A modified MOD16 algorithm to estimate evapotranspiration over alpine meadow on the Tibetan Plateau, China

A modified MOD16 algorithm to estimate evapotranspiration over alpine meadow on the Tibetan Plateau, China

Accepted Manuscript Research papers A Modified MOD16 Algorithm to Estimate Evapotranspiration over Alpine Meadow on the Tibetan Plateau, China Yaping ...

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Accepted Manuscript Research papers A Modified MOD16 Algorithm to Estimate Evapotranspiration over Alpine Meadow on the Tibetan Plateau, China Yaping Chang, Dahe Qin, Yongjian Ding, Qiudong Zhao, Shiqiang Zhang PII: DOI: Reference:

S0022-1694(18)30226-9 https://doi.org/10.1016/j.jhydrol.2018.03.054 HYDROL 22687

To appear in:

Journal of Hydrology

Received Date: Revised Date: Accepted Date:

6 November 2017 3 February 2018 20 March 2018

Please cite this article as: Chang, Y., Qin, D., Ding, Y., Zhao, Q., Zhang, S., A Modified MOD16 Algorithm to Estimate Evapotranspiration over Alpine Meadow on the Tibetan Plateau, China, Journal of Hydrology (2018), doi: https://doi.org/10.1016/j.jhydrol.2018.03.054

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A Modified MOD16 Algorithm to Estimate Evapotranspiration over Alpine Meadow on the Tibetan Plateau, China , 5*

Yaping Chang1, 3, Dahe Qin1, Yongjian Ding1, 2, 3,*, Qiudong Zhao1, 2, and Shiqiang Zhang4 1

State Key Laboratory of Cryospheric Sciences, Northwest Institute of Eco-Environment and

Resources, Chinese Academy of Sciences, Lanzhou 730000, China 2

Key Laboratory of Ecohydrology of Inland River Basin, Chinese Academy of Sciences, Lanzhou

730000, China 3

University of Chinese Academy of Sciences, Beijing 100049, China

4

Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity,

Northwest University, Xi’an, 710127, China 5

College of Urban and Environmental Sciences, Northwest University, Xi’an 710027, China

*

Correspondence: [email protected]; [email protected]

Highlights: •

Five precious representative alpine meadow sites on the Tibetan Plateau were used to evaluate the modified MOD16 algorithm.



A modified MOD16 algorithm was proposed to improve the accuracy of ET estimation.



A modular analysis was performed for understanding the relative importance of each part of the modified MOD16 algorithm.

Abstract The long-term change of evapotranspiration (ET) is crucial for managing water resources in areas with extreme climates, such as the Tibetan Plateau (TP). This study proposed a modified algorithm for estimating ET based on the MOD16 algorithm on a global scale over alpine meadow on the TP in China. Wind speed and vegetation height were integrated to estimate aerodynamic resistance, while the temperature and moisture constraints for stomatal conductance were revised based on the technique proposed by Fisher et al. (2008). Moreover, Fisher’s method for soil evaporation was adopted to reduce the uncertainty in soil evaporation estimation. Five representative alpine meadow sites on the TP were selected to investigate the performance of the modified algorithm. Comparisons were made between the ET observed using the Eddy Covariance (EC) and estimated using both the original and modified algorithms. The results revealed that the modified algorithm performed better than the original MOD16 algorithm with the coefficient of determination (R2) increasing from 0.26 to 0.68, and root mean square error (RMSE) decreasing from 1.56 to 0.78 mm d-1. The modified algorithm performed slightly better with a higher R2 (0.70) and lower RMSE (0.61 mm d-1) for after-precipitation days than for non-precipitation days at Suli site. Contrarily, better results were obtained for non-precipitation days than for after-precipitation days at Arou, Tanggula, and Hulugou sites, indicating that the modified algorithm may be more suitable for estimating ET for non-precipitation days with higher accuracy than for after-precipitation days, which had large observation errors. The comparisons between the modified algorithm and two mainstream methods suggested that the modified algorithm could produce high accuracy ET over the alpine meadow sites on the TP. Keywords:

evapotranspiration;

parameterization; Tibetan Plateau

alpine

meadow;

modified

MOD16

algorithm;

resistance

1. Introduction Evapotranspiration (ET) is an important component for the global terrestrial energy budget balance and water cycle (Jia et al., 2013; Di et al., 2015; Liu et al., 2016). Therefore, the long-term change of regional ET is of significance for improving water management and monitoring climate change (Di et al., 2015; Cleugh et al., 2007; Kustas et al., 2007; Mu et al., 2007; Kim et al., 2012). Conventional ET estimation methods, such as the Lysimeter, Eddy Covariance (EC), and Bowen Ratio, are based on in-situ measurements that cannot acquire regional or global ET. Existing remote sensing models that provide the potential for regional ET estimation can be divided into three categories: (1) empirical regression models (Wang et al., 2007; Wang et al., 2010); (2) Surface Energy Balance (SEB) models, such as the Surface Energy Balance Algorithm for Land (SEBAL) (Bastiaanssen et al., 1998), Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) (Allen et al., 2007), and Surface Energy Balance System (SEBS) (Su, 2002); and (3) traditional methods combined with remote sensing data, such as the Penman-Monteith (PM) method with MODerate resolution Imaging Spectroradiometer (MODIS) imagery (Cleugh et al., 2007; Mu et al., 2007; Mu et al., 2011; Fisher et al.,

2011)

and

the

Priestly-Taylor

equation

with

Advanced

Very

High

Resolution

Spectroradiometer( AVHRR) (Fisher et al., 2008). Empirical regression models have been developed by establishing the relationship between ET and satellite-based net radiation, temperature and vegetation indices (Wang et al., 2007; Wang et al., 2010). Although these models can quickly capture long-term regional ET, it is difficult to ascertain the empirical coefficients for different ecosystems due to the heterogeneity of land use types (Feng et al., 2015). Further, SEB models have been applied successfully in many countries with varying climates (Gao and Long, 2008; Paiva et al., 2011; Tang et al., 2013; Chang et al., 2017) and have been proven

to produce regional ET with high spatial resolution, which is useful for monitoring water resources. However, these models cannot capture long-term change of regional ET due to the availability of continuous remote-sensing data. To address these problems, Cleugh et al. (2007) first developed a remote sensing model based on the PM equation with MODIS vegetation products and meteorological data. Mu et al. (2007) modified Cleugh’s model and produced the first global MODIS ET datasets with a spatial resolution of 1 km and temporal resolutions of 8-day, monthly, and yearly. Later, Mu et al. (2011) further improved the MODIS ET algorithm to produce a more accurate global MODIS ET product (MOD16) from 2000 to 2014. The MOD16 ET product has been validated and applied in many countries with various climates (Jia et al., 2012; Kim et al., 2012; Tang et al., 2015; Chen et al., 2014). Kim et al. (2012) found that the MOD16 product performed best at five forest sites and mismatched with observed ET at two grassland sites in Asia. Chen et al. (2014) demonstrated that the MOD16 product showed substantial differences with PT-JPL (Fisher et al., 2008) and Yuan’s (Yuan et al., 2010) algorithms in China. Tang et al. (2015) pointed out that the MOD16 ET product produced good agreement with EC values at one cropland site but underestimated ET at four irrigated cropland sites in China. Feng et al. (2015) found the MOD16 algorithm showed reduced performance at grassland, savannas, and shrubland sites over semi-arid ecosystems compared with PT-JPL (Fisher et al., 2008) and Yao’s (Yao et al., 2013) algorithms. It is pertinent to note that the performance of the MOD16 ET product varies under different climates and different surfaces (Feng et al., 2016). Yet, few studies focus on the performance of the MOD16 ET product on the Tibetan Plateau (TP), which is recognized as the world’s Third Pole and one of most sensitive areas for climate change (Qiu, 2008). Long-term regional ET variations on the TP are significant to understand local hydrological processes and global climate change.

Although ET on the TP has been studied using the water balance method (Liu et al., 2016; Xue et al., 2013; Li et al., 2014), hydrological models (Yang et al., 2011; Zhou et al., 2014), and satellite-based models (Chen et al., 2013a, b; Peng et al., 2016), these methods are inadequate to evaluate the accuracy of ET estimation over alpine meadow due to lack of observation data, which is an important land cover type on the TP. Further, there are substantial differences among different satellite-based models on the TP (Peng et al., 2016). Therefore, it is important to evaluate and improve the current satellite-based ET algorithm (MOD16 algorithm) for long-term ET estimation over alpine meadow on the TP. In this study, the objective was to evaluate and improve the MOD16 algorithm for daily ET estimation over alpine meadow on the TP by modifying, evaluating, and then finalizing the improved algorithm. First, modification of the canopy resistance parameterization scheme was introduced by integrating wind speed and vegetation height in the MOD16 algorithm. The algorithms for temperature and moisture constraints for canopy stomatal conductance were also improved, and the soil evaporation algorithm was modified by adopting Fisher’s equation. Second, the performance of original and modified MOD16 algorithms were evaluated at five representative sites on the TP. The results of the two algorithms under non-precipitation and after-precipitation days were also analyzed, and the performance of the MOD16 product, original MOD16 algorithm, and modified algorithm at four representative sites with an 8-day time window were compared. Third, the uncertainties and limitations of the modified MOD16 algorithm were determined. The study will help to obtain more accurate long-term and continuous ET over alpine meadow on the TP.

2. Materials and Methods

2.1 Study Sites Observed data at five flux tower sites on the TP during the growing season were collected (Liu et al., 2011; Li et al., 2013; Chen et al., 2015; Chen et al., 2017). Observation data at Arou site can be downloaded from the internet (http://westdc.westgis.ac.cn/data/). The vegetation type was alpine meadow with semiarid climate, and elevation ranges from 3033 to 5100 m at the observed area. The land classification type was grassland provided by MOD12Q1 for all sites. The locations of the flux tower sites are shown in Figure 1, and details of the tower sites are listed in Table 1. Only one growing season was available for each location with different years due to lack of data. The observed half-hourly meteorological data, which included air temperature (Ta), relative humidity (RH), and wind speed (u) at five sites, were aggregated into daily data and used as forcing data for the models in this study. The forcing data also included net radiation (Rn) and soil heat flux (G). The net radiation was measured by a Net Radiometer (CNR), while the surface soil heat flux was measured by soil heat flux plate buried 5 cm underground and was corrected using the Thermal Diffusion Equation and Correction method with soil temperature and soil water content (Yang and Wang. 2008). The LE measured by EC was used as validation. The results of the original MOD16 and modified MOD16 algorithms were both driven by observed meteorological data. Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI) data were extracted from MOD15A2 with spatial resolution of 1 km and temporal resolution of 8-day. The unreliable FPAR and LAI data were linearly filled with the nearest reliable value before and after the missing data (Mu et al., 2011). The mean value of the nine surrounding pixels around each site was considered to be the site point-scale value (Mu et al., 2007). Daily FPAR and LAI data were linearly interpolated by the 8-day MOD15A2 product. We also used meteorological data from the Modern-Era

Retrospective Analysis Research and Applications (MERRA) product by NASA’s Global Modeling and Assimilation Office (GMAO) to explore the limitations of the algorithm (Section 4.5). The MOD16 8-day product data were downloaded from the internet

(https://e4ftl01.cr.usgs.gov/MOLT/), which

were driven by MERRA reanalysis data.

2.2 Data Pre-Processing EdiRe software (http://www.geos.ed.ac.uk/homes/jbm/micromet/EdiRe/) was employed for EC data processing with observation correction and quality assessment. The observation correction included the removal of spikes, coordinate rotation (2-D rotation), frequency response correction, sonic virtual temperature correction and corrections for density fluctuation (Webb-Pearman-Leuning, WPL-correction). Data quality assessment was performed using the turbulence stationary test and integrated turbulence characteristics test. The flag system (0, 1, and 2) (Mauder and Foken, 2015) was used for quality assessment, where a flag of 0 represented the best data. Data were discarded when precipitation occurred within 1 h before and after data collection, or when the data quality control flag was marked as 2, as well as data at night when the friction velocity was below 0.1 m s-1 (Blanken, 1998; Liu et al., 2011; Li et al., 2013). If the number (N) of reliable 30-min measurements was less than 40 per day, the daily measurements were set as null values. The missing half-hourly latent heat flux data were filled by the nonlinear regression method (establish the relationship between the latent heat flux and net radiation, LE=aR2n +bRn+c), which was considered a better interpolation method for half-hourly data (Xu et al., 2009; Xu et al., 2017). Then the daily ET was calculated by summing the half-hourly gap-filled values. Figure 2 displays the nonlinear regression equation at Arou site. Although EC was recognized as the best method for observed ET estimation compared to the

Bowen Ratio and Lysimeter, (Feng et al., 2015; Baldocchi et al., 2001), energy enclosure issues generally exist (Feng et al., 2015) in which turbulent flux measured by the flux tower is less than the available energy over alpine meadow (Gu et al., 2005; Wu et al., 2015). The energy closure ratios (H+LE)/ (Rn-G) (Goulden et al., 1996; Leuning et al., 2012) at Suli, Arou, Nagqu, Tanggula, and Hulugou sites were 0.80, 0.98, 0.89, 0.97, and 0.68, respectively. Since it was essential to address this issue before calculating the actual daily ET, the following correcting method was applied, which was derived from Twine et al. (2010):

ETcor 

( Rn  G)  ETuncor H uncor  LEuncor

(1)

where ETcor is the corrected evapotranspiration (mm d-1); Rn is net radiation (W m-2); G is soil heat flux (W m-2); and Huncor, LEuncor, and ETuncor are the uncorrected sensible heat flux (W m-2), latent heat flux (W m-2), and evapotranspiration (mm d-1), respectively. To make comparisons with the MOD16 8-day product, 8-day ET from EC was integrated from 30-min measurements. If the number (N) of reliable 30-min measurements from the MOD16 8-day period is greater than 352, the 8-day measured ET can be determined using Mu’s equation (Mu et al., 2011): N

ET8d 

 ET  384 i 1

i

(2)

N

Considering the absence of observed meteorological or EC data, the actual number of available days for Suli, Arou, Nagqu, Tanggula, and Hulugou sites during the growing season were 146, 77, 149, 100, and 117, respectively.

2.3 Modified MOD16 Algorithm

The original MOD16 algorithm developed by Mu et al. (2007; 2011) is based on the Penman-Monteith equation (Monteith, 1965). Generally, daily ET can be divided into four parts: (1) evaporation from wet canopy surface; (2) canopy transpiration; (3) evaporation from wet soil; and (4) and evaporation from dry soil surface. Daytime and nighttime results were obtained for each part, where nighttime and daytime were distinguished by hourly downward shortwave radiations less and equal to/ greater than 10 W m-2, respectively. The biophysical parameters used for each site referred to the parameters of grassland in the study by Running et al. (2017) (Table 2). The detailed information for the MOD16 algorithm can be seen in the publications by Mu et al. (2007, 2011) and Running et al. (2017).

E 

s A   c p  esat  e  / ra s   1  rs / ra 

(3)

where λE is the latent heat flux (W m-2); A is the available energy partitioned between sensible heat and latent heat fluxes on land surface (W m-2); s is the slope of saturated water vapor pressure versus temperature (kPa oC-1); cp is the specific heat capacity of air (J Kg-1 K-1); ρ is the air density (kg∙m-3); (esat-e) is the water vapor pressure deficit at reference height (Pa); γ is the psychometric constant (Pa K-1); ra and rs are the aerodynamic and surface resistances (s m-1), respectively. In our study, the algorithms for plant temperature and moisture constraints, aerodynamic resistance for transpiration, and soil evaporation were modified to fit a single algorithm.

2.3.1 Canopy Surface Resistance Estimation Resistance parameterization is significant to estimate ET based on the Penman-Monteith method

(Shuttleworth and Wallace, 1985; McCabe et al., 2005; Mu et al., 2007; Mu et al., 2011; Ershadi et al., 2015), and canopy transpiration is an important partition for ET. In the original MOD16 algorithm, canopy surface resistance is expressed as a function of air temperature, LAI, and RH (Shuttleworth and Wallace, 1985). However, the canopy surface resistance is also affected by the microclimate and vegetation physiological characteristics (Vörösmarty et al., 1998). Jacquemin and Noihan (1990) proposed the Jarvis scheme to estimate canopy resistance, which represented the combination effects of plant stress in terms of solar radiation, humidity, air temperature, and soil moisture, as been implemented in the Noah model. Similarly, Fisher et al. (2008) introduced an eco-physiological theory that considered multiple stresses on plant function through biophysical remote sensing metrics. The canopy surface resistance in the original MOD16 algorithm was estimated as (Mu et al., 2007; 2011):

 Gs2 (Gsi1  Gcu )  Cci   Gs2  Gsi1  Gcu 0  Gcu  g cu rcorr

LAI  0, (1- Fwet )  0 LAI  0, (1- Fwet )  0

Gs2  glsh

(4)

C m(T min)m(VPD )rcorr i  daytime Gsi1   L i  nighttime 0 1 rsi  Cci  1.0   Tmin  Tmin_ close m(Tmin )    Tmin_ open  Tmin_ close  0.1 

Tmin  Tmin_ open Tmin_ close  Tmin  Tmin_ open Tmin  Tmin_ close

(5)

1.0   VPDclose  VPD m(VPD)    VPDclose  VPDopen 0.1 

VPD  VPDopen VPDopen  VPD  VPDclose

(6)

VPD  VPDclose

where i means the variable value at daytime or nighttime; G1s is the stomatal conductance; G2s is the leaf boundary-layer conductance; Gcu is leaf cuticular conductance; gcu is cuticular conductance per unit LAI (0.00001 m s-1); glsh is leaf conductance to sensible heat per unit LAI (Table 2); rcorr is a corrector for atmospheric temperature and pressure with standard conditions; m(Tmin) and m(VPD) are constraints for plant temperature and moisture; close means nearly complete inhibition (full stomatal closure) and open refers to no inhibition to transpiration; and Tmin is the minimum air temperature (oC). The minimum temperature for full stomatal closure of the grassland was set to -8 oC. However, the Tmin_close for alpine meadow may be different with that for grassland due to alpine characteristic. The lower temperature constraint probably induces the underestimation of the stomatal conductance and transpiration. Fisher et al. (2008) considered that when leaf area, light and temperature were high and VPD was low, optimal stomata conductance occurred and thus updating the approach developed by June et al. (2004). While several studies (Running and Nemani, 1988; Granger and Gray, 1989; Mu et al., 2007; Mu et al., 2011) regarded VPD as an indicator of plant moisture, it may fail to capture water stress in semi-arid and arid regions (Song et al., 2012). In our modified algorithm, the temperature and moisture constraint for stomatal conductance in the modified algorithm was expressed using the equations adopted by Fisher et al. (2008).

mT  exp(( mm 

Tmax  Topt Topt f APAR

f APAR max

)2 )

(7)

(8)

where mT and mm are the plant temperature and moisture constraints corresponding to m(Tmin) and m(VPD) in the original algorithm, respectively; Tmax is the maximum temperature during the day; Topt is the optimum plant growth temperature (25 oC), which has been proven to obtain good results in high altitude areas (Yuan et al., 2010); fAPAR is the fraction of PAR absorbed by green vegetation cover; and fAPARmax is the maximum fAPAR during the growing season.

2.3.2 Aerodynamic Resistance Estimation for Transpiration In the original MOD16 algorithm, canopy aerodynamic resistance was obtained from the parallel resistance to convective and radiative heat transfer, which is a function of air temperature that does not need wind speed and soil moisture data (Mu et al., 2011).

rh  rr rh  rr 1 rh  glbl ra 

rr 

(9)

cp 4 (Ti  273.15)3

where rh and rr are the resistances to convective and radiative heat transfer, respectively; glbl is leaf-scale boundary layer conductance (m s-1); and σ is the Stefan-Boltzmann constant (5.67×10-8 W m-2K-4). However, wind speed has been recognized as a critical variable of aerodynamic resistance that influences ET (Sellers et al., 1997). Based on Ershadi et al. (2005) suggesting that Thom’s equation (Thom, 1975) was better than Mu’s equation (2011) for estimating aerodynamic resistance, we applied Thom’s method to estimate aerodynamic resistance with wind speed for canopy transpiration (Thom, 1975):

ln( ra 

zd zd ) ln( ) zom zoh k 2u z

(10)

where z is the reference height (m); d is the zero displacement height (m); uz is the wind speed at the height of z (m s-1); and k is von Karman’s constant (0.41). zom and zoh are the roughness heights of momentum and water vapor transfer (m), respectively, and are calculated by equation 11 (Brutsaert, 2005):

zom  0.123hc zoh  0.1zom

(11)

where hc is the vegetation height (m). We set constant values for each month according to observed vegetation height.

2.3.3 Soil Evaporation Soil surface resistance is expressed as a function of air temperature and VPD in the original MOD16 algorithm, which is questionable due to the ignorance of soil moisture for different soil texture types (Di et al., 2015). Thus, the uncertainty of soil surface resistance induces large uncertainty for soil evaporation in the original MOD16 algorithm. Moreover, there is little knowledge regarding the boundary layer resistance for soil evaporation (Mu et al., 2011). Due to the difficulty of surface and aerodynamic resistances estimation, Fisher et al. (2008) proposed a simple bio-meteorological method based on the Priestley-Taylor for ET estimation. Therefore, Fisher’s method (Fisher et al., 2008) for soil evaporation was adopted in the modified algorithm:

 Ewet _ s   Fwet

sAs s 

sA  Edry _ s   Fsm (1  Fwet ) s s 

(12)

where α is empirical coefficient (1.26); As is the component of available energy on soil surface (1-Fc)Rn-G; Fc is the vegetation cover fraction; Fwet is the relative surface wetness (RH4); and Fsm is soil moisture constraint, which is equal to RHVPD/β (Fisher et al., 2008).

2.4 Statistical Index We used the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean bias (MB), and Nash-Sutcliffe efficiency coefficient (NSE) for statistical evaluation (Table 3).

3. Results 3.1 Estimated Daily ET Comparison between Original MOD16 and Modified Algorithms To make comparisons, observed air temperature, relative humidity, wind speed, net radiation, and soil heat flux data at the five sites were used to force the original MOD16 and modified algorithms (Figure 3). To analyze the performance of two algorithms at different sites, R2, RMSE, MAE, and NSE between the observed ET and simulated ET via different algorithms were used as statistical indices (Figure 4).

ET estimated by the original MOD16 algorithm had no significant correlation with measured ET at Suli, Tanggula, and Nagqu sites, while ET estimated using the original algorithm were in good

agreement with measured values at Arou and Hulugou sites. However, the original MOD16 algorithm underestimated ET for all sites (Figure 3). The modified MOD16 algorithm improved the accuracy of estimated ET with higher R2 and NSE, and lower RMSE and MAE (Figure 4). The ET estimated by the modified algorithm at Suli site was slightly underestimated after July 9, 2010 and overestimated after August 1, 2010 until the end of August, which may be due to the influence of the permafrost thawing. NSE of the modified algorithm at Arou site was 0.18, which demonstrates that the simulation does not perform well at areas with high vegetation cover. It was clear that the modified algorithm underestimated ET before July 3, and overestimated ET after September 20, 2014 at Arou site. The modified algorithm underestimated ET with a relative error of 12.1% at Nagqu site, while it performed better than the original algorithm. The modified algorithm reduced the variation amplitude and improved the accuracy of the estimated ET at Tanggula site. Both algorithms underestimated ET during the growing season, while the modified MOD16 algorithm overestimated ET during the end of April. The average R2 increased from 0.26 using the original algorithm to 0.68 using the modified algorithm for five sites. The RMSE for five sites ranged from 1.09 to 1.98 mm d-1 with a mean value of 1.56 mm d-1 using the original MOD16 algorithm, while RMSE ranged from 0.41 to 1.11 mm d -1 with a mean value of 0.78 mm d-1 using the modified algorithm (Figure 4b). MAE decreased from 1.28 using the original algorithm to 0.61 mm d-1 using the modified algorithm. NSE values increased significantly using the modified algorithm at four sites, except Hulugou site (Figure 4d), and the mean NSE of the modified MOD16 algorithm for all sites was 0.57, which was higher than that of the original MOD16 algorithm (-0.71). RMSE and MB in the modified MOD16 algorithm were 0.78 and 0.09 mm d-1, while those were nearly 0.88 and -0.24 mm d-1 at the Qomolangma Station (Chen et al., 2013b). The monthly R2 and RMSE between the High resOlution Land Atmosphere surface Parameters from Space

(HOLAPS) dataset and the LandFlux-EVAL dataset were 0.98 and 2.69 W m-2 over the whole TP. respectively (Peng et al., 2016). Although it seems that the HOLAPS datasets provided better ET estimation this suggestion was based on cross-comparison of the existing datasets with a monthly scale. These results indicate that more validation with in-situ data and remote-sensing data are still needed to improve the accuracy of daily ET estimation on the TP. Moreover, the original MOD16 algorithm tended to underestimate ET at high values and overestimate at low values, which induced substantial uncertainties (Figure 3; Table 4). The modified MOD16 algorithm reduced deviations by integrating wind speed and vegetation height into canopy aerodynamic resistance and substituting the canopy surface resistance and soil evaporation calculation method proposed by Fisher et al. (2008). The mean bias of peak ET and minimal ET using the modified MOD16 algorithm for all sites were 0.42 and 0.12 mm d -1, respectively, which were lower than those of the original MOD16 algorithm (1.5 and 0.3 mm d-1, respectively). 3.2 Daily ET Comparison of After-Precipitation Days and Non-Precipitation Days In addition to the strong correlation between net radiation and ET (Wang et al., 2007), precipitation is another important factor (Sun et al., 2011) since water is a major constraint on ET in semi-arid areas (Tang et al., 2010). Due to the unavailability of the precipitation data at Nagqu site, we compared the ET estimation by the original MOD16 and modified algorithms for non-precipitation and after-precipitation days at Suli, Arou, Tanggula, and Hulugou sites (Figure 5). The day after precipitation events was defined as the day with short duration (<1 hour) precipitation, such as at Arou and Hulugou sites, or the next day after one with longer precipitation duration or more precipitation events, such as at Suli and Tanggula sites. It can be found that the modified algorithm had better performance than the original MOD16

algorithm for both non-precipitation and after-precipitation days (Figure 5). The modified algorithm performed slightly better with higher R2 (0.70) and lower RMSE (0.61 mm d-1) for after-precipitation days than for non-precipitation days at Suli site, while it had better results for non-precipitation days than for after-precipitation days at Arou, Tanggula and Hulugou sites. Based on such performance, the modified algorithm may be more suitable for estimating ET for non-precipitation days with higher accuracy than ET for after-precipitation days, which had large observation errors.

4. Discussion

4.1 Comparison 8-day Estimated ET using the Original MOD16 Algorithm and Modified MOD16 Algorithm To test the effect of different time windows on ET estimation by the original MOD16 and modified MOD16 algorithms, four sites (Suli, Nagqu, Tanggula and Hulugou) were selected for comparison (Figure 6). The Arou site was not analyzed due to the unavailability of daily data aggregating into 8-day value. For forcing data, the original and modified MOD16 algorithms implemented in-situ data, while the 8-day MOD16 ET used GMAO reanalysis data. The underestimation of the MOD16 ET product was significant at Suli (Figure 6a), Nagqu (Figure 6b), and Tanggula (Figure 6c) stations during the growing season. The MOD16 product overestimated 8-day ET in April and underestimated 8-day ET from May at Hulugou site (Figure 6d). The MAE of the MOD16 product at Suli, Nagqu, and Tanggula sites were 12.64, 14.18 and 17.69, mm 8d-1, respectively, and the RMSE values at these sites were all greater than 10 mm 8d-1, indicating the existence of large errors (Table 5). Moreover, the MOD16 product performed worse with higher RMSE

(8.67 mm 8d-1), higher MAE (6.78 mm 8d-1), and lower NSE (0.55) at Hulugou site, which may be due to the bias of GMAO reanalysis meteorological data. The input data in the MOD16 product (including downward shortwave radiation, air temperature and relative humidity) extracted from the GMAO reanalysis data had large biases with higher RMSE and MAE (Figure 7a, b, and c). This was in agreement with Mu et al. (2011) who stated that large errors exist on a local scale, especially for complex terrain. Compared to the MOD16 ET product, the RMSE of the original MOD16 algorithm decreased by substituting the observed meteorological data at four sites (Table 5). The ET obtained from the modified MOD16 algorithm had the highest correlations with measured ET compared to ET of the MOD16 product and original MOD16 algorithm at the four sites. Furthermore, the MAE values of the modified MOD16 algorithm were lower than those of the MOD16 product and original MOD16 algorithm. The NSE values of the modified MOD16 algorithm at Suli and Tanggula sites were 0.68 and 0.58, respectively, while NSE values of the MOD16 product and original MOD16 algorithm were negative. NSE of the modified MOD16 algorithm at Nagqu site was only 0.02, which was higher than the negative NSE values of the MOD16 product and original MOD16 algorithm. Therefore, it can be concluded that the modified MOD16 algorithm performed best with higher R2 and NSE values and lower RMSE and MAE with an 8-day time window compared to the original MOD16 algorithm and MOD16 ET product.

4.2 Daily ET Comparisons of the Modified algorithm, PT-JPL and Regress Methods Re-parameterizations of the original algorithm have been reported in several studies. For instance, Yuan et al. (2010) modified MOD16 algorithm by substituting the temperature constraint for stomatal

conductance and energy partition equations and recalibrated some parameters to set invariant values for all biome types. Peng et al. (2016) implemented the modified aerodynamic resistance parameterization from SEBS into the MOD16 algorithm (PMSRB_PU) to estimate ET with seasonal and annual variations on the TP. In the latter study, it was found that substantial spatial-temporal differences existed for the PMSRB_PU and other four datasets on the TP. For the modified algorithm in this study and Peng’s work, wind speed and vegetation height were considered into the aerodynamic resistance parameterization. The advantage of the modified algorithm in this study was to introduce temperature and moisture constraints suggested by Fisher et al. (2008) and Yuan et al. (2010) into the stomatal conductance and to reduce the uncertainty of boundary layer resistance for soil evaporation. Although we obtained high accuracy ET over the alpine meadow sites using the parameters of the grassland, the parameters for different species within the same biome type were different. Considering that we have not yet calibrated the parameters for alpine meadow, which may induce uncertainty, further study is needed to calibrate these parameters and improve the parameterization scheme.

4.3 Modular Analysis of the Modified MOD16 Algorithm To understand the relative importance of each part of the modified MOD16 algorithm, a modular analysis was performed by making a single change in aerodynamic resistance, canopy resistance and soil evaporation (Table 6). The fractions of interception, transpiration, and soil evaporation relative to ET with the original MOD16 algorithm for each tower site are listed in Table 7. The soil evaporation accounted for the largest proportion, followed by plant transpiration for all sites. Interception accounted for the lowest proportion, where the negative interception even occurred at Nagqu site in the original MOD16 algorithm. Nearly zero interception occurred at daytime due to the lower RH (< 70%) and

relative higher air temperature, while negative interception (or condensation) occurred at nighttime due to the negative energy partition, higher RH (> 70%), and lower air temperature at Nagqu site. Therefore, the modification of the soil evaporation and plant transpiration could improve the accuracy of ET estimation. The algorithm with the aerodynamic resistance change performed better with higher R2, lower MAE, lower MB and higher NSE values than the original MOD16 algorithm at the five sites (Table 6), implying that the aerodynamic resistance change implemented into the modified MOD16 algorithm could obtain good results. In addition, the algorithm with the canopy resistance change improved the results insignificantly. The algorithm with the soil evaporation change performed better than that with the aerodynamic change, canopy surface change and original MOD16 algorithm with a higher R2, lower MAE, and higher NSE at four sites, except Hulugou site. MAE of the algorithm with the soil evaporation change at Hulugou site increased from 0.86 to 0.91 mm d-1, while the MB increased from 0.64 to 0.89 mm d-1 using the original algorithm. The modified algorithm with soil evaporation change had the highest accuracy with higher R2 (0.60), lower MAE (0.83 mm d-1) and MB (0.69 mm d-1), and higher NSE (0.26) compared to the changes of aerodynamic resistance and canopy surface resistance, and original MOD16 algorithm, which suggests that the modified algorithm was the most sensitive to soil evaporation change. The modified algorithm improved the accuracy most compared with the original algorithm, the algorithm with the aerodynamic resistance change, canopy surface resistance change, and soil evaporation change at four sites except Arou site. The algorithm with the soil evaporation change performed better than the modified algorithm at Arou site. Among the three changes, the algorithm was the most sensitive to soil evaporation change, followed by aerodynamic resistance change and was the least sensitive to canopy resistance change.

The modified MOD16 algorithm performed better with the highest R2 (0.68), lowest RSME (0.78 mm d-1), lowest MAE (0.61 mm d-1), and highest NSE (0.57) value than algorithms with a single change and the original algorithm (Table 6), thus the most effective algorithm should implement all changes.

4.4 Uncertainty and Limitation of the Modified Algorithm The modified MOD16 algorithm produced more accurate results for daily ET and 8-day ET estimations than the original MOD16 algorithm and MOD16 product based on higher R2 and lower RMSE and MAE values. However, many uncertainties still exist for the original and the modified MOD16 algorithms. One source of uncertainty of the original and modified MOD16 algorithms may be due to the forcing data from GMAO reanalysis data, so the modified MOD16 algorithm forced by observation (Figure 8a) and GMAO reanalysis data (Figure 9) were compared. The modified algorithm driven by the GMAO reanalysis data showed poorer performance with a lower R2 (0.47), higher RMSE (1.19 mm d-1) and lower NSE (–0.01) than the observation force algorithm, which suggests that the bias of the GMAO reanalysis data led to substantial errors for ET estimation. Aside from the forcing data, uncertainties from land cover misclassification may induce biases for the MOD16 product (Mu et al., 2011). Mu et al. (2011) developed original MOD16 ET with 12 classifying biome types, where each type had the same biophysical parameter globally that would produce uncertainties. Although there was no mismatched land cover at the five sites (all were alpine meadows), the characteristics and parameters of the alpine meadow were different from those of grassland which have been used in the algorithm. There were no parameters for alpine meadow in the MOD16 algorithm that would induce uncertainty for ET estimation.

In addition, some limitations exist in the physical mechanism of the MOD16 algorithm. The soil moisture constraint has been validated by soil volumetric water with good results (Fisher et al., 2008); however, the flux sites on the TP covered with permafrost, which has thawing and melting processes, have not been verified. Thus, the constant value β, defined as the relative sensitivity to VPD, needs to be studied in future work for areas with permafrost. Moreover, the soil moisture constraint has a large uncertainty when net radiation is large (Yang et al., 2016). The relative surface wetness was used for the distinction of wet and dry surfaces, and no water covering the surface was identified when RH was less than 70%, which is unreasonable for the judgment of surface wetness. Considering that precipitation at the observation sites occurred with short duration on the TP, which changed the soil moisture for a short time, daytime or nighttime RH may not reflect the real status of soil moisture and probably introduce errors for ET estimation. Another type of uncertainty stemming from MODIS FPAR and LAI most likely influenced the ET estimation. The mean value of the nine pixels around each site was considered to represent the site point-scale. However, for the flux sites located at high altitude and mountainous terrain on the TP, the mean value may not represent the site condition due to the heterogeneous land cover over the complex terrain. Mu et al. (2011) proposed that the underestimation of LAI led to the underestimation of ET through the overestimation of surface resistance. Moreover, Fc influenced the available energy portion between the canopy and soil surface. Thus uncertainties in Fc and LAI may be one reason for the obvious underestimation of the MOD16 product during the growing season at representative site (Figure 10). Therefore, we compared the simulated with observed Fc and FPAR values at Suli site, which had monthly observations of Fc and LAI (Qin et al., 2014). It was clear that monthly FPAR and LAI retrieved from the MODIS product were underestimated when compared with the measured value,

causing a decrease in the available energy for the canopy and an underestimation of canopy transpiration. Aside from the above causes, errors from the modified algorithm may be attributable to the use of α as a constant value. For instance, Fisher et al. (2008) set α equal to 1.26 to maintain the potential latent heat flux equation intact. However, the impact of the use of constant α influenced the estimation of the soil evaporation and affected the ET estimation in the modified MOD16 algorithm (Figure 11). Increased α led to an increase in R2, and decreased in RMSE and MAE, which means that the soil evaporation was underestimated with low α value, and vice versa. Although the modified MOD16 algorithm performed better at five sites, there were more uncertainties when applied across the whole TP or to other areas with different surfaces and climates, among which the most uncertainty was the spatial distribution of wind speed and the dynamic of vegetation height. Although many reanalysis data products have provided wind speed data, these data have a coarse resolution that would induce large uncertainties under heterogeneous surfaces. The wind speed extracted from the GMAO reanalysis data had a lower R2, higher RMSE, and lower NSE compared to observed values (Figure 7d). There was no vegetation height dataset for the whole TP region except for the middle reaches of the Heihe River Basin (Li et al., 2017). To validate the applicability of the modified algorithm, four sites with different surfaces and different climates from AmeriFlux was selected and compared (Table 8 and 9; Figure 12). It was found that the modified algorithm had better performances than the original algorithm at sites with different climates and surfaces. This was especially evident at grassland sites, where the modified algorithm improved the accuracy of ET estimation with an increase in R2 from 0.2 to 0.58, decrease in RMSE from 1.28 to 0.74 mm d-1, and increase in NSE from -1.33 to 0.21, indicating that the modified algorithm improved the

accuracy of ET estimation over the grassland under different climates. The modified algorithm slightly improved the performance at evergreen needleleaf forest site, which had relatively higher R2, lower RMSE, and higher NSE compared with the original algorithm. The modified algorithm resulted in an increased correlation (R2) with measured values for closed shrubland site. These results indicate that the modified algorithm could estimate ET with higher accuracy for several land covers compared to the original MOD16 algorithm, for which it is more pronounced for grassland sites. However, the improvement is not obvious for evergreen needleleaf forests and closed shrubland ecosystems. The reason for the better performance of the modified algorithm than needleleaf forests and shrublands may be that the aerodynamic resistance parameterization with wind speed and vegetation height is more suitable for short canopy and bare soil rather than tall vegetation (Yang et al., 2002). Therefore, the proposed algorithm may still need to be improved in the future to fit more land cover types, which we expect to focus on in future studies.

5. Conclusions This paper presented a modified MOD16 algorithm that incorporated recalculation of the canopy surface and aerodynamic resistances and adopted other soil evaporation equations. The performances of both the original MOD16 and modified MOD16 algorithms were compared at five flux sites over alpine meadow on the TP. The following conclusions were based on the obtained results. The modified MOD16 algorithm presented higher accuracy with higher R2 (0.68), lower RMSE (0.78 mm d-1), lower MAE (0.61 mm d-1), and higher NSE (0.57) values than the original MOD16 algorithm at the five sites during the growing season, indicating that the modified MOD16 algorithm is more effective in simulating the water and energy balance on the TP.

The modified algorithm performed slightly better with higher R2 (0.70) and lower RMSE (0.61 mm d-1) values for after-precipitation days than for non-precipitation days at Suli site, while it had better results for non-precipitation days than after-precipitation days at Arou, Tanggula, and Hulugou sites. This finding suggests that the modified algorithm might be more suitable for estimating ET for non-precipitation days with higher accuracy than ET for after-precipitation days, which had large observation errors in our study. The modular analysis suggested that the modified algorithm was the most sensitive to change in soil evaporation, followed by change in aerodynamic resistance. Comparisons of the modified MOD16 algorithm with PT-JPL (Fisher et al., 2008) and Regress (Wang et al., 2010) indicate that the modified algorithm could produce ET with high accuracy over the alpine meadow sites on the TP during the growing season. Overall, the modified MOD16 algorithm improved some equations under the principle of the MOD16 algorithm. We will perform more intense and extensive research in future studies to improve the MOD16 algorithm from point to point by starting with theoretical physics principles in aim to develop a new scheme for ET estimation over the TP.

Acknowledgements: The authors would like to thank the Heihe Watershed Allied Telemetry Experimental Research (HiWATER), the Nagqu Station of plateau Climate and Environment, the Cryosphere Research Station on the Tibetan Plateau and the Qilian Alpine Ecology and Hydrology Research Station, Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences (CAS) for providing meteorological and eddy covariance flux data. Thanks for AmeriFlux data resources funded

by the U.S. Department of Energy’s Office of Science. This work is supported by the National Key Research and Development Plan (2017YFC0404302), China National Natural Science Foundation (Grants Nos. 41730751, 41671056, 41421061).

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Figure Captions Figure 1. Observed flux sites over alpine meadow on the Tibetan Plateau (TP). Figure 2. The nonlinear regression between latent heat flux (LE) and net radiation (Rn) at Arou site. Figure 3. The time series of estimated daily evapotranspiration (ET) using the original MOD16 algorithm and modified algorithm, and EC observed daily ET at Suli (a); Arou (b); Nagqu (c); Tanggula (d); and Hulugou (e) flux sites. Figure 4. The coefficient of determination (R2, a); Root Mean Squared Error (RMSE, b); Mean Absolute Error (MAE, c); and Nash-Sutcliffe Efficiency coefficient (NSE, d) of the original MOD16 algorithm and modified MOD16 algorithm at Suli, Arou, Nagqu, Tanggula, and Hulugou sites. Figure 5. Daily ET comparison for non-precipitation days and precipitation days at the Suli (a, b); Arou (c, d); Tanggula (e, f); and Hulugou (g, h) sites. Figure 6. The time series of 8-day ET estimated with the MOD16 product, original MOD16 algorithm, and modified algorithm forced by in-situ data at the Suli (a); Nagqu (b); Tanggula (c); and Hulugou (d) sites. Figure 7. Validation of downward shortwave radiation (DWS, a), air temperature (Ta, b), relative humidity (RH, c) and wind speed (u, d) extracted from the Global Modeling and Assimilation Office (GMAO) reanalysis data and measured values at five sites. Figure 8. Comparisons of daily ET estimated using the modified MOD16 algorithm (a), PT-JPL (b) and Regress (c) methods with measured ET over the alpine meadow sites on the TP. Figure 9. Daily ET estimated by the modified algorithm driven by GMAO reanalysis data. Figure 10. Monthly vegetation cover fraction (Fc) and leaf area index (LAI) comparison of observation and MODIS product at the Suli site.

Figure 11. The impact of different α used in the modified algorithm. Figure 12. Daily ET comparison using the original and modified algorithms with measured ET over Grassland (Grass; a, b), Evergreen needleleaf forests (ENF; c, d) and Closed Shrubland (CSH; e, f) sites. The left panel is the results of the original algorithm while the right panel represent the results of the modified algorithm.

Table 1. Information of observed flux sites during growing season on the TP.

Site

Suli

Latitude

Longitude

Elevation

Mean Ta

Mean RH

Vegetation

Date

(o)

(o)

(m)

(oC)

(%)

height (m)

collection

38.42

98.32

3885

6.35

67.94

0.6–0.12

2010.5.7–20 10.9.29

Arou

38.05

100.46

3033

7.58

67.23

0.20–0.30

2014.4.22–2 014.10.10

Nagqu

31.37

91.90

4509

7.36

60.54

0.08–0.20

2011.4.27–2 011.10.21

Tanggula

33.07

91.93

5100

4.48

71.09

0.05–0.10

2010.5.17–2 010.9.26

Hulugou

38.25

99.88

3232

6.96

61.99

0.08–0.20

2013.4.14–2 013.10.14

Table 2. Biophysical parameter values of grassland (Grass) (Running et al., 2017).

Grass

Tmin_open

Tmin_close

VPDclose

(oC)

(oC)

(Pa)

12.02

-8.00

4200

VPDopen

glsh

gle_wv

gcu

CL

rblmin

rblmax

(Pa)

(m s-1)

(m s-1)

(m s-1)

(m s-1)

(s m-1)

(s m-1)

650

0.02

0.02

0.00001

0.0055

60.0

95.0

Table 3. Statistical indexes used in this study.

Statistical variable

Abbreviation

Equation

Coefficient of determination

R2

(n (Qest Qobs )   Qest  Qobs ) 2

n

i 1

n

n

n

n

i 1 n

i 1

n

(n Qest  ( Qest ) )(n Qobs 2  ( Qobs ) 2 ) 2

i 1

Root mean square error

2

i 1

i 1

n

RMSE

 (Q

 Qobs ) 2

est

i 1

n Mean absolute error

n

Q

MAE

i 1

est

 Qobs

n

Mean bias

MB

n

 (Q i 1

obs

 Qest )

n Nash-Sutcliffe efficiency coefficient

n

NSE

1

 (Q

obs

 Qest ) 2

 (Q

obs

 Qobs ) 2

i 1 n

i 1

Note: Qest stands for simulations, Qobs is the observed values, and n is the sample number.

i 1

Table 4. Mean bias of peak ET and minimal ET for each site.

Mean bias

algorithm

Suli

Arou

Nagqu

Tanggula

Hulugou

Mean bias for peak ET

Original

1.75

1.00

2.17

1.10

1.01

(mm d-1)

Modified

0.44

0.14

0.79

0.09

0.26

Mean bias for minimal ET

Original

0.22

0.49

0.63

0.03

0.10

(mm d-1)

Modified

–0.02

0.17

0.44

–0.09

0.02

Table 5. Statistical analysis of the MOD16 product, original algorithm and modified algorithm at the Suli; Nagqu; Tanggula; and Hulugou sites.

Site

Suli

Algorithm

MOD16 product

R2

0.1

RMSE (mm 8d-1)

RRMSE

MAE (mm

MAPE

NSE

(%)

8d-1)

(%)

13.82

52.42

12.64

46.73

–4.27

9.73

36.9

8.69

32.74

–1.61

3.40

12.90

2.65

9.62

0.68

14.89

65.06

14.18

61.99

–5.63

11.61

50.72

10.76

44.77

–3.03

5.74

25.06

4.54

20.37

0.02

18.45

70.79

17.69

67.12

-10.4

1 Original Algorithm

0.3 5

Modified Algorithm

0.7 4

Nagqu

MOD16 product

0.3 8

Original Algorithm

0.4 4

Modified Algorithm

0.4 5

Tanggul

MOD16 product

a

0.1 1

Original Algorithm

0.5

9 9.01

34.59

8.14

31.65

-1.74

3.52

13.52

3.46

13.62

0.58

4 Modified Algorithm

0.7

2 Hulugou

MOD16 product

0.9

8.67

57.27

6.78

56.01

0.55

4.19

27.71

3.54

28.12

0.89

2.59

17.12

2.56

35.08

0.96

5 Original Algorithm

0.9 6

Modified Algorithm

0.9 6

Note: RRMSE means relative RMSEs to the mean measured ET, MAPE means the difference between the estimated value and measured value divided by the measured value.

Table 6. Statistics of modular analysis for all sites.

Statistics

R2

Sites

Original

Change

Change

Change soil

Modified

algorithm

aerodynamic

canopy

evaporation

algorithm

resistance

resistance

Suli

0.09

0.20

0.23

0.72

0.71

Arou

0.68

0.71

0.67

0.73

0.74

Nagqu

0.02

0.11

0.05

0.47

0.62

Tanggula

0.04

0.08

0.07

0.49

0.77

Hulugou

0.65

0.70

0.66

0.74

0.78

MAE

Suli

1.32

1.13

1.11

0.64

0.52

(mm d-1)

Arou

1.11

1.05

1.20

0.89

0.96

Nagqu

1.67

1.40

1.56

1.02

0.71

Tanggula

1.23

1.12

1.13

0.89

0.31

Hulugou

0.86

0.81

0.86

0.91

0.62

MB

Suli

1.02

0.82

0.91

0.53

0.17

(mm d-1)

Arou

0.78

0.52

0.44

0.51

0.09

Nagqu

1.46

1.18

1.35

0.93

0.57

Tanggula

0.64

0.47

0.50

0.89

0.01

Hulugou

0.60

0.25

0.48

0.54

0.15

Suli

-1.01

-0.48

–0.55

0.48

0.68

Arou

-0.14

-0.02

–0.32

0.29

0.18

NSE

Nagqu

-2.22

–1.30

–1.78

–0.27

0.35

Tanggula

-2.24

–1.58

-1.364

–0.69

0.76

Hulugou

0.44

0.52

0.45

0.61

0.72

Table 7. The proportion of interception, transpiration and soil evaporation in the original algorithm for each site.

Site

Interception (%)

Transpiration (%)

Soil evaporation (%)

Suli

5.08

34.27

60.65

Arou

1.00

55.16

43.84

Nagqu

-1.71

33.80

67.91

Tanggula

0.67

5.87

93.46

Hulugou

2.26

39.96

57.78

Table 8. Site information from AmeriFlux.

Site ID

Lat, Lon (o)

Climate

Vegetation type

Vegetation cover

Data period

US-Fwf

35.45, -111.77

Mediterranean

Grass

0.25

2010

US-Seg

34.36,-106.7



Grass

0.14

2012-2013

US-Fuf

35.09, -111.76

Mediterranean

ENF

0.52

2009-2010

US-FR3

29.94, -97.99

Humid Subtropical

CSH

0.47

2012

Note: The full names of Grass, ENF and CSH are Grassland, Evergreen Needleleaf forests, and Closed Shrubland.

Vegetation cover is extracted from the MOD15A2 of the study period.

Table 9. Statistics indices of the original and modified algorithms over the Grassland, Evergreen needleleaf forests and Closed shrubland sites.

Algorithm

R2

RMSE

(mm

MAE (mm d-1)

d-1) Grass

ENF

CSH

Original algorithm

0.2

0.88

0.62

Modified algorithm

0.58

0.53

0.37

Original algorithm

0.16

0.93

0.76

Modified algorithm

0.19

0.89

0.7

Original algorithm

0.17

1.15

0.89

Modified algorithm

0.59

1.85

1.52

A Modified MOD16 Algorithm to Estimate Evapotranspiration over Alpine Meadow on the Tibetan Plateau, China

Highlights: •

Five precious representative alpine meadow sites on the Tibetan Plateau were used to evaluate and modified MOD16 algorithm.



A modified MOD16 algorithm was proposed to improve the accuracy of ET estimation



A modular analysis was performed for understanding the relative importance of each part of the modified MOD16 algorithm.