, 张镱锂Elevation-dependent alpine grassland phenologyon the Tibetan Plateau
LILanhui, ZHANGYili通讯作者:
收稿日期:2016-06-12
修回日期:2016-10-14
网络出版日期:2017-01-20
版权声明:2017《地理研究》编辑部《地理研究》编辑部
基金资助:
作者简介:
-->
展开
摘要
关键词：
Abstract
Keywords：
-->0
PDF (12814KB)元数据多维度评价相关文章收藏文章
本文引用格式导出EndNoteRisBibtex收藏本文-->
1 引言
植被物候是气候—植被相互关系变化的重要指示器之一[1, 2]。一方面,植被物候在植被生态系统功能中发挥着重要作用[3],植被返青期、枯黄期和生长季的变化可能引起碳循环和水循环的变化[1,4,5],进而引起区域气候系统的相应变化[6,7]。另一方面,植被物候对气候变化极为敏感,被誉为植被对气候变化响应的“最佳指示器”[8]和全球变化的“诊断指纹”[9,10]。因此,在全球变化的背景下,研究敏感生态区的植被物候变化具有重要意义[11]。青藏高原,被称为“世界屋脊”,平均海拔高于4000 m以及巨大的海拔落差等特殊自然环境使其形成许多独特且脆弱的生态系统类型,是探讨气候—生态系统关系的理想区域[11-13]。气温随海拔上升呈现规律性递减,并且被视为高寒植被物候时空变化的控制性因子[14]。因此,探讨高寒植被物候与海拔梯度的关系有助于揭示气候—植被之间的关系。已有研究结果表明,青藏高原高寒植被物候分布与海拔存在密切关系。从整个高原来看,随海拔上升,高寒植被返青期显著推迟,幅度为0.78~1.1天/100 m[13-15],枯黄期逐渐提前,幅度为-0.3~-0.1 天/100 m[15, 16]。然而,在高原的局部区域,比如藏北地区[17]和三江源地区[18,19]等,高寒植被物候的分布与海拔的关系呈现出明显的地域分异特征,且与整个高原相比也存在较大差异。
近几十年来,青藏高原增温剧烈[20-22],在1990s末期全球变暖趋势出现中断的背景下,仍呈现加速变暖的趋势(0.25 ℃/10年) [23],并且变暖趋势具有显著的海拔敏感性[21, 24]。然而,高寒植被物候年际变化趋势的海拔敏感性研究较少,当前的研究表明：尽管三江源地区植被返青期提前趋势随海拔上升而减缓,但未出现类似于整个高原在海拔4700 m左右的区域由提前趋势转变为推迟趋势的现象[14, 19]。此外,在整个高原地区,高海拔地区的高寒草地物候年际变化要比低海拔地区复杂[15]。
青藏高原作为一个巨型自然地理单元,拥有独特的自然环境和地域分异规律。如果仅从整个高原的尺度来把握物候与海拔的特征,将可能会掩盖许多局地信息。因此,本文基于2000-2013年的SPOT-VGT（NDVI）和MODIS（NDVI和EVI）数据集提取物候结果,并结合不同水热条件的生态地理分区,探讨高寒草地物候分布和年际变化趋势的海拔敏感性差异。
2 数据来源与研究方法
遥感数据主要包括：SPOT-VGT（NDVI）(http://www.vito-eodata.be/) 和MODIS（NDVI和EVI）(https://ladsweb.nascom.nasa.gov/),数据的空间分辨率为1 km×1 km,时段均为2000-2013年,SPOT-VGT（NDVI）的时间分辨率为10天,MODIS（NDVI和EVI）时间分辨率为16天。DEM数据的空间分辨率为30 m×30 m（https://lta.cr.usgs.gov/GTOPO30）,为减少不确定性和误差,将DEM数据的空间分辨率重采样为1 km×1 km。农业气象站点的物候观测数据引自Ding等的研究[25];青藏高原矢量范围引自文献[12]数据,其生态地理区划采用郑度等拟订的中国生态地理区域系统的框架方案[26]（图1）;青藏高原草地矢量数据比例尺为1物候提取过程：首先,利用HANTS平滑方法对NDVI或EVI数据进行平滑处理,并相应得到两种时间分辨率的数据,一种为原始分辨率的NDVI或EVI数据,另一种为1天的NDVI或EVI数据[25]。然后,采用比率阈值法提取返青期和枯黄期数据。在提取过程中,将2-3月的NDVI或EVI均值视为年最小值,且返青期和枯黄期的比率阈值分别为0.2和0.6[25, 28]。高寒草地的返青期和枯黄期分布均为三套数据提取结果的平均值,并采用回归分析方法获得2000-2013年高寒草地返青期和枯黄期的年际变化趋势。
本文分析的生态地理分区位于高寒草地集中分布地区,包括川西藏东山地针叶林区（IIAB1）、果洛那曲高寒灌丛草甸区（IB1）、青东祁连山地针叶林和草原区（IIC2）、青南高寒草甸草原区（IC1）和藏南山地灌丛草原区（IIC1）五个地理分区（下文涉及的分区,均指上述五个地理分区）。考虑到极高海拔区域可能受到雪被等不确定因素对遥感物候反演的干扰[29],本文仅分析海拔高度为2000~5000 m的区域,以100 m为间隔,统计海拔梯度内的物候分布及其年际变化趋势的平均值和标准差。由于在部分海拔梯度内像元缺失或是数量过少,因此,在分析时剔除了地理分区内少于80个像元的海拔梯度。
显示原图|下载原图ZIP|生成PPT图1青藏高原生态地理区及地形
-->Fig. 1Eco-geographical regions of the Tibetan Plateau and its topographic feature
-->
3 结果分析
3.1 高寒草地物候分布与海拔的关系
青藏高原高寒草地物候分布存在显著的空间差异,本文从整个高原和地理分区两个空间尺度对高寒草地物候分布与海拔的关系进行了探讨。3.1.1 高原高寒草地物候分布与海拔的关系 青藏高原高寒草地物候分布随海拔上升呈现规律性变化（图2）。随海拔上升,返青期显著推迟,推迟幅度为1.59天/100 m;枯黄期逐渐提前,提前幅度为-0.23天/100 m,相对于低海拔地区而言,高海拔地区返青期分布随海拔变化而呈现的波动幅度更小,并大致以海拔3400~3500 m地带为分界线。
显示原图|下载原图ZIP|生成PPT图2青藏高原高寒草地物候与海拔的关系
-->Fig. 2Changes in mean alpine grassland spring and autumn phenology along altitude gradient throughout the Tibetan Plateau
-->
3.1.2 不同地理分区高寒草地返青期分布与海拔的关系 在五个地理分区中,高寒草地返青期分布均随海拔上升呈现显著的推迟趋势（图3）,但推迟幅度在不同的地理分区存在差异。海拔每升高100 m,IC1地理分区的高寒草地返青期大致推迟0.92天;IIC1地理分区呈现先迅速上升后保持相对稳定的态势;其余三个地理分区的推迟幅度相近,为1.54~1.70天。从空间上来看,在高原南侧的IIC1和IIAB1地理分区中,随海拔上升,返青期分布推迟幅度不仅高于其余三个地理分区,也高于整个高原。
显示原图|下载原图ZIP|生成PPT图3青藏高原不同地理分区的返青期分布与海拔的关系
-->Fig. 3Changes in mean alpine grassland spring phenology along altitude gradient in differenteco-geographical regions on the Tibetan Plateau
-->
相对于高海拔地区,低海拔地区高寒草地返青期分布随海拔上升推迟幅度较大,这可能是IC1分区推迟幅度较其他分区低的原因。此外,青藏高原东侧的三个海拔落差明显的地理分区（IIAB1、IB1、IIC2）中,相对于海拔高度2600~3500 m的地区,海拔3500~3600 m以上区域的标准差更小。
3.1.3 不同地理分区高寒草地枯黄期分布与海拔的关系 除IIC1分区外,其余四个分区高寒草地枯黄期分布随海拔升高而呈现显著提前的趋势,即海拔每升高100 m,枯黄期提前0.23~0.88天（图4）,但在这四个地理分区中,高寒草地枯黄期分布与海拔的关系差异明显。IIC2分区高寒草地枯黄期在海拔2200 m和3300 m处呈现两个峰值,并在4300 m以上区域趋于平缓;IB1、IIAB1和IC1分区枯黄期分布随海拔上升呈现近乎直线的提前态势,其中,IB1和IIAB1分区的提前幅度为-0.44~-0.34天/100 m,而IC1分区的提前幅度约为前者的两倍。
显示原图|下载原图ZIP|生成PPT图4青藏高原不同地理分区的枯黄期与海拔的关系
-->Fig. 4Changes in mean alpine grassland autumn phenology along altitude gradient in differentphysio-geographical regions on the Tibetan Plateau
-->
在高原东侧的三个分区（IIAB1、IB1、IIC2）中,随着纬度增加,高寒草地枯黄期分布随海拔上升呈现的提前幅度逐渐下降。此外,与返青期类似,相对于高海拔地区,低海拔地区的标准差更大,尤其在IIC2分区表现明显。
3.2 高寒草地物候年际变化趋势与海拔的关系
青藏高原高寒草地物候的海拔敏感性不仅存在于空间分布的差异上,而且表现在年际变化趋势的不同[13, 14]。3.2.1 高原高寒草地物候年际变化趋势与海拔的关系 在青藏高原地区,高寒草地返青期和枯黄期的年际变化趋势均表现出显著的海拔敏感性（图5）。随海拔上升,返青期的提前趋势在海拔2100~3200 m地区显著增强（-0.049天/年
显示原图|下载原图ZIP|生成PPT图5青藏高原高寒草地物候年际变化趋势与海拔的关系
-->Fig. 5Changes in the trends of alpine grassland phenology with altitude throughout the Tibetan Plateau
-->
3.2.2 不同地理分区高寒草地返青期年际变化趋势与海拔的关系 不同地理分区高寒草地返青期年际变化趋势的海拔敏感性存在明显差异（图6）。在高原东侧海拔落差较大的地理分区（IIAB1、IIC2）中,返青期年际变化趋势在海拔3200~3300 m地带出现转折。其中,在海拔低于3200 m地区,随海拔上升,IIAB1分区的返青期提前趋势显著增强（-0.025天/年
显示原图|下载原图ZIP|生成PPT图6青藏高原不同地理分区的返青期年际变化趋势与海拔的关系
-->Fig. 6Changes in the trends of alpine grassland spring phenology with altitude in differenteco-geographical regions on the Tibetan Plateau
-->
显示原图|下载原图ZIP|生成PPT图7青藏高原不同地理分区的枯黄期与海拔的关系
-->Fig. 7Changes in the trends of alpine grassland autumn phenology with altitude in differenteco-geographical regions on the Tibetan Plateau
-->
3.2.3 不同地理分区高寒草地枯黄期年际变化趋势与海拔的关系 高寒草地枯黄期的年际变化趋势在五个分区均表现出显著的海拔敏感性（图7）。其中,在IIAB1、IB1和IC1分区,高寒草地枯黄期提前趋势均随海拔上升而显著增强（-0.04~-0.01天/年
4 讨论
4.1 高寒草地物候变化海拔敏感性的区域分异
从物候分布特征来看,高寒草地返青期分布随海拔上升,推迟幅度为1.59天/100 m,略高于已有的研究结果（0.78~1.3天/100 m）[13-15],这可能与不同研究采用的海拔梯度范围不同有关。本文采用的海拔高度起始为2100 m,而在低海拔地区,返青期分布随海拔高度上升呈现更大的推迟幅度（图3）。整个高原的高寒草地物候分布随海拔上升呈现的变化幅度低于大部分地理分区,并且在同一海拔高度,高原东北部的IIC2分区返青期明显晚于东南部的IIAB1分区,枯黄期则相反,这都可能与纬度因素有密切关系。从物候年际变化趋势特征来看,返青期年际变化趋势沿海拔上升呈现的变化特征与Shen等[14]基于2000-2011年四套遥感数据和五种方法得出的结果基本一致,但在本文中,提前趋势达到最低点时,大致位于海拔3300 m,且由提前转为推迟趋势时,大致位于4800~4900 m地区,均比Shen等[14]的结果约高出100~200 m,这可能与研究时段不同导致变化趋势存在差异有关。在高原中部的IC1分区和南部的IIC1分区的3600~4900 m地区,高寒草地返青期分别呈现提前和推迟趋势,并且随海拔上升,两分区返青期的年际变化趋势也呈现相反的变化方向。相对于返青期,尽管整个高原和五个分区高寒草地枯黄期均表现出随海拔上升,提前幅度增大或推迟幅度减小,但与其他分区不同的是,IIC1分区在海拔3600~4900 m地区均呈现推迟趋势。
由此可见,在探讨高寒草地物候变化的海拔敏感性差异时,需要兼顾整体特征及区域特征。
4.2 基于地面观测数据的高寒草地物候分布与海拔关系探讨
IIC2分区的高寒草地物候分布随海拔上升呈现较大波动,并且返青期分布与海拔梯度的决定系数明显高于枯黄期（图3、图4）。其中,枯黄期分布在海拔2200 m和3300 m处呈现两个峰值。本文基于IIC2地理分区及周边地区的19个农业气象站点物候实地观测资料（2003-2012年）对这一现象进行了探讨,结果表明高寒草地实测返青期分布随海拔上升呈现显著推迟趋势,幅度为1.88天/100 m（图8）,与IIC2分区接近（1.64天/100 m）;实测枯黄期提前0.57天,高于IIC2分区（0.23天/100 m）,但提前趋势不显著。进一步对比图4与图8可以发现,实测返青期与海拔梯度的决定系数明显高于实测枯黄期,并且在海拔3000~3600 m地区,遥感反演和地面观测的枯黄期均出现较大波动,这可能由于影响枯黄期变化的因素较返青期更为复杂[30, 31],从而导致枯黄期的海拔敏感性低于返青期。上述遥感反演和地面观测结果的一致性在一定程度上验证了遥感监测结果的可靠性。但也要注意到,由于观测站点稀少,且集中分布在3000~4000 m区域,难以表征整个区域的物候特征。此外,由于在高原的西南部缺乏站点观测数据,基于遥感反演的物候数据的精度在该地区仍难以验证,比如本文IIC1分区高寒草地枯黄期特征与其他地理分区相比,存在较大的差异,具体原因分析还有待于地面物候数据观测的加强。
显示原图|下载原图ZIP|生成PPT图8基于站点观测的高原东北部高寒草地物候分布与海拔的关系
-->Fig. 8Changes in the spring and autumn phenology of alpine grassland observed in situ along altitude gradient on the northeast part of the Tibetan Plateau
-->
4.3 增温背景下高寒草地物候变化的海拔敏感性特征
尽管青藏高原增温效应具有明显的海拔敏感性[21,22,24],然而随海拔上升,无论是整个高原区域还是各地理分区,均未出现返青期提前幅度或枯黄期推迟幅度持续增强的规律,与之相反的是,在海拔4000 m以上地区,返青期提前幅度显著减弱甚至接近或等于0,且枯黄期的提前趋势也显著增强。前者可能由于冬季的剧烈升温导致高海拔地区草地冬季休眠的低温无法满足而使返青期推迟[28],也可能受到不同海拔地区季前降水量或降雪量变化等因素的影响[11,14,32];后者可能与夏季增温导致水分亏缺,使得枯黄期的年际变化与夏季温度成负相关等有关[33]。因此,无论在整个高原区域还是各个地理分区,基于遥感反演的物候年际变化趋势难以直接反映青藏高原的增温效应。然而,Liu等[34]认为,青藏高原高海拔地区草地返青期的气温敏感性高于低海拔地区。由于高海拔地区气象站点和物候观测站点的匮乏[11,13,14],使得高寒草地物候变化与海拔的关系对气候变化的响应难以验证,仍需要进一步研究。5 结论
（1）青藏高原高寒草地物候分布在海拔梯度上呈现出一定的规律性。随海拔上升,高寒草地返青期和枯黄期分别呈现出显著推迟和提前的趋势（P<0.001）,幅度分别为1.59天/100 m和-0.23 天/100 m。除藏南山地灌丛草原区（IIC1）外,其余四个自然分区高寒草地物候分布均具有显著的海拔敏感性（P<0.001）,即每上升100 m,返青期推迟0.98~1.86天,枯黄期提前0.23~0.88 天。（2）青藏高原高寒草地返青期和枯黄期的年际变化趋势沿海拔梯度上升呈现明显差异。在海拔低于3200 m地区,返青期的提前幅度显著增大（-0.049天/年
（3）高原中部的青南高寒草甸草原区（IC1）和南部的藏南山地灌丛草原区（IIC1）高寒草地物候呈现相反的年际变化趋势;随海拔上升,两分区返青期的年际变化趋势呈现相反的变化方向,枯黄期则呈现基本一致的变化方向。
The authors have declared that no competing interests exist.
参考文献 原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子
| [1] | |
| [2] | |
| [3] | Abstract Using data from 28 flux measurement sites, we performed an analysis of the relationship between annual net ecosystem exchange (NEE) and the length of the carbon uptake period (CUP) (the number of days when the ecosystem is a net carbon sink). The observations suggest a linear correlation between the two quantities. The change in annual carbon exchange per day of the CUP differs significantly between deciduous and evergreen vegetation types. The sites containing vegetation with short-lived foliage (less than 1 year) have higher carbon uptake and respiration rates than evergreen vegetation. The ratio between mean daily carbon exchange rates during carbon uptake and release periods is relatively invariant (2.73卤1.08) across different vegetation types. This implies that a balance between carbon release and uptake periods exists despite different photosynthetic pathways, life forms, and leaf habits. The mean daily carbon sequestration rate for these ecosystems never exceeds the carbon emission rate by more than a factor of 3. Growing season lengths for the study sites were derived from the normalized difference vegetation index (NDVI) of advanced very-high-resolution radiometer and from the enhanced vegetation index (EVI) of VEGETATION SPOT-4. NDVI and EVI were found to be closely related to the CUP, and consequently they also can be used to approximate annual carbon exchange of the ecosystems. This approach has potential for allowing extrapolation of NEE over large areas from remotely sensed data, given a certain amount of ancillary information. This method could complement the currently existing techniques for extrapolation, which rely upon modeling of the individual gross fluxes. |
| [4] | . A number of studies have suggested that the growing season duration has significantly lengthened during the past decades, but the connections between phenology variability and the terrestrial carbon (C) cycle are far from clear. In this study, we used the "ORganizing Carbon and Hydrology In Dynamic Ecosystems" (ORCHIDEE) process based ecosystem model together with observed climate data to investigate spatiotemporal changes in phenology and their impacts on carbon fluxes in the Northern Hemisphere (>25掳N) during 1980-2002. We found that the growing season length (GSL) has increased by 0.30 days yr(R= 0.27, P = 0.010), owing to the combination of an earlier onset in spring (0.16 days yr) and a later termination in autumn (0.14 days yr). Trends in the GSL are however highly variable across the regions. In Eurasia, there is a significant trend toward earlier vegetation green-up with an overall advancement rate of 0.28 days yr(R= 0.32, P = 0.005), while in North America there is a significantly delayed vegetation senescence by 0.28 days yr(R= 0.26, P = 0.013) during the study period. Our results also suggested that the GSL strongly correlates with annual gross primary productivity (GPP) and net primary productivity (NPP), indicating that longer growing seasons may eventually enhance vegetation growth. A 1-day extension in GSL leads to an increase in annual GPP of 5.8 gC myr(or 0.6% per day), and an increase in NPP of 2.8 gC myrper day. However, owing to enhanced soil carbon decomposition accompanying the GPP increase, a change in GSL correlates only poorly with a change in annual net ecosystem productivity (NEP). |
| [5] | [1] In temperate regions, the budburst date of deciduous trees is mainly regulated by temperature variation, but the exact nature of the temperature dependence has been a matter of debate. One hypothesis is that budburst date depends purely on the accumulation of warm temperature; a competing hypothesis states that exposure to cold temperatures is also important for budburst. In this study, variability in budburst is evaluated using 15 years of budburst data for 17 tree species at Harvard Forest. We compare two budburst hypotheses through reversible jump Markov chain Monte Carlo. We then investigate how uncertainties in budburst date mapped onto uncertainties in ecosystem carbon using the Geophysical Fluid Dynamics Laboratory's LM3 land model. For 15 of 17 species, we find that more complicated budburst models that account for a chilling period are favored over simpler models that do not include such dependence. LM3 simulations show that the choice of budburst model induces differences in the timing of carbon uptake commencement of 6511 days, in the magnitude of April–May carbon uptake of 651.03 g C m 612 day 611 , and in total ecosystem carbon stocks of 652 kg C m 612 . While the choice of whether to include a chilling period in the budburst model strongly contributes to this variability, another important factor is how the species-dependent field data gets mapped onto LM3's single deciduous plant functional type (PFT). We conclude budburst timing has a strong impact on simulated CO 2 fluxes, and uncertainty in the fluxes can be substantially reduced by improving the model's representation of PFT diversity. |
| [6] | . 陆地生态系统与气候系统通过地面与大气之间能量平衡、水汽交换和生物地球化学循环相互作用,影响大气中温室气体浓度和气溶胶,继而影响气候变化。较系统分析总结了当代国际上陆地生态系统与气候相互作用的最新研究进展。首先介绍了陆地生态系统与气候相互作用的机制与过程,总结了陆地生态系统与气候相互作用研究的三个发展阶段,以及当代相互作用的过程模拟研究中三类主要的全球生态系统模型,即生物物理模型、生物地理模型和生物地球化学模型。并介绍了气候对生态系统变化的响应,即两种主要的反馈机制。最后,对未来的研究方向和重点作了分析。 陆地生态系统与气候系统通过地面与大气之间能量平衡、水汽交换和生物地球化学循环相互作用,影响大气中温室气体浓度和气溶胶,继而影响气候变化。较系统分析总结了当代国际上陆地生态系统与气候相互作用的最新研究进展。首先介绍了陆地生态系统与气候相互作用的机制与过程,总结了陆地生态系统与气候相互作用研究的三个发展阶段,以及当代相互作用的过程模拟研究中三类主要的全球生态系统模型,即生物物理模型、生物地理模型和生物地球化学模型。并介绍了气候对生态系统变化的响应,即两种主要的反馈机制。最后,对未来的研究方向和重点作了分析。 |
| [7] | An increasing number of studies have reported on shifts in timing and length of the growing season, based on phenological, satellite and climatological studies. The evidence points to a lengthening of the growing season of ca. 10–20 days in the last few decades, where an earlier onset of the start is most prominent. This extension of the growing season has been associated with recent global warming. Changes in the timing and length of the growing season (GSL) may not only have far reaching consequences for plant and animal ecosystems, but persistent increases in GSL may lead to long-term increases in carbon storage and changes in vegetation cover which may affect the climate system. This paper reviews the recent literature concerned with GSL variability. |
| [8] | . <p>植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器，其发生时间可以反映陆地生态系 统对气候变化的快速响应。近年来，遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点， 已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理 方法的基础上，从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆 盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展，并针对目前研究中存 在的问题，提出近期遥感物候研究的主要方向：(1)发展一种更具普适性的物候生长季节划分方法；(2)通过开展植物 群落的物候观测和选择合适的尺度转换方法，统一地面与遥感的空间信息；(3)定量分析植物物候变化对人类活动 的响应机制；(4)选择适宜的数学方法和模型，实现各种不同分辨率遥感数据的融合；(5)通过动态模拟，预测植物物 候对未来气候变化的响应。</p> <p>植物物候现象是环境条件季节和年际变化最直观、最敏感的生物指示器，其发生时间可以反映陆地生态系 统对气候变化的快速响应。近年来，遥感物候观测因其具有多时相、覆盖范围广、空间连续、时间序列较长等特点， 已成为揭示植被动态对全球气候变化响应与反馈的重要手段。文章在介绍植物物候遥感监测的数据集及其预处理 方法的基础上，从植物物候生长季节的划分、植物物候与气候变化、植物物候与净初级生产量、植物物候与土地覆 盖、植物物候与农作物估产等方面系统阐述了近5 年来国内外遥感物候学研究的重要进展，并针对目前研究中存 在的问题，提出近期遥感物候研究的主要方向：(1)发展一种更具普适性的物候生长季节划分方法；(2)通过开展植物 群落的物候观测和选择合适的尺度转换方法，统一地面与遥感的空间信息；(3)定量分析植物物候变化对人类活动 的响应机制；(4)选择适宜的数学方法和模型，实现各种不同分辨率遥感数据的融合；(5)通过动态模拟，预测植物物 候对未来气候变化的响应。</p> |
| [9] | Over the past 100 years, the global average temperature has increased by approximately 0.6 degrees C and is projected to continue to rise at a rapid rate. Although species have responded to climatic changes throughout their evolutionary history, a primary concern for wild species and their ecosystems is this rapid rate of change. We gathered information on species and global warming from 143 studies for our meta-analyses. These analyses reveal a consistent temperature-related shift, or 'fingerprint', in species ranging from to and from grasses to trees. Indeed, more than 80% of the species that show changes are shifting in the direction expected on the basis of known physiological constraints of species. Consequently, the balance of evidence from these studies strongly suggests that a significant impact of global warming is already discernible in animal and plant populations. The synergism of rapid temperature rise and other stresses, in particular habitat destruction, could easily disrupt the connectedness among species and lead to a reformulation of species communities, reflecting differential changes in species, and to numerous extirpations and possibly extinctions. |
| [10] | . <p>利用中国物候观测网观测数据,新编制了哈尔滨地区1985-2012年的自然历。通过与原自然历（1963-1984年）比较,揭示了近30年以来哈尔滨地区21个植物99个物候期的变化特征,并通过物候期与气温的相关分析探讨了物候变化原因。结果表明：自1985年以来,哈尔滨的春季、夏季、秋季的物候期开始日期提前,冬季开始日期推迟。其中春季（以白榆叶芽膨大期为代表）、夏季（以暴马丁香开花始期为代表）、秋季（以金银忍冬果实成熟期为代表）分别提前了7天、6天和19天,冬季（以胡桃楸落叶末期为代表）推迟了2天。各物候期在春季、夏季、秋季的平均日期相较于原自然历提前了3~11天,在冬季推迟了3天。四季各物候期最早日期均以提前为主,夏冬季物候期最晚日期有所推迟。另外,各季节内部分物候期出现的先后次序发生了变化。近30年该地区气温的升高是物候季节开始日期提前的首要原因。且不同植物和物候期对气温变化的响应敏感性不同可解释物候季节内物候期先后次序的变化。</p> <p>利用中国物候观测网观测数据,新编制了哈尔滨地区1985-2012年的自然历。通过与原自然历（1963-1984年）比较,揭示了近30年以来哈尔滨地区21个植物99个物候期的变化特征,并通过物候期与气温的相关分析探讨了物候变化原因。结果表明：自1985年以来,哈尔滨的春季、夏季、秋季的物候期开始日期提前,冬季开始日期推迟。其中春季（以白榆叶芽膨大期为代表）、夏季（以暴马丁香开花始期为代表）、秋季（以金银忍冬果实成熟期为代表）分别提前了7天、6天和19天,冬季（以胡桃楸落叶末期为代表）推迟了2天。各物候期在春季、夏季、秋季的平均日期相较于原自然历提前了3~11天,在冬季推迟了3天。四季各物候期最早日期均以提前为主,夏冬季物候期最晚日期有所推迟。另外,各季节内部分物候期出现的先后次序发生了变化。近30年该地区气温的升高是物候季节开始日期提前的首要原因。且不同植物和物候期对气温变化的响应敏感性不同可解释物候季节内物候期先后次序的变化。</p> |
| [11] | Rapid temperature increase and its impacts on alpine ecosystems in the Qinghai-Tibetan Plateau, the world's highest and largest plateau, are a matter of global concern. Satellite observations have revealed distinctly different trend changes and contradicting temperature responses of vegetation green-up dates, leading to broad debate about the Plateau's spring phenology and its climatic attribution. Large uncertainties in remote-sensing estimates of phenology significantly limit efforts to predict the impacts of climate change on vegetation growth and carbon balance in the Qinghai-Tibetan Plateau, which are further exacerbated by a lack of detailed ground observation calibration. Here, we revealed the spatiotemporal variations and climate drivers of ground-based herbaceous plant green-up dates using 72 green-up datasets for 22 herbaceous plant species at 23 phenological stations, and corresponding daily mean air temperature and daily precipitation data from 19 climate stations across eastern and southern parts of the Qinghai-Tibetan Plateau from 1981 to 2011. Results show that neither the continuously advancing trend from 1982 to 2011, nor a turning point in the mid to late 1990s as reported by remote-sensing studies can be verified by most of the green-up time series, and no robust evidence for a warmer winter-induced later green-up dates can be detected. Thus, chilling requirements may not be an important driver influencing green-up responses to spring warming. Moreover, temperature-only control of green-up dates appears mainly at stations with relatively scarce preseason snowfall and lower elevation, while coupled temperature and precipitation controls of green-up dates occur mostly at stations with relatively abundant preseason snowfall and higher elevation. The diversified interactions between snowfall and temperature during late winter to early spring likely determine the spatiotemporal variations of green-up dates. Therefore, prediction of vegetation growth and carbon balance responses to global climate change on the world's roof should integrate both temperature and snowfall variations. |
| [12] | . <p>长期以来 ,种种因素导致****们对青藏高原确切范围的认识和理解存在差异。根据青藏高原相关领域研究的新成果和多年野外实践 ,从地理学角度 ,充分讨论了确定青藏高原范围和界线的原则与涉及的问题 ,结合信息技术方法对青藏高原范围与界线位置进行了精确的定位和定量分析。得出 :青藏高原在中国境内部分西起帕米尔高原 ,东至横断山脉 ,横跨 31个经度 ,东西长约 2 94 5km ;南自喜马拉雅山脉南缘 ,北迄昆仑山 -祁连山北侧 ,纵贯约 13个纬度 ,南北宽达 15 32km ;范围为 2 6°0 0′12″N～ 39°4 6′5 0″N ,73°18′5 2″E～ 10 4°4 6′5 9″E ,面积为 2 5 72 4× 10 3km2 ,占我国陆地总面积的 2 6 8%。</p> <p>长期以来 ,种种因素导致****们对青藏高原确切范围的认识和理解存在差异。根据青藏高原相关领域研究的新成果和多年野外实践 ,从地理学角度 ,充分讨论了确定青藏高原范围和界线的原则与涉及的问题 ,结合信息技术方法对青藏高原范围与界线位置进行了精确的定位和定量分析。得出 :青藏高原在中国境内部分西起帕米尔高原 ,东至横断山脉 ,横跨 31个经度 ,东西长约 2 94 5km ;南自喜马拉雅山脉南缘 ,北迄昆仑山 -祁连山北侧 ,纵贯约 13个纬度 ,南北宽达 15 32km ;范围为 2 6°0 0′12″N～ 39°4 6′5 0″N ,73°18′5 2″E～ 10 4°4 6′5 9″E ,面积为 2 5 72 4× 10 3km2 ,占我国陆地总面积的 2 6 8%。</p> |
| [13] | Research in phenology change has been one heated topic of current ecological and climate change study. In this study, we use satellite derived NDVI (Normalized Difference Vegetation Index) data to explore the spatio-temporal changes in the timing of spring vegetation green-up in the Qinghai-Xizang (Tibetan) Plateau from 1982 to 2006 and to characterize their relationship with elevation and temperature using concurrent satellite and climate data sets. At the regional scale, no statistically significant trend of the vegetation green-up date is observed during the whole study period (R-2 = 0.00, P = 0.95). Two distinct periods of green-up changes are identified. From 1982 to 1999, the vegetation green-up significantly advanced by 0.88 days year(-1) (R-2 = 0.56, P < 0.001). In contrast, from 1999 to 2006, a marginal delaying trend is evidenced (R-2 = 0.44. P = 0.07), suggesting that the persistent trend towards earlier vegetation green-up in spring between 1980s and 1990s was stalled during the first decade of this century. This shift in the tendency of the vegetation green-up seems to be related to differing temperature trends between these two periods. Statistical analysis shows that the average onset of vegetation green-up over the Qinghai-Xizang Plateau would advance by about 4.1 days in response to 1 degrees C increase of spring temperature. In addition, results from our analysis indicate that the spatial patterns of the vegetation green-up date and its change since 1982 are altitude dependent. The magnitude of the vegetation green-up advancement during 1982-1999, and of its postponement from 1999 to 2006 significantly increases along an increasing elevation gradient. (C) 2011 Elsevier B.V. All rights reserved. |
| [14] | Spring vegetation phenology in temperate and cold regions is widely expected to advance with increasing temperature, and is often used to indicate regional climatic change. The Qinghai-Tibetan Plateau (QTP) has recently experienced intensive warming, but strongly contradictory evidence exists regarding changes in satellite retrievals of spring vegetation phenology. We investigated spatio-temporal variations in green-up date on the QTP from 2000 to 2011, as determined by five methods employing vegetation indices from each of the four sources: three Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), Systeme Pour l'Observation de la Terre (SPOT), MODerate resolution Imaging Spectroradiometer (MODIS), and the Enhanced Vegetation Index (EVI) from MODIS. Results indicate that, at the regional scale, all vegetation indices and processing methods consistently found no significant temporal trend (all P > 0.05). This insignificance resulted from substantial spatial heterogeneity of trends in green-up date, with a notably delay in the southwest region, and widespread advancing trend in the other areas, despite a region-wide temperature increase. These changes doubled the altitudinal gradient of green-up date, from 0.63 days 100 m(-1) in the early 2000s to 1.30 days 100 m(-1) in the early 2010s. The delays in the southwest region and at high altitudes were likely caused by the decline in spring precipitation, rather than the increasing spring temperature, suggesting that spring precipitation may be an important regulator of spring phenological response to climatic warming over a considerable area of the QTP. Consequently, a delay in spring vegetation phenology in the QTP may not necessarily indicate spring cooling. Furthermore, the phenological changes retrieved from the widely used AVHRR NDVI differed from those retrieved from SPOT and MODIS NDVIs and MODIS EVI, necessitating the use of multiple datasets when monitoring vegetation dynamics from space. (C) 2014 Elsevier B.V. All rights reserved. |
| [15] | Plant phenology is the most salient and sensitive indicator of terrestrial ecosystem response to climate change. Studying its change is significantly important in understanding and predicting impressively changes in terrestrial ecosystem. Based on NDVI from SPOT VGT, this paper analyzed the spatiotemporal changes in alpine grassland phenology in Qinghai-Tibetan Plateau from 1999 to 2009. The results are enumerated as follows: (1) The spatial distribution of the average alpine grassland phenology from 1999 to 2009 is closely related to water and heat conditions. Accompanying the deterioration in heat and water conditions from southeast to northwest, the start of growth season (SOG) was delayed gradually, the end of growth season (EOG) advanced slowly, and the length of growth season (LOG) shortened gradually. Elevation played an important role in the regional differentiation of phenology, but a dividing line of approximately 3500 m existed. Below this line, the phenology fluctuated irregularly with altitude change, whereas above the line, the phenology is closely related to altitude change. (2) From 1999 to 2009, SOG of the alpine grassland came earlier by six days per decade ( R 2 =0.281, P =0.093), EOG was late by two days per decade ( R 2 =0.031, P =0.605), and LOG lengthened by eight days per decade ( R 2 =0.479, P =0.018). The early SOG, the late EOG, and the extended LOG mainly occurred at the center and east of the Plateau. SOG in most of the Plateau advanced significantly, especially in the eastern Plateau. (3) The inter-annual phenology changes of the alpine grassland in the Qinghai-Tibetan Plateau exhibited significant differentiation at different elevation and natural zones. The inter-annual changes at high altitude were more complicated than that at low altitude. The most significant phenology changes were found in the eastern Qinghai-Qilian montane steppe zone, and non-significant changes occurred in the Southern Tibet montane shrub-steppe zone. |
| [16] | The spatial and temporal variations in the end date of the vegetation growing season (EGS) and their relationships with climate factors across the Qinghai-Tibetan Plateau yet have not been well researched. In this study, we used the rate of the change in the curvature of the S-curve function which integrated a logistic function and an asymmetric Gaussian function and showed a better performance for fitting the LAI (leaf area index) data to extract the EGS from a long-term time series of AVHRR (advanced very high resolution radiometer) LAI data. The spatial distribution pattern of the EGS averaged from 1982 to 2011 presented a gradual decrease from the southeast to northwest plateau. The various vegetation types showed different phenological EGS timing. The EGS occurred earlier with increasing altitude (slope = -3 day km(-1), p < 0.001). Throughout the entire Qinghai-Tibetan Plateau, the monthly air temperature and precipitation were positively correlated with the EGS, whereas the monthly sunshine duration showed a negative correlation. At the regional scale, a less pronounced increasing EGS trend (shifting about 1 day over 24 years, p = 0.084) was observed during the entire study period. By analyzing the trend turning points, we found that the EGS occurred later during 1982-1994 (slope = 0.155 day yr(-1), p = 0.045) and 1999-2011 (slope = 0.096 day yr(-1), p = 0.3), but occurred earlier during 1994-1999 (slope = -0.373 day yr(-1),p = 0.049). During 1982-2011, the annual changes of EGS negatively correlated with precipitation (p < 0.1) in June, but positively with precipitation (p < 0.1) in August. As the same time, the annual changes of EGS positively correlated with sunshine duration (p < 0.1) in June, yet negatively with sunshine duration (p < 0.1) in August. During 1982-1994, the annual changes of EGS positively correlated with air temperature (p < 0.01) and negatively with precipitation (p < 0.1) in June. During 1994-1999, the annual changes of EGS only negatively correlated with air temperature (p < 0.05)in August. During 1999-2011, the annual changes of EGS only negatively correlated with sunshine duration (p < 0.1) in August. (C) 2014 Elsevier B.V. All rights reserved. |
| [17] | . 基于非对称高斯拟合算法重建了2001—2010年的MODIS EVI时间序列影像，利用动态阈值法提取2001—2010年各年藏北高原植被覆盖的关键物候参数（生长季峰值、返青期、枯黄期及生长季长度），并在此基础上分析了藏北高原植被覆盖的物候参数空间分布特征。结果表明：植被生长季EVI<sub>max</sub>、物候返青期及生长季长度均表现出从东南到西北过渡的水平地带性与东南高山峡谷区的垂直地带性相结合的空间格局;对于不同地表覆盖类型，EVI<sub>max</sub>、返青期、生长季长度均呈现农林混合区＞林灌区＞草甸＞草原＞荒漠草原的特征，枯黄期除农林混合区较迟外，其他4种地表覆盖类型时间接近;对于不同气候区划，植被生长季EVI<sub>max</sub>、返青期、生长季长度表现出半湿润区→半干旱区→干旱区的递变规律;研究区内植被物候受地形影响较大，随着海拔的升高，植被生长季EVI<sub>max</sub>降低、物候返青期推迟、生长季长度减小。 基于非对称高斯拟合算法重建了2001—2010年的MODIS EVI时间序列影像，利用动态阈值法提取2001—2010年各年藏北高原植被覆盖的关键物候参数（生长季峰值、返青期、枯黄期及生长季长度），并在此基础上分析了藏北高原植被覆盖的物候参数空间分布特征。结果表明：植被生长季EVI<sub>max</sub>、物候返青期及生长季长度均表现出从东南到西北过渡的水平地带性与东南高山峡谷区的垂直地带性相结合的空间格局;对于不同地表覆盖类型，EVI<sub>max</sub>、返青期、生长季长度均呈现农林混合区＞林灌区＞草甸＞草原＞荒漠草原的特征，枯黄期除农林混合区较迟外，其他4种地表覆盖类型时间接近;对于不同气候区划，植被生长季EVI<sub>max</sub>、返青期、生长季长度表现出半湿润区→半干旱区→干旱区的递变规律;研究区内植被物候受地形影响较大，随着海拔的升高，植被生长季EVI<sub>max</sub>降低、物候返青期推迟、生长季长度减小。 |
| [18] | . 以8d合成的500m空间分辨率的MODISNDVI时序数据为基础，利用非对称高斯函数拟合法和比值阈值法对2000—2012年黄河源区高寒草地生长季始期（SOG）、生长季末期（EOG）、生长季长度（LOG）的时空变化进行了研究。结果表明：黄河源区高寒草地多在第126～140d开始生长，到第277～290d逐渐停止生长，LOG多集中在140～160d。由东南向西北，随水热条件变化，SOG逐渐推迟，EOG逐渐提前，LOG逐渐缩短。物候的海拔分异明显，随海拔升高，SOG逐渐延迟，EOG逐渐提前，LOG逐渐缩短。2000—2012年，黄河源区高寒草地SOG显著提前，EOG基本不变，LOG显著延长。SOG提前、EOG推迟、LOG延长的区域主要分布在黄河源区西北部和西南部，而SOG推迟、EOG提前、LOG缩短的区域主要分布在黄河源区中部，其中LOG延长和缩短区域分别占植被区面积的82．77％和17．23％。黄河源区高寒草地物候的年际变化在不同海拔上分异显著。高海拔地区SOG与LOG变化幅度均超过了低海拔地区，而EOG变化幅度相当。春季、秋季气温升高可能是引起黄河源区高寒草地SOG提前和EOG推迟的主要原因。 以8d合成的500m空间分辨率的MODISNDVI时序数据为基础，利用非对称高斯函数拟合法和比值阈值法对2000—2012年黄河源区高寒草地生长季始期（SOG）、生长季末期（EOG）、生长季长度（LOG）的时空变化进行了研究。结果表明：黄河源区高寒草地多在第126～140d开始生长，到第277～290d逐渐停止生长，LOG多集中在140～160d。由东南向西北，随水热条件变化，SOG逐渐推迟，EOG逐渐提前，LOG逐渐缩短。物候的海拔分异明显，随海拔升高，SOG逐渐延迟，EOG逐渐提前，LOG逐渐缩短。2000—2012年，黄河源区高寒草地SOG显著提前，EOG基本不变，LOG显著延长。SOG提前、EOG推迟、LOG延长的区域主要分布在黄河源区西北部和西南部，而SOG推迟、EOG提前、LOG缩短的区域主要分布在黄河源区中部，其中LOG延长和缩短区域分别占植被区面积的82．77％和17．23％。黄河源区高寒草地物候的年际变化在不同海拔上分异显著。高海拔地区SOG与LOG变化幅度均超过了低海拔地区，而EOG变化幅度相当。春季、秋季气温升高可能是引起黄河源区高寒草地SOG提前和EOG推迟的主要原因。 |
| [19] | Abstract Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and characterized their driving forces using climatic data sets. A significant advancement trend was observed throughout the entire study area from 1999 to 2013 with a linear tendency of 6.3 days/decade (p < 0.01); the largest advancement trend was over the Yellow River source region (8.6 days/decade, p < 0.01). Spatially, the green-up date increased from the southeast to the northwest, and the green-up date of 87.4% of pixels fell between the 130th and 150th Julian day. Additionally, about 91.5% of the study area experienced advancement in the green-up date, of which 80.2%, mainly distributed in areas of vegetation coverage increase, experienced a significant advance. Moreover, it was found that the green-up date and its trend were significantly correlated with altitude. Statistical analyses showed that a 1-掳C increase in spring temperature would induce an advancement in the green-up date of 4.2 days. We suggest that the advancement of the green-up date in the TRHR might be attributable principally to warmer and wetter springs. |
| [20] | . <p>基于69个气象台站的气象数据,对青藏高原地区1961~2005年来的主要气候因子特征进行了分析。结果表明:1961~2005年的45年间,青藏高原地区年平均温度呈上升趋势,其倾向率为0.265℃/10a,其中青藏高原地区冬季气温变暖趋势明显,春季变暖趋势不明显;20世纪80年代以来青藏高原地区的温度升高有加速的趋势。近45年来青藏高原地区年降水量呈现微弱增加趋势,其倾向率为8.21mm/10a。青藏高原地区春季和冬季降水量都以增加趋势为主,但春季增加趋势远远大于冬季。青藏高原地区降水存在一定的周期性,32个站表现出短周期特性,为2~4年左右;11个站表现出中周期特性,为5~8年;6个站表现出长周期特性,均大于10年。1961~2005年间,青藏高原地区整体气候变化以暖湿化趋势为主,暖湿化站点占总数的67％。</p> <p>基于69个气象台站的气象数据,对青藏高原地区1961~2005年来的主要气候因子特征进行了分析。结果表明:1961~2005年的45年间,青藏高原地区年平均温度呈上升趋势,其倾向率为0.265℃/10a,其中青藏高原地区冬季气温变暖趋势明显,春季变暖趋势不明显;20世纪80年代以来青藏高原地区的温度升高有加速的趋势。近45年来青藏高原地区年降水量呈现微弱增加趋势,其倾向率为8.21mm/10a。青藏高原地区春季和冬季降水量都以增加趋势为主,但春季增加趋势远远大于冬季。青藏高原地区降水存在一定的周期性,32个站表现出短周期特性,为2~4年左右;11个站表现出中周期特性,为5~8年;6个站表现出长周期特性,均大于10年。1961~2005年间,青藏高原地区整体气候变化以暖湿化趋势为主,暖湿化站点占总数的67％。</p> |
| [21] | . 海拔敏感性是当前全球气候变化研究的热点之一,青藏高原作为"世界屋脊",探讨该区域气候变暖与海拔的关系对全球气候变化研究具有重要的参考意义。本文基于1971-2012年青藏高原及周边地区123个气象站的月平均气温数据,采用Mann-Kendall(M-K)趋势分析和突变检验、滑动t检验等方法分析了该地区气温变化的时空分布及其与海拔的关系。结果表明:①1971-2012年研究区年、四季、最热月和最冷月均温均呈现显著上升趋势,但增温幅度空间差异明显,具体表现为中、东部和东北部高,东南部低的态势;②除春季外,研究区增温幅度总体呈现随海拔上升而增加的趋势,且该趋势在青藏高原主体范围内尤为明显,但在不同海拔梯度内存在显著差异,其中海拔2 000~3 000m内增温对海拔的敏感性最强,海拔3 000~4 000m次之,而在海拔4 000m以上区域,增温幅度随海拔增加呈现下降趋势;③年均温的突变年份与海拔存在明显的线性关系,具体表现为:海拔每升高1 000m,突变年份推迟1.1~1.2年(p=0.001);④青藏高原年均温变化趋势及其海拔敏感性对研究时段起、止年份的选取较为敏感。 海拔敏感性是当前全球气候变化研究的热点之一,青藏高原作为"世界屋脊",探讨该区域气候变暖与海拔的关系对全球气候变化研究具有重要的参考意义。本文基于1971-2012年青藏高原及周边地区123个气象站的月平均气温数据,采用Mann-Kendall(M-K)趋势分析和突变检验、滑动t检验等方法分析了该地区气温变化的时空分布及其与海拔的关系。结果表明:①1971-2012年研究区年、四季、最热月和最冷月均温均呈现显著上升趋势,但增温幅度空间差异明显,具体表现为中、东部和东北部高,东南部低的态势;②除春季外,研究区增温幅度总体呈现随海拔上升而增加的趋势,且该趋势在青藏高原主体范围内尤为明显,但在不同海拔梯度内存在显著差异,其中海拔2 000~3 000m内增温对海拔的敏感性最强,海拔3 000~4 000m次之,而在海拔4 000m以上区域,增温幅度随海拔增加呈现下降趋势;③年均温的突变年份与海拔存在明显的线性关系,具体表现为:海拔每升高1 000m,突变年份推迟1.1~1.2年(p=0.001);④青藏高原年均温变化趋势及其海拔敏感性对研究时段起、止年份的选取较为敏感。 |
| [22] | Abstract There is growing evidence that the rate of warming is amplified with elevation, such that high-mountain environments experience more rapid changes in temperature than environments at lower elevations. Elevation-dependent warming (EDW) can accelerate the rate of change in mountain ecosystems, cryospheric systems, hydrological regimes and biodiversity. Here we review important mechanisms that contribute towards EDW: snow albedo and surface-based feedbacks; water vapour changes and latent heat release; surface water vapour and radiative flux changes; surface heat loss and temperature change; and aerosols. All lead to enhanced warming with elevation (or at a critical elevation), and it is believed that combinations of these mechanisms may account for contrasting regional patterns of EDW. We discuss future needs to increase knowledge of mountain temperature trends and their controlling mechanisms through improved observations, satellite-based remote sensing and model simulations. |
| [23] | The surface air temperature change over the Tibetan Plateau is determined based on historical observations from 1980 to 2013. |
| [24] | Adequate knowledge of climatic change over the Tibetan Plateau (TP) with an average elevation of more than 4000 m above sea level (a.s.l.) has been insufficient for a long time owing to the lack of sufficient observational data. In the present study, monthly surface air temperature data were collected from almost every meteorological station on the TP since their establishment. There are 97 stations located above 2000 m a.s.l. on the TP; the longest records at five stations began before the 1930s, but most records date from the mid-1950s. Analyses of the temperature series show that the main portion of the TP has experienced statistically significant warming since the mid-1950s, especially in winter, but the recent warming in the central and eastern TP did not reach the level of the 1940s warm period until the late 1990s. Compared with the Northern Hemisphere and the global average, the warming of the TP occurred early. The linear rates of temperature increase over the TP during the period 1955-1996 are about 0.16掳C/decade for the annual mean and 0.32掳C/decade for the winter mean, which exceed those for the Northern Hemisphere and the same latitudinal zone in the same period. Furthermore, there is also a tendency for the warming trend to increase with the elevation in the TP and its surrounding areas. This suggests that the TP is one of the most sensitive areas to respond to global climate change. |
| [25] | <p>In this study, we have used four methods to investigate the start of the growing season (SGS) on the Tibetan Plateau (TP) from 1982 to 2012, using Normalized Difference Vegetation Index (NDVI) data obtained from Global Inventory Modeling and Mapping Studies (GIMSS, 1982-2006) and SPOT VEGETATION (SPOT-VGT, 1999-2012). SGS values estimated using the four methods show similar spatial patterns along latitudinal or altitudinal gradients, but with significant variations in the SGS dates. The largest discrepancies are mainly found in the regions with the highest or the lowest vegetation coverage. Between 1982 and 1998, the SGS values derived from the four methods all display an advancing trend, however, according to the more recent SPOT VGT data (1999-2012), there is no continuously advancing trend of SGS on the TP. Analysis of the correlation between the SGS values derived from GIMMS and SPOT between 1999 and 2006 demonstrates consistency in the tendency with regard both to the data sources and to the four analysis methods used. Compared with other methods, the greatest consistency between the <i>in situ</i> data and the SGS values retrieved is obtained with Method 3 (Threshold of NDVI ratio). To avoid error, in a vast region with diverse vegetation types and physical environments, it is critical to know the seasonal change characteristics of the different vegetation types, particularly in areas with sparse grassland or evergreen forest.</p> |
| [26] | 正 The Qinghai-Xizang Plateau is a unique physico-geographical region on the earth. As a whole, the spatial differentiation of physico-geographical regions of the plateau is mainly determined by topographic configuration and atmospheric circulation, warm and humid in the southeast, cold and arid in the northwest. The natural landscapes apppear in the following succession: forest → meadow → steppe → desert. The system of physico-geographical regions of the plateau is demarcated on the principle of bio-climate or the principle of three dimension zonality. Based on the thermal conditions, moisture regimes and variation in landform the Qinghai-Xizang Plateau is sequentially demarcated. The duration of mean daily temperature above 10℃ is the principal index, the subsidiary criterion is mean temperature of the warmest month, two temperature belts may be divided: plateau subpolar and plateau temperate. Annual aridity is taken as the principal index, subordinated by annual precipitation. Four moisture regional |
| [27] | . 目前陆地生态系统碳储量方面已有大量实测数据与研究分析结果，但是刻画陆地生态系统碳库容量仍然存在相当大的不确定性。本文运用地理学、生态学、空间分析等相关理论与方法，采用遥感、地理信息系统等技术手段，比较了IGBP-DIS等6个土地利用/覆被分类系统及其分类结果，建立它们与IPCC六大类型的对应关系；采用平均生物量法，计算了各土地利用/覆被类型对应植被的地上和地下生物量及碳储量，分析了碳储量的总体特点及空间格局差异，论述了土地利用/覆被分类系统和分类过程对碳储量估算的影响，探讨了减少碳储量估算不确定性的土地利用/覆被分类方案；在地... 目前陆地生态系统碳储量方面已有大量实测数据与研究分析结果，但是刻画陆地生态系统碳库容量仍然存在相当大的不确定性。本文运用地理学、生态学、空间分析等相关理论与方法，采用遥感、地理信息系统等技术手段，比较了IGBP-DIS等6个土地利用/覆被分类系统及其分类结果，建立它们与IPCC六大类型的对应关系；采用平均生物量法，计算了各土地利用/覆被类型对应植被的地上和地下生物量及碳储量，分析了碳储量的总体特点及空间格局差异，论述了土地利用/覆被分类系统和分类过程对碳储量估算的影响，探讨了减少碳储量估算不确定性的土地利用/覆被分类方案；在地... |
| [28] | |
| [29] | |
| [30] | To investigate the impact of recent climatic changes on the plant development in Europe, this study uses phenological data of the International Phenological Gardens for the period 1969-1998. For this study, the leafing dates of four tree species (Betula pubescens, Prunus avium, Sorbus aucuparia and Ribes alpinum) were combined in an annual leaf unfolding index to define the beginning of growing season. The end of growing season was defined using the average leaf fall of B. pubescens, P. avium, Salix smithiana and R. alpinum. A nearly Europe-wide warming in the early spring (February-April) over the last 30 years (1969-1998) led to an earlier beginning of growing season by 8 days. The observed trends in the onset of spring corresponded well with changes in air temperature and circulation ( North Atlantic Oscillation Index (NAO-index)) across Europe. In late winter and early spring, the positive phase of NAO increased clearly, leading to prevailing westerly winds and thus to higher temperatures in the period February-April. Since the end of the 1980s the changes in circulation, air temperature and the beginning of spring time were striking. The investigation showed that a warming in the early spring (February-April) by 1掳C causes an advance in the beginning of growing season of 7 days. The observed extension of growing season was mainly the result of an earlier onset of spring. An increase of mean annual air temperature by 1掳C led to an extension of 5 days. |
| [31] | Changes in vegetative growing seasons are dominant indicators of the dynamic response of ecosystems to climate change. Therefore, knowledge of growing seasons over the past decades is essential to predict ecosystem changes. In this study, the long-term changes in the growing seasons of temperate vegetation over the Northern Hemisphere were examined by analyzing satellite-measured normalized difference vegetation index and reanalysis temperature during 1982-2008. Results showed that the length of the growing season (LOS) increased over the analysis period; however, the role of changes at the start of the growing season (SOS) and at the end of the growing season (EOS) differed depending on the time period. On a hemispheric scale, SOS advanced by 5.2 days in the early period (1982-1999) but advanced by only 0.2 days in the later period (2000-2008). EOS was delayed by 4.3 days in the early period, and it was further delayed by another 2.3 days in the later period. The difference between SOS and EOS in the later period was due to less warming during the preseason (January-April) before SOS compared with the magnitude of warming in the preseason (June-September) before EOS. At a regional scale, delayed EOS in later periods was shown. In North America, EOS was delayed by 8.1 days in the early period and delayed by another 1.3 days in the later period. In Europe, the delayed EOS by 8.2 days was more significant than the advanced SOS by 3.2 days in the later period. However, in East Asia, the overall increase in LOS during the early period was weakened in the later period. Admitting regional heterogeneity, changes in hemispheric features suggest that the longer-lasting vegetation growth in recent decades can be attributed to extended leaf senescence in autumn rather than earlier spring leaf-out. |
| [32] | The ongoing changes in vegetation spring phenology in temperate/cold regions are widely attributed to temperature. However, in arid/semiarid ecosystems the correlation between spring temperature and phenology is much less clear. We test the hypothesis that precipitation plays an important role in the temperature dependency of phenology in arid/semi-arid regions. We therefore investigated the influence of preseason precipitation on satellite-derived estimates of starting date of vegetation growing season (SOS) across the Tibetan Plateau (TP). We observed two clear patterns linking precipitation to SOS. First, SOS is more sensitive to inter-annual variations in preseason precipitation in more arid than in wetter areas. Spatially, an increase in long-term averaged preseason precipitation of 10 mm corresponds to a decrease of the precipitation sensitivity of SOS by about 0.01 day mm(-1) . Second, SOS is more sensitive to variations in preseason temperature in wetter than in dryer areas of the plateau. A spatial increase in precipitation of 10 mm corresponds to an increase in temperature sensitivity of SOS of 0.25 day 掳C(-1) (0.25-day SOS advance per 1-掳C temperature increase). Those two patterns indicate both direct and indirect impacts of precipitation on SOS on TP. This study suggest a balance between maximizing benefit from the limiting climatic resource and minimizing the risk imposed by other factors. In wetter areas, the lower risk of drought allows greater temperature sensitivity of SOS to maximize the thermal benefit, which is further supported by the weaker inter-annual partial correlation between growing degree days and preseason precipitation. In more arid areas, maximizing the benefit of water requires greater sensitivity of SOS to precipitation, with reduced sensitivity to temperature. This study highlights the impacts of precipitation on SOS in a large cold and arid/semiarid region and suggests that influences of water should be included in SOS module of terrestrial ecosystem models for drylands. This article is protected by copyright. All rights reserved. |
| [33] | ABSTRACT Daily temperature data from 1960 to 2013 and field-observed phenology data were used to investigate the spatiotemporal changes in thermal growing season and their relationship with the response of alpine grassland to climate variability in the Three-Rivers Headwater Region (TRHR) during the recent decades. We found a significant extension of the thermal growing season by 8.3 d per decade (p < 0.01) between 1986 and 2013 due to the combination of earlier start (tGSS; 鈭4.1 d per decade, p < 0.01) and delayed end (tGSE; 4.2 d per decade, p < 0.01) of the thermal growing season. However, earlier tGSS and delayed tGSE were weakened between 2000 and 2013, compared to that between 1986 and 1999, in association with changes in seasonal temperature. Our results also suggested that earlier start of actual growing season (aGSS) was associated with the increasing winter and spring temperature; while the end of actual growing season (aGSE) was triggered by summer temperature and precipitation; and earlier and delayed of aGSE were associated with the increasing summer temperature and precipitation, respectively. Additionally, earlier tGSS was associated with an earlier aGSS response to increased temperature, while delayed tGSE was associated with earlier aGSE. Thus, the actual growing season possibly move forward rather than extended in length, in contrast to the extension of the thermal growing season due to the ongoing warming. |
| [34] | Vegetation phenology is an important indicator of climate change impacts on the seasonal dynamics of the biosphere. However, little is known about the influence of elevation on spring phenological sensitivity to temperature in an alpine ecosystem. Based on remotely sensed land surface phenology and temperature data from 2001 to 2010, this study investigated the rate of spring phenological change of the Tibetan Plateau (TP) grasslands in response to interannual temperature variations at different elevations. Results suggest that spring phenology in the TP grasslands exhibits a stronger response to changes in temperature at higher elevations than at lower ones. In particular, spring phenology advanced by 1–2 days in response to a 102°C increase in May average temperature at elevations from 3,000 to 3,50002m, while the rate was up to 8–902days/°C at 5,000–5,50002m. Analysis using accumulated growing degree days (AGDD) from January 1 through May 31 showed the same general trend with increased elevation associated with increased sensitivity (as measured by phenological change per unit of AGDD change). Such temperature sensitivity gradients in the TP grasslands could be partly explained by the growth efficiency hypothesis which suggests that vegetation adapted to colder climates likely requires less heat energy for the onset of growing season and vice versa in warmer climates. Furthermore, accumulated growing degree days from January 1 to the greenup date were found to decrease with increasing elevations, which provided evidence to support the applicability of the growth efficiency hypothesis in an alpine grassland ecosystem. |
