Abstract Aims The occurrence of extreme climate events is becoming more frequent worldwide because of the global warming. This study investigated the responses of vegetation to climate extremes in southwestern China, in order to assess the regional eco-security of natural ecosystems related to global climate change. Methods The normalized difference vegetation index (NDVI) data from the GIMMS V1.0 datasets with a resolution of 0.083° × 0.083° for the period of January 1982 to December 2015 were used in this study for analysis of the spatiotemporal dynamics of vegetation in the study region. The grid data of regional meteorological variables from the CN05.1 for the period of January 1961 to December 2016 were used to develop the overall climate extreme variables, and the values matching the data period of NDVI were eventually adopted in the analysis on the interrelationships between NDVI and the climate extremes using Pettitt test and trend analysis both before and after detrending. Important findings Results show that in the study region, NDVI generally increased from 1982 to 2015, with occurrence of an abrupt change in 1994. Prior to 1994, the change in NDVI was not significant, but the increase became significant from this point onward. Before data detrending, only the maximum 1-day precipitation was significantly and positively correlated with NDVI in the precipitation-extremes during 1982-2015. The temperature- extreme variables were all significantly correlated with NDVI except the diel air temperature range. From 1994 to 2015, the maximum 1-day precipitation was significantly and positively correlated with NDVI and the number of wet days was significantly and negatively correlated with NDVI. none of the precipitation-extreme variables was significantly correlated with NDVI. The yearly maximum value of daily minimum air temperature, warm days, summer days, length of growing season and diel air temperature range were all significantly and positively correlated with NDVI, but the cool days, frost days, cool nights and icing days were significantly and negatively correlated with NDVI. During 1982-2015, the NDVI was more strongly correlated with annual mean air temperature than with any of the temperature-extreme variables; whereas during 1994-2015, NDVI was more strongly correlated with summer days and diel air temperature range than with annual mean air temperature. After eliminating the trend, there was no significant correlation between the precipitation-extreme variables and NDVI, but the yearly maximum value of daily maximum air temperature, warm days, summer days and diel temperature range were significantly and positively correlated with NDVI for the entire period of 1982-2015 as well as for the period 1994-2015. The response of NDVI to extreme warm events was more pronounced during 1994-2015 than during 1982-2015, with the strongest correlation between diel air temperature range and NDVI. There was a significant and negative correlation between cool days and NDVI for the period 1982-2015. Keywords：normalized differential vegetation index (NDVI);climate extremes variables;global climate change;southwestern China
Table 1 表1 表1极端气候指数的选择及定义 Table 1Selection of and definitions on climate extreme variables
气候指数 Climate variables
缩写(单位) Abbreviation (Units)
定义 Definition
年平均气温 Mean annual air temperature
TM (℃)
日平均气温的年平均值 Yearly mean value of daily mean air temperature
日最高气温的最大值 Maximum value of daily maximum air temperature
TXx (℃)
日最高气温的年最大值 Yearly maximum value of daily maximum air temperature
暖昼日数 Warm days
TX90 (d)
日最高气温>90%分位值的日数 Number of days when daily maximum air temperature >90th percentile
夏季日数 Summer days
SU (d)
日最高气温大于25 ℃的全部天数 Annual count of days when daily maximum air temperature >25 °C
日最高气温的最小值 Minimum value of daily maximum air temperature
TXn (℃)
日最高气温的年最小值 Yearly minimum value of daily maximum air temperature
冷昼日数 Cool days
TX10 (d)
日最高气温<10%分位值的日数 Number of days when daily maximum air temperature <10th percentile
冰冻日数 Icing days
ID (d)
日最高气温<0 ℃的全部天数 Annual count of days when daily maximum air temperature <0 °C
日最低气温的最大值 Maximum value of daily minimum air temperature
TNx (℃)
日最低气温的年最大值 Yearly maximum value of daily minimum air temperature
暖夜日数 Warm nights
TN90 (d)
日最低气温>90%分位值的日数 Number of days when daily minimum air temperature >90th percentile
热带夜数 Tropical nights
TR (d)
日最低气温大于20 ℃的全部天数 Annual count of days when daily minimum air temperature >20 °C
日最低气温的最小值 Minimum value of daily minimum air temperature
TNn (℃)
日最低气温的年最小值 Yearly minimum value of daily minimum temperature
冷夜日数 Cool nights
TN10 (d)
日最低气温<10%分位值的日数 Number of days when daily minimum air temperature <10th percentile
霜冻日数 Frost days
FD (d)
日最低气温小于0 ℃的全部天数 Annual count of days when daily minimum air temperature <0 °C
持续冷期指数 Cold spell duration index
CSDI (d)
至少连续6天最低气温小于基准期内10%分位值的天数 Annual count of days with at least 6 consecutive days when daily minimum air temperature <10th percentile
持续暖期指数 Warm spell duration index
WSDI (d)
至少连续6天最高气温大于基准期内90%分位值的天数 Annual count of days with at least 6 consecutive days when daily maximum air temperature >90th percentile
生长季长度 Growing season length
GSL (d)
第一次连续6天以上日平均气温大于5 ℃至第一次(6月1日后)连续6天日平均气温小于5 ℃的天数 Annual count between first span of at least 6 days with daily mean air temperature >5 °C and first span after June 1st of 6 days with daily mean air temperature <5 °C
气温日较差 Diel temperature range
DTR (℃)
日最高气温与最低气温的差的平均值 Yearly mean difference between daily maximum air temperature and daily minimum air temperature
年降水量 Annual precipitation
PRE (mm)
年总降水量 Total annual precipitation
湿日总降水量 Annual total precipitation on wet days
PRCPTOT (mm)
大于等于1 mm的日降水量的总和 Annual total precipitation on wet days (daily precipitation ≥1 mm)
强降水量 Precipitation amount on very wet days
R95 (mm)
大于基准期内95%分位点的日降水量的总和 Annual total precipitation on days when daily precipitation >95th percentile
极端强降水量 Precipitation amount on extremely wet days
R99 (mm)
大于基准期内99%分位点的日降水量的总和 Annual total precipitation on days when daily precipitation >99th percentile
降水强度 Simple daily intensity index for precipitation
SDII (mm·d-1)
降水量与降水日数的比值 Annual total precipitation divided by the number of wet days (defined as daily precipitation ≥1.0 mm) in the year
1日最大降水量 Maximum 1-day precipitation
Rx1day (mm)
最大的日降水量 Yearly maximum value of daily precipitation
5日最大降水量 Maximum 5-day consecutive precipitation
Rx5day (mm)
最大的连续5天降水量 Yearly maximum 5-day consecutive precipitation
连续湿日数 Consecutive wet days
CWD (d)
最长连续降水日数 Yearly maximum number of consecutive days with a daily precipitation ≥1 mm
连续干日数 Consecutive dry days
CDD (d)
最长连续无降水日数 Yearly maximum number of consecutive days with daily precipitation <1 mm
降水日数 Number of wet days
R1mm (d)
日降水量≥1 mm的天数 Number of days when daily precipitation ≥1 mm
中雨日数 Number of heavy precipitation days
R10mm (d)
日降水量≥10 mm的天数 Annual count of days when daily precipitation ≥10 mm
大雨日数 Number of very heavy precipitation days
R20mm (d)
日降水量≥20 mm的天数 Annual count of days when daily precipitation ≥20 mm
Fig. 1Spatial pattern of mean annual normalized differential vegetation index (NDVI)(A) and inter-annual variations in regional average NDVI (B) in southwestern China from 1982 to 2015.
Fig. 2Spatial patterns of changes in normalized differential vegetation index (NDVI) in southwestern China during periods 1982-2015 (A), 1982-1993 (B) and 1994-2015 (C). Gray-coded areas are where no significant changes in NDVI were detected (p > 0.05).
Table 2 表2 表21982-2015、1982-1993和1994-2015年中国西南部地区区域平均极端气候指数的变化趋势 Table 2Trends of regional average climate extreme variables in southwestern China during periods 1982-2015, 1982-1993 and 1994-2015
气候指数 Climate variables
气候指数变化趋势(每10年) Trends of climate indicators (per 10 a)
1982-2015
1982-1993
1994-2015
TM (℃)
0.375**
0.221
0.368**
TXx (℃)
0.305**
-0.243
0.248
TX90 (d)
6.840**
-1.209
8.914**
SU (d)
3.387**
0.671
4.129**
TXn (℃)
0.262
0.391
-0.099
TX10 (d)
-6.077**
-4.626
-5.243
ID (d)
-3.338**
-3.583
-3.795**
TNx (℃)
0.359**
0.356
0.486**
TN90 (d)
9.127**
6.265
10.896**
TR (d)
1.753**
-1.061
2.159**
TNn (℃)
0.462**
0.660
0.287
TN10 (d)
-6.239**
-5.590
-4.777*
FD (d)
-5.374**
-4.716
-4.468**
CSDI (d)
-0.807*
-1.164
-1.058
WSDI (d)
0.950**
-0.620
1.012
GSL (d)
4.316**
0.273
3.482**
DTR (℃)
-0.005
-0.085
0.012
PRE (mm)
0.699
-21.112
-11.668
PRCPTOT (mm)
2.825
-20.346
-6.832
R95 (mm)
2.461
-17.008
-3.946
R99 (mm)
1.567
-13.383
5.835
SDII (mm·d-1)
0.014
-0.068
-0.041
Rx1day (mm)
0.623
-1.490
1.752*
Rx5day (mm)
0.652
-3.921
1.102
CWD (d)
0.141
0.745
-0.042
CDD (d)
-0.603
-5.610
3.480*
R1mm (d)
-0.002
0.454
-1.947
R10mm (d)
-0.215
-1.307
-0.702
R20mm (d)
0.092
-0.724
-0.088
气候指数含义参照表1。*, p < 0.05; **, p < 0.01。 Definitions of climate variables see Table 1. *, p < 0.05; **, p < 0.01.
Table 3 表3 表31982-2015、1982-1993和1994-2015年中国西南部地区去趋势前后区域平均尺度上极端气候指数与归一化植被指数(NDVI)的相关性 Table 3Correlations between regional average normalized differential vegetation index (NDVI) and climate extreme variables in southwestern China during periods 1982-2015, 1982-1993 and 1994-2015 before and after data detrending.
气候指数 Climate variables
1982-2015
1982-1993
1994-2015
去趋势前 Before data detrending
去趋势后 After data detrending
去趋势前 Before data detrending
去趋势后 After data detrending
去趋势前 Before data detrending
去趋势后 After data detrending
TM (℃)
0.743**
0.348*
0.536
0.154
0.589**
0.408
TXx (℃)
0.547**
0.357*
0.193
0.213
0.400
0.449*
TX90 (d)
0.638**
0.504**
0.291
0.382
0.533*
0.552**
SU (d)
0.692**
0.437*
0.056
0.003
0.648**
0.546*
TXn (℃)
0.395*
0.255
0.482
0.566
0.131
0.119
TX10 (d)
-0.670**
-0.346*
-0.586
0.475
-0.460*
-0.288
ID (d)
-0.616**
-0.241
-0.299
-0.040
-0.522*
-0.348
TNx (℃)
0.662**
0.022
0.382
-0.388
0.524*
0.222
TN90 (d)
0.551**
-0.100
0.098
-0.459
0.364
0.037
TR (d)
0.496**
-0.087
-0.063
-0.046
0.318
-0.106
TNn (℃)
0.544**
0.298
0.600*
0.634*
0.164
0.114
TN10 (d)
-0.688**
-0.329
-0.606*
-0.414
-0.448*
-0.312
FD (d)
-0.710**
-0.075
-0.487
0.253
-0.470*
-0.219
CSDI (d)
-0.342*
-0.038
-0.531
-0.320
-0.106
0.062
WSDI (d)
0.481**
0.229
0.230
0.260
0.287
0.234
GSL (d)
0.695**
0.242
0.522
0.176
0.482*
0.273
DTR (℃)
0.283
0.670**
0.015
0.494
0.617**
0.749**
PRE (mm)
0.033
-0.028
-0.128
0.043
-0.024
-0.063
PRCPTOT (mm)
0.071
-0.011
-0.118
0.043
0.024
-0.035
R95 (mm)
0.132
0.049
-0.219
-0.041
0.156
0.081
R99 (mm)
0.209
0.154
-0.038
0.029
0.355
0.191
SDII (mm·d-1)
0.164
0.044
-0.026
0.016
0.133
0.053
Rx1day (mm)
0.366*
0.281
-0.038
0.117
0.522*
0.320
Rx5day (mm)
0.144
0.046
-0.437
-0.265
0.294
0.147
CWD (d)
-0.009
-0.267
0.029
-0.321
-0.125
-0.252
CDD (d)
-0.122
-0.022
-0.355
-0.307
0.342
0.113
R1mm (d)
-0.103
-0.243
0.022
-0.087
-0.455*
-0.354
R10mm (d)
-0.016
0.100
-0.213
0.158
0.037
0.077
R20mm (d)
0.156
0.161
-0.368
0.195
0.192
0.150
气候指数含义参照表1。*, p < 0.05; **, p < 0.01。 Definitions of climate variables see Table 1. *, p < 0.05; **, p < 0.01.
Fig. 3Spatial patterns of correlation between normalized differential vegetation index (NDVI) and temperature-extreme variables after data detrending in southwestern China from 1982 to 2015. A, Mean annual air temperature. B, Maximum value of daily maximum air temperature. C, Warm days. D, Summer days. E, Cool days. F, Diel temperature range. Gray-coded areas are where no significant correlation was detected between NDVI and temperature-extreme variables (p > 0.05). The warm tone indicates positive significance (r > 0, p < 0.05), and the cold tone indicates negative significance (r < 0, p < 0.05).
Fig. 4Spatial patterns of correlation between normalized differential vegetation index (NDVI) and temperature-variables after data detrending in southwestern China from 1994 to 2015. A, Mean annual air temperature. B, Maximum value of daily maximum air temperature. C, Warm days. D, Summer days. E, Diel temperature range. Gray-coded areas are where no significant correlation was detected between NDVI and temperature-extreme variables (p > 0.05). The warm tone indicates positive significance (r > 0, p < 0.05), and the cold tone indicates negative significance (r < 0, p < 0.05).
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Vegetation response to extreme climate events on the Mongolian Plateau from 2000 to 2010 1 2013
... 极端温度事件的发生随着气候变暖发生了显著的变化(Donat et al., 2013).在全球大部分地区, 极端暖事件逐渐增多, 极端冷事件逐渐减少(Boccolari & Malmusi, 2013; Keggenhoff et al., 2014; Tong et al., 2019).例如, 1961-2015年期间, 兴都库什-喜马拉雅地区的极端冷事件显著减少, 极端暖事件显著增多(Sun et al., 2017); 2000-2010年蒙古高原冬季极端严寒事件频发(John et al., 2013); 中国西北地区呈现暖化趋势(Deng et al., 2014); Qin等(2015)在其研究中指出, 1960-2009年间中国西南部地区呈暖化趋势. ...
Trends in daily temperature and precipitation extremes over Georgia, 1971-2010 1 2014
... 极端温度事件的发生随着气候变暖发生了显著的变化(Donat et al., 2013).在全球大部分地区, 极端暖事件逐渐增多, 极端冷事件逐渐减少(Boccolari & Malmusi, 2013; Keggenhoff et al., 2014; Tong et al., 2019).例如, 1961-2015年期间, 兴都库什-喜马拉雅地区的极端冷事件显著减少, 极端暖事件显著增多(Sun et al., 2017); 2000-2010年蒙古高原冬季极端严寒事件频发(John et al., 2013); 中国西北地区呈现暖化趋势(Deng et al., 2014); Qin等(2015)在其研究中指出, 1960-2009年间中国西南部地区呈暖化趋势. ...
Influence of extreme weather disasters on global crop production 1 2016
... 气候变化对植被的影响一直是众多****关注的热点(徐浩杰和杨太保, 2014; 刘丹和于成龙, 2017), 而近些年来, 极端气候对生态系统影响的研究越来越受到重视.2003年欧洲热浪对欧洲生态系统造成了严重影响, 使其初级生产力下降了大约30% (Ciais et al., 2005), 但在美国和欧洲一些地区, 日最高气温的升高使得植被展叶提前(Piao et al., 2015); 鄱阳湖地区极端温度在月尺度上对植被生长影响显著(Tan et al., 2015); 青藏高原地区植被对5月发生的极端温度事件响应敏感(Liu et al., 2019).极端降水有助于蒙古高原植被生长(Li et al., 2018a), 在内蒙古地区, 年尺度上植被生长对极端降水敏感(Li et al., 2018b); 而黄土高原地区的植被在季节尺度上对极端降水事件响应强烈(Zhao et al., 2018); Yao等(2018)在研究中提到, 极端降水对新疆植被的影响分为两个阶段, 在1982-2010年期间, 极端降水对植被覆盖的影响不太强烈, 在1998年之后, 植被生长对于极端降水的响应加强.极端气候事件对农业发展也存在影响, 有****发现, 极端温度的升高会降低农作物的产量(Hatfield & Prueger, 2015; Lesk et al., 2016).因此进行极端气候对生态系统影响的研究, 对探究未来生态系统的变化具有重要的意义. ...
An assessment of the impacts of climate extremes on the vegetation in Mongolian Plateau: using a scenarios-based analysis to support regional adaptation and mitigation options 1 2018a
... 气候变化对植被的影响一直是众多****关注的热点(徐浩杰和杨太保, 2014; 刘丹和于成龙, 2017), 而近些年来, 极端气候对生态系统影响的研究越来越受到重视.2003年欧洲热浪对欧洲生态系统造成了严重影响, 使其初级生产力下降了大约30% (Ciais et al., 2005), 但在美国和欧洲一些地区, 日最高气温的升高使得植被展叶提前(Piao et al., 2015); 鄱阳湖地区极端温度在月尺度上对植被生长影响显著(Tan et al., 2015); 青藏高原地区植被对5月发生的极端温度事件响应敏感(Liu et al., 2019).极端降水有助于蒙古高原植被生长(Li et al., 2018a), 在内蒙古地区, 年尺度上植被生长对极端降水敏感(Li et al., 2018b); 而黄土高原地区的植被在季节尺度上对极端降水事件响应强烈(Zhao et al., 2018); Yao等(2018)在研究中提到, 极端降水对新疆植被的影响分为两个阶段, 在1982-2010年期间, 极端降水对植被覆盖的影响不太强烈, 在1998年之后, 植被生长对于极端降水的响应加强.极端气候事件对农业发展也存在影响, 有****发现, 极端温度的升高会降低农作物的产量(Hatfield & Prueger, 2015; Lesk et al., 2016).因此进行极端气候对生态系统影响的研究, 对探究未来生态系统的变化具有重要的意义. ...
Relationship between vegetation change and extreme climate indices on the Inner Mongolia Plateau, China, from 1982 to 2013 1 2018b
... 气候变化对植被的影响一直是众多****关注的热点(徐浩杰和杨太保, 2014; 刘丹和于成龙, 2017), 而近些年来, 极端气候对生态系统影响的研究越来越受到重视.2003年欧洲热浪对欧洲生态系统造成了严重影响, 使其初级生产力下降了大约30% (Ciais et al., 2005), 但在美国和欧洲一些地区, 日最高气温的升高使得植被展叶提前(Piao et al., 2015); 鄱阳湖地区极端温度在月尺度上对植被生长影响显著(Tan et al., 2015); 青藏高原地区植被对5月发生的极端温度事件响应敏感(Liu et al., 2019).极端降水有助于蒙古高原植被生长(Li et al., 2018a), 在内蒙古地区, 年尺度上植被生长对极端降水敏感(Li et al., 2018b); 而黄土高原地区的植被在季节尺度上对极端降水事件响应强烈(Zhao et al., 2018); Yao等(2018)在研究中提到, 极端降水对新疆植被的影响分为两个阶段, 在1982-2010年期间, 极端降水对植被覆盖的影响不太强烈, 在1998年之后, 植被生长对于极端降水的响应加强.极端气候事件对农业发展也存在影响, 有****发现, 极端温度的升高会降低农作物的产量(Hatfield & Prueger, 2015; Lesk et al., 2016).因此进行极端气候对生态系统影响的研究, 对探究未来生态系统的变化具有重要的意义. ...
2 ℃全球变暖背景下青藏高原平均气候和极端气候事件变化 1 2015
... 世界气象组织委员会(WMO)为了有效推动世界对极端气候事件的研究, 定义了27个极端指数(李红梅和李林, 2015), 现已广泛应用于国内外极端气候研究中(Chen et al., 2019; Fallah-Ghalhari et al., 2019).这27个指数包含16个极端温度指数以及11个极端降水指数(表1). ...
2 ℃全球变暖背景下青藏高原平均气候和极端气候事件变化 1 2015
... 世界气象组织委员会(WMO)为了有效推动世界对极端气候事件的研究, 定义了27个极端指数(李红梅和李林, 2015), 现已广泛应用于国内外极端气候研究中(Chen et al., 2019; Fallah-Ghalhari et al., 2019).这27个指数包含16个极端温度指数以及11个极端降水指数(表1). ...
Characteristics of climate change in southwest China karst region and their potential environmental impacts 1 2015
... 植被的生长和发育受气候和非气候因素(人类活动)的交互影响, 去趋势从统计上去除了长期变化的复杂影响(Tao et al., 2008; 王柳等, 2014; Lu et al., 2017), 可将植被和气候的年际变化与非气候因素的长期变化解耦(Anderson et al., 2012; Iler et al., 2017).一阶差分是去趋势的主要方法(徐向英, 2019), 常用于评估气候因素对植被的影响, 可在一定程度上减小人类活动对植被生长发育的影响(史文娇等, 2012; 王柳等, 2014), 被广泛应用(任庆民, 1984; Nicholls, 1997).公式如下: ...
江苏小麦综合气象指数构建与产量变化预测和分析 1 2019
... 植被的生长和发育受气候和非气候因素(人类活动)的交互影响, 去趋势从统计上去除了长期变化的复杂影响(Tao et al., 2008; 王柳等, 2014; Lu et al., 2017), 可将植被和气候的年际变化与非气候因素的长期变化解耦(Anderson et al., 2012; Iler et al., 2017).一阶差分是去趋势的主要方法(徐向英, 2019), 常用于评估气候因素对植被的影响, 可在一定程度上减小人类活动对植被生长发育的影响(史文娇等, 2012; 王柳等, 2014), 被广泛应用(任庆民, 1984; Nicholls, 1997).公式如下: ...
Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China 1 2018
... 气候变化对植被的影响一直是众多****关注的热点(徐浩杰和杨太保, 2014; 刘丹和于成龙, 2017), 而近些年来, 极端气候对生态系统影响的研究越来越受到重视.2003年欧洲热浪对欧洲生态系统造成了严重影响, 使其初级生产力下降了大约30% (Ciais et al., 2005), 但在美国和欧洲一些地区, 日最高气温的升高使得植被展叶提前(Piao et al., 2015); 鄱阳湖地区极端温度在月尺度上对植被生长影响显著(Tan et al., 2015); 青藏高原地区植被对5月发生的极端温度事件响应敏感(Liu et al., 2019).极端降水有助于蒙古高原植被生长(Li et al., 2018a), 在内蒙古地区, 年尺度上植被生长对极端降水敏感(Li et al., 2018b); 而黄土高原地区的植被在季节尺度上对极端降水事件响应强烈(Zhao et al., 2018); Yao等(2018)在研究中提到, 极端降水对新疆植被的影响分为两个阶段, 在1982-2010年期间, 极端降水对植被覆盖的影响不太强烈, 在1998年之后, 植被生长对于极端降水的响应加强.极端气候事件对农业发展也存在影响, 有****发现, 极端温度的升高会降低农作物的产量(Hatfield & Prueger, 2015; Lesk et al., 2016).因此进行极端气候对生态系统影响的研究, 对探究未来生态系统的变化具有重要的意义. ...
Effects of spring and summer extreme climate events on the autumn phenology of different vegetation types of Inner Mongolia, China, from 1982 to 2015 1 2020
Spatiotemporal changes of normalized difference vegetation index (NDVI) and response to climate extremes and ecological restoration in the Loess Plateau, China 2 2018
... 气候变化对植被的影响一直是众多****关注的热点(徐浩杰和杨太保, 2014; 刘丹和于成龙, 2017), 而近些年来, 极端气候对生态系统影响的研究越来越受到重视.2003年欧洲热浪对欧洲生态系统造成了严重影响, 使其初级生产力下降了大约30% (Ciais et al., 2005), 但在美国和欧洲一些地区, 日最高气温的升高使得植被展叶提前(Piao et al., 2015); 鄱阳湖地区极端温度在月尺度上对植被生长影响显著(Tan et al., 2015); 青藏高原地区植被对5月发生的极端温度事件响应敏感(Liu et al., 2019).极端降水有助于蒙古高原植被生长(Li et al., 2018a), 在内蒙古地区, 年尺度上植被生长对极端降水敏感(Li et al., 2018b); 而黄土高原地区的植被在季节尺度上对极端降水事件响应强烈(Zhao et al., 2018); Yao等(2018)在研究中提到, 极端降水对新疆植被的影响分为两个阶段, 在1982-2010年期间, 极端降水对植被覆盖的影响不太强烈, 在1998年之后, 植被生长对于极端降水的响应加强.极端气候事件对农业发展也存在影响, 有****发现, 极端温度的升高会降低农作物的产量(Hatfield & Prueger, 2015; Lesk et al., 2016).因此进行极端气候对生态系统影响的研究, 对探究未来生态系统的变化具有重要的意义. ...
... 本文选用27个极端气候指数, 运用趋势分析法、Pearson相关性分析方法来探究西南部地区植被对极端气候事件的响应.目前, 植被对极端气候事件响应的研究大多采取了使用原始数据直接相关的方法(Na et al., 2018; Zhao et al., 2018; Luo et al., 2020), 然而不可否认的是, 使用原始数据直接进行简单相关分析, 混淆了多种因素(而不只是极端气候)对植被的影响.为了降低这些因素对结果的影响, 本研究加入了年平均气温和年降水量这两个变量, 与植被对极端气候指数的响应进行对比.同时, 还对极端气候指数和植被NDVI进行去趋势处理, 计算去趋势后的极端气候指数与NDVI的相关性. ...
Assessing the impacts of extreme climate events on vegetation activity in the north south transect of eastern China (NSTEC) 1 2019
,*, 王鹤松北京林业大学生态与自然保护学院, 北京 100083
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
新窗口打开|下载原图ZIP|生成PPT
