中国旱区GPP时空演变特征及影响因素研究

doi:10.13866/j.azr.2024.06.06

摘要/Abstract

摘要：

为明晰中国旱区生态系统的固碳能力及其变化机制，本研究根据AI指数划分了中国旱区范围，并基于MODIS植被总初级生产力（GPP）数据集，结合气温、降水、饱和水气压差（VPD）、土壤含水量等气象数据和土地利用等人类活动，探究了中国旱区2001—2020年植被固碳能力的时空演变特征及影响因素。结果表明：（1）中国旱区GPP 20 a间呈增长趋势，其中，64.72%的区域GPP呈显著增长趋势；（2）温度对GPP的影响最低，相对贡献率为21.70%，降水和土壤含水量是GPP增长的主导因子，二者贡献率总和超过55%。随干旱程度加剧，水分胁迫作用逐渐增强。不同植被类型下，除混交林和高山植被外，降水是影响其他植被类型GPP变化最重要的气候因子；（3）土壤类型及地貌类型的差异是影响GPP空间分异的主导因子，水分、土地利用类型因素也有重要作用，任意两要素间的交互作用解释力均大于单一要素的解释力，以土壤类型与其他各因子的交互作用最为显著。研究结果对深入理解我国旱区生态系统碳汇演变特征及其对外界环境因子响应机制具有重要理论意义。

关键词: 植被总初级生产力, 气候变化, 响应机制, 地理探测器, 中国旱区

Abstract:

This study aimed to clarify the carbon sequestration capacity and its change mechanisms in the drylands of China. The study used the AI index to delineate the extent of drylands. Based on the MODIS Vegetation Gross Primary Productivity (GPP) dataset, the temporal and spatial characteristics of vegetation carbon sequestration capacity in China’s drylands from 2001 to 2020 were investigated. This investigation considered meteorological data, including temperature, precipitation, Vapor Pressure Deficit (VPD), soil water content, and human activities such as land use. The results reveal the following: (1) Over the 20 year period, GPP in China’s drylands elevated significantly in 64.72% of the regions. (2) Temperature had the lowest impact on GPP, with a relative contribution rate of 21.70%. Precipitation and soil water content emerged as the dominant factors driving GPP growth, with their combined contribution rate exceeding 55%. As drought intensified, the effect of water stress gradually strengthened. In different vegetation types, except for mixed forests and alpine vegetation, precipitation was the most critical climate factor influencing GPP changes. (3) Differences in soil and landform types were the dominant factors influencing the spatial variation of GPP. Moisture and land use type factors also played important roles, with the explanatory power of the interaction between any two factors exceeding that of a single factor. The interaction between soil type and the other factors was particularly remarkable. The study’s findings hold essential theoretical implications for a deeper understanding of the evolution characteristics of carbon sinks in arid ecosystems in China and their response mechanisms to external environmental factors.

Key words: gross primary productivity, climate change, response mechanism, geographic detector, China’s drylands

唐可欣, 郭建斌, 何亮, 陈林, 万龙. 中国旱区GPP时空演变特征及影响因素研究[J]. 干旱区研究, 2024, 41(6): 964-973.

TANG Kexin, GUO Jianbin, HE Liang, CHEN Lin, WAN Long. Characteristics of the spatial and temporal evolution of Gross Primary Productivity and its influencing factors in China’s drylands[J]. Arid Zone Research, 2024, 41(6): 964-973.

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年份	交互解释力排序(前10)
2005年	土壤类型∩地貌类型=0.760>土壤类型∩降水=0.726>土地利用类型∩土壤类型=0.725>土壤类型∩海拔=0.719>土壤类型∩AI指数=0.716=土壤类型∩相对湿度=0.716 >VPD∩土壤类型=0.696>土壤类型∩温度=0.689>土壤类型∩土壤含水量=0.687
2010年	土壤类型∩地貌类型=0.760>土壤类型∩相对湿度=0.744>土地利用类型∩土壤类型=0.728>土壤类型∩海拔=0.725>土壤类型∩AI指数=0.724>土壤类型∩降水=0.722>土壤类型∩温度=0.703>土壤类型∩海拔=0.743>土壤类型∩VPD=0.697>土壤类型∩土壤含水量=0.695
2015年	土壤类型∩地貌类型=0.787>土壤类型∩AI指数=0.785> 土壤类型∩相对湿度=0.780>土壤类型∩降水=0.779>地貌类型∩海拔=0.752> 温度∩土壤类型=0.724>土壤类型∩VPD=0.721>土壤类型∩潜在蒸散发=0.714>土壤类型∩土壤含水量=0.713> 地貌类型∩降水=0.710>地貌类型∩AI指数=0.705
2020年	土壤类型∩AI指数=0.839>地貌类型∩AI指数=0.786>地貌类型∩土壤类型=0.776>土壤类型∩土壤含水量=0.765>土壤类型∩降水=0.764>AI指数∩海拔=0.757>土壤类型∩相对湿度=0.756>土壤类型∩土地利用类型=0.750>土壤类型∩海拔=0.740>土壤类型∩温度=0.717

2020年	2001年
2020年	建筑用地	草地	耕地	灌丛	林地	裸地	湿地	水体	冰/雪地
建筑用地	36479.1	5875.9	21965.2	1.9	289.8	1880.0	0	1327.5	0
草地	281.7	2226644	84251.6	1826.8	5502.1	118799	63.8	2462.6	830.2
耕地	39.6	33231.4	8284.5	1633.6	234645	29.6	4.4	111.6	10.2
灌丛	0	925.9	17.4	2082.1	793.9	0	0	0.1	0
林地	2713.2	87477.7	479383	38.8	4932.5	13465.6	23.7	1829
裸地	54.0	92851.3	1482.38	0	11.0	1761150	16.8	1849.2	8287.2
湿地	0	267.6	16.3	0	0.8	12.2	485.6	4.7	0.2
水体	589.6	6088.0	2688.4	0.1	46.8	9125.8	24.3	53631.8	619.5
冰/雪地	0	889.6	0.03	0	0.2	8902.8	0.1	70.0	44194.3