An R tool for Capturing Dynamics of Actual Evapotranspiration with MEP model and its application in Amazon

&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;amp;#160;&amp;lt;/strong&amp;gt;&amp;lt;strong&amp;gt;Abstract&amp;lt;/strong&amp;gt;: It is challenged to get an accurate estimate of surface energy budget for the investigation of land atmosphere and global ecosystems. In this study, we established a novel tool based the maximum entropy production (MEP) method for the simulation of global energy flux as well as evapotranspiration (ET) processes. This tool (named as RMEP) was built in R for its great convenient for open-source and the feature of easy-use. As only three variables (net radiation, surface temperature, and specific humidity) are need for MEP model, it shows great advantages in simulation for both global or site scales. &amp;lt;strong&amp;gt;Firstly,&amp;lt;/strong&amp;gt; we compare the performances of RMEP in two flux sites, BR-Sa1and BR-Sa3 of Amazon basin, with the simulation of heat fluxes. Although the substantial bias of G flux exist, both the latent and sense heat flux show high R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; in hourly temporal scale. &amp;lt;strong&amp;gt;Then,&amp;lt;/strong&amp;gt; the RMEP was test in large scale by employing the global scale dataset. Since the Global Land Data Assimilation System (GLDAS) product integrates satellite data and ground-based observations at global scale, the variables of radiation, surface temperature, as well as specific humidity of GLDAS were used as inputs for RMEP and the outputs of RMEP were validated with the variables of fluxes and evapotranspiration in GLDAS. The MEP model shows a high performances in simulating surface energy budget in global scale and Amazon basin area of 3-hourly temporal scale. The performances of MEP model using GLDAS data are superior to that of EC data, with higher R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;, lower RMSE and higher, positive NSE. In addition, the MEP accurately estimated ET over regional or global scale. &amp;lt;strong&amp;gt;Especially for Amazon area,&amp;lt;/strong&amp;gt; MEP simulated results of heat fluxes and ET are used in comparisons at their original (3-hourly and daily) and aggregated monthly temporal scales. Generally, the original 3-hourly simulations had a higher accuracy and smaller bias than daily simulations, take the aggregated monthly ET for example, the monthly 3-hourly ET (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.91, NSE=0.85) outperformed than that of daily scale (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=0.29, NSE=-0.98). &amp;lt;strong&amp;gt;Results indicated&amp;lt;/strong&amp;gt; the excellent performances of the MEP model in estimating ET with 3-hourly temporal scale in Amazon area. &amp;lt;strong&amp;gt;In summary,&amp;lt;/strong&amp;gt; the RMEP shows great performances in both site and global scale. It also can deal with the input file with both site measured table and global netcdf types. The resulted figures, global ET values (in netcdf file), source code, and R package can be shared by the request to the first author.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Appendix&amp;lt;/strong&amp;gt;. List of figures and tables.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Table 1.&amp;lt;/strong&amp;gt; Information for two flux sites&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;img 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