Uncertainties in partitioning evapotranspiration by two remote sensing-based models

Accurate estimation of evapotranspiration (ET) and the partitioning of ET into transpiration (Tr), soil evaporation (Es) and interception (Ei) is critical to understand water cycle and land-atmosphere feedback. In this study, we evaluated the performances of two remote sensing-based ET models at multiple scales, and analyzed the uncertainties in ET partitioning due to the model structures. These two models were Simple Terrestrial Hydrosphere Model (SiTH) developed by our team and the Global Land Evaporation Amsterdam Model (GLEAM). As far as ET were concerned, the two models exhibited relatively good performances at different scales. However, it was found that GLEAM performed relatively poor at evergreen broadleaf forest (R2 = 0.34; RMSE = 0.87 mm day- 1; NSE = -0.28). In addition, the seasonal pattern of simulated ET by GLEAM at the tropical rainforest was not consistent with the observations. Furthermore, great discrepancies in ET partitioning were observed between the two models. Generally, GLEAM tended to underestimate Es (slope = 0.02; R2 = 0.004), and overestimate Tr (slope = 1.51; R2 = 0.78) compared to the observations. The underestimations of Es by GLEAM may partly be due to the ignorance of soil evaporation under vegetation canopy. On the contrary, SiTH displayed relatively good performances in estimations of Es (slope = 0.76; R2 = 0.62) and Tr (slope = 0.98; R2 = 0.51). However, both of the two models failed to properly simulate Ei, although GLEAM (slope = 0.55; R2 = 0.83) performed slightly better than SiTH (slope = 0.40; R2 = 0.95). Global multi-year average ratios of Tr, Es, and Ei to ET for GLEAM and SiTH were 0.76, 0.09, 0.15 and 0.67, 0.25, 0.08 respectively. In future studies, it is important to investigate direct observations on different components of ET, especially on the interception, to improve our understanding on the ET processes.

Automated summary

This study evaluated the performances of two remote sensing-based ET models at different scales and found that GLEAM performed relatively poorly in some regions, with significant discrepancies in ET partitioning between the two models.