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

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.     

山体效应对生态垂直格局的影响（英文）

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.     

基于地理探测器的喀斯特不同地貌形态类型区土壤侵蚀定量归因（英文）

The formation mechanism and influencing factors identification of soil erosion are the core and frontier issues of current research. However, studies on the multi-factor synthesis are still relatively lacked. In this study, the simulation of soil erosion and its quantitative attribution analysis have been conducted in different geomorphological types in a typical karst basin based on the RUSLE model and the geodetector method. The influencing factors, such as land use type, slope, rainfall, elevation, lithology and vegetation cover, have been taken into consideration. Results show that the strength of association between the six influencing factors and soil erosion was notably different in diverse geomorphological types. Land use type and slope were the dominant factors of soil erosion in the Sancha River Basin, especially for land use type whose power of determinant(q value) for soil erosion was much higher than other factors. The q value of slope declined with the increase of relief in mountainous areas, namely it was ranked as follows: middle elevation hill small relief mountain middle relief mountain. Multi-factors interactions were proven to significantly strengthen soil erosion, particularly for the combination of land use type with slope, which can explain 70% of soil erosion distribution. It can be found that soil erosion in the same land use type with different slopes(such as dry land with slopes of 5° and above 25°) or in the diverse land use types with the same slope(such as dry land and forest with a slope of 5°), varied much. These indicate that prohibiting steep slope cultivation and Grain for Green Project are reasonable measures to control soil erosion in karst areas. Based on statistics of soil erosion difference between diverse stratifications of each influencing factor, results of risk detector suggest that the amount of stratification combinations with significant difference accounted for 55% at least in small relief mountain and middle relief mountainous areas. Therefore, the spatial heterogeneity of soil erosion and its influencing factors in different geomorphological types should be investigated to control karst soil loss more effectively.     

川渝地区气温随地形、经度和纬度的变化(英文)

Using the daily temperature data of 95 meteorological stations from Si-chuan-Chongqing Region and its surrounding areas, this paper adopted these methods (e.g., linear regression, trend coefficient, geographical statistics, gray relational analysis and spatial analysis functions of GIS) to analyze the relations of temperature variability with topography, latitude and longitude. Moreover, the rank of gray correlation between temperature variability and elevation, longitude, latitude, topographic position and surface roughness also was measured. These results indicated: (1) The elevation affected temperature variability most obviously, followed by latitude, and longitude. The slope of the linear regression between temperature change rate and elevation, latitude and longitude was 0.4142, 0.0293 and -0.3270, respectively. (2) The rank of gray correlation between temperature change rate and geographic factors was elevation > latitude > surface roughness > topographic position > longitude. The gray correla-tion degree between temperature change rate and elevation was 0.865, followed by latitude with 0.796, and longitude with 0.671. (3) The rate of temperature change enhanced with the increase of elevation. Especially, the warming trend was significant in the plateau and mountain areas of western Sichuan, and mountain and valley areas of southwestern Sichuan (with the warming rate of 0.74℃/10a during the 1990s). However, there was a weak warming trend in Sichuan Basin and its surrounding low mountain and hilly areas. (4) The effects of latitude on temperature change rate presented the specific regulation, which the warming rate of low-latitude areas was more significant than that of high-latitude areas. However, they were consistent with the regulation that the increasing of low temperature controlled most of the warming trend, due to the effects of terrain and elevation on annual mean temperature. (5) Ba-sically, temperature variability along longitude direction resulted from the regular change of elevation along longitude. It was suggested that, in Sichuan-Chongqing Region, special features of temperature variability largely depended on the terrain complexity (e.g., undulations, mutations and roughness). The elevation level controlled only high or low annual mean temperature and the range of temperature change rate in the macro sense.     

Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982简

Based on the GIMMS AVHRR NDVI data （8 km spatial resolution） for 1982-2000, the SPOT VEGETATION NDVI data （1 km spatial resolution） for 1998-2009, and observa- tional plant biomass data, the CASA model was used to model changes in alpine grassland net primary production （NPP） on the Tibetan Plateau （TP）. This study will help to evaluate the health conditions of the alpine grassland ecosystem, and is of great importance to the pro- motion of sustainable development of plateau pasture and to the understanding of the func- tion of the national ecological security shelter on the TP. The spatio-temporal characteristics of NPP change were investigated using spatial statistical analysis, separately on the basis of physico-geographical factors （natural zone, altitude, latitude and longitude）, river basin, and county-level administrative area. Data processing was carried out using an ENVI 4.8 platform, while an ArcGIS 9.3 and ANUSPLIN platform was used to conduct the spatial analysis and mapping. The primary results are as follows： （1） The NPP of alpine grassland on the TP gradually decreases from the southeast to the northwest, which corresponds to gradients in precipitation and temperature. From 1982 to 2009, the average annual total NPP in the TP alpine grassland was 177.2x1012 gC yrl（yr represents year）, while the average annual NPP was 120.8 gC m^-2 yr^-1. （2） The annual NPP in alpine grassland on the TP fluctuates from year to year but shows an overall positive trend ranging from 114.7 gC m^-2 yr^-1 in 1982 to 129.9 gC m^-2 yr^-1 in 2009, with an overall increase of 13.3%; 32.56% of the total alpine grassland on the TP showed a significant increase in NPP, while only 5.55% showed a significant decrease over this 28-year period. （3） Spatio-temporal characteristics are an important control on an- nual NPP in alpine grassland： a） NPP increased in most of the natural zones on the TP, only showing a slight decrease in the Ngari montane desert-steppe and desert zone. The positive trend in NPP in the high-cold shrub-meadow zone, high-cold meadow steppe zone and high-cold steppe zone is more significant than that of the high-cold desert zone; b） with in- creasing altitude, the percentage area with a positive trend in annual NPP follows a trend of ＂increasing-stable-decreasing＂, while the percentage area with a negative trend in annual NPP follows a trend of ＂decreasing-stable-increasing＂, with increasing altitude; c） the varia- tion in annual NPP with latitude and longitude co-varies with the vegetation distribution; d） the variation in annual NPP within the major river basins has a generally positive trend, of which the growth in NPP in the Yellow River Basin is most significant. Results show that, based on changes in NPP trends, vegetation coverage and phonological phenomenon with time, NPP has been declining in certain places successively, while the overall health of the alpine grassland on the TP is improving.     

近5年青海省植被覆盖变化的遥感监测

This paper used five years （2001-2006） time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classification approach for different land cover types with special emphasis on vegetation, especially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dynamic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km^2 in 2001 to 374,576 km^2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest （67.92%） among the five grade elevation belts in Qinghai Province. The second largest vegetation cover ratio is in middle mountain belt （61.80%）. Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt （38.98%）, low mountain belt （25.55%） and flat region belt （15.46%）. The area of middle density grassland in high mountain belt is the biggest （94,003 km^2）, and vegetation cover ratio of dense grassland in middle mountain belt is the highest （32.62%）, and the increased area of dense grassland in high mountain belt is the greatest （1280 km^2）. In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km^2.     

Factor analysis to detect the causes of land degradation in Maduo County, northeastern Qinghai-Tibet Plateau

The area of desertified land has increased by 27.3% from 1987 to 2000 in Maduo County,northeastern Qinghai-Tibet Plateau.Driving forces of land degradation has been extensively studied in the region.Using Factor Analysis (FA),we evaluate contribution of human activity and natural environmental change to land degradation.Four common factors were extracted in this study.The result shows that climate related other than human-related factors,are the major inducing factors of land degradation in Maduo County.Climate change and consequent change of permafrost account for 70% to the land degradation.Increasing evaporation and declining precipitation in the beginning of the growing season hamper seedling establishment.Decreasing frozen days and rising active layer lower bound make surface soil loose and less soil moisture available for plant.     

华北平原禹城市耕地变化与驱动力分析(英文)

Taking Yucheng, a typical agricultural county in Shandong Province as a case, this study applied Logistic regression models to spatially identify factors affecting farmland changes. Using two phases of high resolution imageries in 2001 and 2009, the study obtained the land use and farmland change data in 2001-2009. It was found that the farmland was reduced by 5.14% in the period, mainly due to the farmland conversion to forest land and built-up land, although part of forest land and unused land was converted to farmland. The results of Logistic regressions indicated that location, population growth and farmer income were main factors affecting the farmland conversion, while soil types and pro-curvature were main natural factors controlling the distribution of farmland changes. Regional differences and temporal-spatial variables of farmland changes affected fitting capability of the Logistic re-gression models. The ROC fitting test indicated that the Logistic regression models gave a good explanation of the regional land-use changes. Logistic regression analysis is a good tool to identify major factors affecting land use change by quantifying the contribution of each factor.     

Spatio-temporal pattern and attribution analysis of the mass elevation effect in the Tianshan Mountains in China

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 conduct...     

青藏高原高寒草地净初级生产力(NPP)时空分异(英文)

Based on the GIMMS AVHRR NDVI data(8 km spatial resolution) for 1982–2000, the SPOT VEGETATION NDVI data(1 km spatial resolution) for 1998–2009, and observational plant biomass data, the CASA model was used to model changes in alpine grassland net primary production(NPP) on the Tibetan Plateau(TP). This study will help to evaluate the health conditions of the alpine grassland ecosystem, and is of great importance to the promotion of sustainable development of plateau pasture and to the understanding of the function of the national ecological security shelter on the TP. The spatio-temporal characteristics of NPP change were investigated using spatial statistical analysis, separately on the basis of physico-geographical factors(natural zone, altitude, latitude and longitude), river basin, and county-level administrative area. Data processing was carried out using an ENVI 4.8 platform, while an ArcGIS 9.3 and ANUSPLIN platform was used to conduct the spatial analysis and mapping. The primary results are as follows:(1) The NPP of alpine grassland on the TP gradually decreases from the southeast to the northwest, which corresponds to gradients in precipitation and temperature. From 1982 to 2009, the average annual total NPP in the TP alpine grassland was 177.2×1012gC yr-1(yr represents year), while the average annual NPP was 120.8 gC m-2yr-1.(2) The annual NPP in alpine grassland on the TP fluctuates from year to year but shows an overall positive trend ranging from 114.7 gC m-2yr-1in 1982 to 129.9 gC m-2yr-1in 2009, with an overall increase of 13.3%; 32.56% of the total alpine grassland on the TP showed a significant increase in NPP, while only 5.55% showed a significant decrease over this 28-year period.(3) Spatio-temporal characteristics are an important control on annual NPP in alpine grassland: a) NPP increased in most of the natural zones on the TP, only showing a slight decrease in the Ngari montane desert-steppe and desert zone. The positive trend in NPP in the high-cold shrub-meadow zone, high-cold meadow steppe zone and high-cold steppe zone is more significant than that of the high-cold desert zone; b) with increasing altitude, the percentage area with a positive trend in annual NPP follows a trend of"increasing-stable-decreasing", while the percentage area with a negative trend in annual NPP follows a trend of "decreasing-stable-increasing", with increasing altitude; c) the variation in annual NPP with latitude and longitude co-varies with the vegetation distribution; d) the variation in annual NPP within the major river basins has a generally positive trend, of which the growth in NPP in the Yellow River Basin is most significant. Results show that, based on changes in NPP trends, vegetation coverage and phonological phenomenon with time, NPP has been declining in certain places successively, while the overall health of the alpine grassland on the TP is improving.     

山体效应对北半球林线分布的影响(英文)

Alpine timberline, as the "ecological transition zone," has long attracted the attention of scientists in many fields, especially in 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 model. Continentality is calculated using the meteorological data released by WorldClim and 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 differs 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.     

山体基面高度对青藏高原及其周边地区雪线空间分布的影响

山体效应是地理地带性之外,在大尺度上影响垂直带分布的主要因素,山体基面高度则是山体效应的第一影响因子。青藏高原及其周边地区,雪线呈现出中心高、周围低,与山体基面高度相一致的环状分布模式。为分析山体基面高度对雪线分布的影响,本文共收集青藏高原及周边地区雪线数据142个,采用纬度、经度和基面高度为自变量的三元一次方程拟合研究区雪线分布,计算各自的标准回归系数和相对贡献率,再将基面高度划分成5个子集（0~1000 m、1001~2000 m、2001~3000 m、3001~4000 m和4001~5000 m）,分析基面高度不同的山地对雪线的影响差异。结果表明：① 在青藏高原,纬度、经度和基面高度对雪线高度分布的相对贡献率分别为51.49%、16.31%和32.20%;② 随着基面高度的增高,各子集模型的决定系数虽有逐渐降低的趋势,但仍保持在较高的值域（R²=0.895~0.668）,说明模型的有效性;③ 随基面高度的抬升,纬度和山体基面高度对雪线分布高度的相对贡献率分别表现出降低（92.6%~48.99%,R²=0.855）和增大（3.33%~31.76%,R²=0.582）的趋势,表明基面高度越高,其对雪线分布高度的影响越大。     

山体效应对北半球林线分布的影响分析

通过搜集整理了北半球516 个林线数据, 结合WorldClim 气象数据计算了林线数据点上的大陆度, 并依据SRTM高程数据提取了林线处的山体基面高度(作为山体效应的代用因子), 然后以纬度、大陆度和山体基面高度为解释变量, 建立三元回归模型。结果表明:线性回归模型的判定系数R²为0.904, 二次回归模型的R²高达0.912。相比先前不考虑基面高度的林线分布模型(R² = 0.79), 纳入了山体基面高度的林线分布模型能够更加有效的拟合半球尺度的林线分布; 结果还表明, 山体基面高度对北半球林线高度分布的贡献率达到了48.94% (p =0.000), 而纬度和大陆度分别为45.02% (p = 0.000) 和6.04% (p = 0.000)。这揭示了山体效应对半球尺度林线分布具有重要的影响。基面高度在北美洲地区对林线高度的贡献率最大(50.49%, p=0.000), 在欧亚大陆东部地区为48.73% (p = 0.000), 在欧亚大陆西部地区为43.6% (p=0.000)。这一结果说明山体效应对林线分布高度的影响虽有区域差异, 但都有较高的贡献率。     

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.     

山体基面高度对青藏高原及其周边地区雪线空间分布的影响简

山体效应是地理地带性之外,在大尺度上影响垂直带分布的主要因素,山体基面高度则是山体效应的第一影响因子。青藏高原及其周边地区,雪线呈现出中心高、周围低,与山体基面高度相一致的环状分布模式。为分析山体基面高度对雪线分布的影响,本文共收集青藏高原及周边地区雪线数据142个,采用纬度、经度和基面高度为自变量的三元一次方程拟合研究区雪线分布,计算各自的标准回归系数和相对贡献率,再将基面高度划分成5个子集（0~1000 m、1001~2000 m、2001~3000 m、3001~4000 m和4001~5000 m）,分析基面高度不同的山地对雪线的影响差异。结果表明：① 在青藏高原,纬度、经度和基面高度对雪线高度分布的相对贡献率分别为51.49%、16.31%和32.20%;② 随着基面高度的增高,各子集模型的决定系数虽有逐渐降低的趋势,但仍保持在较高的值域（R²=0.895~0.668）,说明模型的有效性;③ 随基面高度的抬升,纬度和山体基面高度对雪线分布高度的相对贡献率分别表现出降低（92.6%~48.99%,R²=0.855）和增大（3.33%~31.76%,R²=0.582）的趋势,表明基面高度越高,其对雪线分布高度的影响越大。     

阿尔卑斯山山体效应及其对林线的影响分析

阿尔卑斯山是欧亚大陆上著名的山地,对欧洲的地理生态格局具有重要的影响。山体效应产生的原因在于隆起的高原或山地吸收了更多的太阳辐射。因此,论文以阿尔卑斯山为研究对象,利用收集到的气象台站观测数据、林线、数字高程数据,以及基于半球视域算法计算得到的太阳辐射数据等,分析阿尔卑斯山气温的空间分布格局以及最热月、最冷月、全年的太阳辐射量,同时以太阳辐射作为山体效应的代用因子,采用逐步回归分析方法构建了阿尔卑斯山林线分布模型,探究该山地的山体效应及其对林线的影响。研究结果表明：① 阿尔卑斯山具有明显的山体效应,山体内部的太阳辐射量远高于山体边缘地区,这也是山体内部气温和林线高度都高于山体边缘地区的主要原因。最热月、最冷月和全年总太阳辐射量在山体内部比边缘地区分别高10~20、20~40和200~400 kWh/m²。② 太阳辐射能更好地定量化山体效应,以太阳辐射为山体效应代用因子建立的林线分布模型具有更高的精度。与基于气温、降水构建的林线分布模型(R²= 0.522)相比,该模型具有更高的模拟精度(R² = 0.736),同时太阳辐射对林线分布的贡献率最大(1月、7月太阳辐射的贡献率分别为34.75%、27.82%),超过了气温和降水的贡献率(分别为26.24%和11.17%)。     

中国高山林线的分布高度与气候的关系

通过研究我国高山林线的分布高度沿纬度、经度的变化格局,和对高山林线处的温度和基带降水等气候指标的分析,对我国高山林线分布高度与气候因子的关系进行探讨。结果表明：(1) 我国高山林线高度表现出明显的纬向和经向变化,总体趋势是：在北纬30^o以北,高山林线高度随纬度升高而下降,下降速率为112 m/度左右;在30^oN以南,则表现出较大的东西部差异：在东部,高山林线高度变化不明显,西部则随纬度增加呈上升趋势。在相似的纬度上,高山林线高度呈现出从东向西升高的趋势。高山林线在藏东南的洛隆、丁青、工布江达一带 (约29^o~32^oN,94^o~96^oE) 达到4 600 m,为世界最高林线高度,并以此为中心向四周降低。(2) 影响高山林线高度的主导气候因子为生长季温度条件。我国高山林线高度的温度指标为年生物温度3.5 ^oC,温暖指数14.2 ^oC·月,生长季平均温度8.2 ^oC。该指标相应海拔高度的地理差异,导致了我国高山林线高度的纬向、经向变化,和从沿海到内陆林线高度的差异。(3) 降水对高山林线高度有显著影响。在中高纬度地区,相同纬度上干旱区域的高山林线高于较湿润区域,降水量是通过温度间接作用于林线高度的。     

青藏高原和阿尔卑斯山山体效应的对比研究

山体效应不仅对气候产生重大影响,也对区域地理生态格局有深远影响,尤其是它对山地垂直带分布和结构类型等的影响已经为地理学家和地植物学家所认识。目前相关研究主要集中在山体效应定量化方面,缺少不同山地山体效应的对比研究,因此对山体效应的区域差异性了解不足。本文选择欧亚大陆上具有明显山体效应的两个山地青藏高原和阿尔卑斯山为研究对象,利用收集到的气象台站观测数据、林线和DEM数据以及基于MODIS地表温度估算的青藏高原和阿尔卑斯山气温数据等,通过对比分析青藏高原与阿尔卑斯山相同海拔高度上的气温以及林线分布高度等来探讨两个山地的山体效应差异性。分析结果表明青藏高原的山体效应比阿尔卑斯山更为强烈,表现为：① 由于山体效应影响,在相同海拔高度上（4500 m）,青藏高原内部气温远高于阿尔卑斯山的气温,尤其是在最热月高原内部气温比阿尔卑斯山内部气温高10~15℃,在最冷月高原内部气温比阿尔卑斯山内部气温高5~10℃。② 由于山体效应影响,青藏高原内部林线也远高于阿尔卑斯山内部林线,约高2000~3000 m。本研究将为山体效应的影响因素分析奠定基础,同时对于揭示欧亚大陆山地生态系统格局具有一定的科学意义。