

Site MBE (mmmonth�1)

Models P-M P-M-d P-T M-B S-WControl �15.80 �14.85 7.30 �16.58 �6.36Fire 4.67 �22.15 �8.30 9.01 5.89Restoration 1.44 �13.25 36.15 0.62 13.77



ET. At the fire site (Figure 4), the P-M-d modelconsistently underpredicted over the range of eddy ET,and the P-M, M-B, and S-W models overpredictedintermediate values of eddy ET. The P-T modelunderpredicted high values of eddy ET at the fire site.At the restoration site (Figure 5), overprediction by the P-T and S-W models generally increased as eddy ETincreased, and the P-M and M-B models underpredictedthe highest values of eddy ET. The P-M-d modelunderpredicted intermediate and high values of eddy ET.

Ecohydrol. (2014)

Figure 2. Monthly actual evapotranspiration (AET) in linear amount of water (mmmonth�1) for the (a) control, (b) fire, and (c) restoration sites for years2007–2010 measured by eddy covariance (eddy) and predicted by the following meteorological models: Penman–Monteith (P-M), P-M with dynamicstomatal conductance (P-M-d), Priestley–Taylor (P-T), McNaughton–Black (M-B), and Shuttleworth–Wallace (S-W). MODIS data are available only for

2007–2009.

W. HA et al.

MODIS AET consistently underpredicted eddy ET at allsites (Figures 3–5).

Annual AET. Annual eddy ET was highest among sites atthe control site for all years except 2010, when ET was7mmyear�1 greater at the restoration site than at thecontrol site (Table III). Average eddy ET over all years was19 and 9% lower at the fire and restoration sites than at thecontrol site, respectively. Average normalized eddy ET(annual total ET/annual total precipitation) was 0.85, 0.68,and 0.80 at the control, fire, and restoration sites,respectively (Dore et al., 2012). Interestingly, averageannual precipitation was the highest at the fire site(568mmyear�1), although averaged eddy ET and normal-ized eddy ET were the lowest (Table III).At the control site, model-predicted annual AET was

closest to eddy ET for the S-W and P-T models. The S-W


model underestimated annual eddy ET by an average of14%. The P-T model overestimated eddy ET by an averageof 18% (Table III). Predicted average annual ET for allother models and the MODIS product at the control sitediffered from eddy ET by at least 30%.At the fire site, model-predicted annual AET was closest

to eddy ET for the P-M model, which overestimated by anaverage of 11%, and the S-W model, which overestimatedby an average of 15% (Table III). Predicted average annualET for all other models and the MODIS product at the firesite differed from eddy ET by at least 24%.At the restoration site, model-predicted annual AET was

closest to eddy ET for the M-B model, which overpredictedby an average of 1%, and the P-M model, whichoverpredicted by an average of 3% (Table III). Predictedaverage annual ET for all other models at the restorationsite differed from eddy ET by at least 35%.

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DISCUSSION

Eddy covariance ET measurements

Our study assumes that stand-level ET measured with theeddy covariance approach is an appropriate standard forevaluation of the accuracy of ET estimates from meteoro-logical models and remote-sensing-based approaches. Manyearlier investigations (e.g. Law et al., 2000; Fisher et al.,2005; Morales et al., 2005; Mu et al., 2011; Domec et al.,2012; Singh et al., 2014) make this assumption based on themore direct measurement and lower uncertainty of stand-scale water vapour flux with the eddy covariance techniquecompared with other approaches (Moncrieff et al., 2000;Baldocchi and Ryu, 2011). Our results on energy balanceclosure at the three study sites (Figure 1) indicate that theaccuracy of our eddy covariance measurements of ET istypical for eddy covariance studies (Wilson et al., 2001;Amiro, 2009; Barr et al., 2012). The poorer energy balance

closure at the restoration site (78%) than at the control andfire sites (>95%) suggests underestimation of ET measure-ment at the restoration site perhaps because of heteroge-neous land surface cover, non-stationary flow, and/orinstrument and measurement errors (Twine et al., 2000;Ruhoff et al., 2012). We note that site-specific environmen-tal and meteorological data used as model inputs hadoccasional gaps of 10 days or longer. Gap filling of thesemissing data (Dore et al., 2010) provided continuous (i.e.30-min interval) estimates of ET by eddy covariance andinput data for the meteorological models, but clearly, gapfilling is a source of error in estimates of ET from eddycovariance and the meteorological models in our study.Our estimates of annual ET with eddy covariance for

ponderosa pine forests of northern Arizona (510mmyear�1

control site, 464mmyear�1 restoration site) appear to bereasonable when compared with other studies. Gouldenet al. (2012) reported annual ET of 429mmyear�1 for a

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Figure 3. Scatter plots of monthly actual evapotranspiration (AET; mmmonth�1) at the control site between 2007 and 2010 measured by eddy covariance(eddy ET) and predicted by the (a) Penman–Monteith, (b) P-M with dynamic stomatal conductance, (c) Priestley–Taylor, (d) McNaughton–Black,and (e) Shuttleworth–Wallace models. MODIS AET (f) and eddy ET data were compared only between 2007 and 2009. The solid line indicates a 1:1 line.


Copyright © 2014 John Wiley & Sons, Ltd. Ecohydrol. (2014)

watershed that includes ponderosa pine forests in the SierraNevada Mountains based on eddy covariance. Estimates of700mmyear�1 for our control site and 400mmyear�1 forour restoration site were produced using the Landsat-basedSimplified Surface Energy Balance model (Singh et al.,2014). Finally, an extensive set of small (<730 ha)catchments was instrumented at the Beaver Creek Exper-imental Watershed that operated 80 km south of Flagstafffrom 1957 to 1981 (Baker, 1986). Fourteen of thecatchments were in ponderosa pine forest with vegetation,climate, and soils similar to our study sites. For unmanagedforests at the Beaver Creek Experimental Watershed, ETcalculated with the water balance approach (precipitation–run-off) averaged 526mmyear�1.

Model validation environment

The forest disturbance gradient of our study sites, the longstudy duration (4 years), and the climatic characteristics of

the sites present a formidable challenge to modelling stand-level ET. First, the disturbance gradient of our study sites isrepresentative of the three most common stand conditionsof ponderosa-pine-dominated forests that result from acentury of lack of disturbance and forest management(control site), intense burning of previous forest (fire site),and restoration thinning of previous forest to reduce fuelsand fire intensity (restoration site). Previous studies basedon the eddy covariance data showed that these disturbanceschanged ET at our study sites (Dore et al., 2012).Specifically, conversion of dense forest to sparse grasslandby intense burning at the fire site reduced annual ET byabout 20% (Dore et al., 2012). In contrast, impacts ofrestoration thinning on annual ET were temporallydynamic, with reductions of about 12% in the first 2 yearsafter thinning (2007 and 2008), followed by return to asimilar ET as the unthinned control site by the fourth post-thinning year (Dore et al., 2012). Most past investigations

R2= 0.62 R2= 0.64

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Figure 4. Scatter plots of monthly actual evapotranspiration (AET; mmmonth�1) at the fire site between 2007 and 2010 measured by eddy covariance(eddy ET) and predicted by the (a) Penman–Monteith, (b) P-M with dynamic stomatal conductance, (c) Priestley–Taylor, (d) McNaughton–Black, and(e) Shuttleworth–Wallace models. MODIS AET (f) and eddy ET data were compared only between 2007 and 2009. The solid line indicates a 1:1 line.

W. HA et al.


of model performance in predicting forest ET avoideddisturbed sites because of the challenges in accuratemodelling of disturbance effects. Second, many pastinvestigations of ET model performance in forests havebeen conducted at the subannual scale, often only duringthe summer when ET flux is highest (e.g. Fisher et al.,2005), rather than for every month over multiple years aswas performed in our study. Finally, the climate of ourstudy site region presents further challenges to modellingforest ET because of a unique combination of influencesfrom winter cold, spring drought, and late-summer heavyrains (Sheppard et al., 2002).

Performance of meteorological models

We used annual total ET (Table III) to discuss modelaccuracy because the most accurate meteorological modelsat the monthly scale (Table II) were generally most

accurate at the annual scale at all sites and because mostwater managers and hydrologists are most interested inannual ET. We found that three meteorological modelsestimated annual ET similar to measurements with eddycovariance at all sites. The S-W model had the most robustperformance over sites. Annual average ET predicted withthe S-W model was within 15% of eddy ET at both theheavily forested control site and the grass-dominated andshrub-dominated fire site. The S-W model was lessaccurate at the recently thinned restoration site but stillpredicted annual ET within 35% of eddy ET. Theunderlying structure of the S-W model, which is designedfor sparse crops and includes separate equations for soilevaporation and canopy transpiration, is well suited forponderosa pine forests that often contain canopy openingsand are exposed to short-duration rain in late summer thatlargely evaporates from surfaces. We suspect that ETpredictions by the S-W model could be improved at the

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Figure 5. Scatter plots of monthly actual evapotranspiration (AET; mmmonth�1) at the restoration site between 2007 and 2010 measured by eddycovariance (eddy ET) and predicted by the (a) Penman–Monteith, (b) P-M with dynamic stomatal conductance, (c) Priestley–Taylor, (d) McNaughton–Black,and (e) Shuttleworth–Wallace models. MODIS AET (f) and eddy ET data were compared only between 2007 and 2009. The solid line indicates a 1:1 line.



Table III. Comparison of yearly total evapotranspiration in linear amount of water (mmyear�1) between eddy covariancemeasurements, the MODIS AET product, and five meteorological models (P-M=Penman–Monteith, P-M-d = P-M with dynamicstomatal resistance, P-T =Priestley–Taylor, M-B=McNaughton–Black, and S-W=Shuttleworth–Wallace) for control, fire, and

restoration sites between 2007 and 2010.

Site Year Precpa Eddya MODISb P-M P-M-d P-T M-B S-W

Control 2007 653 528 288 (�45) 330 (�37) 396 (�25) 605 (15) 321 (�39) 446 (�16)2008 595 562 256 (�54) 339 (�40) 380 (�32) 628 (12) 330 (�41) 458 (�18)2009 296 438 211 (�52) 312 (�29) 221 (�50) 529 (21) 305 (�30) 422 (�4)2010 581 510 N/A 310 (�39) 341 (�33) 640 (25) 299 (�41) 419 (�18)AVG 531 510 252 (�51) 323 (�37) 334 (�34) 600 (18) 314 (�38) 436 (�14)

Fire 2007 680 462 165 (�64) 522 (13) 112 (�76) 307 (�34) 595 (29) 535 (16)2008 574 399 167 (�58) 458 (15) 146 (�63) 297 (�26) 510 (28) 468 (17)2009 408 379 146 (�61) 437 (15) 82 (�78) 290 (�23) 488 (29) 450 (19)2010 608 420 N/A 426 (1) 216 (�48) 327 (�22) 459 (9) 449 (7)AVG 568 415 159 (�62) 461 (11) 139 (�67) 305 (�26) 513 (24) 476 (15)

Restoration 2007 625 443 150 (�66) 484 (9) 284 (�36) 829 (87) 476 (7) 634 (43)2008 564 489 139 (�72) 504 (3) 297 (�39) 978 (100) 493 (1) 660 (35)2009 366 407 126 (�69) 479 (18) 292 (�28) 835 (105) 470 (16) 627 (54)2010 569 517 N/A 439 (�15) 328 (�37) 930 (80) 427 (�17) 576 (11)AVG 531 464 138 (�70) 476 (3) 300 (�35) 893 (92) 467 (1) 624 (35)

Numbers in parenthesis indicate percent difference (%) between eddy ET and modelled AET for each site based on average annual AET. Numbers inbold indicate the best model for each site.Precp represents annual total precipitation in mm year�1; AVG, average, n/a, not available.a Data from Dore et al. (2012) except for AVG.b Data provided by ORNL at http://daac.ornl.gov/

Numbers in parenthesis indicate percent difference (%) between eddy ET and modelled AET for each site based on average annual AET. Numbers inbold indicate the best model for each site.Precp represents annual total precipitation in mm year�1; AVG, average, n/a, not available.a Data from Dore et al. (2012) except for AVG.b Data provided by ORNL at http://daac.ornl.gov/

W. HA et al.

recently thinned restoration site by refining parametervalues that strongly control model predictions and arealtered by thinning. For example, ET prediction by the S-Wmodel is highly sensitive to the stomatal resistanceparameter based on our sensitivity analysis (data notshown), yet we used the same stomatal resistance value forthe control and restoration sites, which likely reducedprediction accuracy by the S-W model. Prediction accuracyof the S-W model in recently disturbed forests likely can beimproved by more investigation of disturbance impacts onstomatal resistance. The P-M model also had goodperformance, with average overestimation of annual eddyET by 3% at the restoration site and 11% at the fire site andunderestimation of 37% at the control site. The third well-performing model was M-B, which overestimated eddy ETby an average of 1% at the restoration site and 24% at thefire site and underestimated by 38% at the control site. Incontrast, the P-M-d and P-T models performed well atsome sites but were not consistently accurate over all sites,with differences from average annual eddy ET of greaterthan 25% at two of three sites.The three models that produced the most accurate ET

values (S-W, P-M, and M-B) use constant canopyresistance rather than the dynamically regulated resistanceused by the P-M-d model (Stewart, 1988), whichconsistently underpredicted eddy ET at all sites. The P-M-d model predicted eddy ET more accurately at thecontrol and restoration sites than at the fire site, as indicatedby higher R2 (Table I). This result may be because of


anisohydric stomatal behaviour and insensitivity of transpi-ration to environmental conditions of the early successionalgrasses and shrubs that dominate the fire site (De Lillis andFederici, 1993). The original dynamic stomatal resistancemodel (Stewart, 1988) was developed for Scots pine (Pinussylvestris) at a more mesic site (Scotland) than our sites.This model likely can be improved for semi-arid regions byrefining the parameters that constrain stomatal resistance.Similar to Fisher et al.’s (2005) study in a ponderosa

pine plantation in California, we found that the relativelysimple P-T model, which approximates aerodynamiccontrols on ET with a simple empirical term rather thanwith physically based functions, predicted ET well (within18% of eddy-measured ET) at the densely forested controlsite. However, the P-T model was not accurate at bothdisturbed sites. Specifically, the P-T model underestimatedannual ET at the fire site by an average of 26% andoverestimated at the restoration site by an average of 92%.The regression equation relating the empirical term α to soilmoisture was determined by Fisher et al. (2005) for anunmanaged ponderosa pine plantation. Our results suggestthat this equation is site specific and that new equations fordisturbance-affected andmanagement-affected sites are needed.

Accuracy of MODIS ET product

The MODIS AET product underestimated annual eddy ETby an average of 51% at the control site, 62% at the firesite, and 70% at the restoration site (Table III). The

Ecohydrol. (2014)


underestimation of ET at the fire site by the MODISproduct may be because of the post-burning shift indominant vegetation, which changes reflectance propertiesand affects LAI estimation (Rogan and Franklin, 2001).Vegetation clumping typical of broadleaf plants, such asshrubs that occur at the fire site, tends to saturate the opticalsignal and reduce the accuracy of LAI measured withremote sensing. The MODIS product underestimatedannual ET of our forested sites possibly because of anunderestimation of LAI by the MODIS LAI product.Empirically measured LAI (based on projected area) atthese sites averaged 2.3 at the control site and 1.2 at therestoration site as compared with MODIS LAI of 1.5 and0.9, respectively (data not shown). At oak/pine and mixedconifer forests in the Sierra Nevada where eddy ET washigher (between 600 and 800mmyear�1) than in our study,Goulden et al. (2012) found that the MODIS productunderestimated annual mean ET by more than 70%, whichwas caused by inaccurate meteorological or biogeophysicalinputs to the MOD16 algorithm. Mu et al. (2007) reportedthat inaccuracy in estimating ET by the MODIS productmay result from using different land cover datasets forestimation of LAI and ET. Further, the 1-km2 square pixelof the MODIS ET and LAI products does not exactlymatch the oval footprint of the eddy covariance measure-ments in our and most studies, which is an additionalsource of uncertainty in comparisons of ET. Singh et al.(2014) compared AET derived from both Land RemoteSensing Satellite System (Landsat) and MODIS with eddyET data in the south-western USA and reported thatLandsat AET data were more strongly correlated with eddyET in part because of the finer spatial resolution of Landsatcompared with MODIS. Given the large offset between theMODIS ET product and the tower ET found in this andother studies, future applications should run the MOD16algorithm using site-specific model parameters.

Challenges in modelling ET in recently thinned forests

The restoration site, where tree LAI was reduced 40% bythinning in 2006 in the year before the start of ourcomparisons (Dore et al., 2010), posed special challengesto model prediction of ET. We know from the eddycovariance data that the thinning treatment reduced ET byabout 10% in the first three post-thinning years(2007–2009) compared with the control site (Dore et al.,2012). Most meteorological models (P-M, P-T, M-B, andS-W), however, predicted higher ET at the restoration sitethan at the control site in those years (Table III), likelybecause these models did not adequately represent thestructural and physiological changes caused by treethinning. Although the P-M model predicted eddy ET wellat the restoration site, predictions of the M-B model werethe most accurate at this site. The M-B model is based on


the P-M model but ignores the aerodynamic resistanceabove the canopy, meaning that stomatal resistance is theonly surface resistance source in this model. Among themeteorological models in this study, the M-B model is theonly one that does not require energy flux data as inputparameters. We speculate that the M-B model performedbest at the disturbed restoration site for two reasons. First,as discussed previously, the heterogeneous land surfacecover and non-stationary air flow field at the restoration sitecould have caused energy flux measurement errors;therefore, using the M-B model eliminated this source oferror. Second, the M-B model does not formally consideraerodynamic resistance, which likely was altered by thethinning treatment.The MODIS product faces challenges associated with

canopy openings in recently thinned forests. Bare groundexposed in forest gaps created by forest thinning reflectsmore short-wave radiation, and this reflectance depends onmoisture and organic and mineral contents in the soil(Varjo, 1997; Heikkonen and Varjo, 2004). Inter-canopyshadowed surfaces that absorb more radiation can decreasereflectance (Gemmell and Varjo, 1999). Olsson (1995)could not reliably assess forest condition using Landsatimagery because a 25% canopy decrease caused by forestthinning decreased NIR reflectance. For the same reasons,Souza and Barreto (2000) could not reliably detect locationsof selective harvest sites in tropical forests with remotesensing. As thinning created larger gaps and more groundarea with exposed soil at the restoration site, groundreflectance of short-wave and NIR radiations may haveincreased and decreased, respectively. Thus, average pixelreflectance at the restoration site likely was influenced byground reflectance causing underprediction of LAI that thenresulted in underprediction of ET by the MODIS product.

CONCLUSIONS

Reliable estimates of ET are needed because ET is a majorcomponent of the hydrologic cycle in upland forestedregions that provide water to downstream agriculture andmetropolitan areas. Increasingly, forests such as theponderosa pine forests that we studied in Arizona aresubjected to disturbance from wildfire and forest restorationprojects, yet the accuracy of ET estimations in disturbedforests with currently available meteorological and remote-sensing-based models is unknown. Based on average annualET measured by eddy covariance as a standard, we foundthat the most accurate model for predicting ET was S-W atthe densely forested control site, P-M at the intensivelyburned fire site, and M-B at the recently thinned restorationsite. All these models (S-W, P-M, and M-B) producedpredictions of annual ET usually within 30% of measure-ments with eddy covariance at all sites.

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W. HA et al.

Our results show that the MODIS ET product currentlyhas a limited capability for estimating ET in bothundisturbed and disturbed ponderosa pine forests of thesouth-western USA. Therefore, we recommend evaluationof finer spatial-resolution-based remote sensing modelsthan MODIS, such as the Advanced Spaceborne ThermalEmission and Reflection Radiometer or Landsat, forimproving remote-sensing-based estimates of ET insouth-western ponderosa pine forests. Because measure-ment of AET with eddy covariance requires considerableresources, land and water resource managers would benefitfrom a simpler and more cost-effective remote-sensing-based technique for estimating AET.

ACKNOWLEDGEMENTS

The authors acknowledge the financial support from theWaterSMART Applied Science Grants for the DesertLandscape Conservation Cooperative of Bureau of Recla-mation (Grant number: R12AC80912). We thank anony-mous reviewers for their contribution to improve themanuscript.

REFERENCES

Agee JK, Skinner CN. 2005. Basic principles of forest fuel reductiontreatments. Forest Ecology and Management 211: 83–96.

Allen RG, Tasumi M, Morse A, Trezza R, Wright JL, Bastiaanssen W,Kramber W, Lorite I, Robinson CW. 2007. Satellite-based energybalance for mapping evapotranspiration with internalized calibration(METRIC)-Applications. Journal of Irrigation and DrainageEngineering-ASCE 133: 395–406.

Amiro B. 2009. Measuring boreal forest evapotranspiration using theenergy balance residual. Journal of Hydrology 366: 112–118.

Arain MA, Black TA, Barr AG, Griffis TJ, Morgenstern K, Nesic Z. 2003.Year-around observations of the energy and water vapour fluxes above aboreal black spruce forest. Hydrological Processes 17: 3581–3600.

Arora VK. 2002. Modeling vegetation as a dynamic component in soil-vegetation-atmosphere transfer schemes and hydrological models.Reviews of Geophysics 40: 1006. DOI: 10.1029/2001RG000103.

Aubinet M, Grelle A, Ibrom A, Rannik Ü, Moncrieff J, Foken T, KowalskiAS, Martin PH, Berbigier P, Bernhofer C, Clement R, Elbers J, GranierA, Grünwald T, Morgenstern K, Pilegaard K, Rebmann C, Snijders W,Valentini R, Vesala T. 2000. Estimates of the annual net carbon andwater exchange of forests: The EUROFLUX methodology. Advances inEcological Research 30: 113–175.

Baker, MB. 1986. Effects of ponderosa pine treatments on water yield inArizona. Water Resources Research 22: 67–73. DOI: 10.1029/WR022i001p00067.

Bala G, Caldeira K, Wickett M, Phillips TJ, Lobell DB, Delire C, Mirin A.2007. Combined climate and carbon-cycle effects of large-scaledeforestation. Proceedings of the National Academy of Sciences ofthe United States of America 104: 6550–6555.

Baldocchi D ,Ryu Y. 2011. A synthesis of forest evaporation fluxes – fromdays to years – as measured with eddy covariance. Forest Hydrologyand Biogeochemistry, Levia DF, Carlyle-Moses D, Tanaka T (eds).Springer: Heidelberg, Germany; 101–115.

Baldocchi DD, Hicks BB, Meyers TP. 1988. Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeo-rological methods. Ecology 69: 1331–1340.

Baldocchi D, Valentini R, Running S, Oechel W, Dahlman R. 1996.Strategies for measuring and modelling carbon dioxide and water


vapour fluxes over terrestrial ecosystems. Global Change Biology 2:159–168.

Barr AG, van der Kamp G, Black TA, McCaughey JH, Nesic Z. 2012.Energy balance closure at the BERMS flux towers in relation to thewater balance of the White Gull watershed 1999-2009. Agricultural andForest Meteorology 153: 3–13.

Bosch JM Hewlett JD. 1982. A review of catchment experiments todetermine the effect of vegetation changes on water yield andevapotranspiration. Journal of Hydrology 55: 3–23.

Bright BC, Hicke JA, Meddens AJH. 2013. Effects of bark beetle-causedtree mortality on biogeochemical and biogeophysical MODIS products.Journal of Geophysical Research-Biogeosciences 118: 974–982. DOI:10.1002/jgrg.20078.

Brown AE, Zhang L, McMahon TA, Western AW, Vertessy RA. 2005. Areview of paired catchment studies for determining changes in wateryield resulting from alterations in vegetation. Journal of Hydrology310: 28–61.

Cienciala E, Running SW, Lindroth A, Grelle A, Ryan MG. 1998.Analysis of carbon and water fluxes from the NOPEX boreal forest:comparison of measurements with FOREST-BGC simulations. Journalof Hydrology 212-213: 62–78.

Cleugh HA, Leuning R, Mu Q, Running SW. 2007. Regional evaporationestimates from flux tower and MODIS satellite data. Remote Sensing ofEnvironment 106: 285–304.

Costa MH, Biajoli MC, Sanches L, Malhado ACM, Hutyra LR, da RochaHR, Aguiar RG, de Araújo AC. 2010. Atmospheric versus vegetationcontrols of Amazonian tropical rain forest evapotranspiration: Are thewet and seasonally dry rain forests any different? Journal ofGeophysical Research 115: G04021. DOI: 10.1029/2009JG001179.

Covington WW, Fulé PZ, Moore MM, Hart SC, Kolb TE, Mast JN,Sackett SS, Wagner MR. 1997. Restoring ecosystem health inponderosa pine forests of the Southwest. Journal of Forestry 95: 23–29.

De Lillis M, Federici FM. 1993. Gas exchange and resource-use patternsalong a Mediterranean successional gradient. Journal of VegetationScience 4: 269–272.

Dingman SL. 2002. Physical hydrology, 2nd edn. Waveland Press, Inc.:Long Grove, IL.

Domec J-C, Sun G, Noormets A, Gavazzi MJ, Treasure EA, Cohen E,Swenson JJ, McNulty SG, King JS. 2012. A comparison of threemethods to estimate evapotranspiration in two contrasting loblolly pineplantations: Age-related changes in water use and drought sensitivity ofevapotranspiration components. Forest Science 58: 497–512.

Dore S, Kolb TE, Montes-Helu M, Sullivan BW, Winslow WD, Hart SC,Kaye JP, Koch GW, Hungate BA. 2008. Long-term impact of a stand-replacing fire on ecosystem CO2 exchange of a ponderosa pine forest.Global Change Biology 14: 1–20.

Dore S, Kolb TE, Montes-Helu M, Eckert SE, Sullivan BW, Hungate BA,Kaye JP, Hart SC, Koch GW, Finkral A. 2010. Carbon and water fluxesfrom ponderosa pine forests disturbed by wildfire and thinning.Ecological Applications 20: 663–683.

Dore S, Montes-Helu M, Hart SC, Hungate BA, Koch GW, Moon JB,Finkral AJ, Kolb TE. 2012. Recovery of ponderosa pine ecosystemcarbon and water fluxes from thinning and stand-replacing fire. GlobalChange Biology 18: 3171–3185.

FAO (Food and Agricultural Organization of the United Nations). 2012.State of the world’s forests 2012. UN FAO: Rome, Italy.

Federer CA, V r smarty CJ, Fekete B. 1996. Intercomparison of methodsfor calculating potential evaporation in regional and global waterbalance models. Water Resources Research 32: 2315–2321.

Finney MA, McHugh CW, Grenfell IC. 2005. Stand- and landscape-leveleffects of prescribed burning on two Arizona wildfires. CanadianJournal of Forest Research 35: 1714–1722.

Fisher JB, DeBiase TA, Qi Y, Xu M, Goldstein AH. 2005. Evapotrans-piration models compared on a Sierra Nevada forest ecosystem.Environmental Modeling & Software 20: 783–796.

Flint AL, Childs SW. 1991. Use of the Priestley-Taylor evaporationequation for soil water limited conditions in a small forest clearcut.Agricultural and Forest Meteorology 56: 247–260.

Foken T. 2008. The energy balance closure problem: An overview.Ecological Applications 18: 1351–1367.

Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, StrahlerAH,Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F,

Ecohydrol. (2014)


Schaaf C. 2002. Global land cover mapping from MODIS: algorithmsand early results. Remote Sensing of Environment 83: 287–302.

Gemmell F, Varjo J. 1999. Utility of reflectance model inversion versustwo spectral indices for estimating biophysical characteristics in aboreal forest test site. Remote Sensing of Environment 68: 95–111.

Gordon WS, Famiglietti JS. 2004. Response of the water balance toclimatic change in the United States over the 20th and 21st centuries:Results from the VEMAP Phase 2 model intercomparisons. GlobalBiogeochemical Cycles 18: GB1030. DOI: 10.1029/2003GB002098.

Goulden ML, Anderson RG, Bales RC, Kelly AE, Meadows M, andWinston GC. 2012. Evapotranspiration along an elevation gradient inCalifornia’s Sierra Nevada. Journal of Geophysical Research 117:G03028. DOI: 10.1029/2012JG002027.

Heikkonen J, Varjo J. 2004. Forest change detection applying landsatthematic mapper difference features: A comparison of differentclassifiers in boreal forest conditions. Forest Science 50: 579–588.

Huxman TE, Wilcox BP, Breshears DD, Scott RL, Snyder KA, Small EE,Hultine K, Pockman WT, Jackson RB. 2005. Ecohydrologicalimplications of woody plant encroachment. Ecology 86: 308–319.

Jackson RB, Anderson LJ, Pockman WT. 2000. Measuring wateravailability and uptake in ecosystem studies. Methods in EcosystemScience, Sala OE, Jackson RB, Mooney HA, Howarth RW (eds).Springer: New York, NY, 199–214.

Jung M, Reichstein M, Ciais P, Seneviratne SI, Sheffield J, Goulden ML,Bonan G, Cescatti A, Chen J, de Jeu R, Dolman AJ, Eugster W, GertenD, Gianelle D, Gobron N, Heinke J, Kimball J, Law BE, Montagnani L,Mu Q, Mueller B, Oleson K, Papale D, Richardson AD, Roupsard O,Running S, Tomelleri E, Viovy N, Weber U, Williams C, Wood E,Zaehle S, Zhang K. 2010. Recent decline in the global landevapotranspiration trend due to limited moisture supply. Nature 467:951–954.

Krishnaswamy J, Bonell M, Venkatesh B, Purandara BK, Lele S, KiranMC, Reddy V, Badiger S, Rakesh KN. 2012. The rain-runoff responseof tropical humid forest ecosystems to use and reforestation in theWestern Ghats of India. Journal of Hydrology 472-473: 216–237.

Lathuillière MJ, Johnson MS, Donner SD. 2012. Water use by terrestrialecosystems: temporal variability in rainforest and agricultural contri-butions to evapotranspiration in Mato Grosso, Brazil. EnvironmentalResearch Letters 7: 024024.

Law BE, Waring RH, Anthoni PM, Aber JD. 2000. Measurements ofgross and net ecosystem productivity and water vapor exchange of aPinus ponderosa ecosystem, and an evaluation of two generalizedmodels. Global Change Biology 6: 155–168.

Lucht W, Schaaf CB, Strahler AH. 2000. An Algorithm for the retrieval ofalbedo from space using semiempirical BRDF models. IEEE Transac-tions on Geoscience and Remote Sensing 38: 977–998.

McIver JD, Stephens SL, Agee JK, Barbour J, Boerner REJ, EdminsterCB, Erickson KL, Farris KL, Fettig CJ, Fiedler CE, Haase S, Hart SC,Keeley JE, Knapp EE, Lehmkuhl JF, Moghaddas JJ, Otrosina W,Outcalt KW, Schwilk DW, Skinner CN, Waldrop TA, WeatherspoonCP, Yaussy DA, Youngblood A, Zack S. 2013. Ecological effects ofalternative fuel-reduction treatments: highlights of the National Fire andFire Surrogate study (FFS). International Journal of Wildland Fire 22:63–82.

McNaughton KG, Black TA. 1973. A study of evapotranspiration from aDouglas fir forest using the energy balance approach. Water ResourcesResearch 9: 1579–1590.

Moncrieff JB, Jarvis PG, Valentini R. 2000. Canopy fluxes. Methods inEcosystem Science, Sala OE, Jackson RB, Mooney HA, Howarth RW(eds). Springer: New York, NY; 161–180.

Monteith JL. 1965. Evaporation and the environment. Symposium of theSociety of Exploratory Biology 19: 205–234.

Montes-Helu MC, Kolb T, Dore S, Sullivan B, Hart SC, Koch G, HungateBA. 2009. Persistent effects of fire-induced vegetation change onenergy partitioning and evapotranspiration in ponderosa pine forests.Agricultural and Forest Meteorology 149: 491–500.

Morales P, Sykes MT, Prentice IC, Smith P, Smith B, Bugmann H, ZierlB, Friedlingstein P, Viovy N, Sabaté S, Sánchez A, Pla E, Gracia CA,Sitch S, Arneth A, Ogee J. 2005. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes inmajor European forest biomes. Global Change Biology 11:2211–2233.


Moreaux V, Lamaud É, Bosc A, Bonnefond J-M, Medlyn BE, Loustau D.2011. Paired comparison of water, energy and carbon exchanges overtwo young maritime pine stands (Pinus pinaster Ait.): effects of thinningand weeding in the early stage of tree growth. Tree Physiology 31:903–921.

Mu Q, Heinsch FA, Zhao M, Running SW. 2007. Development of aglobal evapotranspiration algorithm based on MODIS and globalmeteorology data. Remote Sensing of Environment 111: 519–536.

Mu Q, Zhao M, Running SW. 2011. Improvements to a MODIS globalterrestrial evapotranspiration algorithm. Remote Sensing of Environment115: 1781–1800.

Myneni RB, Hoffman S, Knyazikhin Y, Privette JL, Glassy J, Tian Y,Wang Y, Song X, Zhang Y, Smith GR, Lotsch A, Friedl M, MorisetteJT, Votava P, Nemani RR, Running SW. 2002. Global products ofvegetation leaf area and fraction absorbed PAR from year one ofMODIS data. Remote Sensing of Environment 83: 214–231.

Nagler PL, Cleverly J, Glenn E, Lampkin D, Huete A, Wan Z. 2005.Predicting riparian evapotranspiration from MODIS vegetationindices and meteorological data. Remote Sensing of Environment94: 17–30.

Nobre CA, Sellers PJ, Shukla J. 1991. Amazonian deforestation andregional climate change. Journal of Climate 4: 957–988.

Olsson H. 1995. Reflectance calibration of thematic mapper data for forestchange detection. International Journal of Remote Sensing 16: 81–96.

Penman HL. 1948. Natural evaporation from open water, bare soil andgrass. Royal Society of London Series A-Mathematical and PhysicalSciences 193: 120–146.

Priestley CHB, Taylor RJ. 1972. On the assessment of surface heat fluxand evaporation using large scale parameters. Monthly Weather Review100: 81–92.

R Core Team. 2013. R: A language and environment for statisticalcomputing. R Foundation for Statistical Computing: Vienna, Austria.URL: http://www.R-project.org/.

Reba ML, Link RE, Marks D, Pomeroy J. 2009. An assessment ofcorrections for eddy covariance measured turbulent fluxes over snow inmountain environments. Water Resources Research 45: W00D38. DOI:10.1029/2008WR007045.

Rogan J Franklin J. 2001. Mapping wildfire burn severity in southernCalifornia forests and shrublands using enhanced thematic mapperimagery. Geocarto International 16: 91–106.

Ruhoff AL, Paz AR, Collischonn W, Aragao LEOC, Rocha HR, MalhiYS. 2012. A MODIS-based energy balance to estimate evapotranspi-ration for clear-sky days in Brazillian tropical savannas. Remote Sensing4: 703–725.

Saxton KE, Rawls WJ, Romberger JS, Papendick RI. 1986. Estimatinggeneralized soil-water characteristics from texture. Soil Science Societyof America Journal 50: 1031–1036.

Sheppard PR, Comrie AC, Packin GD, Angersbach K, Hughes MK.2002. The climate of the US Southwest. Climatic Research 21:219–238.

Shuttleworth WJ, Wallace JS. 1985. Evaporation from sparse crops-anenergy combination theory. Quarterly Journal of the Royal Meteoro-logical Society 111: 839–855.

Singh RK, Senay GB, Velpuri NM, Bohms S, Scott RL, Verdin JP. 2014.Actual evapotranspiration (Water Use) Assessment of the Coloradoriver basin at the Landsat resolution using the operational simplifiedsurface energy balance model. Remote Sensing 6: 233–256.

Souza CM, Barreto P. 2000. An alternative approach for detecting andmonitoring selectively logged forests in the Amazon. InternationalJournal of Remote Sensing 21: 173–179.

Stednick JD. 1996. Monitoring the effects of timber harvest on annualwater yield. Journal of Hydrology 176: 79–95.

Stewart JB. 1988. Modeling surface conductance of pine forest.Agricultural and Forest Meteorology 43: 19–35.

Sun G, McNulty SG, Amatya DM, Skaggs RW, Swift Jr. LW, Shepard JP,Riekerk H. 2002. A comparison of the watershed hydrology of coastalforested wetlands and the mountainous uplands in the Southern US.Journal of Hydrology 263: 92–104.

Trabucco A, Zomer RJ, Bossio DA, van Straaten O, Verchot LV. 2008.Climate change mitigation through afforestation/reforestation: A globalanalysis of hydrologic impacts with four case studies. AgricultureEcosystems and Environment 126: 81–97.

Ecohydrol. (2014)

http://www.R-project.org

W. HA et al.

Troendle CA. 1983. The potential for water yield augmentation fromforest management in the Rocky-Mountain Mountain Region. WaterResources Bulletin 19: 359–373.

Twine TE, Kustas WP, Norman JM, Cook DR, Houser PR, Meyers TP,Prueger JH, Starks PJ, Wesely ML. 2000. Correcting eddy-covarianceflux underestimates over a grassland. Agricultural and ForestMeteorology 103: 279–300.

Ueyama M, Ichii K, Hirata R, Takagi K, Asanuma J, Machimura T, NakaiY, Ohta T, Saigusa N, Takahashi Y, Hirano T. 2010. Simulating carbonand water cycles of larch forests in East Asia by the BIOME-BGCmodel with AsiaFlux data. Biogeosciences 7: 959–977.

USDA Forest Service. 2012. 4FRI Draft Environmental Impact StatementChapter 1. pp. 75 Retrieved 14 February 2013. URL: http://www.fs.usda.gov/detail/4fri/planning/?cid=stelprdb5408763. Accessed Decem-ber 2013.

Varjo J. 1997. Change detection and controlling forest information usingmultitemporal Landsat TM imagery. Acta Forestalia Fennica 258: 1–64.

Vickers D, Thomas CK, Pettijohn C, Martin JG, Law B. 2012. Five yearsof carbon fluxes and inherent water-use efficiency at two semi-arid pineforests with different disturbance histories. Tellus Series B-Chemical


and Physical Meteorology 64: 17159. DOI: 10.3402/tellusb.v64i0.17159.

Wan Z. 2008. New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products. Remote Sensing of Environ-ment 112: 59–74.

Wilson KB, Hanson PJ, Mulholland PJ, Baldocchi DD, and WullschlegerSD. 2001. A comparison of methods for determining forest evapo-transpiration and its components: sap-flow, soil water budget, eddycovariance and catchment water balance. Agricultural and ForestMeteorology 106: 153–168.

Yuan WP, Liu SG, Yu GR, Bonnefond JM, Chen JQ, Davis K, Desai AR,Goldstein AH, Gianelle D, Rossi F, Suyker AE, Verma SB. 2010.Global estimates of evapotranspiration and gross primary productionbased on MODIS and global meteorology data. Remote Sensing ofEnvironment 114: 1416–1431.

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