中国气候－植被关系初探

气候－植被分类必须强调气候因子的综合影响及其指标的区域性。一般的气候观测缺乏在生物学上具有重要与综合的作用或代表性,而区域潜在蒸散包括从所有表面的蒸发与植物蒸腾,并涉及到决定植被分布的两大要素：温度和降水。因此,区域潜在蒸散具有作为植被－气候相关分析与分类的综合气侯指标的功能。本文首次根据区域潜在蒸散对气侯－植被关系的热量与水分指标进行了初步探讨,提出了进行气候－植被关系的热量指标（ＴＩ）和区域湿润指标（ＲＭＩ）,并据此对中国气侯－植被关系进行了初步的定量研究。该研究对于了解气候－植被之间的相互关系,正确地评估和预测全球变化对人类及生物所赖以生存的生态环境的影响具有重要的理论和现实意义。     

全球变化的中国气候-植被分类研究

区域潜在蒸散具有作为植被－气候相关分析与分类的综合气候指标的功能。根据区域潜在蒸散对气候－植被分类的热量与水分指标进行了初步探讨,并对中国气候－植被分类进行了初步的定量研究。根据该模式对中国陆地生态系统对全球变化的反应进行了探讨,结果表明我国自然植被在气温增加２℃或４ ℃、降水增加２０％ 时, 森林和草原的面积都有所减少,且随着温度的升高而减少,沙漠化趋势增强。特别是青藏高原地区对全球气候变化非常敏感,因而可以作为全球变化的先兆区或预警区,具有重要的监测和研究意义     

全球变化的中国气体—植被分类研究

区域潜在蒸散具有作为植被－气候相关分析与分类的综合气候指标的功能。根据区域潜在蒸散对气候－植被分类的热量与水分指标进行了初步探讨,并对中国气候－植被分类进行了初步的定量研究。根据该模型对中国陆地生态系统对全球变化的反应进行了探讨,结果表明我国自然植被在气温增加２℃或４℃、降水增加２０％时,森林和草原的面积都有所减少,且随着温度的升高而减少,沙漠化趋势增强。特别是青藏高原地区对全球气候变化非常敏感,     

黄土高原地区主要植被类型的气候梯度分布

应用Thronthwaite的方法,对黄土高原286个气象站(台)的某些气候指标进行了计算,其中包括校正的潜在蒸散(APE)、水分指数(IM)、热量系数(TE)、干旱指数(IA)和湿润指数(IH)。同时,得到了每项气候指标的地理回归模型,并利用各项地理回归模型分别做出了各项气候指标的地区分布图。APE和IM与黄土高原地区植被的主要类型及其分布格局有密切的关系。本区大多数的植被类型的IM均为负值,照例划入干旱亚湿润-半干旱-干旱(dry subhumid-semiarid-arid)地区内,而大部分植被的TE在57.1—85.5之间,应属中温范畴。植被与气候指标的相关性表明:黄土高原南端的地带性植被,实际上为干旱森林、疏林和灌丛,它们在性质上与分布于山地上的辽东栋(Quercus liaotungensis Koiz.)林有所区别。     

中国东北主要植被类型的分布与气候的关系

本文根据吉良的热量指标和作者提出的湿度指数,研究了我国东北主要植被类型的分布与气候之间的关系：1．确定了东北地区10个水平地带性植被类型的热量分布范围和水热指标的平均值。2．研究了东北山地垂直地带性植被类型的水热指标分布特点,并用定量指标讨论了东北东部山地岳桦林带的分布、大兴安岭存在山地冻原和东北植被区域的分界线问题。     

气候变化下中国潜在植被演替及其敏感性

潜在植被的研究能够真实反映气候条件对植被形态变化的影响,是植被-环境分类与关系研究的起点,也是全球变化与陆地生态系统研究的关键,对区域植被生态的恢复和重建具有重要的指导意义。本研究基于综合顺序分类系统,对过去30年(1986—2015年)和IPCC5发布的RCP4.5情景下未来3个时期(2030s、2050s和2080s)潜在植被的空间分布进行了GIS模拟,并分析了潜在植被对气候变化响应的敏感性。结果表明:(1)中国分布的潜在植被类型数量及各类在不同时期所占比例均存在差异,同时炎热极干热带荒漠类(VIIA)为各个时期共同缺失的潜在植被类型。(2)潜在植被的分布面积整体上呈现出冷干型潜在植被类型面积逐渐减少、暖湿型潜在植被类型面积逐渐增加的趋势,具体表现为扩展型、缩减型和波动型3类;潜在植被类组的重心发生了不同方向、不同距离的移动变化。(3)中国潜在植被对气候变化的响应存在敏感性差异,其空间分布形态总体呈现出不同敏感性程度的区域相间分布的特点。敏感性高的区域、敏感性较高的区域和敏感性较低的区域分别占国土总面积的2.28%、14.39%和43.82%。     

水热积指数的估算及其在中国植被与气候关系研究中的应用

试图利用大气年平均气温、年降水量、可能蒸散和土壤水分平衡之间的关系建立一个水热积指数,并应用年平均气温、年土壤水分盈亏值和水热积指数三个气候变量来限定植物群落组合,构成一个圆形的生命-气候图式。根据全国689个标准气象台站的气候资料,计算了中国8个植被地带和26个亚地带的年平均气温、年土壤水分盈亏和水热积指数,绘制了各气候指标在中国的分布图及散点图,较好表现了中国各植被类型与气候指标的关系和格局,包括寒温带针叶林、冷温带针阔叶混交林、暖温带落叶阔叶林、亚热带常绿阔叶林、热带雨林和季雨林、温带草原、温带荒漠、青藏高原高寒植被,并得到了中国各植被地带的气候指标范围及界限。通过分析可以看出,年平均气温的等值线较好地反映了中国大陆的热量梯度,经度和纬度方向的区分均较明显;年土壤水分盈亏曲线的等值线则比较零乱;综合了热量和水分差异的水热积指数等值线与热量梯度和水分梯度均有一定的对应性,与植被类型的对应也较好。这是在宏观尺度上进行的植被与气候关系研究的一种尝试。     

KIRA指标的拓展及其在中国植被与气候关系研究中的应用

根据Kira以月平均气温5℃为界的热量指数和干湿度指数概念,提出了以月平均气温10℃为界的生物热量指数,包括生物温暖指数(BWI)和生物寒冷指数(BCI),并修正其干湿度指数为生物干湿度指数(BK).利用中国689个标准气象台站的资料,分析我国主要植被类型分布与热量因子和干湿度因子的关系,得出两者之间较好的相关性,生物温暖指数、寒冷指数和干湿度指数的散点图较好地表现了中国各植被类型与气候指标的关系和格局.以10℃为界的生物温暖指数不仅对我国森林植被的地理分布和温度气候带的划分具有较好的指示作用,而且对西南部高山、亚高山地区的植被与气候关系指示性较强;生物寒冷指数则对亚热带和热带的指示性很好,能够较好区分亚热带南部及热带地区;由热量指数和降水量综合得出的生物干湿度指数,对中国西北部干旱、半干旱区以至全国的植被分布与水分、热量因子的关系分析有较好的应用价值.     

浙江省植被NDVI动态及其对气候的响应

利用GIMMS和MODIS两种归一化植被指数(NDVI)资料反演了1982—2010年浙江植被覆盖状况,结合同期研究区63个气象站点的气温、降水和湿润指数等气候指标,分析了该地区植被年际变化、月际变化及其对气候要素的响应特征。结果表明:(1)研究期间,浙江气候总体呈暖干化趋势,植被覆盖缓慢下降,主要是由于森林植被遭破坏,农业生产活动受抑制影响所致,其中NDVI显著减少的地区约占全省陆域面积的29.1%,主要发生在6—11月;(2)降水量及干湿程度对浙江植被NDVI年变化起着决定性作用。植被与气候要素年变化相关分析发现,NDVI与湿润指数关系较降水、气温更为密切,两者相关及偏相关系数均通过0.05水平的置信度检验,这表明在年际尺度上,湿度的增加增大了植被的生长势,有利于植被生长;(3)植被与气候要素月变化分析表明冬季的热量供给是影响浙江植被生长的重要因子,而植被变化对夏季降水和干湿程度的最大响应为滞后两个月;(4)农业生产水平的提高使得农作物种植区NDVI有所增加,人类活动对浙江植被覆盖的影响不可忽视。     

湖北省地区植被覆盖变化及其对气候因子的响应

归一化植被指数(NDVI)作为一个重要的遥感参数,能够准确地反映植被覆盖程度和植被生长状况、生物物理化学性质及生态系统参数的变化,其时序数据也已成为基于生物气候特征开展大区域植被和土地覆盖分类的基本手段。基于2001—2012年MODIS-NDVI数据,利用趋势分析法以及线性相关分析等方法对湖北省植被年际变化趋势、月变化趋势进行详细分析;并且研究该区植被覆盖时空变化及其与气温和降水的关系。结果表明近12年来,研究区大部分区域植被覆盖度良好,其中鄂西北及鄂南地区NDVI值较高为0.82,鄂中东部城市NDVI值较低为0.13;2001—2012年间年均NDVI整体呈增加趋势,增速1%/10a;植被覆盖度基本不变区域占研究区总面积的92.8%,大致符合我国中部地区植被覆盖变化趋势;分析NDVI与气候因子的相关关系可知,降水量对湖北植被NDVI年变化起有重要影响;逐月NDVI与月平均气温及月降水量的回归分析表明,降水和气温对生长季不同月份的植被NDVI影响明显不同,同时呈现一定的滞后性。     

Study on Climate Vegetation Classification for Global Change in China

The study on climate-vegetation relationship is the basis for determining the re sponse of terrestrial ecosystem to global change. By means of quantitative analysis on climate-vegetation interaction, vegetation types and their distribution pattern could be corresponded with certain climatic types in a series of mathematical forms. Thus, the climate could be used to predict vegetation types and their distribution, the same is in reverse. Potential evapotranspiration rate is a comprehensive climatological index which combines temperature with precipitation, and could be used to evaluate the effect of climate on vegetation. In this respect, Holdridge life zone system has been drawing much attention and widely applied internationally owing to its simplicity. It is especially used in the assessment of sensibility of terrestrial ecosystems and their distribution in accordance with climate change and in prediction of the changing pattern of vegetation under doubled CO2 condition. However, Prentice (1990) pointed out that the accurancy of Holdridge life zone system is less than 40 % when it is used at global scale. The reason may be that the potential evapotranspiration calculated by Thornthwaite method, which is used in Holdridge life zone system, reflects the potential evapotranspiration from small evaporated area, while climate-vegetation classification is based on the regional scale. The authors try to establish a new climate-vegetation classification system based on the regional potential evapotranspiration. According to the following formula: where E designates regional actual evapotranspiration: Ep local potential evapotran-spiration: Epo, regional potential evapotranspiration. Ed can be calculated from Penman model or other models. E can be calculated from the following model: E=r · Rn (r2+Rn2+r · Rn) / (2) (r+Rn) · (r2+Rn2)where r designates precipitation (mm); Rn, net radiation (mm). Thus, Ep0 can be easily obtained. It is used as the regional thermal index (RTI) of climate-vegetation classification,and can be expressed as: RTl = Epo (3) Moisture index is another index of climate-veggetation classification. Usually, it can be expressed as the ratio between potential evapotranspiration and precipitation. However, this ratio can not reflect soil moisture, which is important for plant. The ratio between regional actual evapotranspiration and regional potential evapotranspiration is associated not only with climatic condition but also with soil moisture. So it can be used as the moisture index of climate-vegetation classification, and is defined as regional moisture index (RMI): RMI = E/Epo (5) Based on the average climatological data of 30 years from 647 meteorological observation stations in China. It was found that RTl could well reflect a regional thermal level. The values of RTI were less than 360 mm in cold temperate zone, 360～650 mm in temperate zone, 650～380 mm in warm temperate zone, 780～1100 mm in subtropical zone. And more than 1100 mm in tropical zone. RMI also reflects a regional moisture level very well. The values of RMI was less than 0.4 in desert area, 0.4～0.7 in grassland area and more than 0.7 in forest area. Thus, the climate-vegetation classification in China is established on the basis of the two indices: RTI and RMI. According to this model, the changing patterns of vegetation zones in China are given under the conditions of mean annual temperature in creasing by 2℃ and 4℃ and mean annual precipitation increasing by 20%. The results showed that the areas of forest and grassland would decrease, the vegetation zones would move northward and upward, and the area of desert would increase. The results also indicate that the Tibetan Plateau is an area highly sensitive to global change. It could be considered as an indicative or forewarning area for global change , and therefore, an area of great siginificance for monitoring and research. The possible beneficial effect of global change on China terrestrial ecosystems is that the plantation boundary will move northwards and upwards; and the disadvantageous effect is the expansion of desertification and the increase of instability in climatic conditions.     

植被的PE（可能蒸散）指标与植被气候分类（三）

Holdridge的生命地带分类系统由于其指标的计算十分简便与对植被的对应性强而受到国际植被生态学界与环境科学研究者的重视。特别是近年来在环境的评价、生态区划与预测全球变化对生态系统的影响等方面得到较多采用。该系统对中国各植被地带的气候台站资料进行计算分析的结果表明有较好的适应性。但由于该系统发展于中美洲的热带地区,因而在中国的亚热带地区须进行局部的调整。但采用该系统将有利于与世界各地的气候—植被分类系统的统一与对比研究。通过回归计算表明,该系统的可能蒸散率(PER)指标与CHIKUGO模型的辐射干燥度(RDI)显著相关。因而可以采用便于取得资料与易于计算的PER来进行潜在第一性生产力(NPP)的估算。对中国各植被地带的计算结果令人满意,可进一步用于在全球变化条件下,中国各植被地带或生态系统主要类型及其NPP变化的预测。     

自然植被净第一性生产力模型初探

本文根据植物的生理生态学特点及联系能量平衡方程和水量平衡方程的区域蒸散模式建立了联系植物生理生态学特点和水热平衡关系的植物的净第一性生产力模型：该模型的建立为宏观地确定地带性景观的生产潜力、植物净第一性生产力的区域分布和全球分布,以及全球变化的影响提供了理论基础,对于合理地利用气候资源,扬长避短,充分发挥气候生产潜力,最大限度地提高植物的产量具有重要的意义。     

近30年中国陆地生态系统NDVI时空变化特征

气候变化已明显影响到陆地植被的活动,但在不同生态系统间存在差异,研究不同陆地生态系统归一化植被指数(NDVI)的时空变化特征,不仅可揭示各生态系统植被活动对气候变化的响应规律,而且可为我国不同生态区制定应对气候变化的策略和生态文明建设提供科学依据。基于1982—2012年GIMMS NDVI3g和中国陆地生态系统类型数据,利用一元线性回归、集合经验模态分解和相关分析等方法,研究了近30年中国各陆地生态系统NDVI的时空变化特征,分析了其与气候事件的关系。结果表明,近30年中国植被活动显著上升,年平均归一化植被指数(ANDVI)的上升幅度为0.0029/10a(P0.05),年最大归一化植被指数(MNDVI)的上升幅度为0.0076/10a(P0.01);植被活动显著增强的区域主要是分布在东部季风区的农田和森林生态系统,显著下降的区域主要是分布于西北的荒漠生态系统和东北的森林生态系统;尽管ANDVI和MNDVI线性趋势的显著性有所差异,但农田、森林、草地和水体与湿地生态系统的NDVI总体呈非稳定的上升趋势,上升过程中伴随着较大波动,荒漠生态系统的NDVI呈下降趋势,植被退化显著;与线性趋势不同,各生态系统植被活动的残差趋势包含"上升—下降"两个阶段,并相继于20世纪90年代到21世纪初发生转折;上述5类生态系统的植被活动存在不同尺度的周期特征,年际周期波动特征(1.9—7.6a)比较显著,而年代际周期(10.7a和22.2a)的显著性相对较差;各生态系统的空间异质性在趋强过程中存在2.1—7.1a的年际周期节律;海洋与大气环流的短周期脉动与各生态系统植被活动的周期性节律有着明显关联,ENSO事件和太阳活动是推动植被活动周期性振荡的重要因素。     

2001-2020年新疆阿勒泰地区归一化植被指数时空变化特征及其对气候变化的响应

气候变化是干旱区植被变化的重要驱动因素,探究干旱区气候与植被关系的时空变化,有助于理解生态系统演化特征。基于MODIS-NDVI与CRU数据集中气候数据（降水、平均气温、最高气温、最低气温、水汽压及潜在蒸散）,采用Sen+Mann-kendall、Hurst指数及相关分析法,在不同时间尺度评价了阿勒泰地区NDVI的时空变化特征及其对气候变化的响应。结果表明：（1）在年尺度上,植被NDVI整体呈上升趋势,但存在弱反持续特征。区域内植被退化现象严重（12.11%）,植被改善区域与退化区域呈破碎化分布。（2）月尺度与季尺度上,NDVI与降水、气温、极端气温、水汽压和潜在蒸散呈正相关,其中降水因素在季尺度上的相关性高于月尺度。（3）不同土地利用方式下NDVI与气候因子的滞后效应表现为短期正效应与长期负效应。     

植被变化对气候的反馈机制及调节效应

植被通过光合作用固定大气中的CO₂来减缓温室效应，同时植被也通过改变地表能量收支影响温室效应。在过去的气候-植被研究中，大多关注气候变化对植被的影响，而植被对气候反馈的研究相对较少。植被通过调节地表能量收支、水通量等重要地气过程影响局地、区域乃至全球气候，在气候变化中的作用十分重要。因此，需要厘清植被对气候的反馈效应机制及其结果，并识别其地域差异。从生物地球物理和生物地球化学过程两方面分析植被与气候之间的作用机制，对全球及关键区域内植被变化对局地、区域乃至全球的气候反馈效应进行了系统总结：（1）生物地球物理反馈的区域特征明显，生物地球化学反馈则表现在全球尺度上，二者相互作用但难以统一；（2）植被破坏带来的气候影响在气温效应方面与生态系统的类型及地理分布相关：热带森林破坏带来增温效应，北方森林破坏带来降温效应，温带森林破坏则会通过增加森林反照率抵消丢失的固碳降温效应，气温效应表现不明显；（3）当前研究对关键过程机制考虑不够完善，不同研究方法的结果差异较大，且缺乏高质量观测数据的验证；同时考虑生物地球物理和生物地球化学的净气候反馈研究尚无法支撑植树造林对气候变化单一减缓作用的常规理解。本文可为科学评估植树造林对气候变化作用的方向与强度提供理论依据。     

植被的PE(可能蒸散)指标与植被-气候分类(三)几种主要方法与PEP程序介绍

Holdridge的生命地带分类系统由于其指标的计算十分简便与对植被的对应性强而受到国际植被生态学界与环境科学研究者的重视。特别是近年来在环境的评价、生态区划与预测全球变化对生态系统的影响等方面得到较多采用。该系统对中国各植被地带的气候台站资料进行计算分析的结果表明有较好的适应性。但由于该系统发展于中美洲的热带地区,因而在中国的亚热带地区须进行局部的调整。但采用该系统将有利于与世界各地的气候一植被分类系统的统一与对比研究。通过回归计算表明,该系统的可能蒸散率(PER)指标与CHIKUGO模型的辐射干燥度(RDI)显著相关。因而可以采用便于取得资料与易于计算的PER来进行潜在第一性生产力(NPP)的估算。对中国各植被地带的计算结果令人满意,可进一步用于在全球变化条件下,中国各植被地带或生态系统主要类型及其NPP变化的预测。     

Future changes and driving factors of global peak vegetation growth based on CMIP6 simulations

Accurate detection and attribution of changes in global peak vegetation growth at the annual scale are prerequisites for characterising the productivity of terrestrial ecosystems and developing strategies for the sustainable management of ecosystems. This study examined the long-term global normalised difference vegetation index during the baseline period (1982–2015) and found widespread greening in 70% of global vegetated areas in response to climate warming. However, climate change is not the only cause of global greening. The spatial variability in the response of global vegetation to environmental factors has not been well established. The Cubist model was used to investigate the relationship between peak vegetation growth and environmental variables. The results showed that 64% of the spatial variation in greening/browning can be explained by climate (including precipitation and temperature), followed by atmospheric components of nitrogen deposition and carbon dioxide concentration (17%), terrain properties (12%), and soil properties (7%). By incorporating future climate and atmospheric component projections from the Coupled Model Intercomparison Project Phase 6 into the model, enhanced vegetation greening was predicted globally, particularly in evergreen needle-leaf forests and grasslands, from 2081 to 2100. Many browning changes were predicted in evergreen and deciduous broadleaf forests, mixed forests, and around areas influenced by human land use. Overall, these findings reveal that environmental factors have relevant integrated impacts on vegetation dynamics under climate change and should be considered during the design of local mitigation and adaptation management strategies.     

Coupling dynamic models of climate and vegetation

Numerous studies have underscored the importance of terrestrial ecosystems as an integral component of the Earth's climate system. This realization has already led to efforts to link simple equilibrium vegetation models with Atmospheric General Circulation Models through iterative coupling procedures. While these linked models have pointed to several possible climate–vegetation feedback mechanisms, they have been limited by two shortcomings: (i) they only consider the equilibrium response of vegetation to shifting climatic conditions and therefore cannot be used to explore transient interactions between climate and vegetation; and (ii) the representations of vegetation processes and land-atmosphere exchange processes are still treated by two separate models and, as a result, may contain physical or ecological inconsistencies. Here we present, as a proof concept, a more tightly integrated framework for simulating global climate and vegetation interactions. The prototype coupled model consists of the GENESIS (version 2) Atmospheric General Circulation Model and the IBIS (version 1) Dynamic Global Vegetation Model. The two models are directly coupled through a common treatment of land surface and ecophysiological processes, which is used to calculate the energy, water, carbon, and momentum fluxes between vegetation, soils, and the atmosphere. On one side of the interface, GENESIS simulates the physics and general circulation of the atmosphere. On the other side, IBIS predicts transient changes in the vegetation structure through changes in the carbon balance and competition among plants within terrestrial ecosystems. As an initial test of this modelling framework, we perform a 30 year simulation in which the coupled model is supplied with modern CO₂ concentrations, observed ocean temperatures, and modern insolation. In this exploratory study, we run the GENESIS atmospheric model at relatively coarse horizontal resolution (4.5° latitude by 7.5° longitude) and IBIS at moderate resolution (2° latitude by 2° longitude). We initialize the models with globally uniform climatic conditions and the modern distribution of potential vegetation cover. While the simulation does not fully reach equilibrium by the end of the run, several general features of the coupled model behaviour emerge. We compare the results of the coupled model against the observed patterns of modern climate. The model correctly simulates the basic zonal distribution of temperature and precipitation, but several important regional biases remain. In particular, there is a significant warm bias in the high northern latitudes, and cooler than observed conditions over the Himalayas, central South America, and north-central Africa. In terms of precipitation, the model simulates drier than observed conditions in much of South America, equatorial Africa and Indonesia, with wetter than observed conditions in northern Africa and China. Comparing the model results against observed patterns of vegetation cover shows that the general placement of forests and grasslands is roughly captured by the model. In addition, the model simulates a roughly correct separation of evergreen and deciduous forests in the tropical, temperate and boreal zones. However, the general patterns of global vegetation cover are only approximately correct: there are still significant regional biases in the simulation. In particular, forest cover is not simulated correctly in large portions of central Canada and southern South America, and grasslands extend too far into northern Africa. These preliminary results demonstrate the feasibility of coupling climate models with fully dynamic representations of the terrestrial biosphere. Continued development of fully coupled climate-vegetation models will facilitate the exploration of a broad range of global change issues, including the potential role of vegetation feedbacks within the climate system, and the impact of climate variability and transient climate change on the terrestrial biosphere.