evaluation of nldas-2 evapotranspiration against tower

HYDROLOGICAL PROCESSESHydrol. Process. 29, 1757–1771 (2015)Published online 20 August 2014 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/hyp.10299

Evaluation of NLDAS-2 evapotranspiration against tower fluxsite observations

Youlong Xia,1,2* Michael T. Hobbins,3 Qiaozhen Mu4 and Michael B. Ek11 Environmental Modeling Center (EMC), National Centers for Environmental Prediction (NCEP), College Park, MD, USA

2 IMSG at NOAA/NCEP/EMC, College Park, MD, USA3 UCAR at NOAA/National Integrated Drought Information System (NIDIS), Earth Systems Research Laboratory (ESRL), Boulder, CO, USA

4 Department of Ecosystem and Conservation Services, University of Montana, Missoula, MT, USA

*CUnE-m

Co

Abstract:

The North American Land Data Assimilation System project phase 2 (NLDAS-2) has run four land surface models for a 30-year(1979–2008) retrospective period. Land surface evapotranspiration (ET) is one of the most important model outputs fromNLDAS-2 for investigating land–atmosphere interaction or to monitor agricultural drought. Here, we evaluate hourly ET using insitu observations over the Southern Great Plains (Atmospheric Radiation Measurement/Cloud and Radiation Testbed network)for 1 January 1997–30 September 1999 and daily ET u-sing in situ observations at the AmeriFlux network over the conterminousUSA for an 8-year period (2000–2007). The NLDAS-2 models compare well against observations, with the National Centers forEnvironmental Prediction’s Noah land surface model performing best, followed, in order, by the Variable Infiltration Capacity,Sacramento Soil Moisture Accounting, and Mosaic models. Daily evaluation across the AmeriFlux network shows that for allmodels, performance depends on season and vegetation type; they do better in spring and fall than in winter or summer and betterfor deciduous broadleaf forest and grasslands than for croplands or evergreen needleleaf forest. Copyright © 2014 John Wiley &Sons, Ltd.

KEY WORDS NLDAS-2; ET comparison; tower ET observations; AmeriFlux; ARM/CART

Received 19 June 2013; Accepted 12 July 2014

INTRODUCTION

Land evapotranspiration (ET, a sum of bare soilevaporation, evaporation from canopy interception, andtranspiration from vegetation) is a central process in theclimate system and an essential component of the water,energy, and carbon cycles, governing land surface–atmosphere interactions of water vapour and heat energy(Betts et al., 1996) and affects regional and global carboncycles. ET data that are spatially distributed, of longduration at high temporal resolution, and with completespace–time coverage are crucial for the development andevaluation of scientific and operational models, includingthose used in meteorology, climatology, hydrology,ecology, water resources management (Glenn et al.,2007), and the monitoring and prediction of drought(Anderson et al., 2011). In general, ET may be estimatedfrom in situ measurements at eddy flux towers, at Bowenratio stations, by scintillometry, by satellites, and byvarious models and algorithms. Examples of the former

orrespondence to: Youlong Xia, IMSG at NOAA/NCEP/EMC, 5830iversity Research Court, College Park, MD 20740, USA.ail: [email protected]

pyright © 2014 John Wiley & Sons, Ltd.

include the Boreal Ecosystem–Atmosphere Study (Sellerset al., 1997), AmeriFlux network of FLUXNET (Baldocchiet al., 2001), and the Atmospheric Radiation Measurement/Cloud and Radiation Testbed (ARM/CART; Robock et al.,2003). The limitation of such in situ measurements is thatdata are often for short periods, have many missing records,and are only available at sparsely located tower sites.As of September 2013, there were only 560 tower sitesworldwide, a number that is primarily constrained bycost. Therefore, the ET data from towers cannot meetthe space–time requirements outlined earlier, whichgenerally leaves modelled ET or remotely sensed ET asan alternative. The currently available modelled ETproducts include estimates from the complementaryrelationship (e.g. Hobbins et al., 2001), remote-sensingmethods (e.g. Mu et al., 2011), machine-learningalgorithms (e.g. Jung et al., 2009, 2010), offline landsurface models (Mitchell et al., 2004), and coupledland surface models (termed ‘reanalyses’, e.g. Kalnayet al., 1996; Mesinger et al., 2006).The North American Land Data Assimilation System

phase 2 (NLDAS-2) has generated long-term (more than30 years) ET data sets at high resolution (0.125° spatialresolution) to support the US operational drought

1758 Y. XIA ET AL.

monitoring and prediction activities (Xia et al., 2012a;http://www.emc.ncep.noaa.gov/mmb/nldas/drought/Evap/). Mean monthly ET data have been evaluated usingin situ ET measurements across conterminous USA(CONUS; Mo et al., 2011). The results showed thatthe ensemble mean ET from the Mosaic, Noah, andVariable Infiltration Capacity (VIC) models is closer tothe observations than ET from the individual models.However, NLDAS-2 ET products at short timescales(i.e. hourly or daily) have not yet been evaluated. Theobjective of this study is to evaluate the robustness ofNLDAS-2 hourly and daily ET products using site-based flux tower observations. The paper is organizedas follows: the Section on Data Set and Methodspresents data sets and the evaluation method used inthis study; the Sections on Hourly ET Evaluated atARM/CART Sites and Daily ET Evaluated atAmeriFlux Sites present the evaluation of NLDAS-2products at hourly and daily timescales; the Discussionsection discusses the challenges in evaluating model ETproducts; finally, the Conclusions section gives thesummary and conclusions.

DATA SETS AND METHODS

Observed ET data sets

Observed ET (in energy unit) data sets used in thisstudy include (1) 14 ARM/CART sites (Table I) and 29AmeriFlux sites (Table II and Figure 1). Both data setsare measured using the eddy covariance technique (alsoknown as ‘eddy correlation’ or ‘eddy flux’). ARM/CART sites are located in north-central Oklahoma andsouth-central Kansas, and the major vegetation coversare grasslands and crops. The AmeriFlux sites aredistributed in forested areas across CONUS, with major

Table I. Descriptions of the 14 Atmospheric Radiation Measuremenand vegetat

Station ID Station name Latitude (°N)

EF-2 Hillsboro 38.31EF-4 Plevna 37.95EF-7 Elk Falls 37.38EF-8 Cold Water 37.33EF-9 Ashton 37.13EF-12 Pawhuska 36.84EF-13 Lamont 36.61EF-15 Ringwood 36.43EF-18 Morris 35.69EF-19 El Reno 35.56EF-20 Meeker 35.56EF-22 Cordell 35.35EF-25 Seminole 35.25EF-26 Cement 34.96

Copyright © 2014 John Wiley & Sons, Ltd.

vegetation covers that include broadleaf forests, crop-lands, grasslands, and mixed and needleleaf forests.

ARM/CART data. The goal of the ARM programme is toobtain field measurements and develop models to support abetter understanding of the processes controlling solar andthermal/infrared radiative transfer in the atmosphere and atthe Earth’s surface. The Southern Great Plains CART site,created in 1992, was the first field measurement siteestablished under the ARM programme and was installedat the 14 grassland sites listed in Table I.The eddy covariance system is a ground-based system

using in situ sensors to estimate the vertical fluxes of sensibleheat (H) and latent heat (LvET).Lv is the specific latent heat ofvaporization for water (kJ/kg). Fluxes are measured every30min and were aggregated to hourly values for this study.Only ET in energy unit (LvET) is used in this study. Althoughthis measured flux is only representative of the grassy areawithin about 50m of the measurement stations (Robocket al., 2003), it can still provide very valuable information inthe evaluation process if the surrounding landscape isrelatively homogeneous at the model grid scale.

AmeriFlux data. The AmeriFlux network wasestablished in 1996 to provide continuous measurementsof ecosystem-level exchanges of CO2, water, energy, andmomentum at hourly to inter-annual timescales (Baldocchiet al., 2001). Information on the site, instrumentation, andprincipal investigators and other documentation are avail-able online (http://public.ornl.gov/ameriflux). Mu et al.(2011) obtained Level-4 measured ET data at 46 AmeriFluxsites and used them to validateModerate-resolution ImagingSpectroradiometer daily ET estimates. We obtaineddaily ET values (in W/m2) for 29 of these sites thatrecorded more than 2 years of measurements (i.e. at least730 recorded data, excluding missing values) during

t/Cloud and Radiation Testbed sites include latitude, longitudes,ion types

Longitude (°W) State Vegetation type

97.30 KS Pasture98.33 KS Rangeland (ungrazed)96.18 KS Pasture99.31 KS Rangeland (grazed)97.27 KS Pasture96.43 OK Native prairie97.49 OK Pasture and Wheat98.28 OK Pasture95.86 OK Pasture (ungrazed)98.02 OK Pasture (ungrazed)96.99 OK Pasture98.98 OK Rangeland (grazed)96.74 OK Pasture98.08 OK Pasture

Hydrol. Process. 29, 1757–1771 (2015)

http://www.emc.ncep.noaa.gov/mmb/nldas/drought/Evap/

http://www.emc.ncep.noaa.gov/mmb/nldas/drought/Evap/

http://public.ornl.gov/ameriflux

Table II. The 29 AmeriFlux sites across conterminous USA used in this study, including tower names, locations, vegetation type, andobservation periods for daily and monthly ET

Site name Abbreviation Vegetation type Years of data period

Daily Monthly

Deciduous broadleaf forest (DBF)Bartlett Experimental Forest USBar DBF 2004–2006 2004–2005Missouri Ozark USMoz DBF 2004–2007 2004–2006Morgan Monroe State Forest USMMS DBF 2000–2006 2000–2004Ohio Oak Openings USOho DBF 2004–2005 2004–2005

Croplands (Crop)ARM Southern Great Plains Main USARM Crop 2003–2006 2003–2006Bondville USBo1 Crop 2000–2007 2000–2006Mead Irrigated USNe1 Crop 2001–2006 2001–2005Mead Irrigated Rotation USNe2 Crop 2001–2006 2002–2005Mead Rainfed USNe3 Crop 2001–2006 2001–2005Rosemount Management USRo3 Crop 2004–2006 —

Grasslands (Grass)ARM Southern Great Plains Burn USARb Grass 2005–2006 —Audubon Grasslands USAud Grass 2002–2006 —Kendall Grasslands USWkg Grass 2004–2006 —Walnut River USWlr Grass 2001–2004 —Fort Peck USFPe Grass 2000–2006 —

Mixed forest (MF) and woodland (WL)Fort Dix USDix MF 2005–2006 —Little Prospect Hill USLPH MF 2002–2005 —Sylvania Wilderness USSyv MF 2001–2003 —Flagstaff Wildfire USFwf WL 2005–2006 —Freeman Ranch USFR2 WL 2004–2006 —Tonzi Ranch USTon WL 2001–2007 —

Evergreen needleleaf forest (ENF)Blodgett Forest USblo ENF 2000–2006 2000–2006Donaldson USSP3 ENF 2000–2004 2000–2004Flagstaff Unmanaged Forest USFuf ENF 2005–2007 2005–2006Metolius Young Pine USMe5 ENF 2000–2005 —Metolius Pine USMe2 ENF 2002–2007 2003–2005Niwot Ridge USNR1 ENF 2000–2007 2000–2003Wind River USWrc ENF 2000–2006 2000–2006

Wisconsin Pine USwi4 ENF 2002–2005 —

The monthly data are obtained from Jung et al. (2010) with an incomplete energy balance closure correction.

1759EVALUATION OF NLDAS-2 EVAPOTRANSPIRATION

2000–2007 (Table II and Figure 1). These sites representthe following vegetation types: deciduous broadleafforest, croplands, grasslands, evergreen needleleaf forest,mixed forest and woodland, and evergreen needleleafforest (for more details, see Mu et al., 2011).In addition, monthly mean ET products at a subset of

15 AmeriFlux sites (four broadleaf sites, five croplandssites, and six needleleaf sites) with corrections forincomplete energy balance closure were obtained fromthe supplementary data of Jung et al. (2010) in order toexamine the effects of energy closure errors.

Simulated ET data sets

The NLDAS-2 ET (in depth unit) is simulated by thefollowing four land surface models: the Mosaic model,the Noah model, the VIC model, and the Sacramento Soil


Moisture Accounting (SAC-SMA) model. The Noah andMosaic models grew from the legacy of the atmosphericsurface vegetation–atmosphere transfer scheme commu-nity of coupled modelling in regional and global weatherand climate models, whereas the VIC and SAC-SMAmodels emerged from the hydrological community asuncoupled models.The Noah land surface model (Ek et al., 2003) is a land

component of the National Oceanic and AtmosphericAdministration National Centers for EnvironmentalPrediction regional and global weather and climatemodels that represent land surface–atmosphere interac-tions of water and energy using a dominant vegetation-type approach, which has proven effective in reproducingobserved energy and water budget without the complexityof tiling (Robock et al., 2003).

Hydrol. Process. 29, 1757–1771 (2015)

Figure 1. Locations of the 29 AmeriFlux stations shown by vegetation type. Hourly evapotranspiration data are measured by using the eddy covarianceapproach for the period from 1 January 1997 to 30 September 1999

1760 Y. XIA ET AL.

TheMosaic land surface model was developed by Kosterand Suarez (1994) as a land component of the NationalAeronautics and SpaceAdministration global climatemodelthat accounts for the sub-grid heterogeneity of vegetationand soil moisture with a mosaic-like approach.The VIC model (Liang et al., 1994) is a semi-

distributed, grid-based hydrological model that includestwo versions: the water budget mode and the energybudget mode. The former considers precipitation-partitioning processes only, whereas the latter includesboth precipitation and net radiation-partitioning processesand is used in NLDAS-2. In contrast to the Mosaic andNoah models, the VIC model’s distinguishing hydrologicfeature is its representation of sub-grid variability in soilstorage capacity as a spatial probability: at such scales,the spatial variability in soil properties and topographiceffects is represented statistically, without infiltrationparameters assigned to specific sub-grid locations.The SAC-SMA model is a conceptual rainfall–runoff,

storage-type model. It treats only the surface waterbudget, does not attempt to model the surface energybudget, and uses the SNOW-17 snowpack model(Anderson, 1973), which is driven by hourly snowfalland air temperature at a 2-m height.The basic inputs of the Mosaic, Noah, and VIC models

are hourly downward shortwave radiation, downwardlongwave radiation, precipitation, 10-m wind speed, 2-mair temperature, 2-m specific humidity, and surface


pressure. Unlike the other three models, the inputs forthe SAC-SMA model are 2-m air temperature, hourlyprecipitation plus snowmelt from the SNOW-17model, andpotential ET (PET). SAC-SMA uses a climatologicallybased PET with seasonal variation (but no intra-monthly orinter-annual variation) to drive its streamflow and ETsimulation (Xia et al., 2012a, b). Thus, SAC-SMA does notaccount for ET inter-annual variability well: even in energy-limited regions, the intra-monthly and inter-annualvariability of ET is driven only by moisture availabilityand T, not by PET.All four models compute actual ET as a fraction of PET.

The Noah,Mosaic, and VICmodels use a Penman–Monteithapproach (Mahrt and Ek, 1984) to compute PET dynamical-ly, and SAC-SMA uses the aforementioned climatologicallybased PET, loosely derived from pan evaporation(Farnsworth et al., 1982). The derivation of ET from theNoah, Mosaic, and VIC models can be expressed as

ET ¼ Eb þ Et þ Ec (1)

whereET is total ET,Eb is direct evaporation from the groundsurface, Et is the transpiration via canopy and roots, and Ec isthe evaporation of precipitation intercepted by the canopy. Inthe Noah and Mosaic models, Eb = f(ʘ, σf)PET, where ʘ istop 10-cm soil moisture, σf is the green vegetation fraction,and PET is potential evapotranspiration. Note that thefunction f is different for Noah (Chen et al., 1996) and

Hydrol. Process. 29, 1757–1771 (2015)


Mosaic (Koster and Suarez, 1994). In the VIC model, Ebvaries within the base soil area (Liang et al., 1994) when top10-cm soil moisture is unsaturated; when the top 10-cm soilmoisture is saturated,Eb=PET. For thesemodels,Et=h(ʘ,Rs,σf)PET, where Rs is the effective surface resistance and thefunction h varies between models. For further details, seeChen et al. (1996) for the Noah model, Koster et al.(1994) for the Mosaic model, and Liang et al. (1994) forthe VIC model. Et processes vary between vegetationtypes. Root-zone depths vary between models: Noah’sroot zone is the top 1m for short vegetation such asgrasses, crops, and shrubs and 2m for trees and woodyvegetation; Mosaic’s root-zone depth is 40 cm for allvegetation types; and VIC’s root zone varies up to 1.5 mdepending on vegetation type and soil moisture. For thesethree models, Ec =V(Wc, Ra)PET, where Wc is the waterintercepted by the canopy and Ra is the aerodynamicresistance. Function V varies between models dependingon the parameterization scheme used (Chen et al., 1996;Koster et al., 1994; Liang et al., 1994).The SAC-SMA model also has no controls on the

movement of water to satisfy evaporation. In SAC-SMA, if the potential evaporation rate is not satisfiedfrom the upper storage, water is withdrawn directlyfrom lower storages. As a result, the soil moisture ofthe SAC-SMA lower zone may be underestimatedconsiderably in dry basins. Furthermore, SAC-SMAdoes not account well for the effects of vegetation, withtranspired water drawn from various soil layers basedon root depth, distribution, and resistance to transpira-tion demand. Under severely dry conditions, soilmoisture in the upper layer and part of the lower layermay be further underestimated because the vegetationresistance and the source of transpiration withdrawalsare not considered. As SAC-SMA is heavily calibratedwith streamflow observations across CONUS, its ETestimates compare well with the other models (Xiaet al., 2012a, b). All models can output hourly ET.However, SAC-SMA ET and soil moisture have littlediurnal cycle, owing to the use of monthly constantPET. SAC-SMA ET has reasonable daily and seasonalvariation. Thus, we evaluate SAC-SMA ET at daily andmonthly timescales rather than at the hourly timescale.Evapotranspiration is obtained for all models for the

same period for which observations are available. InNLDAS-2, all four models used the same vegetation type,vegetation cover fraction, and soil-type data, but eachmodel used its own set of parameter values for a givensoil and vegetation type (Mitchell et al., 2004).To obtain modelled ET in energetic units (W/m2), we

convert modelled ET from depth units (mm) usingET= fscLsETs + (1� fsc)LvETv at each grid point, where fscis snow cover fraction, ETs is snow sublimation rate (mm),Ls is the specific latent heat of fusion for snow (kJ/kg),ETv is


a sum of evaporation and transpiration rate for the grid point(mm), and Lv is the specific latent heat of vaporization forwater (kJ/kg).

Evaluation method

A direct comparison between the modelled andobserved ET is possible (grid cell to point) at all ARM/CART stations and AmeriFlux stations, but thesecomparisons may suffer from the spatial-scale incompat-ibility issues (Entin et al., 2000; Crow and Wood, 1999)as ET has a small scale related to soil and vegetationcharacteristics and soil moisture. Although spatial andtemporal averaging is a simple way to get around thisproblem as it reduces the spatial and temporal noise andgives a more meaningful comparison, it incurs theexpense of averaging out error (i.e. bias) characteristics.This method has been used in many similar valuation andevaluation studies (Robock et al., 2003; Mo et al., 2011).Sites can be grouped according to their locations,climates, and vegetation types (Mo et al., 2011). Asvegetation is closely related to ET generation, we grouped29 sites on the basis of vegetation type (listed for each sitein Table II) although different soil textures between sitesand grid cells also affect daily ET simulation. Wespatially average by aggregating tower observations fromsimilar dominant plant types and do the same for modelgrid cells corresponding to those locations. There ispotential to increase the robustness of the comparisons byupscaling the point measurements, taking into accounttheir correlation structure rather than simple averaging,although this would require information from a densenetwork of observations or extra-high-resolution landsurface modelling (Crow et al., 2011). Kustas et al.(2004), McCabe and Wood (2006), and Li et al. (2008)used high-resolution remote-sensing data to investigatethe effect of different spatial resolutions (i.e. 30, 120, and950m) on interpreting the variability and magnitude oftower-based flux observations and showed that the remotesensing of ET at resolutions of less than 100m compareswell with tower-measured ET. However, in the presentstudy, the land surface model products cannot achieve theseresolutions (i.e. 100m), for various reasons. Primarily, atsuch high resolutions, neither forcing data nor soil andvegetation characteristics data (e.g. soil texture classes andvegetation-type classification) nor parameter fields for landmodel run (e.g. albedo, vegetation fraction, and greennessfraction) exist. Further, the computational burden of runningthe land surface model run at such resolution is currentlyprohibitive. The simple spatial averaging discussed earlierrepresents a compromise necessary to compare NLDAS-2products with tower-measured ET.To reduce the impact of the seasonal cycle on our

evaluation, we divided all available averaged data intofour seasons: winter (December, January, and February),

Hydrol. Process. 29, 1757–1771 (2015)

1762 Y. XIA ET AL.

spring (March, April, and May), summer (June, July, andAugust), and fall (September, October, and November),pooled over 8 years (2000–2007). This compromiseapproach accounts for the high number of missing tower-measured ET records that render both month-by-monthcomparisons and daily anomalies impossible, both of whichwould otherwise have reduced the effect of ET’s seasonalcycle. Note that daily correlation values may be exaggeratedin spring and fall, owing to the sharp increase (decrease) inspring (fall). We make no monthly comparisons withAmeriFlux data, as for some vegetation types, sample sizesof available data are too small. Nevertheless, for thefollowing analysis, the spatial average of the observed andmodelled ET time series for a given vegetation type andseason is generated and used.In order to evaluate the relative impacts of different factors

on daily ET (i.e.weather processes represented by season,vegetation types, and models) for a given statistic [e.g. dailycorrelation and daily root mean square error (RMSE)], weused a one-factor analysis of variance method (Lane D.,2013; Online Statistics Education: A Multimedia Course ofStudy, http://onlinestatbook.com/). A similar concept is usedto investigate the impact of land surface states on precipitationand runoff in the Global Land–Atmosphere CouplingExperiment (Koster et al., 2006). We used a ratio betweenthe conditional sum of squares (V) of the quantity for a givenfactor considered alone and the total sum of squares (TV) ofthe quantity with all three factors considered. In our case, thedaily correlationC for four seasons, five vegetation types, andfour models can be expressed as Ci,j,k, where i representsseason, j represents vegetation type, and k represents modelchoice. The total sum of squares of daily correlation can becalculated as follows:

TV ¼ ∑4

i¼1∑5

j¼1∑4

k¼1Ci;j;k � C� �2

(2)

where C represents the grand mean of daily correlationbetween observations and simulation across all vegetationtypes, seasons, and models, which is calculated as follows:

C ¼ 14�5�4

∑4

i¼1∑5

j¼1∑4

k¼1Ci;j;k (3)

Considering a single given factor (e.g. deciduous broad-leaf forest for vegetation type), the conditional variance ofdaily correlation for model effect can be expressed as

Vj ¼ ∑4

kVSj;k (4)

whereVSj;k ¼ Cj;k � C� �2

andCj;k is the four-season meanfor vegetation type j and model k. If we assume that the


effects are linear, the impact of a given factor on daily ETsimulation can be expressed as the ratio Raj=Vj /TV. Ravalues are calculated for each vegetation type, with highervalues indicating more important factors. In a similar way,the conditional sum of squares of daily correlation forseasonal effect can be calculated when the four-model meanis used. For a given model or given season, a similarcalculation process can be applied to generate such ratiovalues. Higher values imply a greater effect for that factor(i.e. model, season, or vegetation).

EVALUATION OF NLDAS-2 ET PRODUCTS

Hourly ET evaluated at ARM/CART sites

Figure 2 shows a comparison between diurnal cycles ofmodelled ET by three models (Noah, Mosaic, and VIC)and their ensemble mean and the observations in January,April, July, and October 1999 (as noted, the SAC-SMAmodel does not simulate ET at the hourly timescale).Ensemble mean ET is used in this study as it has beenshown to be more reliable at monthly timescales whencompared with monthly mean AmeriFlux observations(Mo et al., 2011). The diagram presents the monthlymean diurnal cycle of ET averaged over 14 ARM/CARTsites. The Mosaic model has the highest modelled ET forall four months, exceeding observations for three of thefour months (except July). The VIC model has generallylower ET values across the day and specifically in termsof peak values for three of the four months (except April).The Noah-modelled ET is the closet to the observations inthree of the four months (except July) and outmatches thethree-model ensemble mean, presumably because it hasbeen calibrated at the ARM/CART network by Wei et al.(2012). Overall, these results reveal that models are able tocapture the general features of ET observations, although allmodels have worse performance in terms of diurnal cycle inwinter than other seasons. In addition, some models delayET relative to observations, e.g. January Mosaic andOctober VIC. The reason for this delay remains unclearand warrants further investigation.Figure 3 presents the differences (bias) betweenmodelled

ET and observations (model minus observation). Clearly,the Mosaic model largely overestimates midday ETsystematically throughout the 33months by up to65W/m2, particularly in the spring and fall. The VICmodel also greatly overestimates midday ET in the springand largely underestimates midday ET in the summer. TheNoahmodel is the closest to the observations throughout the33months with a bias smaller than 35W/m2 for mostcases, although it moderately underestimates midday ETin summer. The ensemble mean has smaller bias than theMosaic and VIC models and larger bias than the Noahmodel. As indicated by Xia et al. (2012a), the Noah

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http://onlinestatbook.com/

Figure 2. Monthly mean diurnal cycle of modelled and observed (OBS) land surface evapotranspiration (ET) for (a) January, (b) April, (c) July, and (d)October in 1999. The dotted lines are observations averaged spatially on the basis of all available observations from 14 Atmospheric Radiation

Measurement/Cloud and Radiation Testbed sites. EM, ensemble mean; VIC, Variable Infiltration Capacity

Figure 3. Difference of mean diurnal cycle of land surface evapotranspiration (ET) across each month from January 1997 to January 2000 betweenmodels and observations for (a) Noah, (b) Mosaic, (c) Variable Infiltration Capacity (VIC), and (d) the three-model (Noah, Mosaic, and VIC) ensemblemean (EM). Values plotted are model ET minus observed ET, with warmer colours representing overestimation by the models; cool colours

underestimation. The y-axis represents different months, and the x-axis represents local time


upgrade (Wei et al., 2012) improved ET simulations inNLDAS-2 as compared with those in NLDAS-1.Although the VIC upgrade reduced streamflow simula-


tion in the southeastern USA (Xia et al., 2012b), it showslittle improvement for ET simulation at these 14 ARM/CART sites.

Hydrol. Process. 29, 1757–1771 (2015)

1764 Y. XIA ET AL.

Daily ET evaluated at AmeriFlux sites

Figure 4 shows annual and seasonal correlationcoefficients between modelled ET and observations forthe four models (SAC-SMA is included here as itgenerates daily ET) and their ensemble mean, across fivevegetation types. For all models, year-around correlationsgenerally exceed seasonal correlations, owing to theinclusion of the seasonal cycle in the correlated data.Most annual correlation coefficients exceed 0.8 except fora few model/vegetation type combinations. These valuesare comparable with Moderate-resolution ImagingSpectroradiometer daily ET evaluation results (Muet al., 2011). Overall, the models display betterperformance for a given season at broadleaf forest,grassland, and mixed-forest sites than at cropland andneedleleaf forest sites. Overall, the Noah model tends toperform the best, and the Mosaic model the worst. This

Figure 4. Correlation coefficients between modelled and observed daily evapseasons and annually for (a) deciduous broadleaf forest, (b) croplands, (c) g

forest. SAC-SMA, Sacramento Soil Moisture Ac


result is consistent with the ARM/CART evaluationdescribed in Section on Hourly ET Evaluated at ARM/CART Sites. In general, all models and their ensemblemean exhibit the strongest correlations in the spring andfall, owing partially to the shape of the annual cycle,which exhibits the highest seasonal changes during theseseasons and the lowest in summer and winter. In winter,all models also have generally weak correlations,particularly for broadleaf forests and cropland sitesrelative to grasslands and mixed-forest sites. This poorperformance is possibly due to poor simulation ofsublimation: broadleaf forests and cropland sites are athigher latitudes where the effects of sublimation aregreater. In fact, the SAC-SMA/SNOW-17 model does notpermit sublimation at all. Simulation of sublimation is anissue of ongoing development in the modelling commu-nity. In the meantime, the combination of large simulation

otranspiration for the four models and their ensemble mean (EM) and fourrasslands, (d) mixed forest and woodlands, and (e) evergreen needleleafcounting; VIC, Variable Infiltration Capacity

Hydrol. Process. 29, 1757–1771 (2015)


errors and low winter ET may lead to larger relative errorsand lower correlation values than in other seasons. Insummer, all models also have low correlation coefficientsfor all vegetation types, in particular for needleleaf forest andcrop sites. This is primarily due to greater summerprecipitation variability relative to other seasons (Figure 5).This leads to high ET variability for all vegetation typesexcept the mixed-forest type, whose sites are widelydistributed across CONUS (Figure 1), leading to similarprecipitation variabilities all seasons. For regions with largeprecipitation variability, nomodel captures daily ET variationwell. In addition, for cropland sites, irrigation may also affectdaily ET simulations. Table III shows the ratio Ra of

Figure 5. Percentage of days with precipitation larger than 0.5mm/day for four s

Table III. Ra, the ratio of conditional sum of squares to total sum oother factors ar

Model

Ra for R Ra for RMSE

Vegetation type is fixedVegetation type Broadleaf 0.002 0.014

Crop 0.010 0.030Grasslands 0.018 0.021Mixed 0.007 0.012Needleleaf 0.031 0.003

Model is fixedModel Noah

MosaicSAC-SMAVIC

Season is fixedSeason Winter 0.027 0.024

Spring 0.022 0.041Summer 0.032 0.029Fall 0.031 0.030

RMSE, root mean square error; SAC-SMA, Sacramento Soil Moisture Acco


conditional sum of squares to total sum of squares for dailycorrelation and RMSE, considering season, vegetation type,and model. The results show the following:

(i) When vegetation type is fixed and model andseasonal effects are compared, season has the greatereffect for both R and RMSE for all vegetation types,although this effect varies between types. The largestseasonal effects are for broadleaf, crop, and grass-lands; the lowest seasonal effects, for mixed andevergreen needleleaf forest.

(ii) When the model is fixed and the effects of season andvegetation types are compared, vegetation type is the

easons and five vegetation typeswhen the same data size is used as in Figure 4

f squares when vegetation type, model, or season is given and thee considered

Season Vegetation

Ra for R Ra for RMSE Ra for R Ra for RMSE

0.059 0.0370.052 0.0910.079 0.0390.012 0.0210.045 0.012

0.027 0.052 0.052 0.0700.016 0.021 0.041 0.0300.029 0.041 0.054 0.0820.040 0.009 0.060 0.017

0.016 0.0140.028 0.0350.014 0.0120.011 0.019

unting; VIC, Variable Infiltration Capacity.

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1766 Y. XIA ET AL.

dominant effect for both R and RMSE for all fourmodels, with some variation between models.

(iii) When season is fixed and the effects of model andvegetation type are compared, model choice has thedominant effect in summer, fall, and winter for bothR and RMSE; in spring, model and vegetation-typeeffect are comparable for both R and RMSE as this isthe period of greatest vegetation fraction. In summer,model structural errors increase errors under highprecipitation variability (Figure 5).

Figures 6 and 7 show daily RMSE and bias analysisrelative to observations for four models and theirensemble mean for five vegetation types across fourseasons and annually. Figure 6f shows mean seasonal andannual observed daily ET for five vegetation types.Significantly higher ET is observed in broadleaf forest,

Figure 6. Root mean square errors (RMSE) between modelled and observed(EM), across four seasons and annually for (a) deciduous broadleaf forest, (bneedleleaf forest, and (f) observed daily ET for five vegetation types across

SMA, Sacramento Soil Moisture Account


cropland, and needleleaf forest than in grassland andmixed forest for summer and fall, a pattern that isrepeated for the RMSE and biases. Overall comparison ofRMSEs of the modelled ET (Figure 6a–e) reveals that, ingeneral, the Mosaic model has the largest RMSE,followed, in order, by the VIC model, the SAC-SMAmodel, the ensemble mean, and the Noah model (theorder varies somewhat across different vegetation typesand seasons). RMSEs vary from 3W/m2 for the Noahwinter grassland simulation to 45W/m2 for the VICsummer cropland simulation. Overall comparison ofbiases (Figure 7) shows that the Mosaic model has thelargest positive biases for most simulation cases,followed, in order, by the VIC model, the SAC-SMAmodel, the ensemble mean, and the Noah model. Thismeans that the Mosaic model overestimates daily ETobservations for almost all vegetation types and seasons,

daily evapotranspiration (ET) for four models and their ensemble mean) croplands, (c) grasslands, (d) mixed forest and woodlands, (e) evergreenfour seasons and annually. Note the different scale for Figure 6(f). SAC-ing; VIC, Variable Infiltration Capacity

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Figure 7. The same as Figure 6(a–e), but for biases (modelled� observed). SAC-SMA, Sacramento SoilMoistureAccounting; VIC,Variable InfiltrationCapacity


consistent with the conclusion drawn in the water budgetapproach of Xia et al. (2012b). The impact of forest ondaily ET simulation (e.g. impact of leaf growth and rootdepth on transpiration) is demonstrated by the largerbiases at broadleaf and needleleaf forest sites than atcropland, grassland, or mixed-forest sites. For broadleafforest, almost all models largely overestimate daily ETobservations for all seasons and annually (Figure 7a).Conversely, almost all models underestimate daily ETobservations for evergreen needleleaf forest (Figure 7e).In addition, all models also largely overestimate springET observations for cropland (Figure 7b).In order to assess whether these systematic biases are

due to incomplete energy balance errors at the AmeriFluxsites (Mu et al., 2011), we used monthly mean ETobservations with a correction for the incomplete energybalance closure (Jung et al., 2010) for three vegetationtypes from four to six sites (depending on vegetation type,Table II) to make this comparison. Figure 8 shows the


annual cycle for broadleaf forest, cropland, and needleleafforest as a comparison of observed monthly mean ET tomodel outputs. The results here are in good agreementwith the bias analysis from Figure 7a, b, and e, implyingthat the energy balance closure errors at the AmeriFluxsites are not the primary drivers of the large biases atbroadleaf forest, croplands, and needleleaf sites. Givenhigh-quality NLDAS-2 forcing (Xia et al., 2012a), we canreasonably assume that NLDAS-2 forcing data errorsdriving the four land models are small. Therefore, thesystematic biases discussed earlier primarily arise frommodel structure and parameter errors. This means that thefour land models in NLDAS-2 cannot simulate daily ETobservation well for deciduous broadleaf forest, ever-green needleleaf forest, and croplands (i.e. spring andsummer). The reason may be that none of the four modelsincludes either vegetation dynamics affecting vegetationgrowth (for forest) or irrigation and groundwaterprocesses (for croplands).

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Figure 8. Seasonal cycle of 7-year (2000–2006) averaged monthlymodelled [by the Noah, Mosaic, Sacramento Soil Moisture Accounting(SAC-SMA), and Variable Infiltration Capacity (VIC) models and theirfour-model ensemble mean (EM)] and observed ET for (a) deciduousbroadleaf forest, (b) croplands, and (c) evergreen needleleaf forest. Themonthly observations are obtained from Jung et al. (2010) with anincomplete energy balance closure correction. The sites containing

monthly data listed in Table II are used in these plots

1768 Y. XIA ET AL.

The evaluation against AmeriFlux data demonstratesthat, for daily ET simulation, the Noah model has the bestperformance (largest correlation and smallest RMSE andbias), followed, in order, by the ensemble mean, the SAC-SMA model, the VIC model, and the Mosaic model,consistent with the evaluation of hourly ET simulationagainst ARM/CART data.

Discussion

In general, with respect to ecological modelling,broadleaf, grasslands, and mixed forests were modelledthe best. However, various modelling choices and


ecohydrologic dynamics affected the performance of themodels in simulating ET from the various ecotones.Winter modelling suffered from the effects of eachmodel’s treatment of snow dynamics, particularly thoseecotones where snow plays an important ecohydrologicrole, such as broadleaf forests and croplands, whereassummer modelling of some ecotones was affected by theeffects of high precipitation variability at regional scales,particularly in needleleaf and croplands. Forest dynamics(such as leaf growth and rooting depth) play a significantrole in determining the accuracy of daily ET modelling,leading to larger errors at broadleaf and needleleafecotones. It should be noted that all four models usestatic vegetation process (e.g. predetermined monthlyvegetation fraction and greenness fraction) rather thandynamic vegetation process (e.g. vegetation growth andcarbon cycle), which limits the simulation of ETassociated with ecological processes. In future, usingreal-time remote-sensing vegetation parameters (e.g. dailyor weekly vegetation fraction and greenness fraction) andupgraded land surface models with dynamic vegetationprocess (Niu et al., 2012) should improve ET simulation,particularly at the ecosystem scale.Although we used high-quality tower ET observations

available across CONUS to assess hourly and dailyNLDAS-2 ET products, errors and biases in this studymay come from many sources, particularly errors inforcing data to NLDAS-2 land surface models errors,model structure deficiencies and model parameter errors,measure data errors, and incompatibility issues resultingfrom scaling from towers to model pixels. In thefollowing, we discuss each of these sources:

a. Model forcing data errors. As described in the Sectionon Simulated ET Data Sets, running all four modelsrequires hourly forcing data (we force the Noah,Mosaic, and VIC models with precipitation, downwardshortwave and longwave radiation, specific humidity,air temperature, and wind speed and the SAC-SMAmodel with hourly precipitation and 2-m air temper-ature). These forcing data have been evaluated forOklahoma in NLDAS phase 1 (Luo et al., 2003),where good agreement was shown with site observa-tions for all hourly meteorological variables exceptprecipitation. In addition, large negative biases inprecipitation in mountainous regions are also found inNLDAS phase 1 (Pan et al., 2003), owing to thedifficulties resulting from orographic effects and wind-driven undercatch. In NLDAS-2, precipitation includesan orographic adjustment based on the widely appliedParameter-elevation Regression on Independent SlopesModel climatology (Daly et al., 1994) to reduceprecipitation biases caused by topography. However,wind-driven undercatch in NLDAS-2 precipitation has

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not been corrected yet, which may introduce biases inNLDAS-2 ET simulation.

b. Model structure deficiencies and parameter errors. Alarge number of physical factors and model parametersare involved in simulating soil and vegetation surfaceevaporation, plant transpiration, and snow surfacesublimation processes. These include parameterizationsof microclimate, plant biophysics for site-specificvegetation types, and landscape heterogeneity; theymake an accurate assessment of ET a challenge (Friedl,1996). Different parameterizations (functions) andparameter values in Noah, Mosaic, SAC, and VICmodel as discussed in the Section on Simulated ETData Sets are used in this study. Although performingaccurate comparisons between models is challenging,the model sensitivity analysis as used by Xia et al.(2014) can find some model deficiencies. As shown inFigure 7a, the Noah model greatly underestimates ETobservations in spring and early summer as comparedwith the other three models. This inconsistency isbecause the evaporation from Noah is small andincreases only slowly after April (Xia et al., 2012a).Rather than being an issue related to frozen ground, thelow evaporation in late spring and early summer ismost likely due to the introduction of an ‘intermediatefix’ (Slater et al., 2007) meant to constrain large snowsublimation values under stable boundary layers(Livneh et al., 2010; Xia et al., 2014).

c. Model calibration differences. As SAC and VIC aremainly developed for simulating streamflow simula-tion, they are not calibrated explicitly to match ET;instead, they match observed annual streamflow (Troyet al., 2008). Mosaic has not been calibrated usingeither observed ET or observed streamflow sinceNLDAS was initiated (Mitchell et al., 2004). Noahwas calibrated using latent heat flux (LvET) at theARM/CART network and the observed streamflow(Wei et al., 2012), and this may have yielded aperformance advantage with respect to the othermodels. At the Environmental Modeling Center, weare currently evaluating Noah land model physics viaProtocol for the Analysis of Land Surface models(PALS, http://www.pals.unsw.edu.au/pals/Welcome.action) at 20 sites around the world, with furtherdata sets from other sites to be added. At the sametime, we acknowledge this potential drawback in thisstudy, and we encourage Mosaic, SAC, and VICmodel development groups to evaluate their models’physics via PALS.

d. Measured data errors. Eddy covariance flux towershave been shown to underestimate ET by about 10–30%, on the basis of a comparison of multiple towers atthe same site or comparison with other methods such aslysimeters or sap-flux sensors (Glenn et al., 2008). In


addition, eddy covariance flux towers have anincomplete energy balance closure (Wilson et al.,2002). The correction and reduction of this error arestill uncertain (Shuttleworth, 2007) for all timescales, inparticular for hourly and daily timescales although Junget al. (2010) corrected the data at monthly timescales.

e. Scaling from tower to grid box. The measurementheight (2–5m) and the horizontal scale (about 50m) ofmeasurements of the tower fluxes have significantinfluences on the footprint and the size of thecontributing underlying surface. Furthermore, thecomplex terrain and complicated canopy structureand the stochastic nature of turbulence can affect theeddy covariance measurements.

CONCLUSIONS

We compared hourly and daily ET from the NLDAS-2-driven models with observations from 14ARM/CART sitesand 29 AmeriFlux sites. Spatially averaged ET values werecompared to reduce small-scale noise, although it is still acompromise method. The spatial averaging is based onvegetation types – deciduous broadleaf forest, cropland,grassland, mixed forest, and evergreen needleleaf forest.Overall results show that NLDAS-2 models capture broadfeatures of hourly and daily ET variations (except fordeciduous broadleaf, evergreen needleleaf, and croplands),but inter-model variability is often significant. The Mosaicmodel overestimates hourly and daily ET observations formost cases; Noah is the closet to the observations; and SAC-SMA andVICmodels lie in between. TheRMSE and biasesbetween modelled and observed daily ET vary with seasonand vegetation type.It is not surprising that, for all models and their

ensemble mean, the highest correlation coefficients are forthe annual analysis as compared with any season, as itincludes the seasonal cycle of ET. All models have highercorrelation coefficients in spring and fall than in winterand summer for all five vegetation types. The reason maybe the small ET values and models’ inabilities to simulatesnow sublimation, large summertime precipitation vari-ability, and the strong intra-seasonal cycle in ET. For agiven season, all models have higher correlation coeffi-cients for deciduous broadleaf forest, grasslands, andmixed forest than for croplands or evergreen needleleafforest, indicating the impact of land surface vegetation ondaily ET simulations. For a given season and vegetationtype, there is a large inter-model variability for somecases (e.g. summer croplands, spring mixed forest, andsummer and fall needleleaf forest). The importance of thefactors generating errors and biases between modelledand observed ET varies between seasons, vegetationtypes, and models.

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http://www.pals.unsw.edu.au/pals/Welcome.action

http://www.pals.unsw.edu.au/pals/Welcome.action

1770 Y. XIA ET AL.

Notwithstanding errors in both modelled and observedET, this study yields important conclusions. All modelsand their ensemble mean can capture the general featuresof the observations at hourly and daily timescales. TheNoah model is able to closely simulate hourly and daily ETfor all vegetation types over CONUS as it was calibrated atthe ARM/CART network byWei et al. (2012). The Mosaicmodel has the largest positive biases when compared withobservations because of shallow root zone and small totalrunoff (Mitchell et al., 2004). The performances of the SAC-SMA and VIC models lie between those of Noah andMosaic. This study provides a first-order evaluation andassessment for newly released NLDAS-2 ET products (Xiaet al., 2012a) and suggests a future benchmark forcontinental-scale ET studies and analyses.

ACKNOWLEDGEMENTS

Y. Xia was supported by the National Oceanic andAtmospheric Administration’s Climate Program OfficeModeling, Analysis, Prediction and Projection pro-gramme. Michael Hobbins was supported by the NationalIntegrated Drought Information System under the UCARVisiting Scientist Program. We also thank Jeff McQueenand Helin Wei at the Environmental Modeling Center andtwo anonymous reviewers whose comments greatlyimproved the quality and readability of this paper.

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evaluation of nldas-2 evapotranspiration against tower

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