, 张强, 黄文琳Vegetation phenology change in Tibetan Plateau from 1982 to2013 and its related meteorological factors
KONGDongdong, ZHANGQiang, HUANGWenlin通讯作者:
收稿日期:2016-09-18
修回日期:2016-11-28
网络出版日期:2017-01-20
版权声明:2017《地理学报》编辑部本文是开放获取期刊文献，在以下情况下可以自由使用：学术研究、学术交流、科研教学等，但不允许用于商业目的.
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1 引言
植物物候是指植物受气候和其他环境因子的影响而出现的以年为周期的自然现象[1],是植物长期适应环境的季节性变化而形成的生长发育节律[2]。物候是植被的一个敏感性和关键性特征,它能够反应植被生长变化与气候变化,帮助理解植被生态过程、量化气象变化对陆地生态系统的影响[3]。青藏高原作为世界的第三极,高寒草地约152.15×104 km2 （占青藏高原总面积257.24×104 km2的59.15%）,是中国乃至亚洲的重要牧区之一,青藏高原的生态过程对保障中国乃至东亚生态安全具有独特的屏障作用[4],因此,研究青藏高原物候变化具有重要意义。虽然关于青藏高原的植被物候已经很多研究,但是较多关注于生长季开始时间的变化及成因[3, 5-7]。尽管如此,关于生长季开始时间的变化和成因依然具有较大争议[3, 5-7]。不仅仅针对生长季开始时间,本文定义18个物候指标研究生长季各个阶段物候指标的变化情况。不同区域,不同植被类型,植被物候也存在较大的差异,简单地对青藏高原物候指标求整体平均会覆盖物候指标的空间差异性,因此本文对青藏高原植被物候按照植被分区进行研究。对物候变化比较显著的区域,本文使用PLS判断气象变量的影响。在分析气候变化对植被物候影响时,植被对气象变化的影响具有滞后效应[8]。沈妙根等根据气象变量前n天值进行叠加,判断生长季前期气象变量的影响[9-10]。但是气候变量对植被物候的影响可能不是连续的,简单的进行前期叠加可能会掩盖一些现象。而PLS可以识别出是哪段时间气象因子对植被物候造成的影响,且兼具主成分分析和多元回归的优点。本文主要围绕以下两个问题进行研究：① 青藏高原物候到底发生了怎样的变化;② 气象因子对青藏高原物候变化的影响。本文根据NDVI3g数据,定义了18种植被物候指标研究植被物候变化情况。根据1:100万植被区划,把青藏高原划分为8个植被分区;对物候变化比较显著的区域,采用最高温度、最低温度、平均温度、降水、太阳辐射数据等,运用PLS研究物候变化的气候成因。
2 数据
2.1 NDVI数据
本文采用归一化植被指数（Normalized Difference Vegetation Index, NDVI）分析植被物候变化趋势。NDVI与植被初级生产力、叶覆盖、生物量[11]具有很好的相关性,已经被广泛应用于量化植被生长趋势与生长过程[11]。本文使用的NDVI是由全球监测与模型组利用NOAA系列卫星合成分辨率为1/12°的半月NDVI第三代数据集NDVI3g（时间跨度为1981年7月-2013年12月,http://ecocast.arc.nasa.gov/data/pub/gimms/）。为减少云、大气对NDVI的干扰,数据首先采用谐波时间序列分析法（Harmonic Analysis of NDVI Time Series, HANTS）对NDVI数据进行平滑处理。HANTS能够解析的代表生长季NDVI,被广泛的应用于物候指标提取[4, 12]。由于物候提取方法很容易受到非生长季NDVI影响,而积雪覆盖会显著影响非生长季NDVI[13],因此NDVI数据在提取物候指标时需要进行除雪处理[7]。本文利用日平均温度（半月中含有5天连续低于0°）确定可能存在的积雪覆盖[13]。在选择研究区域时,把植被较为稀疏的荒漠区和NDVI季节性变化不明显的区域进行截除。超过10年以上NDVI年内变化小于0.1的区域判定为该地区NDVI季节性变化比较微弱。同时除去含有多个生长季与生长季跨年的区域,截除方法参考GARONNA等[12]。
2.2 气象数据
本文使用的气象数据来源于中国气象驱动数据集,该数据集是中科院青藏高原研究所开发的一套近地面气象与环境要素再分析数据集[14],空间精度为0.1°×0.1°。本文使用的温度数据为该数据集3小时时间尺度温度数据,据此计算逐日最高温度、最低温度、平均温度;降水和太阳辐射数据采用日时间尺度数据。根据1:100万植被图集[15],本文把青藏高原划分为9个植被分区[13],其中热带雨林区域植被季节性变化不明显,在选择研究区域过程中已截去,余下8个植被分区[16]（图1）。
显示原图|下载原图ZIP|生成PPT图1青藏高原植被类型分区
-->Fig. 1Vegetation clusters in the Tibetan Plateau
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3 方法
3.1 植被物候指标的计算方法
NDVI数据经过除噪、除雪处理之后,本文定义了18种植被物候指数研究植被物候变化情况。NDVI数据计算物候之前,首先采用spline样条函数把NDVI数据插值到逐日,和参数函数不同,spline可以最大程度保留NDVI数据的变化形状[12, 17]。（1）NDVI阈值法（TRS）
式中：
（2）NDVI最大斜率法（DES） NDVI的斜率的峰值和谷值分别对应生长季开始与生长季结束时间,同样生长季开始和结束时间之间的间隔为生长季长度[12, 18]。同时NDVI最大值对应的时间定义为峰值时间。
（3）曲率曲线法 对于S型曲线,物候转移日期都可以根据局部曲率
式中：
（4）Gu法
3.2 分段回归
分段回归在植被NDVI量级,及植被物候转折[3]方面有较为广泛的应用。本文采用分段回归分析植被物候指标突变转折点。式中：t为时间（年）;y为响应变量（植被物候指标）;β0、β1、β2是回归系数;β0为截距,β1与β1+β2分别为转折前后趋势;ε为误差项;α为响应变量转折点的位置。t检验用来检测原假设H0：β2是否显著不为0（即突变前后趋势有无显著差别）[3]。当检验显著性水平达到0.1时,转折点达到了变异程度,α称为变异点。为保证趋势分析具有一定的数据长度,令转折点的范围在1986-2009年间。同时,采用Mann-Kendall（M-K）法检测转折前后物候指标变化趋势。
3.3 偏最小二乘法回归
偏最小二乘法回归（Partial least regression, PLS）,兼具主成分分析和多元回归的优点,克服了预测变量相关导致的多元共线性[22],PLS在植被变化归因[22]以及近年来植被物候变化研究中均有应用[6]。采用VIP指数（Variable importance Index）挑选显著影响变量。VIP>0.8,说明该变量具一定的解释意义;VIP ≥ 1,说明该变量具有明显解释意义。本文采用较为严格的限制条件,VIP ≥ 1时认为该变量影响显著。有关PLS的具体计算过程请参考文献 [23]。本文采用PLS研究植被物候对气候的响应,对于物候发生显著变化的植被分区,首先把物候指标和气象变量根据植被分区计算区域平均。对于某一物候指标,该物候指标的区域平均值作为响应变量,对应的前期1-12月的区域平均气象变量（月平均最高温度、月平均最低温度、月平均平均温度、月累积降水、月平均太阳辐射共12×5=60个气象变量）作为独立变量。PLS模型系数类似于多元回归斜率,反映了物候指标对气候变量的敏感性,VIP ≥ 1时说明自变量对因变量具有显著的解释意义。4 结果分析
4.1 不同植被分区物候均值
图2显示了青藏高原15种物候指标在各个植被分区的多年均值情况（未包含生长季长度指标）,青藏高原生长季主要在4底到10月初,青藏高原东北地区物候明显早于西南地区。不同方法定义的物候指标均值分布存在明显差异。其中返青期、上升期、TRS2生长季开始时间对应的时间比较接近,大致在4月底5月初。成熟期主要分布在7月底到9月初,不同分区差异较大,温带草原物候偏早,温性草原偏晚,相差将近1个半月。NDVI峰值时间主要集中在8月初。衰老期、下降期分布相对集中,且较为吻合,平均出现在8月底9月初。在9月中下旬进入生长季末期。TRS2生长季结束时间不同分区波动较大,尤其是在常绿阔叶林和温性草原地区,可能是因为这些地区植被覆盖度比较高,优势物种衰退之后,底层的草本植被仍然可以保持一定的NDVI值[24],因此这些地区TRS2生长季结束时间波动较大,阈值偏小（0.2）。Yu等[6]认为在使用阈值法提取青藏高原物候指标时,生长季结束时间取阈值0.6拟合情况较为接近真实。同时,TRS5生长季结束时间与DES生长季结束时间分布集中且较为相似,说明采用TRS5与DES推求的生长季结束时间较为接近真实。10月中下旬植被逐渐进入衰退期,11月中旬植被进入休眠期。衰退期与休眠期明显晚于采用阈值法和最大斜率法定义的生长季结束日期。
显示原图|下载原图ZIP|生成PPT图2青藏高原不同植被分区物候指标多年平均分布情况
-->Fig. 2Multi-annual (1982-2013) average of phenological metrics in Tibetan Plateau in different vegetation clusters
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4.2 物候指标变化
青藏高原生长季初期物候指标,转折发生在1997-2000年（图4）,很多研究认为1998年左右青藏高原生长季初期物候发生转折[25-26],这与本文研究非常吻合。对于具有显著突变点的植被物候指标,上升期、TRS2、TRS5、DES生长季开始时间,转折前均呈现显著下降趋势（显著性水平达0.05）（图3a、3b）,相应的下降趋势分别为2.3 d/10a、3.5 d/10a、3.7 d/10a、3.3 d/10a,生长季初期物候指标平均提前2~3 d/10a。之前研究认为1982-1999年,区域平均生长季开始时间提前速率达4.5~10.2 d/10a[3, 19, 25],可能是本文采用MK计算线性趋势剔除异常值的影响,使本文得出的提前速率略小于前人研究。转折之后,上升期、TRS5、DES生长季开始时间等指标,呈推迟现象,延迟幅度在7~10 d/10a,但显著性水平只达到0.1。其中TRS2生长季开始时间,转折之后推迟趋势达0.05显著性水平,但推迟幅度较小,仅4.4 d/10a。众多生长季初期物候指标只有TRS2在转折之后趋势较为显著,因此转折之后初期物候指标推迟可信度较低。关于青藏高原生长季初期物候指标转折之后趋势具有较大争议[3, 6-7],但是青藏高原西南地区初期物候推迟被普遍接受[4, 19],只有西南地区的温带草原和北部的荒漠呈推迟显著（图3b）。对于青藏高原生长季末期物候指标,转折发生在2004-2007年左右（图4）。休眠期、衰退期、DES、TRS5生长季结束时间,转折前显著上升,这些物候指标平均延迟1~2 d/10a。转折后生长季末期物候指标具有提前趋势,衰退期对应的提前趋势为-17.6 d/10a（显著性水平0.1）;TRS5生长季结束提前趋势为-7.8 d/10a（显著性水平达0.05）。Che等[27]认为青藏高原末期物候指标趋势不明显,1999-2011年趋势为0.96 d/10a（p = 0.3）。同时,除衰退期、TRS5生长季结束时间,其余末期物候指标趋势不显著。对于青藏高原生长季长度物候指标,其突变时间与生长季末期物候指标突变时间相近,约在2005年。转折之前TRS5生长季长度平均延长2.4 d/10a,DES生长季长度延长1.3 d/10a。转折之后生长季长度变化趋势不显著。
显示原图|下载原图ZIP|生成PPT图3青藏高原物候指标突变前后趋势及全部年份整体趋势
-->Fig. 3phenological metrics' trends before and after turning points and trends of the whole research period
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显示原图|下载原图ZIP|生成PPT图4青藏高原分段回归物候指标突变年份
-->Fig. 4The turning points of phenological metrics detected by piecewise
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对于具有显著突变点的植被物候指标的全年趋势,大多数分区植被物候指标并未有明显的推迟或提前趋势,这可能由突变前后变化趋势完全相反导致的（图3a、3b、3c）。尽管如此,高寒草甸和高寒灌木草甸分区依然有物候指标趋势显著性水平达0.05。全年而言青藏高原的高寒草甸与高寒灌木草甸是物候指标变化最剧烈的区域（图3d、3e）,可能是这些地区草地对气候变化比较敏感。在高寒草甸区,返青期、上升期提前幅度较大,达到2~5 d/10a（显著性水平达0.05）;TRS5、DES生长季长度显著延长,而生长季结束时间等物候指标与休眠期、衰退期等趋势均不显著,这说明高寒草甸区生长季长度的延长主要是由生长季初期物候指标提前导致的。在高寒灌木草甸区,TRS2、TRS5、DES生长季长度指数均显著延长（显著性水平均达0.05）;返青期、TRS2生长季开始时间均显著提前,但提前幅度较小,分别为1.5 d/10a、1.1 d/10a;休眠期、衰退期推迟显著,平均推迟1~2 d/10a。这说明高寒灌木草甸区生长季长度的延长可能是由于生长季前物候指标的提前,以及生长季后期物候指标的推迟共同作用导致的。高寒草甸与高寒灌木草甸是物候指标变化最剧烈的区域。因此下文,对这两个植被分区趋势变化显著的8个物候指标（返青期、上升期、休眠期、衰退期、DES、TRS2、TRS5生长季开始时间与生长季长度）,采用PLS进一步探究其原因。
4.3 气象因素对植被物候的影响
对于最高温度,全部植被分区、全部月份均呈现显著上升趋势,除10月份高寒草甸与常绿阔叶林地区显著性水平为0.1,其余均是0.05显著性水平显著上升（图5）。11月-2月增温幅度达到0.15 ℃/a,冬季和初春升温幅度最高,升温幅度明显高于全国。最低温度的升温幅度不如最高温度,且2-6月、10-11月很多分区升温不显著。和最高温度类似,平均温度在冬季升温幅度最大,夏季和秋季升温幅度较小,且一些地区升温不显著。对于降水,大多数地区呈现上升趋势,降水显著上升主要集中在5-8月,而同时期太阳辐射则呈显著下降趋势。
显示原图|下载原图ZIP|生成PPT图51982-2013年青藏高原气象因素平均温度、最高温度、最低温度、降水、太阳辐射变化趋势
-->Fig. 5Multi-annual (1982-2013) trends of average temperature, maximum temperature, minimumtemperature, precipitation and solar radiation
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4.3.1 高寒草甸区
（1）生长季初期物候指标（返青期、上升期、DES,TRS2,TRS5生长季开始时间）
温度对植被物候指标的影响强于降水和太阳辐射,平均温度、最高温度、最低温度对生长季初期物候指标的影响大致相同（图6a~6e）。上年9月-次年2月平均温度与返青期、上升期、DES,TRS5生长季开始时间的PLS模型VIP均大于1,具有显著解释意义。其中上年9-12月温度与生长季初期物候指标正相关,该时段温度上升（图5a~5c）导致初期物候指标延迟;而当年1-2月、4月、6月温度升高导致植被物候提前（图6a、6c、6e）。温度升高会使植被生长发育所需的有效积温提前达到;同时温度升高会提升酶的活性,加快植被物候进程[28]。上年秋末冬初降水与初期物候指标模型系数为负（图6a~6e）,尤其在上年10、12月份,高寒草甸10月份降水显著上升（显著性水平达0.05）,12月份降水趋势不显著。3、5月份降水对DES、TRS5生长季开始时间的影响,逐渐由正转负（图6c、6e）。5月份降水与上升期、TRS2生长季的PLS模型系数为正,可能是上升期、TRS2生长季开始时间定义的生长季开始时间偏早（图2）。上年夏末秋初太阳辐射与生长季初期物候指标正相关（图6a~6e）,而当年的2-3月的太阳辐射对TRS2、TRS5生长季开始时间具有负的影响。可能是因为上年夏末秋初高太阳辐射会导致生长季结束时间推迟[13],从而导致当年生长季开始时间推迟;而2-3月太阳辐射升高说明降雪减少,有利于有效积温的积累。同时太阳辐射升高,意味着光照时间延长、光周期缩短,刺激植被返青。
显示原图|下载原图ZIP|生成PPT图6青藏高原高寒草甸均值方差标准化物候指标与气象变量PLS系数
-->Fig. 6PLS model coefficients of the centered and scaled phenological and meteorological data in alpine meadows
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（2）生长季末期物候指标（休眠期,衰退期）
对于末期物候指标,温度对植被物候指标的影响依然占主导地位,相对于最高温度与平均温度,最低温度对末期物候指标的影响减弱（图6f、6g）。温度对末期物候指标的影响主要集中在当年夏末与秋季。上年7月-当年8月,温度与末期物候指标的模型系数接近于0;当年秋季温度对生长季末期物候指标的影响最大,模型系数接近于0.1。夏末最高温度、太阳辐射与末期物候指标负相关,最低温度为正相关。秋季温度上升是生长季末期物候指标延长的主要因素。降水与末期物候指标的关系比较复杂,模型系数正负变化较大。6月、8月太阳辐射对末期物候指标具有负的影响。
（3）生长季长度物候指标（DES、TRS2、TRS5生长季长度）
温度对生长季长度物候指标的影响占主导地位,平均温度与最高温度PLS模型系数类似（图6h~6j）。除当年8月份,其余时间最高温度对生长季长度的影响强于最低温度。上年9月至当年4月温度,对应的生长季长度与初期物候指标的PLS模型系数刚好吻合,说明该时段温度影响初期物候指标;夏末与秋季温度主要影响末期物候指标,从而调节生长季长度。夏末与秋季最小温度与最高温度对生长季长度指标的影响差别较大。8月份,最低温度、降水对物候有正的影响,太阳辐射为负的影响。可能是该时段太阳辐射升高,增加蒸腾作用,使植物可利用水分减少,该时段主要限制因素为降水。对于生长季长度物候指标,上年12月份降水影响为正,当年4月份降水为负,而8月份影响又转为正。可能是因为上年12月份与当年8月份降水增多,可以增加植被的可利用水资源,而4月份降水增多,意味着降雪增多,天气变冷不利于植被返青。而太阳辐射对生长季长度物候指标的影响主要体现在上年和当年8月份。8月份太阳辐射上升会提前生长季结束时间,上年生长季结束时间推迟可能会对当年生长季开始时间带来负的影响。
4.3.2 高寒灌木草甸区 从生长季长度PLS模型系数来看,TRS2与DES、TRS5生长季长度指标PLS模型系数差别较大,在夏、秋季与降水、太阳辐射的模型系数甚至和DES、TRS5对应的完全相反,这可能是因为TRS2在计算生长季结束时间时误差较大,从而生长季长度误差也很大（图7h~7j）。
显示原图|下载原图ZIP|生成PPT图7青藏高原高寒灌木草甸均值方差标准化物候指标与气象变量PLS系数
-->Fig. 7PLS model coefficients of the centered and scaled phenological and meteorological data in alpine shrub meadows
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（1）生长季初期物候指标（返青期、上升期、DES,TRS2,TRS5生长季开始时间）
温度对高寒灌木草甸初期物候指标的影响不如高寒草甸（图7a~7f）。返青期PLS模型系数与其他初期物候指标差别较大,可能是采用GU法推求的返青期在高寒灌木草甸明显偏早导致的（图2）。而上升期、DES、TRS2、TRS5生长季开始时间PLS模型结果较为相似。与高寒草甸现象类似,上年温度（尤其是9、10月份）对初期物候指标具有正的影响。年后平均温度、最高温度对春季物候指标的影响则转为负,尤其是4月、6月（图7c~7e）。与高寒草甸不同的是,冬季平均温度、最高温度、最低温度,对初期物候指标不具显著解释意义。降水对初期物候指标的影响强于温度和太阳辐射。上年7月至当年4-6月,降水与初期物候指标PLS模型系数从正转负,年后又由负转正,尤其是在当年4月份。
（2）生长季末期物候指标（休眠期,衰退期）
对于生长季末期物候指标,温度的影响强于降水与太阳辐射（图7f、7g）。生长季末期物候指标的前3个月,平均温度、最高温度、最低温度对末期物候指标的影响较为一致,温度升高有利于末期物候指标推迟。可能此时升温会增强植被光合作用,减少叶绿素退化,延迟进入生长季末期[13]。5-6月份最低温度的PLS模型系数为负,此时最高温度对末期物候指标的影响不显著,可能5-6月份温度首先影响初期物候指标,最小温度升高使初期物候指标提前,从而使末期物候指标提前。降水、太阳辐射对末期物候指标的影响主要集中在当年8月份,此时降水、最低温度对末期物候指标具有正的影响,太阳辐射为负的影响,平均温度与最高温度对末期物候指标的影响比较微弱。可能此时限制植被生长的主要因素是最低温度与降水,而太阳辐射、最高温度增加会导致植被可利用水分减少[10]。
（3）高寒灌木草甸生长季长度（DES、TRS2、TRS5生长季长度）
与高寒草甸类似,上年温度对生长季长度有负的影响。可能的原因为,一方面上年末期物候指标推迟,会导致当年生长季初期物候指标相应的延迟;另一方面,气温升高不利于植被冬季休眠[6, 29],从而对生长季长度造成负的影响。但是高寒灌木草甸年前温度对生长季长度的影响主要集中在夏末秋初,且影响相对微弱（图7h、7j）。当年4-9月份平均温度、最高温度对生长季长度的影响都比较大,PLS模型系数为正,温度升高导致生长季长度延长;6月份,最高温度、太阳辐射对生长季长度具有正的影响,而最低温度对生长季长度具有负的影响。降水对生长季长度的影响集中在上年的秋冬季节,以及当年的秋季。上年冬末,降水对生长季长度有正的影响,该时段降水增多导致植被返青期可利用水分增多,诱导提前进入生长季。上年及当年秋季降水对生长季长度具有负的影响,可能是因为秋季限制植被生长发育的主要因素主要是最高温度与太阳辐射[28],而降水增多,可能会导致最高温度和太阳辐射降低。太阳辐射对生长季长度物候指标的影响主要集中在当年6月、9月份,且具有正的影响（图7h、7j）。
5 讨论与结论
5.1 讨论
数据上,本文对NDVI3g数据进行了精细的除雪处理。同时,为避免单一方法提取物候指标的误差,定义了18种植被物候指标研究植被物候变化情况。考虑到区域的差异性,根据1:100万植被区划,把青藏高原划分为8个植被区分。方法上,PLS兼具主成分分析和多元回归的优点,克服了预测变量相关导致的多元共线性[22],其得到了广泛的认可,并应用于诸多自然科学领域。对青藏高原物候变化最剧烈的两个植被分区（高寒草甸与高寒灌木草甸）,综合考虑最高温度、最低温度、平均温度、降水、太阳辐射等,采用PLS进行成因分析。物候变化是温度、降水、太阳辐射等因素综合作用的结果,温度对物候的影响占主导地位。研究发现,上年秋季、冬初温度对生长季初期物候具有正的影响。该结论与之前许多研究相符：张福春等采用积分回归法研究北京春季树木开花期实测物候资料,得出冬季气温偏高不利于休眠,反而使开花期推迟[29]。Guo[30]采用实测物候资料研究中国杏树物候变化、Eike等[31]采用实测物候指标研究加利福尼亚胡桃物候、Yu等[6]采用GIMMS-NDVI研究青藏高原物候变化,均发现冬季升温导致生长季初期物候指标推迟。夏季温度对植被物候的影响较为特殊,最小温度与最高温度对植被物候的影响存在明显差异：在高寒草甸,8月份最高温度、太阳辐射与末期物候指标负相关、最低温度正相关,高寒草甸多年平均最高温度与最大降水均在7月份,8月份降水明显减少,而太阳辐射、温度依然很高,可能是该时段较高的太阳辐射、温度导致植被蒸腾耗水增加,可利用水分减少,使植被加快进入枯黄。同时8月份降水与末期物候指标正相关也佐证了这一观点（图6f、6g）;在高寒灌木草甸,6月份最高温度、太阳辐射对生长季长度具有正的影响,而最低温度对生长季长度具有负的影响。可能是6月份最低温度的升高会导致植被夜间呼吸作用消耗的有机物增多,不利于植被生长。而最高温度、太阳辐射增加有利于植被进行光合作用,叶绿素增多。此外,秋季温度对末期物候指标具有正的影响,相对于夏季,秋季植被需水减少,温度升高会增强光合作用酶的活性,减缓叶绿素退化,从而推迟末期物候指标[13]。
降水对物候的影响不同月份波动较大。在高寒草甸,上年秋末冬初降水与初期物候指标模型系数为负,尤其在上年10、12月份;在高寒灌木草甸,上年冬季降水与初期物候指标模型系数为负。这与沈妙根等人的研究一致,生长季前期降水增多,会使青藏高原大多数地区春季物候提前[32]。反之,缺水则会推迟物候[28]。春初,降水与初期物候指标模型系数由负转正,可能是因为3、4月份降水主要以降雪的形式表现[26],降雪增多会致使温度、太阳辐射偏低,不利于植被返青;在高寒草甸5月份降水与初期物候指标模型系数为负,可能5月气温回暖,降水的主要形式是雨水,降水增多有利于提高植被可利用水分,加速植被返青。8月份限制植被生长的主要因素为降水,降水增多促使生长季末期物候指标推迟,这与Che等人[27]研究结果一致。
相对于温度与降水,太阳辐射对初期物候指标影响较小,主要集中在6、8、9月份。这与Liu等[13]人结论一致,这可能是因为太阳辐射主要因纬度而不同,对气候变化不敏感（除多云的条件下）。在高寒草甸与高寒灌木草甸,6月份、8月份太阳辐射与末期物候指标负相关。6月份太阳辐射升高导致春季物候提前,间接导致生长季末期物候指标提前。8月份可能是夏季太阳辐射过高,太阳辐射下降反而有利于末期物候指标推迟[27]。在高寒灌木草甸,9月份太阳辐射对生长季长度指标具有正的影响,此时高太阳辐射可以加强植被光合作用能力,延缓脱落酸的堆积[13],因此对生长季长度具有正的影响。
5.2 结论
（1）青藏高原初期物候指标,转折发生在1997-2000年,转折前初期物候指标平均提前2~3 d/10a;青藏高原末期物候指标转折发生在2004-2007年左右,生长季长度物候指标突变发生在2005年左右,转折前末期物候指标平均延迟1~2 d/10a、生长季长度平均延长1~2 d/10a;转折之后生长季初期物候指标推迟趋势的显著性水平仅为0.1,生长季末期物候指标、生长季长度指标趋势不显著。（2）高寒草甸与高寒灌木草甸是青藏高原物候变化最剧烈的植被分区。高寒草甸区生长季长度的延长主要是由生长季初期物候指标提前导致的。在高寒灌木草甸区生长季长度的延长主要是由于初期物候指标的提前,以及末期物候指标的推迟共同作用导致的。
（3）采用PLS进一步分析气象因素对高寒草甸与高寒灌木草甸物候剧烈变化的成因。两植被分区均显示上年秋季、冬初温度对生长季初期物候具有正的影响,该时段温度一方面会导致上年末期物候指标推迟,间接推迟生长季开始时间;另一方面高温不利用冬季休眠,从而秋季及冬初温度升高对初期物候具有推迟作用。除夏季外,其余月份最小温度对植被物候的影响与平均温度、最高温度的影响类似。降水对植被物候的影响不同月份波动较大,上年秋冬季节降水对初期物候指标具有负的影响;当年春初降水对初期物候指标具有正的影响。8月份限制植被生长季的主要因素是降水,此时降水与末期物候指标模型系数为正。太阳辐射对植被物候的影响主要在6、8、9月份。
PLS方法在物候变化研究应用中具有较好的效果,结果符合实际,大多数现象能在他人研究结果中的得到印证。本文研究结果将会对植被物候模型改进,提供有力的科学依据。
The authors have declared that no competing interests exist.
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| [5] | ABSTRACT Not Available Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints |
| [6] | Climate change has caused advances in spring phases of many plant species. Theoretically, however, strong warming in winter could slow the fulfillment of chilling requirements, which may delay spring phenology. This phenomenon should be particularly pronounced in regions that are experiencing rapid temperature increases and are characterized by highly temperature-responsive vegetation. To test this hypothesis, we used the Normalized Difference Vegetation Index ratio method to determine the beginning, end, and length of the growing season of meadow and steppe vegetation of the Tibetan Plateau in Western China between 1982 and 2006. We then correlated observed phenological dates with monthly temperatures for the entire period on record. For both vegetation types, spring phenology initially advanced, but started retreating in the mid-1990s in spite of continued warming. Together with an advancing end of the growing season for steppe vegetation, this led to a shortening of the growing period. Partial least-squares regression indicated that temperatures in both winter and spring had strong effects on spring phenology. Although warm springs led to an advance of the growing season, warm conditions in winter caused a delay of the spring phases. This delay appeared to be related to later fulfillment of chilling requirements. Because most plants from temperate and cold climates experience a period of dormancy in winter, it seems likely that similar effects occur in other environments. Continued warming may strengthen this effect and attenuate or even reverse the advancing trend in spring phenology that has dominated climate-change responses of plants thus far. |
| [7] | Zhang et al. (1) report that the green-up dates in the Tibetan Plateau (TP) have continuously advanced from 1982 to 2011. They retrieved green-up dates from the multiannual time-series of Normalized Difference Vegetation Index (NDVI) using the method proposed by Piao et al. (2). This method assumes that the nongrowing... |
| [8] | The change in spring phenology is recognized to exert a major influence on balance dynamics in temperate ecosystems. Over the past several decades, several studies focused on shifts in spring phenology; however, large uncertainties still exist, and one understudied source could be the method implemented in retrieving satellite-derived spring phenology. To account for this potential uncertainty, we conducted a multimethod investigation to quantify changes in vegetation green-up date from 1982 to 2010 over temperate China, and to characterize climatic controls on spring phenology. Over temperate China, the five methods estimated that the vegetation green-up onset date advanced, on average, at a rate of 1.3 卤 0.6 days per decade (ranging from 0.4 to 1.9 days per decade) over the last 29 years. Moreover, the sign of the trends in vegetation green-up date derived from the five methods were broadly consistent spatially and for different vegetation types, but with large differences in the magnitude of the trend. The large intermethod variance was notably observed in arid and semiarid vegetation types. Our results also showed that change in vegetation green-up date is more closely correlated with temperature than with precipitation. However, the temperature sensitivity of spring vegetation green-up date became higher as precipitation increased, implying that precipitation is an important regulator of the response of vegetation spring phenology to change in temperature. This intricate linkage between spring phenology and precipitation must be taken into account in current phenological models which are mostly driven by temperature. |
| [9] | Abstract Spatial variations in phenological responses to temperature have not been reported for grasslands of the Qinghai-Tibetan Plateau. Using satellite-derived normalized difference vegetation index and meteorological records from 1982 to 2006, we characterized the spatial patterns of grassland green-up onset in relation to air temperature and precipitation before the growing season (“preseason” henceforth) in the central and eastern plateau by combining linear programming with correlation analysis. Green-up onset near half of the meteorological stations was significantly correlated (p |
| [10] | Abstract Understanding vegetation responses to climate change on the Tibetan Plateau (TP) helps in elucidating the land–atmosphere energy exchange, which affects air mass movement over and around the TP. Although the TP is one of the world's most sensitive regions in terms of climatic warming, little is known about how the vegetation responds. Here, we focus on how spring phenology and summertime greenness respond to the asymmetric warming, that is, stronger warming during nighttime than during daytime. Using both in situ and satellite observations, we found that vegetation green-up date showed a stronger negative partial correlation with daily minimum temperature (Tmin) than with maximum temperature (Tmax) before the growing season (‘preseason’ henceforth). Summer vegetation greenness was strongly positively correlated with summer Tmin, but negatively with Tmax. A 1-K increase in preseason Tmin advanced green-up date by 4 days (P 0.10) and higher summer Tmax even reduced greenness by 2.6% K611 (P < 0.05). The stimulating effects of increasing Tmin were likely caused by reduced low temperature constraints, and the apparent negative effects of higher Tmax on greenness were probably due to the accompanying decline in water availability. The dominant enhancing effect of nighttime warming indicates that climatic warming will probably have stronger impact on TP ecosystems than on apparently similar Arctic ecosystems where vegetation is controlled mainly by Tmax. Our results are crucial for future improvements of dynamic vegetation models embedded in the Earth System Models which are being used to describe the behavior of the Asian monsoon. The results are significant because the state of the vegetation on the TP plays an important role in steering the monsoon. |
| [11] | Changes in the phenology of vegetation activity may accelerate or dampen rates of climate change by altering energy exchanges between the land surface and the atmosphere and can threaten species with synchronized life cycles. Current knowledge of long-term changes in vegetation activity is regional, or restricted to highly integrated measures of change such as net primary productivity, which mask details that are relevant for Earth system dynamics. Such details can be revealed by measuring changes in the phenology of vegetation activity. Here we undertake a comprehensive global assessment of changes in vegetation phenology. We show that the phenology of vegetation activity changed severely (by more than 2 standard deviations in one or more dimensions of phenological change) on 54% of the global land surface between 1981 and 2012. Our analysis confirms previously detected changes in the boreal and northern temperate regions. The adverse consequences of these northern phenological shifts for land-surface-climate feedbacks, ecosystems and species are well known. Our study reveals equally severe phenological changes in the southern hemisphere, where consequences for the energy budget and the likelihood of phenological mismatches are unknown. Our analysis provides a sensitive and direct measurement of ecosystem functioning, making it useful both for monitoring change and for testing the reliability of early warning signals of change. |
| [12] | Land Surface Phenology (LSP) is the most direct representation of intra-annual dynamics of vegetated land surfaces as observed from satellite imagery. LSP plays a key role in characterizing land-surface fluxes, and is central to accurately parameterizing terrestrial biosphere-atmosphere interactions, as well as climate models. In this paper we present an evaluation of Pan-European LSP and its changes over the past 30 years, using the longest continuous record of Normalized Difference Vegetation Index (NDVI) available to date in combination with a landscape-based aggregation scheme. We used indicators of Start-Of-Season, End-Of-Season and Growing Season Length (SOS, EOS and GSL, respectively) for the period 1982-2011 to test for temporal trends in activity of terrestrial vegetation and their spatial distribution. We aggregated pixels into ecologically representative spatial units using the European Landscape Classification (LANMAP) and assessed the relative contribution of spring and autumn phenology. GSL increased significantly by 18-24 days/decade over 18-30% of the land area of Europe, depending on methodology. This trend varied extensively within and between climatic zones and landscape classes. The areas of greatest growing-season lengthening were the Continental and Boreal zones, with hotspots concentrated in southern Fennoscandia, Western Russia and pockets of continental Europe. For the Atlantic and Steppic zones, we found an average shortening of the growing season with hotspots in Western France, the Po valley, and around the Caspian Sea. In many zones, changes in the NDVI-derived end-of-season contributed more to the GSL trend than changes in spring green-up, resulting in asymmetric trends. This underlines the importance of investigating senescence and its underlying processes more closely as a driver of LSP and global change. This article is protected by copyright. All rights reserved. |
| [13] | ABSTRACT Autumn phenology plays a critical role in regulating climate-biosphere interactions. However, the climatic drivers of autumn phenology remain unclear. In this study, we applied four methods to estimate the date of the end of the growing season (EOS) across China's temperate biomes based on a 30-year normalized difference vegetation index (NDVI) dataset from Global Inventory Modeling and Mapping Studies (GIMMS). We investigated the relationships of EOS with temperature, precipitation sum, and insolation sum over the preseason periods by computing temporal partial correlation coefficients. The results showed that the EOS date was delayed in temperate China by an average rate at 0.12 0.01 days per year over the time period of 1982-2011. EOS of dry grassland in Inner Mongolia was advanced. Temporal trends of EOS determined across the four methods were similar in sign, but different in magnitude. Consistent with previous studies, we observed positive correlations between temperature and EOS. Interestingly, the sum of precipitation and insolation during the preseason were also associated with EOS, but their effects were biome dependent. For the forest biomes, except for evergreen needle-leaf forests, the EOS dates were positively associated with insolation sum over the preseason, whereas for dry grassland, the precipitation over the preseason was more dominant. Our results confirmed the importance of temperature on phenological processes in autumn, and further suggested that both precipitation and insolation should be considered to improve the performance of autumn phenology models. This article is protected by copyright. All rights reserved. |
| [14] | . |
| [15] | |
| [16] | 中国科学院机构知识库(中国科学院机构知识库网格（CAS IR GRID）)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。 中国科学院机构知识库(中国科学院机构知识库网格（CAS IR GRID）)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。 |
| [17] | The software capabilities are illustrated by analyzing one year of data from a selection of seven sites belonging to the PhenoCam network ( http://phenocam.sr.unh.edu/ ), including an unmanaged subalpine grassland, a tropical grassland, a deciduous needle-leaf forest, three deciduous broad-leaf temperate forests and an evergreen needle-leaf forest. One of the novelties introduced by the package is the spatially explicit, pixel-based analysis, which potentially allows to extract within-ecosystem or within-individual variability of phenology. We examine the relationship between phenophases extracted by the traditional ROI-averaged and the novel pixel-based approaches, and further illustrate potential applications of pixel-based image analysis available in the phenopix R package. |
| [18] | ABSTRACT Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8 km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16 000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by 卤60 days and in standard deviation by 卤20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS trends could be detected for only 12% of North America and were divided between trends towards both earlier and later spring. |
| [19] | . ABSTRACT 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), Système 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 m611 in the early 2000s to 1.30 days 100 m611 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. |
| [20] | ABSTRACT Incluye índice |
| [21] | ABSTRACT The seasonal cycle of plant community photosynthesis is one of the most important biotic oscillations to mankind. This study built upon previous efforts to develop a comprehensive framework to studying this cycle systematically with eddy covariance flux measurements. We proposed a new function to represent the cycle and generalized a set of phenological indices to quantify its dynamic characteristics. We suggest that the seasonal variation of plant community photosynthesis generally consists of five distinctive phases in sequence each of which results from the interaction between the inherent biological and ecological processes and the progression of climatic conditions and reflects the unique functioning of plant community at different stages of the growing season. We applied the improved methodology to seven vegetation sites ranging from evergreen and deciduous forests to crop to grasslands and covering both cool-season (vegetation active during cool months, e.g. Mediterranean climate grasslands) and warm-season (vegetation active during warm months, e.g. temperate and boreal forests) vegetation types. Our application revealed interesting phenomena that had not been reported before and pointed to new research directions. We found that for the warm-season vegetation type, the recovery of plant community photosynthesis at the beginning of the growing season was faster than the senescence at the end of the growing season while for the cool-season vegetation type, the opposite was true. Furthermore, for the warm-season vegetation type, the recovery was closely correlated with the senescence such that a faster photosynthetic recovery implied a speedier photosynthetic senescence and vice versa. There was evidence that a similar close correlation could also exist for the cool-season vegetation type, and furthermore, the recovery-senescence relationship may be invariant between the warm-season and cool-season vegetation types up to an offset in the intercept. We also found that while the growing season length affected how much carbon dioxide could be potentially assimilated by a plant community over the course of a growing season, other factors that affect canopy photosynthetic capacity (e.g. nutrients, water) could be more important at this time scale. These results and insights demonstrate that the proposed method of analysis and system of terminology can serve as a foundation for studying the dynamics of plant community photosynthesis and such studies can be fruitful. |
| [22] | 植被变化往往受到不同气候变量的综合作用,人类活动影响又使得植被对气候响应变得更为复杂,如何准确判别各种影响因素的相对重要性是植被变化归因研究中的一个关键点。研究基于偏最小二乘(PLS)回归方法,使用1982—2006年的归一化植被指数(NDVI)数据,分析了降水、气温、日照、相对湿度和风等气候变量对中国东部植被变化的相对影响,并选取了NDVI变化较为典型的区域,量化了农业活动对该地区植被变化的相对贡献。PLS回归方法兼具了主成分分析和多元回归的优点,克服了众多自变量之间存在强烈交互相关导致的多元共线性问题。研究结果表明:1 1982—2006年间,中国东部逐月NDVI的年际变化呈现出明显的南北差异。在12、1—5月,NDVI以显著上升为主,上升区域主要位于淮河以北。在6—10月,NDVI以显著下降为主,下降区域主要为淮河以南的部分区域,特别是6月江苏一带NDVI的大范围下降尤为明显。不过与NDVI发生显著变化的区域相比,更多区域的NDVI在各月并没有出现显著变化。2在NDVI显著上升的站点,对NDVI变化最具解释意义的气候变量为气温,特别是冬末春初(2—3月)的升温对黄淮海区域NDVI的显著上升具有主导控制作用。而对于NDVI显著下降的站点,多数都不能从气候角度解释这些区域的NDVI变化。3江苏省NDVI在6月出现的大范围显著下降,与农业种植结构的调整,主要是棉花种植面积的减少以及油菜面积的增加具有显著关系。 . 植被变化往往受到不同气候变量的综合作用,人类活动影响又使得植被对气候响应变得更为复杂,如何准确判别各种影响因素的相对重要性是植被变化归因研究中的一个关键点。研究基于偏最小二乘(PLS)回归方法,使用1982—2006年的归一化植被指数(NDVI)数据,分析了降水、气温、日照、相对湿度和风等气候变量对中国东部植被变化的相对影响,并选取了NDVI变化较为典型的区域,量化了农业活动对该地区植被变化的相对贡献。PLS回归方法兼具了主成分分析和多元回归的优点,克服了众多自变量之间存在强烈交互相关导致的多元共线性问题。研究结果表明:1 1982—2006年间,中国东部逐月NDVI的年际变化呈现出明显的南北差异。在12、1—5月,NDVI以显著上升为主,上升区域主要位于淮河以北。在6—10月,NDVI以显著下降为主,下降区域主要为淮河以南的部分区域,特别是6月江苏一带NDVI的大范围下降尤为明显。不过与NDVI发生显著变化的区域相比,更多区域的NDVI在各月并没有出现显著变化。2在NDVI显著上升的站点,对NDVI变化最具解释意义的气候变量为气温,特别是冬末春初(2—3月)的升温对黄淮海区域NDVI的显著上升具有主导控制作用。而对于NDVI显著下降的站点,多数都不能从气候角度解释这些区域的NDVI变化。3江苏省NDVI在6月出现的大范围显著下降,与农业种植结构的调整,主要是棉花种植面积的减少以及油菜面积的增加具有显著关系。 |
| [23] | Two examples are used as illustrations: First, a Quantitative Structure–Activity Relationship (QSAR)/Quantitative Structure–Property Relationship (QSPR) data set of peptides is used to outline how to develop, interpret and refine a PLSR model. Second, a data set from the manufacturing of recycled paper is analyzed to illustrate time series modelling of process data by means of PLSR and time-lagged X-variables. |
| [24] | ABSTRACT The phenology of growth in temperate deciduous forests, including the timing of leaf emergence and senescence, has strong control over ecosystem properties such as productivity and nutrient cycling, and has an important role in the carbon economy of understory plants. Extended leaf phenology, whereby understory species assimilate carbon in early spring before canopy closure or in late autumn after canopy fall, has been identified as a key feature of many forest species invasions, but it remains unclear whether there are systematic differences in the growth phenology of native and invasive forest species or whether invaders are more responsive to warming trends that have lengthened the duration of spring or autumn growth. Here, in a 3-year monitoring study of 43 native and 30 non-native shrub and liana species common to deciduous forests in the eastern United States, I show that extended autumn leaf phenology is a common attribute of eastern US forest invasions, where non-native species are extending the autumn growing season by an average of 4 eeks compared with natives. In contrast, there was no consistent evidence that non-natives as a group show earlier spring growth phenology, and non-natives were not better able to track interannual variation in spring temperatures. Seasonal leaf production and photosynthetic data suggest that most non-native species capture a significant proportion of their annual carbon assimilate after canopy leaf fall, a behaviour that was virtually absent in natives and consistent across five phylogenetic groups. Pronounced differences in how native and non-native understory species use pre- and post-canopy environments suggest eastern US invaders are driving a seasonal redistribution of forest productivity that may rival climate change in its impact on forest processes. |
| [25] | 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 02=020.00, P 02=020.95). Two distinct periods of green-up changes are identified. From 1982 to 1999, the vegetation green-up significantly advanced by 0.8802days02year 611 ( R 2 02=020.56, P 02<020.001). In contrast, from 1999 to 2006, a marginal delaying trend is evidenced ( R 2 02=020.44, P 02=020.07), 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 102°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. |
| [26] | |
| [27] | . 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-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, < 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, = 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, = 0.045) and 1999–2011 (slope = 0.096 day yr, = 0.3), but occurred earlier during 1994–1999 (slope = −0.373 day yr, = 0.049). During 1982–2011, the annual changes of EGS negatively correlated with precipitation ( < 0.1) in June, but positively with precipitation ( < 0.1) in August. As the same time, the annual changes of EGS positively correlated with sunshine duration ( < 0.1) in June, yet negatively with sunshine duration ( < 0.1) in August. During 1982–1994, the annual changes of EGS positively correlated with air temperature ( < 0.01) and negatively with precipitation ( < 0.1) in June. During 1994–1999, the annual changes of EGS only negatively correlated with air temperature ( < 0.05) in August. During 1999–2011, the annual changes of EGS only negatively correlated with sunshine duration ( < 0.1) in August. |
| [28] | 植物物候及其变化是多个环境因子综合影响的结果,其中气候是最重要、最活跃的环境因子.主要从气候环境角度分析了植物物候与气候以及气候变化间的相互关系,概述了国内外有关植物物候及物候模拟等方面的研究进展.表明,温度是影响物候变化最重要的因子;同时,水分成为胁迫因子时对物候的影响也十分重要.近50a左右,世界范围内的植物物候呈现出了春季物候提前,秋季物候推迟或略有推迟的特征,从而导致了多数植物生长季节的延长,并成为全球物候变化的趋势.全球气候变暖改变了植物开始和结束生长的日期,其中冬季、春季气温的升高使植物的春季物候提前是植物生长季延长的主要因为.目前对物候学的研究方向主要集中在探讨物候与气候变化之间的关系,而模型模拟是定量研究气候变化与植物物候之间关系的重要方式,国内外已经开发出多种物候模型来分析气候驱动与物候响应之间的因果关系.另外遥感资料的应用也为物候模型研究提供了新的方向.物候机理研究、物候与气候关系以及物候模型研究将是研究的重点. . 植物物候及其变化是多个环境因子综合影响的结果,其中气候是最重要、最活跃的环境因子.主要从气候环境角度分析了植物物候与气候以及气候变化间的相互关系,概述了国内外有关植物物候及物候模拟等方面的研究进展.表明,温度是影响物候变化最重要的因子;同时,水分成为胁迫因子时对物候的影响也十分重要.近50a左右,世界范围内的植物物候呈现出了春季物候提前,秋季物候推迟或略有推迟的特征,从而导致了多数植物生长季节的延长,并成为全球物候变化的趋势.全球气候变暖改变了植物开始和结束生长的日期,其中冬季、春季气温的升高使植物的春季物候提前是植物生长季延长的主要因为.目前对物候学的研究方向主要集中在探讨物候与气候变化之间的关系,而模型模拟是定量研究气候变化与植物物候之间关系的重要方式,国内外已经开发出多种物候模型来分析气候驱动与物候响应之间的因果关系.另外遥感资料的应用也为物候模型研究提供了新的方向.物候机理研究、物候与气候关系以及物候模型研究将是研究的重点. |
| [29] | 本文根据我国的３０年的物候资料和气候资料的统计分析，论证了气温是影响中国木本植物物候的主要因子，在此基础上建立了物候与年平均气温的线性统计模式，又利用此模式分析计算了未来全球年平均气温升高０．５－２．０℃和未来大气中ＣＯ２浓度倍增而增暖情况下，我国主要木本植物物候期的大致变幅。 . 本文根据我国的３０年的物候资料和气候资料的统计分析，论证了气温是影响中国木本植物物候的主要因子，在此基础上建立了物候与年平均气温的线性统计模式，又利用此模式分析计算了未来全球年平均气温升高０．５－２．０℃和未来大气中ＣＯ２浓度倍增而增暖情况下，我国主要木本植物物候期的大致变幅。 |
| [30] | The timing of spring phenology in most temperate zone plants results from the combined effects of both autumn/winter cold and spring heat. Temperature increases in spring can advance spring phases, but warming in autumn and winter may slow the fulfilment of chilling requirements and lead to later onset of spring events, as evidenced by recent phenology delays in response to warming at some locations. As warming continues, the phenology-delaying impacts of higher autumn/winter temperatures may increase in importance, and could eventually attenuate 鈥 or even reverse 鈥 the phenology-advancing effect of warming springs that has dominated plant responses to climate change so far. To test this hypothesis, we evaluated the temperature responses of apricot bloom at five climatically contrasting sites in China. Long-term records of first flowering dates were related to temperature data at daily resolution, and chilling and forcing periods were identified by Partial Least Squares (PLS) regression of bloom dates against daily chill and heat accumulation rates. We then analyzed the impacts of temperature variation during the chilling and forcing periods on tree flowering dates for each site. Results indicated that in cold climates, spring timing of apricots is almost entirely determined by forcing conditions, with warmer springs leading to earlier bloom. However, for apricots at warmer locations, chilling temperatures were the main driver of bloom timing, implying that further warming in winter might cause delayed spring phases. As global warming progresses, current trends of advancing phenology might slow or even turn into delays for increasing numbers of temperate species. |
| [31] | . Observations of first female bloom, first male bloom and leaf emergence of three walnut cultivars at Davis, CA were coupled with daily temperature data since 1951. The dataset was analyzed by PLS, using three temperature inputs: (1) daily mean temperatures, (2) 11-day running means of daily mean temperatures and (3) monthly mean temperatures. For all data constellations, the Variable-Importance-in-the-Projection (VIP) statistic indicated a number of periods, during which temperatures were important determinants of phenological events, and the model-coefficients-of-the-centered-and-scaled-data (MC) statistic showed the direction, in which high temperatures during these phases influenced walnut flowering and leaf emergence. In all analyses, a delaying effect of warm winters, and an advancing effect of warm springs were clearly visible. It was also possible to identify the transition between the chilling and forcing phases, and the VIP and MC plots indicated quantitative differences in the effectiveness of winter chill during different phases of the dormancy season. Such effects have not been captured in any phenology models currently applied to fruit trees, indicating that PLS has potential to help refine such models. PLS can also be used for guiding experimental research by pinpointing the parts of the season that are most important for the timing of budburst. Results suggested that more than 20 years of observed data were necessary for producing clearly recognizable temperature response patterns, limiting the applicability of PLS to long time series. |
| [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 C(-1) (0.25-day SOS advance per 1- 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. |
