Driving force analysis of carbon storage spatial-temporal variation in Xiaoxing'anling Mountains and its contribution to carbon emission reduction
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摘要:
研究目的 生态系统在固碳释氧方面发挥着重要作用，可有效减缓气候变化。碳储量是衡量生态系统碳汇能力的重要指标，可为制定碳减排策略提供科学依据。但当前关于碳储分布的研究多聚焦于单一因素影响，对多因子协同作用及其空间分异规律的系统探讨相对较少，系统分析了中国小兴安岭地区碳储量的时空分布格局及其变化驱动机制，并评估了其对区域碳平衡管理与碳减排目标实现的潜在贡献。
研究方法 针对多因子对生态系统碳储量空间分异的作用机制，借助2000年、2010年、2020年土地利用数据，基于InVEST模型估算小兴安岭地区的碳储量及其空间分布特征，使用随机森林模型对地形、气象、土壤等重要变量进行筛选，并选择地理探测器探索驱动研究区碳储量空间分异的主要驱动因子，探究其驱动机制。
研究结果 ①研究区土地利用类型主要以林地（77%）、耕地（20%）为主，地类结构变化明显，表现为耕地、建设用地面积增加，林地、草地面积减少；②研究区近20年碳储总量提升14.72%，耕地与林地、草地的相互转化是碳储量减少的主要原因；③碳储量空间分异的主导因子为植被覆盖度（0.254~0.278）和土壤质地（0.146~0.168）。
结论 尽管小兴安岭地区近20年林草面积有所减少，削弱了生态系统的碳汇能力，但局部区域碳储量的增长掩盖了局部区域的减少，体现出研究区具有一定的生态恢复潜力。然而，人类活动导致区域碳汇能力承压变大，增加了碳排放控制的难度。因此，研究区虽然具有生态恢复能力，但为提升区域碳汇功能，缓解区域碳排放压力，应持续加强生态保护和恢复措施。
Abstract:Objective Ecosystems play an important role in carbon fixation and oxygen release, which can effectively mitigate climate change. Carbon storage is an important index to measure the carbon sink capacity of ecosystems, which can provide a scientific basis for formulating carbon emission reduction strategies. However, the current research on the distribution of carbon storage mostly focuses on the influence of a single factor, and there are relatively few systematic discussions on the multi-factor synergy and its spatial differentiation. Therefore, this study systematically analyzes the spatial and temporal distribution pattern of carbon storage in the Xiaoxing'anling region of China and its driving mechanism of change, and evaluates its potential contribution to regional carbon balance management and carbon emission reduction targets.
Methods Aiming at the mechanism of multi-factors on the spatial differentiation of ecosystem carbon storage, this study used the land use data of 2000, 2010 and 2020 to estimate the carbon storage and its spatial distribution characteristics in the Lesser Khingan Mountains based on the InVEST model. The random forest model was used to screen important variables such as topography, meteorology and soil, and the geographical detector was selected to explore the main driving factors driving the spatial differentiation of carbon storage in the study area and explore its driving mechanism.
Results ①The spatial distribution of carbon stocks in the study area is characterized by the following factors. The land use types in the study area are mainly dominated by forest land (77%) and cropland (20%), and the change of land structure is obvious, which is manifested in the increase of cropland and construction land area, and the decrease of forest land and grassland area; ②The total amount of carbon stock in the study area has increased by 14.72% in the last 20 a, and the mutual transformation of cropland and forest land and grassland is the main reason for the decrease of carbon stock; ③The dominant factors in the spatial differentiation of carbon stocks were vegetation cover (0.254~0.278) and soil texture (0.146~0.168).
Conclusions Although the area of forest and grass in the Xiaoxing'anling Mountains has decreased in the past 20 years, which has weakened the carbon sink capacity of the ecosystem, the increase of carbon storage in local areas has covered up the reduction of local areas, reflecting that the study area has certain ecological restoration potential. However, human activities have led to greater pressure on regional carbon sink capacity, increasing the difficulty of carbon emission control. Therefore, although the study area has the ability of ecological restoration, in order to enhance the regional carbon sink function and alleviate the pressure of regional carbon emissions, the ecological protection and restoration measures in the study area should be continuously strengthened.
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Key words:
- carbon storage /
- driving factors /
- ecosystem service /
- interactive detection /
- random forest /
- Xiaoxing'an Mountains
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表 1 数据来源及类型
Table 1. Data sources and types
数据源 空间分
辨率数据来源 土地利用数据（2000s、2010s、2020s） 1 km zenodo（ https://doi.org/10.5281/zenodo.4417810 ）数字高程模型（DEM） 30 m 国家地球系统科学数据中心( http://www.geodata.cn )归一化植被指数 1 km 年均气温 1 km 年均降水量 1 km 年均蒸散量 1 km 夜间灯光数据 1 km 土壤数据集(HWSD) 500 m 国家青藏高原科学数据中心( http://data.tpdc.ac.cn )人口密度数据 1 km WorldPop
（https://www.worldpop.org/ ）中国GDP空间分布公里网格数据集 1 km 中国科学院资源环境数据中心（ https://www.resdc.cn/ ）
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表 2 不同土地利用碳密度
Table 2. Carbon density values of different land use types
t/ha 用地类型 地上碳密度 地下碳密度 土壤碳密度 死亡有机物碳密度 2000年 2010年 2020年 2000年 2010年 2020年 2000年 2010年 2020年 2000年 2010年 2020年 耕地 10.10 10.47 10.63 26.80 27.77 28.21 147.70 153.48 152.35 0.00 0.00 0.00 林地 41.88 43.40 44.08 31.32 32.46 32.97 173.90 180.71 179.38 2.25 2.33 2.37 草地 6.96 7.21 7.33 51.00 52.85 53.68 74.60 77.52 76.95 2.84 2.94 2.99 水域 8.72 9.04 9.18 2.21 2.29 2.33 23.01 23.91 23.73 0.00 0.00 0.00 建设用地 8.75 9.07 9.21 4.39 4.55 4.62 27.78 28.87 28.66 1.16 1.20 1.22 未利用地 10.03 10.39 10.56 0.00 0.00 0.00 44.79 46.54 46.20 0.00 0.00 0.00 湿地 0.65 0.67 0.68 0.32 0.33 0.34 28.43 29.54 29.33 0.38 0.39 0.40
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表 3 交互作用类型
Table 3. Types of interactions
判断依据 类型 q(x1∩x2)<Min[q(x1),q(x2)] 非线性减弱 Min[q(x1),q(x2)]<q(x1∩x2)<Max[q(x1),q(x2)] 单因子非线性减弱 q(x1∩x2)>Max[q(x1),q(x2)] 双因子增强 q(x1∩x2)=q(x1)+q(x2) 独立 q(x1∩x2)>q(x1)+q(x2) 非线性增强
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表 4 研究区1990—2020年土地利用面积
Table 4. The land use area of the study area from 1990 to 2020
ha 土地利用类型 2000年 2010年 2020年 耕地 2143093.32 2338291.08 2429273.16 林地 8267553.45 8146704.6 8028870.75 草地 204012.54 107879.22 109856.16 水域 41408.28 44268.75 43688.34 未利用地 333.27 220.05 418.86 建设用地 64975.86 86683.23 112958.73 湿地 6986.97 4316.76 3297.69
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