Characterizing the Mass Elevation Effect across the Tibetan Plateau

It is over 110 years since the term Mass Elevation Effect(MEE) was proposed by A. D. Quervain in 1904. The quantitative study of MEE has been explored in the Tibetan Plateau in recent years; however, the spatial distribution of MEE and its impact on the ecological pattern of the plateau are seldom known. In this study, we used a new method to estimate MEE in different regions of the plateau, and, then analyzed the distribution pattern of MEE, and the relationships among MEE, climate, and the altitudinal distribution of timberlines and snowlines in the Plateau. The main results are as follows:(1) The spatial distribution of MEE in the Tibetan Plateau roughly takes on an eccentric ellipse in northwestsoutheast trend. The Chang Tang Plateau and the middle part of the Kunlun Mountains are the core area of MEE, where occurs the highest MEE of above 11℃; and MEE tends to decreases from this core area northwestward, northeastward and southward;(2) The distance away from the core zone of the plateau is also a very important factor for MEE magnitude, because MEE is obviously higher in the interior than in the exterior of the plateau even with similar mountain base elevation(MBE).(3) The impacts of MEE on the altitudinal distribution of timberlines and snowlines are similar, i.e., the higher the MEE, the higher timberlines and snowlines. The highest timberline(4600–4800 m) appears in the lakes and basins north of the Himalayas and in the upper and middle reach valleys of the Yarlung Zangbo River, where the estimated MEE is 10.2822℃–10.6904℃. The highest snowline(6000–6200 m) occurs in the southwest of the Chang Tang Plateau, where the estimated MEE is 11.2059°C–11.5488℃.

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 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.

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.

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.

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.

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.

山体效应对台湾常绿阔叶林分布上限的影响

山体效应使山体内部的垂直植被带相对升高,影响山地的立体生态格局。台湾岛中央山脉在3 500 m以上,山地植被的分布高度不仅受到纬度和季风的影响,也必然受到山体效应的影响。采用台湾生物多样性信息中心发布的数据,利用多元线性回归模型分析纬度、山体效应(以山体基面高度为简单量化指标)以及季风(以冬雨量占全年降水量百分比为简明代表)对台湾常绿阔叶林分布上限的影响。结果表明,纬度、山体效应和季风为自变量的线性回归模型R2为0.562,回归方程显著,具有统计学意义,三个变量的贡献率分别为26.32%、64.12%与9.56%。这表明山体效应对台湾山地垂直带的影响非常显著,远远超过了纬度与季风的作用。同时还发现,冬雨量与垂直带分布高度的相关性以24.13°N为界,南北完全相反。该纬度以南,冬雨量与垂直带分布高度呈现较强的正相关性;而在以北,正相关性显著下降甚至出现了一定的负相关。后者应该与冬雨量过多有密切关系。

青藏高原山体效应的遥感估算及其生态效应分析

山体效应是隆起的山体所产生的热力效应,其结果之一就是相同垂直带界限自外围向内部有升高的趋势。本文结合MOD11C3地表温度产品和地面144个气象台站实测气象数据,估算青藏高原内外相同高度上的温差(也即高原山体效应值)。具体结论如下:(1)最大温差(10.04℃~11.70℃)出现在高原中南部,即雅鲁藏布江以北藏北高原以南。由此为核心向北、向东、向西均逐渐减小;(2)数据点上同高度内外温差与局部基面高度有紧密关系,基面高度每抬升100 m,温差增加约0.051℃,并有加速增大的趋势;(3)山体基面高度与山体效应存在明显的线性关系,其决定系数R2高达0.5306。但山体基面高度最高的区域山体效应并非最大,说明还有其他因子影响山体效应的大小,可能的因子包括大气湿度、纬度、地形开阔程度等,在建立山体效应数字模型时必须加以考虑;(4)高原山体效应对雪线分布高度的抬升作用更甚于其对林线。山体效应估值最大的区域,分布着6 000 m以上极高雪线;最高林线(4 900 m)分布于本研究中山体效应估算值较低的相对多雨区,因为林线的发育还要求一定的降水量。

台阶深孔爆破振动控制

对某水电站蠕变体高边坡的台阶深孔爆破振动进行观测和分析,得到考虑高程效应的爆破地震波衰减经验公式.根据该蠕变体高边坡的特点,提出爆破振动控制标准.由经验公式和控制标准,得出蠕变体高边坡新浇混凝土支护区爆破安全控制参量.最后结合该蠕变体高边坡爆破实测,建议了台阶深孔爆破所需要采取的降震措施.从工程实施效果看,该研究成果与结论得到了较好的应用,保障了该工程爆破的顺利完成.

青藏高原山体效应对近地表层垂直大气昼夜温差的影响

青藏高原巨大隆起不仅塑造了欧亚大陆的气候格局,也深远地影响了高原的地理生态格局。青藏高原巨大隆起而产生的山体效应不仅可对近地表温度产生显著影响,其对近地表层垂直大气亦可产生显著作用,然而目前仍缺乏这一方面的研究。因此,本研究基于MODIS大气廓线数据产品,以昼夜温差为切入点,分析了青藏高原不同季节、不同气压面(500~200hPa)的昼夜温差差异。结果表明:①青藏高原内部不同季节、不同气压面高度处的昼夜温差均大于外部地区,整体符合山体效应的格局。②青藏高原海拔越高,不同季节的垂直层昼夜温差越大。③随着气压面高度的增加(500~200 hPa),海拔对冬季大气昼夜温差的影响逐渐降低,对春季、夏季和秋季的影响程度先升高后降低,作用最大处分别出现在300 hPa、250 hPa和300 hPa。

中国天山山体效应评估及空间分异归因

山体效应是隆起山地产生的热力效应,其对山区水热条件格局和生态地理过程有着普遍且深远的影响,也是山地科学相关研究的突破口之一。本文基于多源遥感数据和气象观测数据进行中国天山气温空间降尺度反演,开展研究区山体效应估算和时空格局分析,借助地理探测器及GWR模型探析中国天山山体效应时空异质性成因规律。结果表明:①中国天山气温格局复杂多样,整体呈南高北低、东高西低的分布特征;气温的地带性特征明显,且与海拔、内外程度均呈负相关。②研究区山体增温效应普遍且突出,同海拔气温由西向东、由北向南均呈阶梯式递增;大型沟谷和山间盆地等地貌单元削弱了同海拔气温的纬度地带性和海拔依赖性特征,其中天山南脉、额尔宾及巴里坤等隆起区山体增温效应尤其明显。③影响中国天山山体效应整体格局的主导因子为地形及区位,其中内外程度与高程的贡献突出;因子间的交互作用对山体效应空间分异的影响大于单一因子,地形与气候、降水、NDVI等因子的交互作用强烈。④主要驱动因子对研究区山体效应空间变化的作用方向及强度存在明显的空间异质性。绝对高程与山体效应强度呈显著正相关,降水和NDVI则以负反馈作用为主。总体而言,地形与降水、下垫面等因子耦合形成独特的山地环流系统和气候特征,进而增强天山山体效应的时空异质性。本文研究结果是对山体效应成因分析及其生态效应研究的有益补充。

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.

山体基面高度对欧亚大陆东南部林线分布的影响——山体效应定量化研究

根据收集到173个林线数据,采用纬度、经度和基面高度的三元一次方程拟合欧亚大陆东南部林线分布,计算各自的标准回归系数和贡献率,以此来确定山体基面高度(山体效应的简明表达形式)对林线分布高度的影响。结果表明,纬度、经度和山体基面高度对林线分布高度的贡献率分别为30.60%、26.53%、42.87%。以北纬32o为界线,对其以北、以南区域也分别进行了分析,基面高度的贡献率达到24.10%和39.11%。分析不同尺度和区域山体基面高度作用于林线的贡献率不难发现:在欧亚大陆东南部以基面高度代表的山体效应对于林线高度的影响显著,明显地超过了纬度和经度。基面高度的作用受气候条件和海陆位置影响较小,不论大陆内部或沿海,基面高度分异对山地垂直带分异的影响都相对独立和稳定。该结果定量地表明了山体效应对林线分布高度的重要作用。

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。

山体基面高度的提取方法——以台湾岛为例

山体基面高度的差异影响山体自身对其水热条件的再分配,进而影响山地垂直带谱的结构和分布,是决定垂直带分布高度的重要因子之一。目前,山体基面高度还没有一个准确科学的定义,也缺乏一个有效的数字化、定量化提取方法。本文以台湾岛为例,使用30m分辨率的ASTER GDEM数据,提出了一种提取山体基面高度的方法。首先,以地形特征与水文特征提取方法获得主山脊线与主山谷线,然后,以地形地貌单元自动提取方法获得山体轮廓界线,再依据提取出的主山脊线、山体轮廓界线及主山谷线,划分山体基面高度分区,依据山体基面分布特征确定各分区的基面高度值,将台湾山地划分出6个不同的山体基面高度(0m、150m、200m、600m、630m和650m)。该方法为大范围山体基面高度的快速、准确提取,以及山体效应定量化研究提供了重要的技术支撑。

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。

科罗拉多落基山脉山体效应定量化研究

落基山脉作为北美最大的内陆山地,其山体效应对林线分布具有很大影响,导致林线海拔远高于周围内陆山体及其他海岸山地。然而,以往落基山脉山体效应研究多集中于定性研究,但是山体效应如何量化,如何根据落基山脉的地形气候条件构建区域山体效应的定量化模型,目前鲜有研究。通过分析台站处山体增温及量化落基山脉山体效应的影响因子,并计算最热月均温10℃等温线的海拔高度,来定量化地估算科罗拉多落基山脉山体效应值大小及其对林线分布的影响。结果表明:(1)用山体增温值表示山体效应大小是合理且比较理想的指标。科罗拉多落基山脉增温显著,所有台站的增温均值为2.07℃,增温幅度为0.78~4.29℃。(2)科罗拉多落基山脉山体效应的主要影响因素为山体基面高度和降水大陆度,二者与山体增温构建的线性拟合模型具有较高的解释能力,判定系数高达71.2%。(3)科罗拉多落基山脉不同纬度带山体内外最热月10℃等温线分布高度对比表明,山体内部理想林线高度均高于山体外部的理想林线分布,内外分布差异为400~700 m。定量分析科罗拉多落基山脉的山体效应模型,优化了区域尺度的山体效应模型精度,有助于深入认识山体效应及其对垂直带分布的影响。

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200-400kWh/m^(2)。(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(R^(2)=0.522)相比,该模型具有更高的模拟精度(R^(2)=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%)。

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。