Abstract
In this study, a Bayesian-based three-cornered hat (BTCH) method is developed to improve the estimation of terrestrial evapotranspiration (ET) by integrating multisource ET products without using any a priori knowledge. Ten long-term (30 years) gridded ET datasets from statistical or empirical, remotely-sensed, and land surface models over contiguous United States (CONUS) are integrated by the BTCH and ensemble mean (EM) methods. ET observations from eddy covariance towers (ETEC) at AmeriFlux sites and ET values from the water balance method (ETWB) are used to evaluate the BTCH- and EM-integrated ET estimates. Results indicate that BTCH performs better than EM and all the individual parent products. Moreover, the trend of BTCH-integrated ET estimates, and their influential factors (e.g., air temperature, normalized differential vegetation index, and precipitation) from 1982 to 2011 are analyzed by the Mann–Kendall method. Finally, the 30-year (1982 to 2011) total water storage anomaly (TWSA) in the Mississippi River Basin (MRB) is retrieved based on the BTCH-integrated ET estimates. The TWSA retrievals in this study agree well with those from the Gravity Recovery and Climate Experiment (GRACE).
Highlights
Evapotranspiration (ET) refers to the amount of water vapor evaporated from the land surface to the atmosphere [1,2]
The ET estimates in the southeast of contiguous United States (CONUS) are relatively larger than those in the center and north of CONUS
Higher ET estimates in the southeast of CONUS are mainly due to heavy precipitation and dense vegetation cover (Figure 3)
Summary
Evapotranspiration (ET) refers to the amount of water vapor evaporated from the land surface to the atmosphere [1,2]. ET can be estimated directly by flux tower systems (e.g., FLUXNET, AmeriFlux, EuroFlux, HiWATER, etc.) [7,8,9]. These measurements have a sparse distribution and limited time periods. The second group (called the remotely-sensed methods) estimates ET by incorporating remote sensing observations into the empirical models [13,14,15,16], surface energy balance equation [17,18], Penman–Monteith or Priestley–Taylor equation [19,20,21], and data assimilation methods [22,23,24,25,26,27,28,29]. The third group (named the land surface models) utilizes physical models (land surface models) or combines physical models with data assimilation algorithms to predict ET [30,31,32,33]
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