Evapotranspiration Comparisons Between Eddy Covariance Measurements and Meteorological and Remote-Sensing-Based Models in Disturbed Ponderosa Pine Forests

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

Evapotranspiration comparisons between eddy covariance measurements 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. Koch5,6 1 School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ, USA 2 School of Forestry, Northern Arizona University, Flagstaff, AZ, USA 3 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, USA 5 Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA 6 Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA

ABSTRACT Evapotranspiration (ET) comprises a major portion of the water budget in forests, yet few studies have measured or estimated ET in semi-arid, high-elevation ponderosa pine forests of the south-western USA or have investigated the capacity of models to predict ET in disturbed forests. We measured actual ET with the eddy covariance (eddy) method over 4 years in three ponderosa pine forests near Flagstaff, Arizona, that differ in disturbance history (undisturbed control, wildfire burned, and restoration thinning) and compared these measurements (415–510 mm year 1 on average) with actual ET estimated from five meteorological models [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 most accurate estimates of annual eddy ET (average percent differences ranged between 11 and 14%), but their accuracy varied across sites. The P-M-d consistently underpredicted ET at all sites. The more simplistic P-T model performed well at the control site (18% overprediction) but strongly overpredicted annual eddy ET at the restoration sites (92%) and underpredicted at the fire site (26%). The MODIS ET underpredicted annual eddy ET at all sites by at least 51% primarily because of underestimation of leaf area index. Overall, we conclude that with accurate parameterization, micrometeorological models can predict ET within 30% in forests of the south-western USA and that remote sensing-based ET estimates need to be improved through use of higher resolution products. Copyright © 2014 John Wiley & Sons, Ltd.

KEY WORDS evapotranspiration; latent heat; eddy covariance; forest ecosystems; ponderosa pine; Moderate Resolution Imaging Spectroradiometer (MODIS) Received 19 May 2014; Revised 16 September 2014; Accepted 20 November 2014

INTRODUCTION spruce (Picea mariana) forest (Arain et al., 2003), and more than 85% in a ponderosa pine (Pinus ponderosa) Forests occur over approximately 31% of the land surface forest in Arizona (Dore et al., 2012). Consequently, the of the Earth (FAO, 2012) and are important regulators of magnitude and seasonality of forest ET are important terrestrial water balance (Arora, 2002; Ueyama et al., regulators of water resources available to humans and 2010). Evapotranspiration (ET) is the largest ﬂux of annual ecosystems. precipitation from most forests except in cool and wet The question of how forest management affects ET is climate zones. For example, ET has been reported to use long standing (e.g. Bosch and Hewlett, 1982) but is still not approximately 70% of annual precipitation in a loblolly adequately answered for many forest types. This question pine (Pinus taeda) plantation in the south-eastern USA is increasingly relevant to current restoration projects in (Sun et al., 2002), more than 85% in a Canadian black semi-arid forests that use tree thinning to reduce the risk of wildﬁre (e.g. Covington et al., 1997; Agee and Skinner, 2005; McIver et al., 2013). Studies in other forest types *Correspondence to: Abraham E. Springer, School of Earth Sciences and report that reforestation generally increases ET (Bosch and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA. Hewlett, 1982; Trabucco et al., 2008), whereas deforesta- E-mail: [email protected] tion decreases ET (Nobre et al., 1991; Bala et al., 2007;

Costa et al., 2010; Krishnaswamy et al., 2012; Lathuillière on ET (Law et al., 2000; Gordon and Famiglietti, 2004; et al., 2012; Bright et al., 2013). Impacts on ET of more Morales et al., 2005). Estimation of ET from remotely subtle changes in forest cover produced by tree thinning sensed spectral data, such as the Moderate Resolution have been investigated at only a few sites (Moreaux et al., Imaging Spectroradiometer (MODIS) satellite (Yuan et al., 2011; Dore et al., 2012). 2010; Mu et al., 2011; Goulden et al., 2012), is another More information on impacts to ET of forest restoration approach that has potential application in investigations of thinning and of intense wildfire, which often occurs in forest water balance, but more site-specific comparisons to dense semi-arid forests in the absence of thinning (e.g. eddy ET are needed to assess accuracy. Although the Finney et al., 2005), is needed for upland forest landscapes. spatial resolution of MODIS ET products (1 km2) often These forests are critical for supplying water to downslope does not match the flux tower footprint coverage (Goulden ecosystems and human settlements (e.g. Troendle, 1983) et al., 2012), MODIS provides spatially and temporally and are the targets of major new management initiatives. continuous data of land surface and atmosphere interac- Landscape-scale forest restoration treatments (e.g. Coving- tions (Wan, 2008). ton et al., 1997) are planned for 1.5 million ha of semi-arid, The objective of this study was to evaluate the accuracy dense ponderosa pine forests in upland watersheds of of ET predictions from five meteorological models and the Arizona (USDA Forest Service, 2012). Because the impact MODIS ET product over 4 years at three sites in the of vegetation manipulation on ET is highly variable in ponderosa pine forest region of northern Arizona that have semi-arid regions (Bosch and Hewlett, 1982; Stednick, different types of recent disturbance. We compare ET 1996; Brown et al., 2005; Huxman et al., 2005), better predictions from these models with ET measured directly understanding of the coupled land management and at each site by the eddy covariance approach (Dore et al., hydrological response processes is needed. 2012). The sites consist of (1) a dense, unmanaged forest, A major challenge to understanding impacts of forest (2) a similar forest treated with restoration thinning, and management actions on ET arises from the difficulty in (3) a former forest that was converted to grassland by estimating ET accurately over large areas. Forest ET can be intense wildfire. The meteorological models that we used measured by numerous methods, such as site water have shown potential for accurate ET prediction (e.g. balance, lysimeters, sap flow, Bowen ratio, and plant Fisher et al., 2005; Morales et al., 2005), yet they have not chambers (Jackson et al., 2000; Moncrieff et al., 2000), but been adequately evaluated for the ponderosa pine region of the eddy covariance (eddy) approach is considered to be the south-western USA where landscape-scale forest accurate (Wilson et al., 2001; Baldocchi and Ryu, 2011; restoration treatments are being implemented and intense Barr et al., 2012). Despite advances in ET measurement by wildfires are common. The meteorological models that we the eddy approach, accurate estimation of annual ET in investigated are Penman–Monteith (P-M), P-M with forests using this approach remains challenging over broad dynamic stomatal resistance (P-M-d), Priestley–Taylor landscapes because of the difficulty of establishing and (P-T), McNaughton–Black (M-B), and Shuttleworth– maintaining eddy systems in remote locations and the Wallace (S-W). influence of complex topography that can prevent adequate measurement of energy balance closure (Baldocchi et al., 1988; Foken, 2008; Reba et al., 2009). To overcome these MATERIAL AND METHODS challenges, models that predict ET from site and climate data have been used, and ET predictions from these models Study sites have been compared with eddy ET for coniferous forests in We used three study sites located within the ponderosa- several studies (e.g. Federer et al., 1996; Cienciala et al., pine-dominated forest region of northern Arizona that 1998; Sun et al., 2002; Fisher et al., 2005). differ in disturbance and for which ET was measured with Comparisons of modelled and measured ET have the eddy covariance method over several years by Dore generally used only short time series, have neither et al. (2008, 2010, 2012). The three sites (control, fire, and included multiple years nor included recently disturbed restoration) were located less than 35 km apart near forests (e.g. Fisher et al., 2005) nor focused on forests Flagstaff, AZ, USA. Site characteristics were described in with complex climatic influences on the seasonality of ET, detail by Dore et al. (2010). In brief, the control site was a such as is the case in the south-western USA, where the ponderosa pine stand located in the Northern Arizona bimodal precipitation regime of winter snow and late- University Centennial Forest (35°5′20.5″N, 111°45′43.33″ summer rainfall is punctuated by a distinct dry season, and W, elevation 2180 m a.s.l.) that was excluded from inter-annual variability in precipitation is pronounced harvesting, thinning, and fire over the last century. The (Sheppard et al., 2002). Modelling ET is known to be control site had an average leaf area index (LAI; projected challenging in seasonally water-stressed forests because area) of 2.3 m2 m2, basal area of 30 m2 ha1, and tree most models use energy availability as the primary control density of 853 trees ha1 (Dore et al., 2010). The fire site

Copyright © 2014 John Wiley & Sons, Ltd. Ecohydrol. (2014) EVAPOTRANSPIRATION COMPARISONS IN PONDEROSA PINE FORESTS was part of a 10 500-ha area in the Coconino National plates (Hukseflux HFP01SC, Delft, the Netherlands). The Forest (35°26′43.43″N, 111°46′18.64″W, elevation eddy instruments were mounted on towers 23 m above 2270 m a.s.l.) burned by an intense fire in 1996. The fire ground surface at the control and restoration sites and 2.5 m killed all trees in the stand, which prior to the fire had tree above ground surface at the fire site (Dore et al., 2008). Soil density and basal area similar to the control site (Dore temperature and water content were measured at depths of 2, et al., 2010). No ponderosa pine was regenerated in this 10, 20, and 50 cm, and soil heat flux was measured at a depth site. During the measurement period, more than a decade of 8 cm. All data were collected and stored by CR-1000 and after the fire, vegetation on the fire site consisted of grasses, CR-10X dataloggers (Campbell Scientific, USA) and forbs, and shrubs, with average ground cover of 40% averaged over 30-min intervals. Methods for processing vegetation, 50% bare soil, and 10% snags and logs and gap filling of the eddy data are described in Dore et al. (Montes-Helu et al., 2009). The restoration site was a (2008, 2010). Energy balance closure is a comparison of ponderosa pine stand located in the Centennial Forest (35° available energy (Rn G) and turbulent heat fluxes (LE + H) 8′33.48″N, 111°43′38.37″W, 2155 m a.s.l.) about 6 km and is necessary for long-term flux measurements from the control site that was treated with fuel-reduction (Baldocchi et al., 1996). We calculated energy balance thinning in 2006, several months before the measurements closure of the eddy data for all 4 years of our study reported in this study. To reduce tree density and fire risk (2007–2010) for daily gap-filled data following Aubinet and to restore pre-settlement forest structure, approximately et al. (2000). 90 ha of the restoration site was thinned in September 2006. The treatment focused on removal of small-diameter trees Meteorological models and reduced tree density 70%, basal area 35%, tree LAI We used five previously developed meteorological models 40%, and stand LAI (including understory) 30%. Canopy that have been shown to be useful for predicting ET of height was approximately 18 m at the control and vegetated sites (Federer et al., 1996; Fisher et al., 2005). fi restoration sites and approximately 0.3 m at the re site All of these models predict potential ET (PET), which we (Dore et al., 2010). Climatic and edaphic conditions at the scaled to actual ET (AET) using a soil moisture limitation three sites were similar because of their close proximity. factor (Fisher et al., 2005), except for the P-M-d model, The region is characterized by cold winters and dry springs which directly predicts AET. The models, which differ in with precipitation as snow in the winter and rain in late input data and complexity, are briefly described in the summer. succeeding texts. Model input parameters introduced in this section are listed in Table A in the Supporting Eddy covariance measurements Information. Fluxes of water vapour, sensible and latent heats (H and LE), P-M model. The P-M model (Monteith, 1965) improved fl net solar radiation (Rn), and soil heat ux (G) were measured the earlier Penman model (Penman, 1948) by including continuously with the eddy covariance (eddy) method effects of canopy stomatal resistance (rs) and above-canopy between 2007 and 2010 at each site using identical aerodynamic resistance (ra) on PET. The P-M equation is instruments and sensors. The eddy instruments and methods ρ ρðÞ are described in detail by Dore et al. (2008, 2010, 2012). ΔðÞþ C es e Rn G r Instrumentation included a sonic anemometer (CSAT3, λΕ ¼ a (1) Δ þ ϒ 1 þ rs Campbell Scientific, Logan, UT, USA) for measurement of ra wind velocity; a closed-path CO2/H2O infrared gas analyser (LI-COR Li-7000, Lincoln, NE, USA) for measurement of where λE is PET as evaporative LE flux (W m2), Δ is the water vapour density; incoming and outgoing short-wave slope of saturated vapour pressure and air temperature (hPa 1 2 and long-wave radiometers (CNR1, Kipp and Zonen, Delft, °C ), Rn is the net solar radiation flux (W m ), G is the the Netherlands) for measurement of global and net soil heat flux (W m2), ρ is the air density of 1.23 kg m3, radiation; a photosynthetic photon flux density (PPFD) Cρ is the specific heat of air, which is equal to 1 1 sensor (BF3 Delta-T devices, Cambridge, UK); a reflected approximately 1006 J kg °C , es is the saturation vapour PPFD sensor (LI-COR Li-190); precipitation sensors pressure (kPa), e is the vapour pressure (kPa), ϒ is the 1 (5.4103.20.041, Thies Clima, Göttingen, Germany; TR- psychrometric constant (hPa °C ), rs is the bulk stomatal 525-R3 gauge, Texas Instruments, USA); a weather resistance of the canopy (s m1; 416.67 for control and transmitter (WXT510, Vaisala, Helsinki, Finland) for restoration sites and 210.0 for the fire site), and ra is the measurement of atmospheric pressure, air humidity, and aerodynamic resistance above the canopy (s m1; 6.0 for air temperature; volumetric water content sensors (model control and restoration sites and 30.0 for the fire site). Data 615, Campbell Scientific, USA); soil temperature probes used are attached in the Supporting Information. The term (model 107, Campbell Scientific, USA); and soil heat flux es e is known as the vapour pressure deficit (kPa).

P-M model with dynamic stomatal resistance. This model content (SWC) and often less than 1.0 are recommended enhanced the P-M model by including environmental (Flint and Childs, 1991; Fisher et al., 2005). We calculated controls on canopy resistance, which is the inverse of daily α at each site with the Flint and Childs (1991) ð Þ canopy conductance gs . These controls for our study were approach using a regression equation developed by Fisher taken from Stewart’s (1988) investigation of a pine forest et al. (2005) for a ponderosa pine forest in California: near Thetford Forest, Norfolk, UK, that quantified α = 0.84·SWC + 0.72, where SWC equals soil volumetric relationships between gs and four environmental variables, water content in the rooting zone. Soil volumetric water incident short-wave solar radiation (Kin), specific humidity content was measured every 30 min at each flux tower deficit (Δρv), air temperature (Ta), and soil moisture deficit location (Dore et al., 2012) and averaged over depths of 2, (Δθ), as 10, 20, and 50 cm to calculate α.Weinvestigated correspondence between ET predicted with the P-T model g ¼ f LAI g f ðÞKin f ρ ðÞΔρ f ðÞTa f θðÞΔθ s s leaf K v T and eddy ET for three temporal averaging approaches for (2) the calculation of α: daily averages, monthly averages, and – – – where g is the canopy conductance (m s 1), f is the shelter seasonal averages (January March, April June, July s s – factor that represents the degree of sheltered portions of the September, and October December) for each year and site combination. We found that all approaches produced leaves from the sun and wind, LAI is the LAI, gleaf is the maximum value of leaf conductance (m s1; 5.3 × 103 for similar results based on root-mean-square error (RMSE), fi 2 control and restoration sites and 8.0 × 10 3 for the fire site), coef cient of determination (R ), and mean bias error α and f , fρ, f , and fθ are functions of environmental variables (MBE); thus, we used average seasonal values over all k T fi explained previously (Stewart, 1988; Dingman, 2002). We 4 years in our nal predictions of AET with the P-T model. α – used site-specific f , LAI, and input environmental data to Seasonal average ranged between 0.91 (October s – calculate g in Equation 2 for each time period. The f December 2009) and 1.08 (January March 2008) at the s s – values were 0.65 for the control site, 0.85 for the control site, 0.90 (October December 2009) and 1.06 – fi – restoration site, and 1.0 for the fire site. We calculated (January March 2008) at the re site, and 1.08 (October – atmospheric resistance (r ) for Equation 1 following December 2009) and 1.24 (January March 2008 and 2009) a fi α fi Dingman (2002): at the restoration site. Speci c values used in the nal P- T model predictions are shown in Table B in the 1 ¼ ¼ u Supporting Information. While others (Flint and Childs, ga hi (3) r 2 1991; Fisher et al., 2005) have suggested that adjustment of a 6:25 ln zm zd z0 α based on SWC is adequate for converting predictions 1 from the P-T model from PET to AET, we further adjusted where ga is the atmospheric conductance (m s ), u is the predictions of ET from the P-T model by a scaling factor wind speed (m s 1), z is the height of wind speed m that expressed SWC as a fraction of SWC at field capacity measurement at the top of the eddy tower, which is (section on Conversion of PET to AET) to obtain final approximately 2.5 m above the vegetation canopy (m), z is d values of AET. This second adjustment produced AET the zero-plane displacement (m), and z is the roughness 0 estimates from the P-T model that were closer to AET height (m). measured by eddy covariance, likely because the modification of α based on SWC alone does not adequately P-T model. The Priestley and Taylor model (1972) consider SWC at field capacity. predicts PET based primarily on variation in net radiation without formal use of wind velocity, stomatal or atmo- M-B model. McNaughton and Black (1973) derived the spheric resistance, and vapour pressure data. The P-T ET following equation that assumes that stomatal resistance of model is the canopy, rs, is generally much larger than atmospheric R G resistance, ra. The equation starts from the P-M equation λE ¼ αΔ n (4) Δ þ ϒ and assumes that ra is close to zero: where α is the unitless ‘P-T coefficient’, which adjusts PET ρ Cp ðÞes e λE ¼ (5) for differences in water availability, advection, and ϒ rs aerodynamic resistance among surface and vegetation types (Flint and Childs, 1991). The correct α value for a These assumptions are made because well-documented given vegetation type is not known with certainty. The wind profile data often are unavailable or inaccurate, which constant α value of 1.26 suggested by Priestley and Taylor could introduce error to the calculation of λE. The model (1972) for saturated surfaces is known to be inaccurate for implies that vapour pressure deficit controls diurnal drier sites where dynamic values based on soil water changes in ET over a small range of rs (McNaughton and

Black, 1973). The model is theoretically valid only for MODIS-based remote sensing model forest environments (Federer et al., 1996). Forest canopy reflectance in the red and near-infrared (NIR) regions can be related to indicators of forest S-W model. Shuttleworth and Wallace (1985) improved structure, providing a functional basis for LAI estimation. previous models, which were limited to a closed, stable, Because of global coverage of imagery by the Terra and and uniform canopy, by accounting for soil evaporation in Aqua MODIS at 1-km resolution and 8-day frequency, a model for sparse crops that separates soil evaporation estimates of green vegetation LAI and the fraction of λ λ ( Es) from transpiration from the plant canopy ( Ec). They photosynthetically active radiation absorbed by vegetation used a Monteith-type equation with horizontal averages of (FPAR) have been produced on a continuous basis since fl fi energy uxes and resistance terms de ned for heteroge- the year 2000. This information is used to estimate ET λ neous land cover. Total evaporation ( E) is modelled as regionally and globally (Nagler et al., 2005; Allen et al., 2007; Cleugh et al., 2007; Mu et al., 2007; Jung et al., λ ¼ λ þ λ ¼ þ E Es Ec CsPMs CcPMc (6) 2010; Yuan et al., 2010; Mu et al., 2011). The MOD16 remote-sensing-based ET algorithm pre- C C fi PM where s and c are partitioning coef cients, and s dicts ET globally at 86% accuracy when compared with and PM are P-M-like terms to describe evaporation from c eddy measurements of ET over many sites in the the soil substrate and a closed canopy, respectively. The AmeriFlux network (Mu et al., 2011). Building on PM term includes surface resistance at the soil ( rs ), s s previous algorithms, it uses a physically based P-M aerodynamic resistance leaving the soil surface before approach driven by MODIS-derived vegetation data. ET fl s incorporation into the mean canopy ow (ra), and transfer fl is calculated as the sum of daytime and night-time resistance between the mean canopy ow and the screen components using vapour pressure deficit and minimum height (r ). The PM term accounts for stomatal a c temperature to control stomatal resistance. Stomatal r resistance of the vegetation ( s) and bulk boundary layer resistance is scaled up to the canopy level using LAI to resistance of the vegetative elements in the canopy (rc ) a calculate canopy resistance for plant transpiration. The and r . The partitioning coefficients and resistance terms a algorithm also models soil heat flux and separates are parameterized for a sparse canopy based on LAI, a fi fi evaporation from a wet canopy and transpiration from a vegetation-type-speci c extinction coef cient, wind dry canopy. Actual soil evaporation is calculated from a speed, and parameters describing the aerodynamic potential evaporation value. The required data inputs to the characteristics and diffusivity of the canopy and soil. MOD16 algorithm are (1) Collection 4 MODIS global land Full equations for these terms are given by Shuttleworth cover maps (MOD12Q1; Friedl et al., 2002), (2) Collection and Wallace (1985). 5 MODIS FPAR/LAI layers (MOD15A2; Myneni et al., 2002), and (3) Collection 5 MODIS albedo products Conversion of PET to AET . PET data obtained from the (MCD43B2 and MCD43B3; Lucht et al., 2000). Daily meteorological models were adjusted to AET prior to meteorological data inputs are from National Aeronautics comparison with ET data estimated by eddy covariance and Space Administration’s (NASA’s) Modern-Era Retro- except for the P-M-d model, which estimates AET directly spective Analysis for Research and Applications Global without the need for further adjustment. Based on the Modeling and Assimilation Office (Goddard Earth Observ- modification of the Saxton et al. (1986) method described ing System Model, version 5). The global ET raster in Fisher et al. (2005), AET was calculated as PET multiplied by a scaling parameter equal to the average datasets obtained from MOD16 are now available from the SWC over four depths (2, 10, 20, and 50 cm) divided by University of Montana (ftp://ftp.ntsg.umt.edu/pub/MODIS/ field capacity. Although ponderosa pine can have deep NTSG_Products/MOD16/MOD16A3.105_MERRAG roots in deep soils (approximately 150 cm; Vickers et al., MAO/Geotiff/) and NASA through the Oak Ridge National 2012), we averaged SWC over depths and assumed this Laboratory (ORNL) Distributed Active Archive Center averaged value to represent SWC in the rooting depth web data portal (http://daac.ornl.gov/MODIS/modis.html). because our results were not sensitive to specific SWC The available MODIS products have pixel resolution of 2 2 depth (data not shown) and because measurements of SWC 1km and cover 109 million km of vegetated land areas at depths greater than 50 cm were not possible at our sites globally at 8-day, monthly, and annual intervals since because of shallow bedrock. When SWC was greater than 2000. We obtained 1-km2 8-day MODIS AET products field capacity, AET was equal to PET. Site-specific values between 2007 and 2009 for each study site from the ORNL of field capacity, equal to volumetric SWC of 0.391, 0.395, website to calculate monthly and annual ET (mm month 1 and 0.452 m3 m3, at the control, fire, and restoration sites, and mm year1). Annual ET was obtained by summing all respectively, were calculated based on soil texture (Dore 8-day AET per year, while monthly ET was obtained by et al., 2010) following Saxton et al. (1986). adding 8-day values that better corresponded to the month

Copyright © 2014 John Wiley & Sons, Ltd. Ecohydrol. (2014) W. HA et al. of each year. Eight-day LAI data were also downloaded AET from model simulation versus ET from eddy from the same website and were averaged per year for covariance comparisons with measured LAI at three sites. The control, Monthly AET. For monthly data pooled over all years fire, and restoration flux tower locations were overlaid to (Table I), the model with the highest R2 in comparisons MODIS product layers using ArcGIS to select the location with eddy ET was the P-T model at the control and of pixels and to extract ET and LAI pixel values that restoration sites and the P-M-d model at the fire site. RMSE represented the land cover conditions of tower measure- was lowest at the control site for the P-T model and at fire ments at each site. Data were extracted for only one pixel and restoration sites for the P-M model. At the control site, per site because the eddy tower footprint approximately the P-T and P-M-d models had similar strong predictive matches the 1-km2 pixel size of the MODIS data. For performance (R2 = 0.84, 0.81; RMSE = 13.2, 18.5; respec- control and fire sites, the south-east pixel from the tower tively). At the fire site, all models had similar predictive location was selected to represent eddy tower footprint. For performance, with R2 ranging between 0.54 and 0.64 and the restoration site, the north-east pixel was selected. The RMSE ranging between 14.7 and 26.2 among models. At selected pixels had vegetation most similar to vegetation of the restoration site, the P-T and P-M-d models had the best the measurement footprint of each eddy instrument tower predictive performance based on R2 (0.81 and 0.78, based on visual inspection of recent Google Earth images respectively), whereas the P-M and M-B models had the (Figure A in the Supporting Information). lowest RMSE (13.6 and 13.9, respectively). Comparisons of MBE for monthly data (Table II) ET comparisons showed that the P-M, M-B, and S-W models consistently overpredicted monthly eddy ET at fire and restoration sites We ran each meteorological model using both daily (24 h) (0.62 to 13.77 mm month 1), whereas the P-M-d model and average monthly environmental data for each site. The consistently underpredicted at all sites (22.15 to environmental data for each site were obtained from the 13.25 mm month 1). The P-T model overpredicted at eddy tower instruments and associated soil measurements the control and restoration sites and underpredicted at the (Dore et al., 2012). We evaluated fit between model- fire site. The two models producing the closest predictions predicted AET and eddy ET at daily and monthly scales of monthly eddy ET based on the lowest MBE were P-T using RMSE, R2, and MBE. Patterns of results for and S-W at the control site, P-M and S-W at the fire site, predictions based on monthly data were similar to results and P-M and M-B at the restoration site (Table II). based on daily data, and RMSE, R2, and MBE showed All models simulated similar seasonal variation in better fit of model-predicted values to eddy ET for monthly AET, which was characterized by the highest predictions based on monthly data. Thus, we do not AET in the summer and the lowest AET in the winter present predictions based on daily data for brevity. In (Figure 2). At the control site, the P-M, M-B, and S-W addition, we converted monthly data of eddy ET and AET models consistently underpredicted monthly eddy ET predicted by each of the five meteorological models from during all periods, the P-M-d model underpredicted eddy LE to linear amount of water (mm month1) and then ET in late winter and spring, and the P-T model summed over months to obtain annual values for each year overpredicted eddy ET in late winter and spring (Figure 2). and site, which were compared with annual ET derived At the fire site, consistent overprediction by the P-M, M-B, from the MODIS product. Data analysis was performed in R (R Core Team, 2013). and S-W models in the spring and underprediction by the P-M-d model also occurred. Predictions by the P-T model at the fire site fluctuated between small overpredictions and underpredictions. At the restoration site, the P-T model RESULTS overpredicted eddy ET the most, followed by S-W, P-M, Energy balance closure and M-B models. The P-M-d model consistently The fraction of energy balance closure for the eddy underpredicted eddy ET at the restoration site (Table I). measurements, defined as (H + LE)/(Rn G) (Wilson MODIS AET also consistently underpredicted eddy ET at et al., 2001; Amiro, 2009; Barr et al., 2012), averaged all sites. over all years was 0.95, 1.12, and 0.78 for the control, fire, Scatter plots of monthly AET data show how model and restoration sites, respectively. Monthly values of LE prediction accuracy varied over the range of eddy ET. At + H were strongly correlated (R2 > 0.9) with Rn G for all the control site (Figure 3), underprediction of eddy ET by years at each site (Figure 1). The slope of the linear the P-M, M-B, and S-W models increased nonlinearly as regression between LE + H and Rn G was 1.02, 1.10, eddy ET increased, the P-M-d model consistently and0.95atthecontrol,fire, and restoration sites, underpredicted over the range of eddy ET, and the P-T respectively. model tended to overpredict intermediate values of eddy

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 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 (mm month 1) R2

Models P-M P-M-d P-T M-B S-W P-M P-M-d P-T M-B S-W Control 22.25 18.50 13.19 23.18 15.49 0.67 0.81 0.84 0.64 0.67 Fire 14.73 26.18 17.45 17.26 17.77 0.62 0.64 0.54 0.60 0.59 Restoration 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), and Shuttleworth–Wallace (S-W). Numbers in bold indicate the best model for each site.

ET. At the fire site (Figure 4), the P-M-d model Table II. Comparisons of mean bias error (MBE) among five ET consistently underpredicted over the range of eddy ET, models from three (control, fire, and restoration) sites between and the P-M, M-B, and S-W models overpredicted 2007 and 2010 for monthly data. intermediate values of eddy ET. The P-T model fi Site MBE (mm month 1) underpredicted high values of eddy ET at the re site. At the restoration site (Figure 5), overprediction by the P- Models P-M P-M-d P-T M-B S-W T and S-W models generally increased as eddy ET Control 15.80 14.85 7.30 16.58 6.36 increased, and the P-M and M-B models underpredicted Fire 4.67 22.15 8.30 9.01 5.89 Restoration 1.44 13.25 36.15 0.62 13.77 the highest values of eddy ET. The P-M-d model underpredicted intermediate and high values of eddy ET.

Figure 2. Monthly actual evapotranspiration (AET) in linear amount of water (mm month 1) for the (a) control, (b) ﬁre, and (c) restoration sites for years 2007–2010 measured by eddy covariance (eddy) and predicted by the following meteorological models: Penman–Monteith (P-M), P-M with dynamic stomatal 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.

MODIS AET consistently underpredicted eddy ET at all model underestimated annual eddy ET by an average of sites (Figures 3–5). 14%. The P-T model overestimated eddy ET by an average of 18% (Table III). Predicted average annual ET for all Annual AET. Annual eddy ET was highest among sites at other models and the MODIS product at the control site the control site for all years except 2010, when ET was differed from eddy ET by at least 30%. 7 mm year1 greater at the restoration site than at the At the fire site, model-predicted annual AET was closest control site (Table III). Average eddy ET over all years was to eddy ET for the P-M model, which overestimated by an 19 and 9% lower at the fire and restoration sites than at the average of 11%, and the S-W model, which overestimated control site, respectively. Average normalized eddy ET by an average of 15% (Table III). Predicted average annual (annual total ET/annual total precipitation) was 0.85, 0.68, ET for all other models and the MODIS product at the fire and 0.80 at the control, fire, and restoration sites, site differed from eddy ET by at least 24%. respectively (Dore et al., 2012). Interestingly, average At the restoration site, model-predicted annual AET was annual precipitation was the highest at the fire site closest to eddy ET for the M-B model, which overpredicted (568 mm year1), although averaged eddy ET and normal- by an average of 1%, and the P-M model, which ized eddy ET were the lowest (Table III). overpredicted by an average of 3% (Table III). Predicted At the control site, model-predicted annual AET was average annual ET for all other models at the restoration closest to eddy ET for the S-W and P-T models. The S-W site differed from eddy ET by at least 35%.

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Predicted AET R2= 0.67 Predicted AET R2= 0.81 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET

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Predicted AET R2= 0.84 Predicted AET R2= 0.64 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET

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Predicted AET R2= 0.67 Predicted AET R2= 0.50 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET Figure 3. Scatter plots of monthly actual evapotranspiration (AET; mm month 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.

DISCUSSION closure at the restoration site (78%) than at the control and fire sites (>95%) suggests underestimation of ET measure- Eddy covariance ET measurements ment at the restoration site perhaps because of heteroge- Our study assumes that stand-level ET measured with the neous land surface cover, non-stationary flow, and/or eddy covariance approach is an appropriate standard for instrument and measurement errors (Twine et al., 2000; evaluation of the accuracy of ET estimates from meteoro- Ruhoff et al., 2012). We note that site-specific environmen- logical models and remote-sensing-based approaches. Many tal and meteorological data used as model inputs had earlier investigations (e.g. Law et al., 2000; Fisher et al., occasional gaps of 10 days or longer. Gap filling of these 2005; Morales et al., 2005; Mu et al., 2011; Domec et al., missing data (Dore et al., 2010) provided continuous (i.e. 2012; Singh et al., 2014) make this assumption based on the 30-min interval) estimates of ET by eddy covariance and more direct measurement and lower uncertainty of stand- input data for the meteorological models, but clearly, gap scale water vapour flux with the eddy covariance technique filling is a source of error in estimates of ET from eddy compared with other approaches (Moncrieff et al., 2000; covariance and the meteorological models in our study. Baldocchi and Ryu, 2011). Our results on energy balance Our estimates of annual ET with eddy covariance for closure at the three study sites (Figure 1) indicate that the ponderosa pine forests of northern Arizona (510 mm year1 accuracy of our eddy covariance measurements of ET is control site, 464 mm year1 restoration site) appear to be typical for eddy covariance studies (Wilson et al., 2001; reasonable when compared with other studies. Goulden Amiro, 2009; Barr et al., 2012). The poorer energy balance et al. (2012) reported annual ET of 429 mm year1 for a

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Predicted AET R2= 0.62 Predicted AET R2= 0.64 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET

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Predicted AET R2= 0.54 Predicted AET R2= 0.60 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET

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Predicted AET R2= 0.59 Predicted AET R2= 0.39 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET Figure 4. Scatter plots of monthly actual evapotranspiration (AET; mm month 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. watershed that includes ponderosa pine forests in the Sierra the sites present a formidable challenge to modelling stand- Nevada Mountains based on eddy covariance. Estimates of level ET. First, the disturbance gradient of our study sites is 700 mm year 1 for our control site and 400 mm year 1 for representative of the three most common stand conditions our restoration site were produced using the Landsat-based of ponderosa-pine-dominated forests that result from a Simplified Surface Energy Balance model (Singh et al., century of lack of disturbance and forest management 2014). Finally, an extensive set of small (<730 ha) (control site), intense burning of previous forest (fire site), catchments was instrumented at the Beaver Creek Exper- and restoration thinning of previous forest to reduce fuels imental Watershed that operated 80 km south of Flagstaff and fire intensity (restoration site). Previous studies based from 1957 to 1981 (Baker, 1986). Fourteen of the on the eddy covariance data showed that these disturbances catchments were in ponderosa pine forest with vegetation, changed ET at our study sites (Dore et al., 2012). climate, and soils similar to our study sites. For unmanaged Specifically, conversion of dense forest to sparse grassland forests at the Beaver Creek Experimental Watershed, ET by intense burning at the fire site reduced annual ET by – calculated with the water balance approach (precipitation about 20% (Dore et al., 2012). In contrast, impacts of 1 run-off) averaged 526 mm year . restoration thinning on annual ET were temporally dynamic, with reductions of about 12% in the first 2 years Model validation environment after thinning (2007 and 2008), followed by return to a The forest disturbance gradient of our study sites, the long similar ET as the unthinned control site by the fourth post- study duration (4 years), and the climatic characteristics of thinning year (Dore et al., 2012). Most past investigations

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50 50 Predicted AET Predicted AET R2= 0.81 R2= 0.63 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET

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Predicted AET R2= 0.65 Predicted AET R2= 0.62 0 0 0 50 100 150 0 50 100 150 Eddy ET Eddy ET Figure 5. Scatter plots of monthly actual evapotranspiration (AET; mm month 1) at the restoration 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. of model performance in predicting forest ET avoided accurate at the annual scale at all sites and because most disturbed sites because of the challenges in accurate water managers and hydrologists are most interested in modelling of disturbance effects. Second, many past annual ET. We found that three meteorological models investigations of ET model performance in forests have estimated annual ET similar to measurements with eddy been conducted at the subannual scale, often only during covariance at all sites. The S-W model had the most robust the summer when ET flux is highest (e.g. Fisher et al., performance over sites. Annual average ET predicted with 2005), rather than for every month over multiple years as the S-W model was within 15% of eddy ET at both the was performed in our study. Finally, the climate of our heavily forested control site and the grass-dominated and study site region presents further challenges to modelling shrub-dominated fire site. The S-W model was less forest ET because of a unique combination of influences accurate at the recently thinned restoration site but still from winter cold, spring drought, and late-summer heavy predicted annual ET within 35% of eddy ET. The rains (Sheppard et al., 2002). underlying structure of the S-W model, which is designed for sparse crops and includes separate equations for soil evaporation and canopy transpiration, is well suited for Performance of meteorological models ponderosa pine forests that often contain canopy openings We used annual total ET (Table III) to discuss model and are exposed to short-duration rain in late summer that accuracy because the most accurate meteorological models largely evaporates from surfaces. We suspect that ET at the monthly scale (Table II) were generally most predictions by the S-W model could be improved at the

Table III. Comparison of yearly total evapotranspiration in linear amount of water (mm year 1) between eddy covariance measurements, the MODIS AET product, and ﬁve meteorological models (P-M = Penman–Monteith, P-M-d = P-M with dynamic stomatal 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 in boldNumbers indicate in parenthesis the best model indicate for percenteach site. difference (%) between eddy ET and modelled AET for each site based on average annual AET. Numbers in bold indicate the best model for each site. 1 Precp represents annual total precipitation in mm year1; AVG, average, n/a, not available. Precpa Data represents from Dore annual et al. (2012) total precipitation except for AVG. in mm year ; AVG, average, n/a, not available. ab DataData from provided Dore byet ORNL al. (2012) at http://daac.ornl.gov except for AVG. / b Data provided by ORNL at http://daac.ornl.gov/ recently thinned restoration site by refining parameter anisohydric stomatal behaviour and insensitivity of transpi- values that strongly control model predictions and are ration to environmental conditions of the early successional altered by thinning. For example, ET prediction by the S-W grasses and shrubs that dominate the fire site (De Lillis and model is highly sensitive to the stomatal resistance Federici, 1993). The original dynamic stomatal resistance parameter based on our sensitivity analysis (data not model (Stewart, 1988) was developed for Scots pine (Pinus shown), yet we used the same stomatal resistance value for sylvestris) at a more mesic site (Scotland) than our sites. the control and restoration sites, which likely reduced This model likely can be improved for semi-arid regions by prediction accuracy by the S-W model. Prediction accuracy refining the parameters that constrain stomatal resistance. of the S-W model in recently disturbed forests likely can be Similar to Fisher et al.’s (2005) study in a ponderosa improved by more investigation of disturbance impacts on pine plantation in California, we found that the relatively stomatal resistance. The P-M model also had good simple P-T model, which approximates aerodynamic performance, with average overestimation of annual eddy controls on ET with a simple empirical term rather than ET by 3% at the restoration site and 11% at the fire site and with physically based functions, predicted ET well (within underestimation of 37% at the control site. The third well- 18% of eddy-measured ET) at the densely forested control performing model was M-B, which overestimated eddy ET site. However, the P-T model was not accurate at both by an average of 1% at the restoration site and 24% at the disturbed sites. Specifically, the P-T model underestimated fire site and underestimated by 38% at the control site. In annual ET at the fire site by an average of 26% and contrast, the P-M-d and P-T models performed well at overestimated at the restoration site by an average of 92%. some sites but were not consistently accurate over all sites, The regression equation relating the empirical term α to soil with differences from average annual eddy ET of greater moisture was determined by Fisher et al. (2005) for an than 25% at two of three sites. unmanaged ponderosa pine plantation. Our results suggest The three models that produced the most accurate ET that this equation is site specific and that new equations for values (S-W, P-M, and M-B) use constant canopy disturbance-affected and management-affected sites are needed. resistance rather than the dynamically regulated resistance used by the P-M-d model (Stewart, 1988), which consistently underpredicted eddy ET at all sites. The P- Accuracy of MODIS ET product M-d model predicted eddy ET more accurately at the The MODIS AET product underestimated annual eddy ET control and restoration sites than at the fire site, as indicated by an average of 51% at the control site, 62% at the fire by higher R2 (Table I). This result may be because of site, and 70% at the restoration site (Table III). The

Copyright © 2014 John Wiley & Sons, Ltd. Ecohydrol. (2014) EVAPOTRANSPIRATION COMPARISONS IN PONDEROSA PINE FORESTS underestimation of ET at the fire site by the MODIS the P-M model but ignores the aerodynamic resistance product may be because of the post-burning shift in above the canopy, meaning that stomatal resistance is the dominant vegetation, which changes reflectance properties only surface resistance source in this model. Among the and affects LAI estimation (Rogan and Franklin, 2001). meteorological models in this study, the M-B model is the Vegetation clumping typical of broadleaf plants, such as only one that does not require energy flux data as input shrubs that occur at the fire site, tends to saturate the optical parameters. We speculate that the M-B model performed signal and reduce the accuracy of LAI measured with best at the disturbed restoration site for two reasons. First, remote sensing. The MODIS product underestimated as discussed previously, the heterogeneous land surface annual ET of our forested sites possibly because of an cover and non-stationary air flow field at the restoration site underestimation of LAI by the MODIS LAI product. could have caused energy flux measurement errors; Empirically measured LAI (based on projected area) at therefore, using the M-B model eliminated this source of these sites averaged 2.3 at the control site and 1.2 at the error. Second, the M-B model does not formally consider restoration site as compared with MODIS LAI of 1.5 and aerodynamic resistance, which likely was altered by the 0.9, respectively (data not shown). At oak/pine and mixed thinning treatment. conifer forests in the Sierra Nevada where eddy ET was The MODIS product faces challenges associated with higher (between 600 and 800 mm year1) than in our study, canopy openings in recently thinned forests. Bare ground Goulden et al. (2012) found that the MODIS product exposed in forest gaps created by forest thinning reflects underestimated annual mean ET by more than 70%, which more short-wave radiation, and this reflectance depends on was caused by inaccurate meteorological or biogeophysical moisture and organic and mineral contents in the soil inputs to the MOD16 algorithm. Mu et al. (2007) reported (Varjo, 1997; Heikkonen and Varjo, 2004). Inter-canopy that inaccuracy in estimating ET by the MODIS product shadowed surfaces that absorb more radiation can decrease may result from using different land cover datasets for reflectance (Gemmell and Varjo, 1999). Olsson (1995) estimation of LAI and ET. Further, the 1-km2 square pixel could not reliably assess forest condition using Landsat of the MODIS ET and LAI products does not exactly imagery because a 25% canopy decrease caused by forest match the oval footprint of the eddy covariance measure- thinning decreased NIR reflectance. For the same reasons, ments in our and most studies, which is an additional Souza and Barreto (2000) could not reliably detect locations source of uncertainty in comparisons of ET. Singh et al. of selective harvest sites in tropical forests with remote (2014) compared AET derived from both Land Remote sensing. As thinning created larger gaps and more ground Sensing Satellite System (Landsat) and MODIS with eddy area with exposed soil at the restoration site, ground ET data in the south-western USA and reported that reflectance of short-wave and NIR radiations may have Landsat AET data were more strongly correlated with eddy increased and decreased, respectively. Thus, average pixel ET in part because of the finer spatial resolution of Landsat reflectance at the restoration site likely was influenced by compared with MODIS. Given the large offset between the ground reflectance causing underprediction of LAI that then MODIS ET product and the tower ET found in this and resulted in underprediction of ET by the MODIS product. other studies, future applications should run the MOD16 algorithm using site-specific model parameters. CONCLUSIONS Challenges in modelling ET in recently thinned forests Reliable estimates of ET are needed because ET is a major The restoration site, where tree LAI was reduced 40% by component of the hydrologic cycle in upland forested thinning in 2006 in the year before the start of our regions that provide water to downstream agriculture and comparisons (Dore et al., 2010), posed special challenges metropolitan areas. Increasingly, forests such as the to model prediction of ET. We know from the eddy ponderosa pine forests that we studied in Arizona are covariance data that the thinning treatment reduced ET by subjected to disturbance from wildfire and forest restoration about 10% in the first three post-thinning years projects, yet the accuracy of ET estimations in disturbed (2007–2009) compared with the control site (Dore et al., forests with currently available meteorological and remote- 2012). Most meteorological models (P-M, P-T, M-B, and sensing-based models is unknown. Based on average annual S-W), however, predicted higher ET at the restoration site ET measured by eddy covariance as a standard, we found than at the control site in those years (Table III), likely that the most accurate model for predicting ET was S-W at because these models did not adequately represent the the densely forested control site, P-M at the intensively structural and physiological changes caused by tree burned fire site, and M-B at the recently thinned restoration thinning. Although the P-M model predicted eddy ET well site. All these models (S-W, P-M, and M-B) produced at the restoration site, predictions of the M-B model were predictions of annual ET usually within 30% of measure- the most accurate at this site. The M-B model is based on ments with eddy covariance at all sites.

Our results show that the MODIS ET product currently vapour fluxes over terrestrial ecosystems. Global Change Biology 2: 159–168. has a limited capability for estimating ET in both Barr AG, van der Kamp G, Black TA, McCaughey JH, Nesic Z. 2012. undisturbed and disturbed ponderosa pine forests of the Energy balance closure at the BERMS flux towers in relation to the water balance of the White Gull watershed 1999-2009. Agricultural and south-western USA. Therefore, we recommend evaluation – fi Forest Meteorology 153:3 13. of ner spatial-resolution-based remote sensing models Bosch JM Hewlett JD. 1982. A review of catchment experiments to than MODIS, such as the Advanced Spaceborne Thermal determine the effect of vegetation changes on water yield and Emission and Reflection Radiometer or Landsat, for evapotranspiration. Journal of Hydrology 55:3–23. Bright BC, Hicke JA, Meddens AJH. 2013. Effects of bark beetle-caused improving remote-sensing-based estimates of ET in tree mortality on biogeochemical and biogeophysical MODIS products. south-western ponderosa pine forests. Because measure- Journal of Geophysical Research-Biogeosciences 118: 974–982. DOI: ment of AET with eddy covariance requires considerable 10.1002/jgrg.20078. fi Brown AE, Zhang L, McMahon TA, Western AW, Vertessy RA. 2005. A resources, land and water resource managers would bene t review of paired catchment studies for determining changes in water from a simpler and more cost-effective remote-sensing- yield resulting from alterations in vegetation. Journal of Hydrology based technique for estimating AET. 310:28–61. 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. Journal – ACKNOWLEDGEMENTS of Hydrology 212-213:62 78. Cleugh HA, Leuning R, Mu Q, Running SW. 2007. 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