Recent studies have shown that global Penman‐Monteith equation based (PM‐based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite‐retrieved vegetation information to simulate water stress in a PM‐based model (RS‐WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM‐based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS‐WBPM is successful. The daily ET resulting from RS‐WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 ( normalRnormalMnormalSnormalE = 18.72 W m−2) for all 27 sites and R2=0.62 ( normalRnormalMnormalSnormalE = 18.21 W m−2) for 25 nonagricultural sites. However, combined results from the optimum older PM‐based models at specific sites show R2 normalvnormalanormallnormalunormalenormals normalonormalf normalonormalnnormallnormaly 0.50 ( normalRnormalMnormalSnormalE = 20.74 W m−2) for all 27 sites. RS‐WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS‐WBPM are globally available, the results from RS‐WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

Global warming threatens ecosystem functions, biodiversity, and rangeland productivity in Mongolia. The study analyzes the spatial and temporal distributions of the Net Primary Production (NPP) and its response to climatic parameters. The study also highlights how various land cover types respond to climatic fluctuations from 2003 to 2018. The Boreal Ecosystem Productivity Simulator (BEPS) model was used to simulate the rangeland NPP of the last 16 years. Satellite remote sensing data products were mainly used as input for the model, where ground-based and MODIS NPP were used to validate the model result. The results indicated that the BEPS model was moderately effective (R2 = 0.59, the Root Mean Square Error (RMSE) = 13.22 g C m−2) to estimate NPP for Mongolian rangelands (e.g., grassland and sparse vegetation). The validation results also showed good agreement between the BEPS and MODIS estimates for all vegetation types, including forest, shrubland, and wetland (R2 = 0.65). The annual total NPP of Mongolia showed a slight increment with an annual increase of 0.0007 Pg (0.68 g C per meter square) from 2003 to 2018 (p = 0.82) due to the changes in climatic parameters and land cover change. Likewise, high increments per unit area found in forest NPP, while decreased NPP trend was observed in the shrubland. In conclusion, among the three climatic parameters, temperature was the factor with the largest influence on NPP variations (r = 0.917) followed precipitation (r = 0.825), and net radiation (r = 0.787). Forest and wetland NPP had a low response to precipitation, while inter-annual NPP variation shows grassland, shrubland, and sparse vegetation were highly sensitive rangeland types to climate fluctuations.

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