Advances in the Remote Sensing of Terrestrial Evaporation

Diurnal Evapotranspiration and Its Controlling Factors of Alpine Ecosystems during the Growing Season in Northeast Qinghai-Tibet Plateau

It is generally believed that evapotranspiration at night is too miniscule to be considered. Thus, few studies focus on the nocturnal evapotranspiration (ETN) in alpine region. In this study, based on the half-hour eddy and meteorological data of the growing season (from May to September) in 2019, we quantified the ETN of alpine desert (AD), alpine meadow (AM), alpine meadow steppe (AMS), and alpine steppe (AS) in the Qinghai Lake Basin and clarified the different response of evapotranspiration to climate variables in daytime and nighttime with the variation of elevation. The results show that: (1) ETN accounts for 9.88~15.08% of total daily evapotranspiration and is relatively higher in AMS (15.08%) and AD (12.13%); (2) in the daytime, net radiation (Rn), temperature difference (TD), vapor pressure difference (VPD), and soil moisture have remarkable influence on evapotranspiration, and Rn and VPD are more important at high altitudes, while TD is the main factor at low altitudes; (3) in the nighttime, VPD and wind speed (WS) control ETN at high altitudes, and TD and WS drive ETN at low altitudes. Our results are of great significance in understanding ETN in the alpine regions and provide reference for further improving in the evapotranspiration estimation model.

Spatio-Temporal Assessment of Global Gridded Evapotranspiration Datasets across Iran

Estimating evapotranspiration (ET), the main water output flux within basins, is an important step in assessing hydrological changes and water availability. However, direct measurements of ET are challenging, especially for large regions. Global products now provide gridded estimates of ET at different temporal resolution, each with its own method of estimating ET based on various data sources. This study investigates the differences between ERA5, GLEAM, and GLDAS datasets of estimated ET at gridded points across Iran, and their accuracy in comparison with reference ET. The spatial and temporal discrepancies between datasets are identified, as well as their co-variation with forcing variables. The ET reference values used to check the accuracy of the datasets were based on the water balance (ETwb) from Iran’s main basins, and co-variation of estimated errors for each product with forcing drivers of ET. The results indicate that ETERA5 provides higher base average values and lower maximum annual average values than ETGLEAM. Temporal changes at the annual scale are similar for GLEAM, ERA5, and GLDAS datasets, but differences at seasonal and monthly time scales are identified. Some discrepancies are also recorded in ET spatial distribution, but generally, all datasets provide similarities, e.g., for humid regions basins. ETERA5 has a higher correlation with available energy than available water, while ETGLEAM has higher correlation with available water, and ETGLDAS does not correlate with none of these drivers. Based on the comparison of ETERA5 and ETGLEAM with ETwb, both have similar errors in spatial distribution, while ETGLDAS provided over and under estimations in northern and southern basins, respectively, compared to them (ETERA5 and ETGLEAM). All three datasets provide better ET estimates (values closer to ETWB) in hyper-arid and arid regions from central to eastern Iran than in the humid areas. Thus, the GLEAM, ERA5, and GLDAS datasets are more suitable for estimating ET for arid rather than humid basins in Iran.