As the highest and most extensive plateau on earth,the Tibetan Plateau has strong thermodynamic effect,which not only affects regional climate around the plateau but also temperature and precipitation patterns of itself.However,due to scattered meteorological stations,its spatial precipitation pattern and,especially,the mechanism behind are poorly understood.The availability of spatially consistent satellite-derived precipitation data makes it possible to get accurate precipitation pattern in the plateau,which could help quantitatively explore the effect and mechanism of mass elevation effect on precipitation pattern.This paper made full use of TMPA 3B43 V7 monthly precipitation data to track the trajectory of precipitation and identified four routes(east,southeast,south,west directions) along which moisture-laden air masses move into the plateau.We made the assumption that precipitation pattern is the result interplay of these four moistureladen air masses transportation routes against the distances from moisture sources and the topographicbarriers along the routes.To do so,we developed a multivariate linear regression model with the spatial distribution of annual mean precipitation as the dependent variable and the topographical barriers to these four moisture sources as independent variables.The result shows that our model could explain about 70% of spatial variation of mean annual precipitation pattern in the plateau;the regression analysis also shows that the southeast moisture source(the Bay of Bengal) contributes the most(32.56%) to the rainfall pattern of the plateau;the east and the south sources have nearly the same contribution,23.59% and 23.48%,respectively;while the west source contributes the least,only 20.37%.The findings of this study can greatly improve our understanding of mass elevation effect on spatial precipitation pattern.
The altitudinal pattern of vegetation is usually identified by field surveys,however,these can only provide discrete data on a local mountain.Few studies identifying and analyzing the altitudinal vegetation pattern on a regional scale are available.This study selected central Inner Mongolia as the study area,presented a method for extracting vegetation patterns in altitudinal and horizontal directions.The data included a vegetation map at a 1∶1 000 000 scale and a digital elevation model at a 1∶250 000 scale.The three-dimensional vegetation pattern indicated the distribution probability for each vegetation type and the transition zones between different vegetation landscapes.From low to high elevations,there were five vegetation types in the southern mountain flanks,including the montane steppe,broad-leaved forest,coniferous mixed forest,montane dwarf-scrub and sub-alpine shrub-meadow.Correspondingly,only four vegetation types were found in the northern flanks,except for the montane steppe.This study could provide a general model for understanding the complexity and diversity of mountain environment and landscape.
Mass elevation effect(MEE) refers to the thermal effect of huge mountains or plateaus, which causes the tendency for temperature-related montane landscape limits to occur at higher elevations in the inner massifs than on their outer margins. MEE has been widely identified in all large mountains, but how it could be measured and what its main forming-factors are still remain open. This paper, supposing that the local mountain base elevation(MBE) is the main factor of MEE, takes the Qinghai-Tibet Plateau(QTP) as the study area, defines MEE as the temperature difference(ΔT) between the inner and outer parts of mountain massifs, identifies the main forming factors, and analyzes their contributions to MEE. A total of 73 mountain bases were identified, ranging from 708 m to 5081 m and increasing from the edges to the central parts of the plateau. Climate data(1981–2010) from 134 meteorological stations were used to acquire ΔT by comparing near-surface air temperature on the main plateau with the free-air temperature at the same altitude and similar latitude outside of the plateau. The ΔT for the warmest month is averagely 6.15℃, over 12℃ at Lhatse and Baxoi. A multivariate linear regression model was developed to simulate MEE based on three variables(latitude, annual mean precipitation and MBE), which are all significantly correlated to ΔT. The model could explain 67.3% of MEE variation, and the contribution rates of three independent variables to MEE are 35.29%, 22.69% and 42.02%, respectively. This confirms that MBE is the main factor of MEE. The intensive MEE of the QTP pushes the 10℃ isotherm of the warmest month mean temperature 1300–2000 m higher in the main plateau than in the outer regions, leading the occurrence of the highest timberline(4900 m) and the highest snowline(6200 m) of the Northern Hemisphere in the southeast and southwest of the plateau, respectively.
