The climatological characteristics of water vapor transport over the Tibetan Plateau (TP) were investigated in this study by using the ERA-interim and JRA55 monthly reanalysis dataset. The trends of water vapor budget and water vapor sources during the past 40 years were also revealed. The analyses show that the TP is a water vapor convergence area, where the convergence was enhanced from 1979 to 2018. In addition, the convergence is much stronger in JJA, with a linear trend that is twice the annual average trend. The climatological water vapor sources over the TP were identified mainly at the southern and western boundaries, with the vapor sources at the southern boundaries originating from the Arabian Sea and Bay of Bengal and the vapor sources at the western boundary being transported by mid-latitude westerlies. The TP is a moisture sink at a climatological mean, with an annual average net water vapor flux of 11.86 × 10 6 kg • s −1 . Water vapor transport is much stronger in JJA than in other times of the year, and the net water vapor flux is 29.60 × 10 6 kg • s −1 . The net water vapor flux in the TP increased with a linear trend of 0.12×10 6 kg • s −1 • year −1 (α = 0.01), while the increase in the flux was more significant in JJA than in other times of the year with a linear trend of 0.30 ×10 6 kg • s −1 • year −1 (α = 0.01). Detailed features in the water vapor flux and transport changes across the TP's four boundaries were explored by simulating backward trajectories with a Lagrangian trajectory model (hybrid singleparticle Lagrangian integrated trajectory model, HYSPLIT). In the study period, the water vapor contribution rate of western channel is increased. However, the Southern channel's water vapor contribution decreased.
Evapotranspiration (ET) is one of the major uncertain components of the energy and water cycle and was derived for the Nagqu River Basin based on remote sensing data and atmospheric surface layer observations under cloudless conditions. Two process‐based models were used to determine the ET: a Priestley‐Taylor (PT)‐based model and a topographical enhanced surface energy balance system (TESEBS) model. Improved broadband albedo, downward shortwave radiation flux, and reconstructed normalized difference vegetation index (NDVI) were coupled into the TESEBS model and PT‐based model to estimate the actual ET. The atmospheric surface layer meteorological data, SPOT Vegetation (VGT) data, and Moderate Resolution Imaging Spectroradiometer data were used as inputs for 10‐day ET calculations. The model‐estimated results were compared with ground truths calculated via the combinatory method. The results indicated that the ET determined by both models well fit the actual ET, with correlation coefficients (R) of 0.88 and 0.82, respectively. However, the TESEBS model showed a better performance than the PT model, with a lower mean bias error (−0.02 mm/hr) and lower root–mean‐square error (0.08 mm/hr). Although the PT model is computationally simple and requires few parameters, the strong weighting of the NDVI may lead to some overestimations, especially during the growing season.
Downwelling shortwave radiation (DSWR) and downwelling longwave radiation (DLWR) are two important components of the Earth's surface radiation balance. In this study, the Heliosat method and the parameterization of Crawford and Duchon (1999, https://doi.org/10.1175/1520‐0450(1999)038<0474:AIPFEE>2.0.CO;2, hereafter CD99) were calibrated to make them suitable for estimation of DSWR and DLWR over the Tibetan Plateau (TP). Based on meteorological data, forcing data, and observations from polar‐orbiting satellites, the cloud albedo was calculated, and the clear‐sky index estimation scheme of the Heliosat method was improved. These improvements were then applied to derive 10‐day DSWR under all‐sky conditions over the TP by combining the clear‐sky shortwave radiation scheme with a clear‐sky index. The coefficient of the CD99 parameterization scheme clear‐sky DLWR was also calibrated, and 10‐day all‐sky DLWR was then determined and validated using ground‐based measurements. The spatiotemporal distributions of DSWR and DLWR were analyzed in detail. The results showed that the modified methods are efficient and applicable for downward radiation retrieval under all‐sky conditions over the TP with a reasonable accuracy. The mean percentage errors for DSWR and DLWR were −4.75% and 0.11%, respectively. The variation in the monthly DSWR (DLWR) showed a convex shape, with a maximum appearing in May (July). The spatial distributions of DLWR showed a southeast‐high and northwest‐low pattern. As the subsolar point moves northward, DSWR increases gradually and is clearly influenced by the Asian summer monsoon.
During the process of land–atmosphere interaction, one of the essential parameters is the land surface temperature (LST). The LST has high temporal variability, especially in its diurnal cycle, which cannot be acquired by polar-orbiting satellites. Therefore, it is of great practical significance to retrieve LST data using geostationary satellites. According to the data of FengYun 2C (FY-2C) satellite and the measurements from the Enhanced Observing Period (CEOP) of the Asia–Australia Monsoon Project (CAMP) on the Tibetan Plateau (CAMP/Tibet), a regression approach was utilized in this research to optimize the split window algorithm (SWA). The thermal infrared data obtained by the Chinese geostationary satellite FY-2C over the Tibetan Plateau (TP) was used to estimate the hourly LST time series. To decrease the effects of cloud, the 10-day composite hourly LST data were obtained through the approach of maximal value composite (MVC). The derived LST was used to compare with the product of MODIS LST and was also validated by the field observation. The results show that the LST retrieved through the optimized SWA and in situ data has a better consistency (with correlation coefficient (R), mean absolute error (MAE), mean bias (MB), and root mean square error (RMSE) values of 0.987, 1.91 K, 0.83 K and 2.26 K, respectively) than that derived from Becker and Li’s SWA and MODIS LST product, which means that the modified SWA can be applied to achieve plateau-scale LST. The diurnal variation of the LST and the hourly time series of the LST over the Tibetan Plateau were also obtained. The diurnal range of LST was found to be clearly affected by the influence of the thawing and freezing process of soil and the summer monsoon evolution. The comparison between the seasonal and diurnal variations of LST at four typical underlying surfaces over the TP indicate that the variation of LST is closely connected with the underlying surface types as well. The diurnal variation of LST is the smallest at the water (5.12 K), second at the snow and ice (5.45 K), third at the grasslands (19.82 K) and largest at the barren or sparsely vegetated (22.83 K).
Calculation of actual evapotranspiration (AET) is of vital importance for the study of climate change, ecosystem carbon cycling, flooding, drought, and agricultural water demand. It is one of the more important components in the hydrological cycle and surface energy balance (SEB). How to accurately estimate AET especially for the Tibetan Plateau (TP) with complex terrain remains a challenge for the scientific community. Using multi-sensor remote sensing data, meteorological forcing data, and field observations, AET was derived for the Nagqu river basin of the Northern Tibetan Plateau from a surface energy balance system (SEBS) model. As inputs for SEBS, improved algorithms and datasets for land surface albedo and a cloud-free normalized difference vegetation index (NDVI) were also constructed. The model-estimated AET were compared with results by using the combinatory method (CM). The validation indicated that the model estimates of AET agreed well with the correlation coefficient, the root mean square error, and the mean percentage error of 0.972, 0.052 mm/h, and −10.4%, respectively. The comparison between SEBS estimation and CM results also proved the feasibility of parameterization schemes for land surface parameters and AET.
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