Analysis of Climate Change and Vegetation Change in the Yangtze River Source
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摘要:
长江源处于高寒、高海拔的青藏高原腹地，生态系统脆弱。受全球气候变化和人类活动的影响，与源区生态系统密切相关的冰川冻土、沼泽湿地和植被等出现了不同程度的退化现象，引起了社会各界的广泛关注。笔者利用长时序、高时空分辨率的气象和MODIS NDVI数据，结合数理统计方法，分析了长江源气候和生态植被的时空演化特征，探讨了植被对气候和土壤水的响应机制。结果表明：近40年来长江源降雨量和地面气温在空间上从东南向西北呈递减特征，各区域气温整体上均有递增趋势；东部植被生长状况要好于西部地区，2000年以来长江源整体植被生长状况有逐渐转好趋势；受冻土对气温的敏感性影响，长江源生长季植被受气温的影响程度高于降雨；长江源生态植被受土壤含水率影响较大，影响程度由表层到深部有逐渐减弱趋势。
Abstract:The source of the Yangtze River is located in the remote and high-altitude Qinghai-Tibet Plateau, where the ecosystem is fragile. Influenced by global climate change and human activities, various elements closely related to the ecological system of the source area, such as glaciers, permafrost, swamps, wetlands, and vegetation, have experienced different degrees of degradation, which has aroused widespread concern from various sectors of society. This article analyzes the spatiotemporal evolution characteristics of the climate and ecological vegetation in the Yangtze River source region using long-term, high spatiotemporal resolution meteorological and MODIS NDVI data, combined with mathematical and statistical methods, and explores the response mechanism of vegetation to climate and soil water. The results show that over the past 40 years, the rainfall and surface temperature in the Yangtze River source region have decreased from southeast to northwest, and the temperature in each region has generally increased. Vegetation growth in the eastern region is better than that in the western region, and since 2000, the overall vegetation growth in the Yangtze River source region has gradually improved. Due to the sensitivity of permafrost to temperature, vegetation growth during the growing season in the Yangtze River source region is more affected by temperature than rainfall. The ecological vegetation in the Yangtze River source region is greatly influenced by soil water content, and the degree of influence gradually weakens from the surface to the deep layers.
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Keywords:
- Yangtze River source /
- climate /
- vegetation /
- trend analysis /
- response mechanisms
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图 3 长江源气温动态变化曲线分布
Figure 3. Distribution of temperature dynamics curves in the Yangtze River source
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图 4 长江源年平均降雨量空间分布
Figure 4. Spatial distribution of average annual rainfall in the Yangtze River source
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图 5 长江源年平均气温空间分布图
Figure 5. Spatial distribution of annual mean temperatures in the Yangtze River source
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图 6 长江源年均降雨量增量空间分布图
Figure 6. Spatial distribution of annual mean rainfall increments in the Yangtze River source
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图 7 长江源年平均气温增量空间分布图
Figure 7. Spatial distribution of annual mean temperature increments in the Yangtze River source
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图 10 长江源区2000～2021年NDVI植被指数空间分布图
Figure 10. Spatial distribution of NDVI vegetation index in the source area of the Yangtze River from 2000 to 2021
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图 11 长江源区2000～2021年NDVI植被指数变化空间分布图
增量<0面积占35%；增量>0面积占65% 增量<0面积占38%；增量>0面积占62%
Figure 11. Spatial distribution of NDVI vegetation index changes in the Yangtze River source area from 2000 to 2021
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图 12 气象监测站点分布图（冻土分布数据来源于青藏高原新绘制冻土分布图（2017）（赵林，2019））
Figure 12. Map of meteorological monitoring stations
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图 13 长江源区生长季（7～9月）NDVI均值时序变化曲线图
T1～T9：图12中各特征点
Figure 13. Time-series variation of NDVI mean values in the Yangtze River source area during the growing season (July-September)
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图 14 降雨、气温与植被指数NDVI值相关性分析图
Figure 14. Correlation analysis of rainfall and temperature on NDVI value of vegetation index
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图 15 植被指数与土壤含水率相关性热图
Figure 15. Heat map of correlation between vegetation index and soil moisture content
表 1 长江源降雨与气温突变分析
Table 1 Analysis of sudden changes in rainfall and temperature in the Yangtze River source
气象要素 区域 M-K突变检验 滑动t检验 综合分析 n=5 n=10 降雨 沱沱河 2008，2010，2020 - 1999 - 五道梁 2018 2007 - - 曲麻莱 1981，2019 2012 1999，2003 - 玉树 1981，2020 - - - 气温 沱沱河 2001 2001，2015 2001 2001 五道梁 2000 1986 1997，2000 2000 曲麻莱 2002 2002 - 2002 玉树 1987 2010 - - 
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表 2 生长季（7～9月）逐月植被指数与降雨量相关性分析
Table 2 Vegetation index and rainfall correlation analysis for the growing season （July-September）
研究区域 皮尔逊相关性系数 Sig.（双尾） 统计数（个） 曲麻莱 0.568** 0.000 65 沱沱河 0.518** 0.000 65 五道梁 0.587** 0.000 65 玉树 0.331** 0.000 64 注：“**”表明在 0.01 级别（双尾），相关性显著。
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表 3 生长季（7～9月）逐月植被指数与气温相关性分析
Table 3 Vegetation index and temperature correlation analysis for the growing season （July-September）
研究区域 皮尔逊相关性系数 Sig.（双尾） 统计数（个） 曲麻莱 0.666** 0.000 65 沱沱河 0.626** 0.000 65 五道梁 0.635** 0.000 65 玉树 0.729** 0.000 64 注：“**”表明在 0.01 级别（双尾），相关性显著。
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表 4 植被指数与土壤含水率相关性分析
Table 4 Correlation analysis between vegetation index and soil water content
研究
区域土壤
深度/cm皮尔逊相
关性系数Sig.（双尾） 统计数（个） 10 0.563** 0.000 108 西大滩 40 0.487** 0.000 108 100 0.475** 0.003 37 180 −0.016 0.845 144 5 0.686** 0.000 132 50 0.653** 0.000 132 五道梁 90 0.542** 0.000 132 180 0.034 0.701 132 240 −0.123 0.159 132 沱沱河 10 0.772** 0.000 60 60 0.757** 0.000 60 90 0.763** 0.000 60 210 0.210 0.107 60 270 −0.146 0.265 60 唐古拉 50 0.648** 0.000 84 140 0.450** 0.000 84 300 −0.179 0.103 84
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陈婷. 长江源区生态水文学研究[D]. 北京: 中国地质大学(北京), 2009 CHEN Ting. Study on the ecohydrology of the Yangtze River source area [D]. Beijing: China University of Geosciences (Beijing), 2009.
杜臻, 张茂省, 冯立等. 鄂尔多斯盆地煤炭采动的生态系统响应机制研究现状与展望[J]. 西北地质, 2023, 56(03): 78-88 DU Zhen, ZHANG Maosheng, Feng Li, et al. Research Status and Prospect of Ecosystem Response Mechanism to Coal Mining in Ordos Basin [J]. Northwest Geology, 2023, 56 (03): 78-88.
方欣, 刘小槺, 岳大鹏. 毛乌素沙地1960-2018年气候变化特征及影响因子分析[J]. 水土保持研究, 2022, 29(02): 163-169 FANG Xin, LIU Xiaokang, YUE Dapeng. Analysis of climate change characteristics and impact factors in the Maowusu Sands from 1960-2018[J]. Soil and water conservation research, 2022, 29(02): 163-169.
黄荟羽, 李恩键, 安娟, 等. 克拉默法与曼-肯德尔法对降水突变检验的对比分析[J]. 现代农业科技, 2018, 8: 2 doi: 10.3969/j.issn.1007-5739.2018.06.002 HUANG Aiyu, LI Enjian, AN Juan, et al. A comparative analysis of the Cramer method and the Mann-Kendall method for precipitation mutation testing[J]. Modern Agricultural Science and Technology, 2018, 8: 2. doi: 10.3969/j.issn.1007-5739.2018.06.002
韩海辉, 李健强, 易欢等. 遥感技术在西北地质调查中的应用及展望[J]. 西北地质, 2022, 55(03): 155-169 HAN Haihui, LI Jianqiang, YI Huan, et al. Application and prospect of remote sensing technology in geological survey of Northwest China [J]. Northwest Geology, 2022, 55 (03): 155-169.
李雪银, 张志强, 孙爱芝. 1982—2021年黄河流域植被覆盖时空演变及影响因素研究[J]. 地球环境学报, 2022, 13(04), 428–436 LI Xueyin, ZHANG Zhiqiang, SUN Aizhi. Spatial and temporal evolution of vegetation cover in the Yellow River basin from 1982 to 2021 [J]. Journal of Earth Environment, 2022, 13(04), 428-436.
李元寿, 王根绪, 赵林, 等. 青藏高原多年冻土活动层土壤水分对高寒草甸覆盖变化的响应[J]. 冰川冻土, 2010, 32(1): 157-165 LI Yuanshou, WANG Genxu, ZHAO Lin, et al. Soil moisture response to alpine meadow cover change in the permafrost active layer of the Qinghai-Tibet Plateau[J]. Glacial Permafrost, 2010, 32(1): 157-165.
钱开铸. 长江源区水文周期特征及其对气候变化的响应[D]. 北京: 中国地质大学(北京), 2013 QIAN Kaizhu. Characteristics of the hydrological cycle in the Yangtze River source area and its response to climate change [D]. Beijing: China University of Geosciences (Beijing), 2013.
汪柳皓, 魏显虎, 张宗科, 等. 青藏高原地区植被指数变化及其与温湿度因子的关系[J]. 森林与环境学报, 2022, 42(02): 141-148 WANG Liuhao, WEI Xianhu, ZHANG Zongke, et al. Changes in vegetation index and its relationship with temperature and humidity factors in the Qinghai-Tibet Plateau[J]. Journal of Forest and Environment, 2022, 42(02): 141-148.
强建华. 遥感技术在新疆南部地区矿山环境调查及生态修复中的应用[J]. 西北地质, 2021, 54(03): 253-258 doi: 10.19751/j.cnki.61-1149/p.2021.03.023 QIANG Jianhua. Application of remote sensing technology in mine environment investigation and ecological restoration in Southern Xinjiang [J]. Northwest Geology`, 2021, 54 (03): 253-258. doi: 10.19751/j.cnki.61-1149/p.2021.03.023
吴小丽. 近30年来青藏高原多年冻土区与季节性冻土区土壤水分变化差异[D]. 兰州: 兰州交通大学, 2020 WU Xiaoli. Differences in soil moisture changes between perennial and seasonal permafrost areas on the Qinghai-Tibet Plateau over the past 30 years[D]. Lanzhou: Lanzhou Transportation University, 2020.
阳坤, 何杰. 中国区域地面气象要素驱动数据集(1979-2018)[R]. 国家青藏高原科学数据中心, 2019 YANG Kun, HE Jie. Regional ground meteorological element-driven dataset for China (1979-2018) [R]. National Qinghai-Tibet Plateau Scientific Data Center, 2019.
朱艺旋, 张扬建, 俎佳星, 等. 基于MODIS NDVI、SPOT NDVI数据的GIMMS NDVI性能评价[J]. 应用生态学报, 2019, 30(2), 536–544 ZHU Yixuan, ZHANG Yangjian, ZU Jiaxing, et al. Performance evaluation of GIMMS NDVI based on MODIS NDVI and SPOT NDVI data[J]. Journal of Applied Ecology, 2019, 30(2), 536-544.
赵林. 青藏高原新绘制冻土分布图(2017)[R]. 国家青藏高原科学数据中心, 2019. 赵林, 胡国杰, 邹德富, 等.青藏高原多年冻土综合监测数据集（2002-2018）[R].北京: 国家青藏高原科学数据中心, 2021. Ba R, Lovallo M, Song W G, et al. Multifractal Analysis of MODIS Aqua and Terra Satellite Time Series of Normalized Difference Vegetation Index and Enhanced Vegetation Index of Sites Affected by Wildfires[J]. Entropy, 2022, 24(12), 1748. doi: 10.3390/e24121748
Essaadia A, Abdellah A, Ahmed A, et al. The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics[J]. Heliyon, 2022, 8(12), 12204. doi: 10.1016/j.heliyon.2022.e12204
Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems [J]. Nature, 2003, 421(6918), 37–42. doi: 10.1038/nature01286
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