1. Introduction
Latent heat of evapotranspiration, ET, is a primary process driving the energy and water exchange between the hydrosphere, atmosphere, and biosphere (Priestley and Taylor 1972). It is required by short-term numerical weather predication models and longer-term climate simulations (Rowntree 1991).
Conventional techniques essentially provide point measurements, which usually are not representative of the regions because of the heterogeneity of land surfaces and the dynamic nature of heat transfer processes. Unfortunately, current models cannot simulate ET accurately (Betts et al. 2003; Robock et al. 2003; Mitchell et al. 2004; Betts et al. 2006; Yang et al. 2006).
Satellite remote sensing is a promising tool that provides reasonable estimates of ET or the evaporative fraction that is defined as the ratio of ET to available energy (the difference between Rn and ground heat flux). A large number of techniques has been proposed to estimate ET in the last few decades (Kite and Droogers 2000; Drexler et al. 2004; Verstraeten et al. 2005; Wang et al. 2005c).
Methods that use Ts − Ta require unbiased Ts retrievals and Ta interpolated from ground-based point measurements (Cleugh et al. 2007). Estimates of the spatial variability in Ta at regional scales with remote sensing suggest an uncertainty of 3–4 K (Goward et al. 1994; Prince et al. 1998). The uncertainties of these data are on the order of several kelvins (Prata and Cechet 1999; Oku and Ishikawa 2004; Peres and DaCamara 2004; Sun et al. 2004; Wang et al. 2007a). Thus, the magnitude of the derived Ts − Ta is often comparable to the uncertainty in its measurement, except for sparsely vegetated surfaces (Caselles et al. 1998; Norman et al. 2000). In other words, the methods are sensitive to an error in Ts or Ta. For example, Timmermans et al. (2007) showed that a ±3 K error in Ts results in an average error of about 75% in sensible heat flux for the surface energy balance algorithm for land (Bastiaanssen et al. 1998) and an average error of about 45% for the two source energy balance methods (Norman et al. 1995) over subhumid grassland and semiarid rangeland.
Two different approaches propose to reduce the sensitivity of flux estimates to uncertainties of Ts and Ta: methods using the temporal variation of Ts (Anderson et al. 1997; Norman et al. 2000; Nishida et al. 2003) and the method using the spatial variation of Ts (e.g., Jiang and Islam 2001). The Ts and normalized difference vegetation index (NDVI) spatial variation (Ts − NDVI) method uses spatial information of Ts and NDVI to reduce the accuracy requirement of the Ts retrievals (Venturini et al. 2004). Jiang and Islam (2001) estimate evaporative fraction using the triangular distribution of the Ts − NDVI spatial variation. Wang et al. (2006) point out that it is the information of temporal variation of Ts, not Ts itself, used in the spatial variation (Ts − NDVI) method, and they propose to estimate evaporative fraction using the day–night Ts difference (ΔTs − NDVI) spatial variation method. Evaporative fraction retrieved from the ΔTs − NDVI is substantially better than that from Ts − NDVI. However, given surface net radiation, it is still difficult to obtain ET directly from the evaporative fraction because ground heat flux is required, which is not measured by most routine observations. Furthermore, the warm edge of Ts − NDVI or ΔTs − NDVI spatial variation, a key parameter of the methods, is determined manually, which hinders their wide usage.
In a previous study, Wang et al. (2007b) proposed to estimate ET using a simple and relative accurate equation that combines Rn, the vegetation index, and Ta or Ts. However, Wang et al. (2007b) failed to effectively take into account the influence of soil moisture (SM). Studies indicate that SM has a potentially important effect on ET (Detto et al. 2006; Gu et al. 2006; Krishnan et al. 2006). This paper incorporates the influence of SM into the ET parameterization. The revised method is implemented to estimate ET globally, and the details are presented in section 6.
2. Data
Two different kinds of ET measurements are used to calibrate and validate the proposed method at site scale: ET measured by the Energy Balance Bowen Ratio (EBBR) method at eight enhanced surface facility (EF) sites over the Southern Great Plains (SGP) in the United States and ET measured by the eddy covariance (EC) method at four AmeriFlux sites, where the measurements of ET, ground heat flux, and surface upwelling longwave radiation are available. Table 1 shows that these sites represent a variety of land types (grassland, native prairie, cropland, and evergreen forest), SM, and vegetation conditions.
At each EBBR facility, soil heat flux G is estimated as the average of data from five soil heat plate sensors (Soil Heat Flow Probes, Radiation & Energy Balance Systems, Inc., Model #HFT3.1) buried at a depth of 5 cm. The ground heat storage term is calculated as a function of the soil heat capacity (computed as a function of SM and estimated at a depth of 2.5 cm) and the integrated soil temperature as observed from five soil heat plate sensors buried between 0 and 5 cm. The ARM EBBR facilities estimate the percent soil water (ratio of the mass of soil water to the mass of dry soil) from the soil water potential measured from five resistance-type SM sensors (SM Probe, Soil test, Inc., REBS Model #SMP-2). The calculated average from the five soil heat plate and SM sensors is used.
3. Outline of the earlier ET algorithm
4. Algorithm improvement
SM has potentially substantial effects on ET (Gu et al. 2006). According to the two-stage theory of ET, atmospheric demand and available energy determine ET when water supply is sufficient, while soil moisture becomes an important factor controlling ET after soil water supply is deficient (Salvucci 1997).
Unfortunately, there is no reliable global or regional SM dataset at the spatial scale of kilometers and the temporal resolution of days. For example, Dirmeyer et al. (2004) compared and validated eight available global SM products and found that SM climatologies vary greatly among the products. Schaake et al. (2004) compared the SM fields simulated by four land surface models in North American Land Data Assimilation System (NLDAS) driven by the same meteorological forcing data and initiated at the same time with the same relative SM. Significant differences are found between NLDAS-simulated SM fields and the different models.
5. Validity at 12 sites over the United States
We used the daily ET data collected from 2001 to 2006 at six sites (EF08, EF12, EF15, EF19, EF20, and RA) to derive the four parameters in Eq. (14). The parameters a0, a1, and a2 are similar to those of our previous study (Wang et al. 2007b; Table 2). Here, a3 is negative and its value is similar for various combinations of vegetation indices and temperatures, demonstrating that DTsR is an important and stabilizing influence on ET.
Using Eq. (14) and the parameters shown in Table 2, we calculate daytime-averaged ET for the twelve sites. The 16-day-averaged values are used in Eq. (14) because the vegetation indices datasets are 16-day averages. Figure 3 demonstrates that measured and predicted ET using Eq. (14) for a range of EVI, daily maximum Ts, and DTsR are comparable at the six sites used to obtain the parameters. Measured and predicted ET are also comparable at the six validation sites (Fig. 4). Figure 5 is a comparison of the measured and predicted ET for a combination of EVI, daily maximum Ts, and DTsR at all 12 sites. Figures 3 –5 demonstrate that Eq. (14) accurately predicts ET over time and space. The equation also accurately simulates the interannual variation at the Goodwin Creek site. Also, notice that there is no substantial difference between the sites used to obtain the parameters and the validation sites.
Table 3 demonstrates that Eq. (14) accurately estimates ET. The average correlation coefficient between the measured and predicted 16-day-averaged ET for a combination of Rn, EVI, daily maximum Ts, and DTsR is about 0.92 for all sites. The correlation coefficient ranges from 0.89 to 0.98 for the 12 sites. The bias of the predicted 16-day-averaged ET is −1.9 W m−2 and varies from −15.2 to +13.6 W m−2 for the 12 sites; the RMSE is about 28.6 W m−2 for all sites and ranges from 17.3 to 33.2 W m−2 for the 12 sites.
EVI appears to more accurately estimate ET than NDVI. The range in the range of bias of ET predicted using EVI is about 10 W m−2 less than that of NDVI. The smaller range of bias of EVI-estimated ET is because the influence of soil background on NDVI is greater than that of EVI.
A statistical comparison of measured and predicted ET for the original and revised methods indicates that the revised method is more accurate based on the correlation coefficients and RMSE (Table 4). More importantly, the revised algorithm works better for water-deficient conditions. This is reflected in an improvement of the accuracy of predicted ET at two drought-stricken sites (EF08 and EF15). The advantage of the revised method is also shown when used to estimate global ET with the original and revised methods (see section 6).
EC-method-measured ET must be corrected, because of the energy imbalance issue, before it is compared with ground-based measurements. We compare predicted ET to measured ET at other sites where there was not enough data to correct the ET measurement by the EC method. For example, the correlation coefficients between predicted ET- and EC-method-measured ET at the Morgan Monroe temperate deciduous forest (39.32°N, 86.41°W) and the Willow Creek temperate broadleaf evergreen forest (45.91°N, 90.08°W) sites are 0.96 and 0.94, respectively, and the RMSEs are both less than 33 W m−2. However, the bias is larger because the EC method underestimated ET without correcting for its imbalance issue.
To investigate how the joint uncertainties of the input data affected ET error, we allowed every input parameter to change in increments of 10% error over an error range of ±20%. For example, Rn varies from 280 to 420 W m−2 in increments of 35 W m−2. We calculated the error on ET for every possible combination of input data error (total in 54 = 625). The error histogram has a standard deviation of 19.1% and an average of 4% (Fig. 6).
6. Global implementation of the improved method
We selected the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly datasets at a spatial resolution of 1° × 1° for the period 1986–95 to estimate global ET (http://www.daac.ornl.gov; Hall et al. 2006). Fifty-two ISLSCP datasets, consisting of a common series for the 10-yr period from 1986–95, are coregistered to a common grid and gap filled for continuity using uniform procedures. Hall et al. (2006) supplies a detailed description of the datasets. We provide only a summary of the data used in this study.
Here, Rn is calculated from a 3-hourly surface radiation budget (SRB). The SRB parameters are derived using radiative transfer–based algorithms applied to the cloud data provided by the International Satellite Cloud Climatology Project (ISCCP; Rossow et al. 1996; Rossow and Schiffer 1999). The Initiative II SRB data differ from a similar set of radiative flux parameters derived from ISCCP, called ISCCP-FD (Zhang et al. 2004). Daytime-averaged Ta is calculated from the Climatic Research Unit (CRU), version 5 (New et al. 1999, 2000), 3-hourly meteorological reanalysis data. The DTaR is also from the CRU, version 5, reanalysis dataset. The NDVI is from the Global Inventory Modeling and Mapping Studies (GIMMS) group at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (Tucker et al. 2005), which is obtained from National Oceanic and Atmospheric Administration (NOAA)/AVHRR observations. All of the above datasets are monthly averaged and at a spatial resolution of 1° × 1°. We used Eq. (20) to predict monthly global ET with the described datasets, which are available globally, except for Greenland Island. The ET over the desert is set to zero. We used the University of Maryland, College Park (UMD; Hansen et al. 2000) and MODIS (Friedl et al. 2002) land cover datasets at a spatial resolution of 1° × 1° to detect desert regions.
We compare ET predicted by Eq. (20) to the 15-model-simulation-averaged ET from the Global Soil Wetness Project-2 (GSWP-2; Dirmeyer et al. 2006). A total of 15 different state-of-the-art land surface models participated in the project (Dirmeyer et al. 2006). The models are also forced by ISLSCP Initiative II datasets. However, their parameterizations of ET, mainly based on the Monin–Obukhov similarity theory, are entirely different from our proposed method (Sellers et al. 1997; Dirmeyer et al. 2006). At the beginning of this paper, we noted that current models cannot accurately simulate ET (Betts and Jakob 2002; Betts et al. 2003; Robock et al. 2003; Mitchell et al. 2004; Yang et al. 2006). Fortunately, studies show that multiple models provide superior results to any individual model (Dirmeyer et al. 2006). ET is equivalent to “latent heat flux” of the GSWP-2 datasets. Although the latent heat flux from GSWP-2 is not accurate enough to be used for reference data, the comparison between measured ET and revised-algorithm-predicted ET will supply useful information when the two independent datasets are in close agreement.
Note that daytime-averaged ET is used above, and in the following discussion, whole-day-averaged (daytime and nighttime) ET is compared with the latent heat flux from the GSWP-2 multiple-model simulation. Therefore, the maximum of the ET decreases from about 350 to about 180 W m−2. An example of the global ET predicted by Eq. (20) using June 1989 ISCCP Initiative II datasets is compared with the corresponding latent heat flux from GSWP-2 datasets in Fig. 8. Clearly the two datasets have a similar spatial distribution pattern. Their scatterplots are shown in Fig. 9. Predicted and model simulations scatter homogeneously around the 1:1 line, indicating that Eq. (20) can be used on a global scale. In a very few cases, ET predicted by Eq. (20) may be overestimated over arid regions where ET is relatively small, such as over the great deserts of Australia. This results in a small positive bias in the predicted ET because the correspondence of DTaR over arid regions is less than of DTsR. We expect the difference to be reduced when DTsR is used.
Figure 10 shows the bias, correlation coefficient, and RMSE of the comparison between the ET predicted by Eq. (20) and the latent heat flux from the GSWP-2 multiple-model average during the 118 months from January 1986 to October 1995. The bias varies from −0.8 to 9.2 W m−2, with an average of 4.5 W m−2, the RMSE varies from 16.2 to 22.5 W m−2, with an average of 19.8 W m−2, and the correlation coefficient varies from 0.71 to 0.91, with an average of 0.82. The 10-yr-averaged global ET available is 47.5 W m−2. The bias for the Northern Hemisphere winter is relatively large because snow and ice cover the earth’s surface at high latitudes and the proposed method tends to overestimate ET over ice–snow surfaces. The seasonal variation of the bias and RMSE partly reflects the differences in the amount of ratio of land to ocean in the Northern and Southern Hemispheres.
We also calculated global ET using the combination of Rn, daytime-averaged Ta, and NDVI [Eq. (11)] to demonstrate the improvement of the accuracy of global ET estimates by incorporating DTaR. Without DTaR, the ET tends to be overestimated when ET is relatively low (e.g., semiarid or arid area) and underestimated when ET is relatively high [e.g., dense forest (not shown here)]. This overestimating and underestimating of ET are solved by incorporating DTsR or DTaR, because arid areas have a higher DTR, which results in lower ET using Eq. (20) or (14). We also calculated the bias, correlation coefficient, and RMSE of the comparison between the ET predicted by Eq. (11) and the latent heat flux from the GSWP-2 multiple-model average during the 118 months from January 1986 to October 1995. The bias varies from 3.0 to 10.3 W m−2, with an average of 6.2 W m−2, the RMSE varies from 17.2 to 24.5 W m−2, with an average of 21.3 W m−2, and the correlation coefficient varies from 0.58 to 0.89, with an average of 0.78. The correlation coefficient is much less than that predicted by Eq. (20).
7. Conclusions
Satellite remote sensing is a promising technique for estimating global and regional ET. Current methods to estimate ET, using Ts − Ta, are sensitive to retrieval errors of Ts and the interpolation errors of Ta from the ground-based point measurements. To improve the accuracy of ET prediction, it is necessary to reduce the sensitivity of the methods to input data error.
In a previous study, Wang et al. (2007b) proposed estimating ET by combining Rn, the vegetation index, and temperature. However, the influence of SM on ET was not addressed: SM content does influence ET. Unfortunately, studies show that the relationship between ET and SM is very complicated and varies for different land cover types. In addition, there is no reliable global or regional SM dataset at the spatial scale of kilometers and the temporal resolution of days. This paper uses DTsR (or DTaR) as a variable in an improved algorithm that estimates ET with greater accuracy over vegetated surfaces with an insufficient water supply.
Incorporating DTsR solves the problem of using daily-averaged or maximum Ta to parameterize ET (Wang et al. 2007b). That is, ET increases with Ta or Ts for water-sufficient conditions. However, when the soil water is deficient, Ta or Ts increases dramatically because less evaporation occurs. Under water-deficient conditions, ET decreases with increasing Ta or Ts. The revised algorithm solves this problem by using daytime-averaged or daytime-maximum Ta or Ts to represent the influence of temperature on ET when the water supply is sufficient and using DTsR to represent the influence of water-deficient conditions on ET. Global model simulation demonstrates that DTaR and vapor pressure deficit are tightly coupled (Betts 2004). Therefore, incorporating DTsR into the revised method addresses the effect of vapor pressure deficit on ET.
We use ET measured by the EBBR method at eight EF sites on the SGP in the United States and ET measured by the EC method at four AmeriFlux sites from 2001 to 2006 to validate the method. This requires that measurements of ET, ground heat flux, and surface upwelling longwave radiation are available.
Our method can accurately estimate ET using only satellite data and Eq. (14). The correlation coefficient between the measured and predicted 16-day-averaged ET for a combination of Rn, EVI, daily maximum Ts, and DTsR values is about 0.92 for all sites and years. The correlation coefficients vary from 0.89 to 0.98 for the 12 sites; the bias of the predicted 16-day-averaged ET is −1.9 W m−2. The biases for all sites range from −15.2 to +13.6 W m−2. The RMSE of the predicted 16-day-averaged ET is about 28.6 W m−2 for all sites and varies from 17.3 to 33.2 W m−2 across the 12 sites. The correlation coefficient and RMSE are both better than the previous study, especially for sites affected by drought. The sensitivity of the proposed method to input data error is small.
We implemented the revised method to estimate global ET. Because DTsR is not available yet, we used easily obtainable DTaR data to estimate global ET instead. The ET estimated from DTaR is less accurate than DTsR-estimated ET. We calculated global 10-yr monthly ET from the ISLSCP Initiative II global interdisciplinary monthly dataset at a spatial resolution of 1° × 1° and compared it to the averaged latent heat flux from 15 model simulations of GSWP-2. Although the proposed method is derived from site measurements that have a footprint of several kilometers, depending on wind speed, tower height, and heterogeneity of the surface around the sites, it is reasonable to apply it on a 1° × 1° scale because both are at the canopy scale. The results indicate that the bias varies from −0.8 to 9.2 W m−2, with an average of 4.5 W m−2; the RMSE varies from 16.2 to 22.5 W m−2, with an average of 19.8 W m−2; and the correlation coefficient varies from 0.71 to 0.91, with an average of 0.82. For ET without DTaR, the bias varies from 3.0 to 10.3 W m−2, with an average of 6.2 W m−2; the RMSE varies from 17.2 to 24.5 W m−2, with an average of 21.3 W m−2; and the correlation coefficient varies from 0.58 to 0.89, with an average of 0.78. Thus, incorporating DTaR greatly improves the accuracy of the global ET estimates. Predicted ET is even more accurate when DTsR and EVI are used.
This study demonstrates that vegetation indexes (NDVI and EVI) from satellite sensors of MODIS accurately predict both the seasonal and the annual variation of ET (Wang et al. 2008, manuscript submitted to Climate Dyn.). Vegetation indexes derived from AVHRR, the Visible Infrared Imager/Radiometer Suite (VIIRS), and the Medium Resolution Imaging Spectrometer (MERIS) on the Envisat satellite data are potentially useable as datasets (Brown et al. 2006; Fensholt et al. 2006; Gallo et al. 2005).
Acknowledgments
The ground-based measurements were obtained from the AmeriFlux network (http://public.ornl.gov/ameriflux/data-get.cfm) and the ARM Program of the U.S. Department of Energy (http://www.archive.arm.gov/). MODIS satellite data were obtained online (http://redhook.gsfc.nasa.gov/~imswww/pub/imswelcome/plain.html). The International Satellite Land Surface Climatology Project Initiative II global interdisciplinary monthly datasets were downloaded from the Internet (http://www.daac.ornl.gov). GSWP-2 datasets were also downloaded from the Internet (http://haneda.tkl.iis.u-tokyo.ac.jp/gswp2/). We would also like to thank Mike Sparrow for his helpful comments on this manuscript.
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Comparison of the original (uncorrected) ET measured by the eddy covariance method and ET corrected for the energy imbalance issue [refer to Eq. (7)] at the Goodwin Creek (GC) site from 2002 to 2007.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the original (uncorrected) ET measured by the eddy covariance method and ET corrected for the energy imbalance issue [refer to Eq. (7)] at the Goodwin Creek (GC) site from 2002 to 2007.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the original (uncorrected) ET measured by the eddy covariance method and ET corrected for the energy imbalance issue [refer to Eq. (7)] at the Goodwin Creek (GC) site from 2002 to 2007.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of the time series of the ET, surface soil moisture (2.5-cm depth), and DTsR at the enhanced facility site Elk Falls (EF07) on the U.S. SGP 2002–05.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of the time series of the ET, surface soil moisture (2.5-cm depth), and DTsR at the enhanced facility site Elk Falls (EF07) on the U.S. SGP 2002–05.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of the time series of the ET, surface soil moisture (2.5-cm depth), and DTsR at the enhanced facility site Elk Falls (EF07) on the U.S. SGP 2002–05.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of measured (pluses) and predicted ET (points) using Eq. (14) for a range of conditions reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR at the six sites used to obtain the coefficients.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of measured (pluses) and predicted ET (points) using Eq. (14) for a range of conditions reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR at the six sites used to obtain the coefficients.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of measured (pluses) and predicted ET (points) using Eq. (14) for a range of conditions reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR at the six sites used to obtain the coefficients.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the measured (pluses) and predicted ET (points) using Eq. (14) for a range of conditions reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR data at the six validation sites.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the measured (pluses) and predicted ET (points) using Eq. (14) for a range of conditions reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR data at the six validation sites.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the measured (pluses) and predicted ET (points) using Eq. (14) for a range of conditions reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR data at the six validation sites.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of predicted ET using Eq. (14) and measured ET for all 12 sites. The 12 sites demonstrate a range of topographic, meteorological, and land cover conditions that are reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of predicted ET using Eq. (14) and measured ET for all 12 sites. The 12 sites demonstrate a range of topographic, meteorological, and land cover conditions that are reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of predicted ET using Eq. (14) and measured ET for all 12 sites. The 12 sites demonstrate a range of topographic, meteorological, and land cover conditions that are reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, and DTsR.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Histogram of the relative error in ET, calculated when the amount of error on net radiation (Rn), daytime air temperature (Ta), diurnal air temperature range (DTaR), and EVI are all varied over a range of ±20% error in increments of 10% error.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Histogram of the relative error in ET, calculated when the amount of error on net radiation (Rn), daytime air temperature (Ta), diurnal air temperature range (DTaR), and EVI are all varied over a range of ±20% error in increments of 10% error.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Histogram of the relative error in ET, calculated when the amount of error on net radiation (Rn), daytime air temperature (Ta), diurnal air temperature range (DTaR), and EVI are all varied over a range of ±20% error in increments of 10% error.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of ET predicted by Eq. (20) and measured ET for all 12 sites. The 12 sites demonstrate a range of topographic, meteorological, and land cover conditions that are reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, DTsR, and soil moisture.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of ET predicted by Eq. (20) and measured ET for all 12 sites. The 12 sites demonstrate a range of topographic, meteorological, and land cover conditions that are reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, DTsR, and soil moisture.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of ET predicted by Eq. (20) and measured ET for all 12 sites. The 12 sites demonstrate a range of topographic, meteorological, and land cover conditions that are reflected in variable surface net radiation, EVI, daily maximum land surface temperatures, DTsR, and soil moisture.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of (top) global ET predicted by Eq. (20) using June 1989 ISCCP Initiative II datasets and (bottom) the corresponding latent heat flux from GSWP-2 datasets. The color bar for both maps is shown at the base of the bottom panel. ET is set at −50 W m−2 over the ocean in the figure.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of (top) global ET predicted by Eq. (20) using June 1989 ISCCP Initiative II datasets and (bottom) the corresponding latent heat flux from GSWP-2 datasets. The color bar for both maps is shown at the base of the bottom panel. ET is set at −50 W m−2 over the ocean in the figure.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of (top) global ET predicted by Eq. (20) using June 1989 ISCCP Initiative II datasets and (bottom) the corresponding latent heat flux from GSWP-2 datasets. The color bar for both maps is shown at the base of the bottom panel. ET is set at −50 W m−2 over the ocean in the figure.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of the comparison of ET predicted by Eq. (20) using June 1989 ISCCP Initative II datasets and the corresponding latent heat flux from GSWP-2 datasets.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of the comparison of ET predicted by Eq. (20) using June 1989 ISCCP Initative II datasets and the corresponding latent heat flux from GSWP-2 datasets.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
An example of the comparison of ET predicted by Eq. (20) using June 1989 ISCCP Initative II datasets and the corresponding latent heat flux from GSWP-2 datasets.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the bias, correlation coefficient, and RMSE of ET predicted by Eq. (20) and latent heat flux from GSWP-2 multiple-model average during the 118 months from January 1986 to October 1995.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the bias, correlation coefficient, and RMSE of ET predicted by Eq. (20) and latent heat flux from GSWP-2 multiple-model average during the 118 months from January 1986 to October 1995.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Comparison of the bias, correlation coefficient, and RMSE of ET predicted by Eq. (20) and latent heat flux from GSWP-2 multiple-model average during the 118 months from January 1986 to October 1995.
Citation: Journal of Hydrometeorology 9, 4; 10.1175/2007JHM911.1
Brief description of the eight EF sites over the U.S. SGP and the four AmeriFlux sites. Multiyear average of NDVI and its maximum values are obtained from the MODIS 16-day vegetation indices product for the period 2001–06. Multiyear mean SM (kg kg−1) is obtained from surface soil moisture measurements taken at a depth of 2.5 cm. ETs (W m−2) are measured by the EBBR method at the EF sites over the SGP, while the EC method is used to measure ET at the AmeriFlux sites. The energy imbalance problem of the EC method is corrected by the method proposed by Twine et al. (2000).
A summary of the regression coefficients in Eq. (14) for different combinations of temperature, vegetation index, and DTsR. We used data collected from 2001 to 2006 at six sites (EF08, EF12, EF15, EF19, EF20, and RA) to derive the parameters (see Table 1 for site information). The vegetation indices EVI and NDVI are obtained from MODIS global 16-day vegetation indices products. We obtained daytime-averaged air temperature (Ta,d), daily maximum air temperature (Ta,m), daytime-averaged land surface temperature (Ts,d), daily maximum land surface temperature (Ts,m), and diurnal land surface temperature range (TDsR) from ground-based measurements. Surface net radiation is measured by the SIRS system.
The statistical parameters (correlation coefficient R bias, and RMSE) for the comparison between 16-day-averaged measured and predicted ET. We used data collected from 2001 to 2006 at six sites (EF08, EF12, EF15, EF19, EF20, and RA) to derive the coefficients in Table 2 by regression. Equation (14) was then used to predict ET with the coefficients in Table 2. ET is measured by the EBBR method at the EF sites over the SGP. ET measured by the EC method at BH, BI, GC, and RA is corrected by the method proposed by Twine et al. (2000). The data are averaged into 16-day periods before the comparison. Bias and RMSE are reported in W m−2.
