Estimation of mass elevation effect and its annual variation based on MODIS and NECP data in the Tibetan Plateau

The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the main determinant of MEE. In this study, we improved the method of estimating MEE with MODIS and NECP data, by refining temperature laps rate, and dividing MBE plots, and then analyzed the spatio-temporal variation of MEE in the Plateau. The main conclusions include: 1) the highest average annual MEE of the plateau is as high as 11.5488°C in the southwest of the plateau, where exists a high-MEE core and MEE takes on a trend of decreasing from the core to the surrounding areas; 2) in the interior of the plateau, the maximum monthly MEE is 14.1108°C in the highest MBE plot(4934 m) in August; while the minimum monthly MEE appeared primarily in January and February; 3) in the peripheral areas of the plateau, annual mean MEE is relatively low, mostly between 3.0068°C–5.1972°C, where monthly MEE is high in January and December and low in June and July, completely different from the MEE time-series variation in the internal parts of the plateau.

Magnitude and Forming Factors of Mass Elevation Effect on Qinghai-Tibet Plateau

Mass elevation effect （MEE） refers to the thermal effect of huge mountains or plateaus, which causes the tendency for tem- perature-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 （AT） 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 ATby comparing near-surface air temperature on the main plateau with the free-air temperature at the same altitude and simi- lar latitude outside of the plateau. The AT for the warmest month is averagely 6.15 ~C, over 12~C 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 AT. 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~C 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.

What factors determine the mass elevation effect of the Tibetan Plateau?

The mass elevation effect(MEE)of the Tibetan Plateau(TP)has attracted the attention of geographers because of its significant influence on the Asian climate,snow line,timberline,and other important climate-ecological boundaries of the plateau and on global ecological patterns.In recent years,much progress has been made in quantifying the MEE of TP.However,factors that affect the size of MEE have not been examined in depth,and the key factors still remain unclear.Based on quantification of MEE for each mountain basal elevation plot,this study identifies the factors that contribute significantly to MEE of the plateau.Seven factors are considered,including mountain basal elevation,distance from the core zone of MEE,thermal continentality,maximum elevation,height difference,area,and difference of underlying surface(with the yearly max"Normalized Difference Vegetation Index"(NDVI)serving as a quantitative indicator).We also used these seven factors as independent variables to develop a multiple linear regression model for MEE of the plateau.Results show that:(1)the determination coefficient(R2)of the model reaches as high as 0.877,and the contributions of mountain basal elevation,distance from the core zone of MEE,thermal continentality,maximum elevation,topographical height difference,area,and NDVI are 39.77%,23.02%,14.48%,5.78%,11.41%,2.92%,and 2.62%,respectively,with mountain basal elevation and the distance from the core of MEE as the most important factors;(2)thermal continentality and MEE are significantly correlated,and maximum elevation only has a coupling relationship with MEE,with height difference and NDVI contributing little to MEE.This study deepens our understanding of MEE and its forming factors in the Tibetan Plateau.

Comparative analysis of the mass elevation effect and its implication for the treeline between the Tibetan and Bolivian plateaus based on solar radiation

As one of the main non-zonal factors,the mass elevation effect(MEE)has significant impacts on both regional climates and mountain ecological patterns.In recent years,with the development of quantitative techniques and methods,quantitative studies on the MEE and its implication on mountain altitudinal belts have developed rapidly.However,some issues have not been solved yet,such as high errors in spatial temperature estimations and difficulties in the definition and extraction of intramountain base elevation.Moreover,there is still a lack of comparative studies on the MEE and its influence on treelines and snowlines as most studies were conducted on specific mountains or plateaus.To compare the MEE magnitudes of the Tibetan Plateau(TP)and the Bolivian Plateau(BP),we estimated the correspondent air temperatures and simulated the solar radiations based on MODIS surface temperature,station observation,and treeline data.Then,we analyzed the elevation of the 10℃isotherms on the two plateaus,the temperatures at the same elevation,and the solar radiations.According to the mechanism of the MEE and the relationship of solar radiation and treeline,we constructed treeline models for the two plateaus through a stepwise regression analysis by considering several influencing factors of the MEE(e.g.,air temperature and precipitation)and using solar radiation as its proxy.The results showed that:(1)the MEE magnitude on the TP is equivalent to that on the BP although the former is slightly higher than the latter;(2)the MEE strongly influences the highest treelines in the northern and southern hemispheres,which both occur on the two plateaus.Notably,the treeline distribution models based on solar radiation had higher accuracies than those models with parameters of temperature and precipitation(the adjusted R^(2) values were 0.76 for the TP and 0.936 for the BP),indicating that solar radiation can be used to quantify the MEE and its implications on treelines.Overall,the results of this study can serve as a basis for subsequent analyses on the MEE’s impact factors.

Implications of mass elevation effect for the altitudinal patterns of global ecology

The varied altitudinal gradient of climate and vegetation is further complicated by mass elevation effect(MEE), especially in high and extensive mountain regions. However, this effect and its implications for mountain altitudinal belts have not been well studied until recently. This paper provides an overview of the research carried out in the past 5 years. MEE is virtually the heating effect of mountain massifs and can be defined as the temperature difference on a given elevation between inside and outside of a mountain mass. It can be digitally modelled with three factors of intra-mountain base elevation(MBE), latitude and hygrometric continentality; MBE usually acts as the primary factor for the magnitude of MEE and, to a great extent, could represent MEE. MEE leads to higher treelines in the interior than in the outside of mountain masses. It makes montane forests to grow at 4800–4900 m and snowlines to develop at about 6000 m in the southern Tibetan Plateau and the central Andes, and large areas of forests to live above 3500 m in a lot of high mountains of the world. The altitudinal distribution of global treelines can be modelled with high precision when taking into account MEE and the result shows that MEE contributes the most to treeline distribution pattern. Without MEE, forests could only develop upmost to about 3500 m above sea level and the world ecological pattern would be much simpler. The quantification of MEE should be further improved with higher resolution data and its global implications are to be further revealed.

A study of the contribution of mass elevation effect to the altitudinal distribution of timberline in the Northern Hemisphere

Alpine timberline, as the ＂ecologica tion of scientists in many fields, especially in transition zone,＂ has long attracted the atten- recent years. Many unitary and dibasic fitting models have been developed to explore the relationship between timberline elevation and latitude or temperature. However, these models are usually on regional scale and could not be applied to other regions; on the other hand, hemispherical-scale and continental-scale models are usually based on about 100 timberline data and are necessarily low in precision. The present article collects 516 data sites of timberline, and takes latitude, continentality and mass elevation effect （MEE） as independent variables and timberline elevation as dependent variable to develop a ternary linear regression meteorological data released by WorldClim and model. Continentality is calculated using the mountain base elevation （as a proxy of mass elevation effect） is extracted on the basis of SRTM 90-meter resolution elevation data. The results show that the coefficient of determination （R2） of the linear model is as high as 0.904, and that the contribution rate of latitude, continentality and MEE to timberline elevation is 45.02% （p=0.000）, 6.04% （p=0.000） and 48.94% （p=0.000）, respectively. This means that MEE is simply the primary factor contributing to the elevation distribution of timberline on the continental and hemispherical scales. The contribution rate of MEE to timberline altitude dif- fers in different regions, e.g., 50.49% （p=0.000） in North America, 48.73% （p=0.000） in the eastern Eurasia, and 43.6% （p=0.000） in the western Eurasia, but it is usually very high.

Mass elevation effect and its forcing on timberline altitude

The concept of mass elevation effect （massenerhebungseffect, MEE） was introduced by A. de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and snowline to occur at higher elevations in the central Alps than on their outer margins. It also has been widely observed in other areas of the world, but there have not been significant, let alone quantitative, researches on this phenomenon. Especially, it has been usually completely neglected in developing fitting mod- els of timberline elevation, with only longitude or latitude considered as impacting factors. This paper tries to quantify the contribution of MEE to timberline elevation. Considering that the more extensive the land mass and especially the higher the mountain base in the interior of land mass, the greater the mass elevation effect, this paper takes mountain base elevation （MBE） as the magnitude of MEE. We collect 157 data points of timberline elevation, and use their latitude, longitude and MBE as independent variables to build a multiple linear regression equation for timberline elevation in the southeastern Eurasian continent. The results turn out that the contribution of latitude, longitude and MBE to timberline altitude reach 25.11%, 29.43%, and 45.46%, respectively. North of northern latitude 32°, the three factors＇ contribution amount to 48.50%, 24.04%, and 27.46%, respectively; to the south, their contribution is 13.01%, 48.33%, and 38.66%, respectively. This means that MBE, serving as a proxy indi- cator of MEE, is a significant factor determining the elevation of alpine timberline. Compared with other factors, it is more stable and independent in affecting timberline elevation. Of course, the magnitude of the actual MEE is certainly determined by other factors, including mountain area and height, the distance to the edge of a land mass, the structures of the mountains nearby. These factors need to be inctuded in the study of MEE quantification in the future. This paper could help build up a high-accuracy and multi-scale elevation model for alpine timberline and even other altitudinal belts.

The implication of mass elevation effect of the Tibetan Plateau for altitudinal belts

The heating effect （or mass elevation effect, MEE） of the Tibetan Plateau （TP） is intense due to its massive body. Some studies have been undertaken on its role as the heat source in summer and its implications for Asian climate, but little has been known of the im- plications of its MEE for the distribution of mountain altitudinal belts （MABs）. Using air tem- perature data observed and remotely sensed data, MAB/treeline data, and ASTER GDEM data, this paper compares the height of MABs and alpine treelines in the main TP and the surrounding mountains/lowland and explains the difference from the point of view of MEE. The results demonstrate： 1） at same elevation, air temperature and the length of growing season gradually increase from the eastern edge to the interior TP, e.g., at 4500 m （corre- sponding to the mean altitude of the TP）, the monthly mean temperature is 3.58℃ higher （April） to 6.63℃ higher （June） in the interior plateau than in the Sichuan Basin; the 10℃ iso- therm for the warmest month goes upward from the edge to the interior of the plateau, at 4000 m in the Qilian Mts. and the eastern edges of the plateau, and up to 4600-5000 m in Lhasa and Zuogong; the warmth index at an altitude of 4500 m can be up to 15℃-month in the in- terior TP, but much lower at the eastern edges. 2） MABs and treeline follow a similar trend of rising inwards： dark-coniferous forest is 1000-1500 m higher and alpine steppe is about 700-900 m higher in the interior TP than at the eastern edges.

中国天山山体效应评估及空间分异归因

The mass elevation effect(MEE)is a thermal effect,in which heating produced by long wave radiation on a mountain surface generates atmospheric uplift,which has a profound impact on the hydrothermal conditions and natural geographical processes in mountainous areas.Based on multi-source remote sensing data and field observations,a spatial downscaling inversion of temperature in the Tianshan Mountains in China was conducted,and the MEE was estimated and a spatio-temporal analysis was conducted.The Geo Detector model(GDM)and a geographically weighted regression(GWR)model were applied to explore the spatial and temporal heterogeneity of the study area.Four key results can be obtained.(1)The temperature pattern is complex and diverse,and the overall temperature presented a pattern of high in the south and east,but low in the north and west.There were clear zonal features of temperature that were negatively correlated with altitude,and the temperature difference between the internal and external areas of the mountains.(2)The warming effect of mountains was prominent,and the temperature at the same altitude increased in steps from west to east and north to south.Geomorphological units,such as large valleys and intermontane basins,weakened the latitudinal zonality and altitudinal dependence of temperature at the same altitude,with the warming effect of mountains in the southern Tianshan Mountains.(3)The dominant factors affecting the overall pattern of the MEE were topography and location,among which the difference between the internal and external areas of the mountains,and the absolute elevation played a prominent role.The interaction between factors had a greater influence on the spatial differentiation of mountain effects than single factors,and there was a strong interaction between terrain and climate,precipitation,nthe normalized difference vegetation index(NDVI),and other factors.(4)There was a spatial heterogeneity in the direction and intensity of the spatial variation of the MEE.Absolute elevation was significantly positively correlated with the change of MEE,while precipitation and the NDVI were dominated by negative feedback.In general,topography had the largest effect on the macroscopic control of MEE,and coupled with precipitation,the underlying surface,and other factors to form a unique mountain circulation system and climate characteristics,which in turn enhanced the spatial and temporal heterogeneity of the MEE.The results of this study will be useful in the further analysis of the causes of MEE and its ecological effects.

CO_2 permeability of fractured coal subject to confining pressures and elevated temperature: Experiments and modeling

The CO_2 permeability of fractured coal is of great significance to both coalbed gas extraction and CO_2 storage in coal seams, but the effects of high confining pressure, high injection pressure and elevated temperature on the CO_2 permeability of fractured coal with different fracture extents have not been investigated thoroughly. In this paper, the CO_2 permeability of fractured coals sampled from a Pingdingshan coal mine in China and artificially fractured to a certain extent is investigated through undrained triaxial tests. The CO_2 permeability is measured under the confining pressure with a range of 10–25 MPa, injection pressure with a range of 6–12 MPa and elevated temperature with a range of 25–70°C. A mechanistic model is then proposed to characterize the CO_2 permeability of the fractured coals. The effects of thermal expansion, temperature-induced reduction of adsorption capacity, and thermal micro-cracking on the CO_2 permeability are explored. The test results show that the CO_2 permeability of naturally fractured coal saliently increases with increasing injection pressure. The increase of confining pressure reduces the permeability of both naturally fractured coal and secondarily fractured coal. It is also observed that initial fracturing by external loads can enhance the permeability, but further fracturing reduces the permeability. The CO_2 permeability decreases with the elevation of temperature if the temperature is lower than 44°C, but the permeability increases with temperature once the temperature is beyond 44°C. The mechanistic model well describes these compaction mechanisms induced by confining pressure, injection pressure and the complex effects induced by elevated temperature.