气候情景驱动下秦巴山区植被生长动态模拟与气候驱动机制

doi:10.12118/j.issn.1000-6060.2025.148

摘要/Abstract

摘要：

秦巴山区位于我国南北气候与暖温带-亚热带生态过渡带交汇区，作为气候变化敏感区，研究其植被与气候的耦合关系有助于揭示气候变化下生态系统的演变机制。基于2001—2023年MODIS数据和气候因子数据，利用多元线性回归模型对2024—2100年3种共享社会经济路径（SSPs）气候情景下的核归一化植被指数（kNDVI）进行预测，结合Theil-Sen和Mann-Kendall方法分析植被时空变化趋势，并利用通径分析揭示气候因子驱动机制。结果表明：（1）气温是植被变化的主导因子，其面积占比67.27%，正效应区域集中于秦岭—大巴山地区，而蒸散发与降水的影响呈显著空间异质性。（2） 2001—2023年植被kNDVI增速呈“先快后慢”特征，退化区集中于低海拔城市化区域及高海拔水热受限区。（3）未来情景模拟显示，低碳路径（SSP119）情景下植被变化趋于稳定，高碳路径（SSP585）情景则呈现两极分化，蒸散发的直接抑制效应与高温驱动的间接促进效应并存。（4）降水对植被的补给效能随气候极端化减弱，而气温的直接驱动强度随排放情景升高显著增强。（5）区域植被响应存在显著空间分异，需针对高海拔脆弱区、低海拔人类活动干扰带及中东部蒸散发敏感区实施差异化生态修复策略。通过揭示秦巴山区植被对气候变化的非线性响应，证实SSP119的生态稳定性优势，为区域碳中和目标下的植被保护与碳汇功能提升提供空间优化路径。

关键词: kNDVI, SSPs, 秦巴山区, 气候因子, 多元线性回归模型, 趋势分析

Abstract:

The Qinling-Daba Mountains are located at the convergence of the climatic transition zone between northern and southern China and the ecotone between warm-temperate and subtropical regions. As a climate-sensitive area, investigating the coupling relationships between vegetation and climate in this area is critical for understanding the evolutionary mechanisms of ecosystems under climate change. This study employs a multiple linear regression model to predict kernel normalized difference vegetation index (kNDVI) values under three Shared Socioeconomic Pathways scenarios from 2024 to 2100, based on MODIS data and climatic factor datasets from 2001 to 2023. The Theil Sen Median estimator and Mann-Kendall test were used to analyze spatiotemporal trends of vegetation changes, and path analysis was applied to dissect the driving mechanisms of key climatic factors. The results reveal that (1) Temperature is the dominant factor driving vegetation changes, spatially covering 67.27% of the study area, with its positive effects concentrated in the Qinling-Daba Mountain region, whereas the impacts of evapotranspiration and precipitation exhibit significant spatial heterogeneity. (2) The vegetation kNDVI increased by 0.1 from 2001 to 2023, demonstrating a “rapid initial growth followed by a gradual slowdown” trend, with degradation areas concentrated in low-altitude urbanized zones and high-altitude regions constrained by water-heat limitations. (3) Future scenario simulations reveal that vegetation dynamics stabilize under SSP119, whereas SSP585 demonstrates divergent trends, with the direct inhibitory effects of evapotranspiration coexisting with indirect facilitative effects driven by increased temperatures. (4) The replenishment efficiency of precipitation for vegetation diminishes with increasing climate extremes, whereas the direct climatic forcing of temperature significantly intensifies under elevated emission scenarios. (5) Regional vegetation responses indicate significant spatial heterogeneity, requiring differentiated ecological restoration strategies. These strategies should prioritize high-altitude vulnerable zones, low-altitude areas disturbed by human activities, and evapotranspiration-sensitive regions in the central-eastern sectors. This study reveals the nonlinear response of vegetation to climate change in the Qinling-Daba Mountains, thereby confirming the ecological stability advantages of the low-carbon pathway (SSP119) and providing spatially optimized strategies for vegetation conservation and carbon sequestration enhancement under regional carbon neutrality goals.

Key words: kNDVI, SSPs, the Qinling-Daba Mountains, climate factors, multiple linear regression analysis, trend analysis

高锦涛, 张翀, 井静, 钟春霞, 杨瑞霞. 气候情景驱动下秦巴山区植被生长动态模拟与气候驱动机制[J]. 干旱区地理, 2026, 49(2): 316-331.

GAO Jintao, ZHANG Chong, JING Jing, ZHONG Chunxia, YANG Ruixia. Simulation of vegetation growth dynamics and climatic driving mechanisms in the Qinling-Daba Mountains under climate scenarios[J]. Arid Land Geography, 2026, 49(2): 316-331.

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β	Z	趋势特征
β>0	\|Z\|≥2.58	极显著增加
	1.96≤\|Z\|<2.58	显著增加
	\|Z\|<1.96	不显著增加
β=0	Z	无变化
β<0	\|Z\|<1.96	不显著减少
	1.96≤\|Z\|<2.58	显著减少
	\|Z\|≥2.58	极显著减少

相关性	蒸散发		降水		气温		占比总和/%
相关性	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	占比总和/%
正影响	31382	10.09	27922	8.98	159638	51.34	70.41
负影响	39884	12.83	2589	0.83	49551	15.93	29.59
总和	71266	22.92	30511	9.81	209189	67.27	100.00

变化类型	2001—2023年		2024—2100年SSP119		2024—2100年SSP245		2024—2100年SSP585
变化类型	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
极显著增加	234525	75.42	9512	3.06	169609	54.54	183585	59.04
显著增加	29988	9.64	12141	3.90	16161	5.20	7610	2.45
不显著增加	36169	11.63	188182	60.52	47201	15.18	21594	6.94
不显著减少	8133	2.62	98003	31.52	29646	9.53	18526	5.96
显著减少	1043	0.34	1330	0.43	6257	2.01	5208	1.67
极显著减少	1108	0.36	1798	0.58	42092	13.54	74443	23.94