2001—2022年中国西南地区干旱时空演变特征及其对植被的影响

doi:10.11755/j.issn.1006-7639-2026-04-0554

摘要/Abstract

摘要：

干旱是全球主要自然灾害之一，严重影响生态环境和农业生产。中国西南地区地形复杂、气候多变，干旱对植被生态系统威胁尤为突出。基于多时间尺度的标准化降水蒸散指数表征干旱过程，结合MODIS遥感影像提取的核归一化植被指数（Kernel Normalized Difference Vegetation Index，kNDVI）与增强型植被指数（Enhanced Vegetation Index，EVI）监测植被生长状况。其中，kNDVI通过核方法缓解了传统归一化植被指数在高覆盖度区域的饱和问题，提高了密集植被环境中的敏感性和抗噪声能力，与EVI共同提供更稳健的植被响应监测。通过像元尺度相关分析揭示干旱与植被响应关系，采用游程理论和多元线性回归方法，系统分析2001—2022年干旱事件的持续时间、严重度和强度特征，并评估植被变化趋势，进一步揭示干旱对植被的累积影响。结果表明：1）西南地区干旱存在显著空间差异，云南东北部与四川盆地干旱频发且严重，持续时间长、强度大；横断山地、若尔盖高原存在一定干旱风险，云贵高原、广西丘陵干旱风险较低。2）在时间尺度上，干旱影响特征不同：短期干旱影响广泛但持续时间短，严重度低；而中长期干旱则表现出更强的累积效应，持续时间和强度显著增加，对植被的影响更为持久。3）植被对干旱的响应存在时空异质性。kNDVI与EVI共同揭示，植被生长主要受中短期（3、6个月）干旱的累积效应主导，约50%的植被面积对该时间尺度的干旱最敏感；两种指数结论的高度一致增强了发现的稳健性，其局部敏感性差异则揭示了植被从短期生理胁迫到长期生物量累积的多路径响应机制。

关键词: 西南地区, 多尺度干旱, 累积效应, 植被变化

Abstract:

Drought is one of the major natural hazards worldwide, severely impacting ecosystems and agricultural production. The southwestern region of China has complex terrain and variable climate, and drought poses an especially significant threat to the vegetation ecosystem. This study characterized drought processes using multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) and monitored vegetation growth with Kernel Normalized Difference Vegetation Index (kNDVI) and Enhanced Vegetation Index (EVI) derived from MODIS imagery. The kNDVI, based on kernel methods, effectively mitigates the saturation problem of traditional Normalized Difference Vegetation Index (NDVI) in high-cover areas, enhancing sensitivity and noise resistance in dense vegetation environments. Together with EVI, it provides more robust monitoring of vegetation responses. Pixel-scale correlation analysis was employed to reveal the relationship between drought and vegetation, and run theory and multiple linear regression were applied to systematically analyze the duration, severity, and intensity of drought events from 2001 to 2022, and assessed vegetation change trends, further elucidated the cumulative impacts of drought on vegetation. The results indicate that: 1) Drought in southwest China exhibits significant spatial heterogeneity, with high frequency, long duration, and high intensity in northeastern Yunnan and the Sichuan Basin, and notable drought risks in the Hengduan Mountains and the Zoige Plateau, while the Yunnan-Guizhou Plateau and Guangxi Hills experience relatively low drought risk. 2) Drought characteristics vary with time scales: short-term drought (SPEI-1) affects a wide area but with short duration and low severity. In contrast, medium- to long-term droughts (SPEI-6 and SPEI-12) display stronger cumulative effects, with significantly increased duration and intensity, exerting more persistent impacts on vegetation. 3) Vegetation response to drought shows clear spatiotemporal heterogeneity. Both kNDVI and EVI reveal that vegetation growth is predominantly influenced by the cumulative effects of medium-short-term (3, 6 months) droughts, with approximately 50% of the vegetation area being most sensitive to this time scale. The high consistency of the two indices strengthens the robustness of the findings, while their local sensitivity differences help uncover the multi-path response mechanisms of vegetation from short-term physiological stress to long-term biomass accumulation.

Key words: southwest China, multi-scale drought, cumulative effect, vegetation change

中图分类号:

P467

张辉, 李舟鑫, 吕敬, 蒋尚雄, 陈波. 2001—2022年中国西南地区干旱时空演变特征及其对植被的影响[J]. 干旱气象, 2026, 44(4): 554-566.

ZHANG Hui, LI Zhouxin, LYU Jing, JIANG Shangxiong, CHEN Bo. Spatiotemporal evolution characteristics of drought in southwest China from 2001 to 2022 and its impact on vegetation[J]. Journal of Arid Meteorology, 2026, 44(4): 554-566.

图/表 9

图1 研究区概况

Fig.1 Overview of the study area

图2 游程理论示意图

Fig.2 Schematic diagram of the run theory

表1 植被变化趋势分级

Tab.1 Vegetation change trend classification

S值	Z值	kNDVI、EVI趋势变化
S<-0.000 5	Z<-1.96	显著退化
S<-0.000 5	-1.96≤Z<1.96	轻微退化
-0.000 5≤S<0.000 5	-1.96≤Z<1.96	稳定不变
S≥0.000 5	-1.96≤Z<1.96	轻微改善
S≥0.000 5	Z≥1.96	显著改善

图3 基于SPEI的2001—2022年西南地区不同时间尺度干旱特征空间分布

Fig.3 Spatial distribution of drought characteristics at different time scales based on SPEI in southwest China from 2001 to 2022

图4 基于SPEI的2001—2022年西南地区不同时间尺度干旱次数（单位：次）空间分布

Fig.4 Spatial distribution of drought frequency （Unit：times）at different time scales based on SPEI in southwest China from 2001 to 2022

图5 2001—2022年西南地区kNDVI（a）与EVI（b）年际变化趋势的空间分布

Fig.5 Spatial distribution of interannual change trends of kNDVI （a） and EVI （b） during 2001-2022 in southwest China

图6 2001—2022年西南地区不同时间尺度SPEI与kNDVI的相关性空间分布 [相关系数通过显著性检验（p<0.05），下同]

Fig.6 The spatial distribution of correlations between SPEI at different time scales and kNDVI during 2001-2022 in southwest China （the correlation coefficients passing the significance test （p<0.05）， the same as below）

图7 2001—2022年西南地区不同时间尺度SPEI与EVI的相关系数空间分布

Fig.7 The spatial distribution of correlations between SPEI at different time scales and EVI during 2001-2022 in southwest China

图8 2001—2022年西南地区对kNDVI（a）与EVI（b）影响最大的SPEI空间分布

Fig.8 The spatial distribution of SPEI with different time scales having the greatest impact on kNDVI （a） and EVI （b） from 2001 to 2022 in southwest China

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