毛乌素沙地生态系统碳储量时空变化及其驱动因素分析:以乌审旗为例

    Spatio-temporal Dynamics and Driving Factors of Carbon Storage in the Mu Us Sandy Land: A Case Study in Wushen Banner

    • 摘要: 随着防沙治沙任务的不断推进,毛乌素沙地植被盖度显著提升,其中草原和森林植被的增加主要源自荒漠植被类型转变。预测碳储量的时空变化,探究其变化的驱动因素,可为毛乌素沙地生态系统保护和土地利用规划提供科学依据。本研究以乌审旗为例,基于1990—2020年土地利用数据,采用PLUS-InVEST模型探讨1990—2020年及2035年4种情景〔自然发展情景(NE)、可持续协调发展情景(SC)、生态保护情景(EP)和经济发展情景(ED)〕下碳储量时空变化,并借助地理探测器模型和对数平均迪氏指数法(LMDI)探究引起碳储量变化的驱动因素。结果显示:(1)30年间乌审旗耕地、林地、水域和建设用地面积扩张,草地和沙地面积缩减;草地和林地是主要碳储存地,30年间生态系统碳储量增加266.59万t,林地面积的增长是碳储量增加的主要原因。(2)至2035年,在4种预测情景中,经济发展情景下区域碳储量增长面积比例最高,而在自然发展情景下碳储量减少的区域面积比例达到最大。(3)利用地理探测器模型得出DEM、NDVI和转入林地面积对碳储量变化的解释力较大;利用LMDI模型得出林地转入面积和经济发展促进碳储量增加。研究结果表明:多情景分析不仅有助于全面深入地了解碳储量时空格局演变特征,还有利于制定满足城镇集约化和生态高质量发展且兼顾增汇的土地利用规划策略。

       

      Abstract: Advancements in sand control measures have significantly enhanced vegetation cover in the Mu Us sandy land with the growth in grassland and forest vegetation mainly stemming from the transformation of desert vegetation types. Since the 1990s, socioeconomic growth and ecological engineering initiatives have markedly altered land use patterns in the region, impacting carbon storage. Yet, accurately assessing carbon storage and its determinants remains challenging. This study focuses on Wushen Banner, a representative county in the Mu Us sandy land. Based on the land use data from 1990 to 2020, the PLUS-InVEST model was used to explore the spatial and temporal variation of carbon storage under four scenarios, i.e. natural development scenario (NE), sustainable coordinated development scenario (SC), ecological protection scenario (EP) and economic development scenario (ED), from 1990 to 2020 and in 2035, and with the help of geography-detector model and Log-mean Dez exponential method (LMDI) to explore the drivers of the changes of carbon storage. The results of the study show that: (1) Analysis over the past three decades indicates increases in cultivated, forest, construction, and watershed areas, while grassland and sand areas have declined. The expansion of forest areas has been the primary contributor to the 266.59×104 ton increase in the ecosystem′s carbon stock; (2) By 2035, among four forecast scenarios, the scenario of economic development exhibits the highest proportional increase in regional carbon storage, while the scenario of natural evolution sees the largest proportional decrease in carbon storage areas. (3) The geodetector model highlights DEM, NDVI, and transferred forest land area as key explanatory variables for changes in carbon stock, while the LMDI model confirms a positive correlation between transferred forest land area and economic development impacts on carbon stock. These findings underscore the importance of multi-scenario analysis in understanding the evolution of carbon stocks and in crafting land use strategies that support urban intensification and high-quality ecological growth, alongside an increase in carbon sinks.

       

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