Spatial and temporal distribution characteristics and control mechanisms of the karst inorganic carbon sink in the Guangxi karst region from 2002 to 2022
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摘要: 碳酸盐岩化学风化过程是一个大气或土壤CO2汇的过程，其降低了大气CO2浓度和土壤碳排放速率。目前对岩溶碳汇还缺乏精确、系统的评估，对碳汇产生的机制仍存在争议。文章根据广西岩溶地质背景、溶解性无机碳（DIC）浓度实测数据、高分辨率遥感数据，利用机器学习模拟2002—2022年的岩溶地下水DIC含量，结合流量数据，采用水化学径流法评估岩溶无机碳汇，使用地理探测器讨论其控制机制。结果表明：（1）2002—2022年广西岩溶区累积无机碳汇量为5.03×107 tCO2，年均无机碳汇量为2.40×106 tCO2·a−1；（2）2002—2022年广西年均无机碳汇量整体呈增加趋势，2008年峰值（3.56×106 tCO2·a−1）为最低值（2009年）的2.5倍；（3）岩溶无机碳汇强度均值为30.24 tCO2·km−2·a−1，较高的碳汇强度出现在研究区的中部和东北部，而较低值出现在西南部和东南部；（4）降水量是影响广西岩溶无机碳汇时空分布的首要因子，各因子间交互作用均表现为增强作用，其中气温和降水量发挥核心作用。Abstract:
Carbonate rock chemical weathering is a fundamental yet complex process within the global carbon cycle and constitutes an important long-term sink for atmospheric carbon dioxide (CO2). During carbonate dissolution, CO2 derived from the atmosphere and/or soil is consumed and transformed into Dissolved Inorganic Carbon (DIC), predominantly in the form of bicarbonate (${\rm{HCO}}_3^{-}$). This DIC is subsequently transported downstream through groundwater and surface runoff systems and may ultimately be stored in the ocean over long timescales. Because this process reduces atmospheric CO2 concentrations and, to some extent, mitigates the release of soil carbon to the atmosphere, carbonate weathering is widely regarded as an important natural mechanism for buffering anthropogenic climate warming. However, the magnitude of karst inorganic carbon sinks at the regional scale remains highly uncertain. This uncertainty is particularly pronounced in extensive karst regions, where strong spatial heterogeneity in lithological conditions, aquifer connectivity, recharge processes, and land-surface environments makes it difficult to upscale point-based hydrochemical observations to the regional level. Consequently, significant discrepancies persist in regional estimates of karst carbon sinks, and the relative contributions of climatic and environmental factors to DIC production and export have yet to be fully resolved. The Guangxi Zhuang Autonomous Region, located in southern China, is one of the most extensive and representative karst regions in the country. It is characterized by widespread carbonate rock outcrops, well-developed epikarst and subsurface drainage systems, and hydrogeochemical processes that are highly sensitive to monsoonal climatic forcing. The region has a subtropical humid monsoon climate, abundant precipitation, and diverse geomorphological settings, all of which provide favorable conditions for carbonate weathering and dissolved carbon export. At the same time, Guangxi exhibits marked regional variability in climate, hydrology, topography, and karst development, making it an ideal study area for reassessing karst inorganic carbon sinks and exploring their controlling mechanisms at the regional scale. Against this background, the present study systematically re-evaluates the karst inorganic carbon sink in Guangxi from 2002 to 2022 and employs the Geodetector model to investigate its controlling factors. This study employs multidisciplinary methodology, integrating field observations, remote sensing data, machine learning modeling, and spatial attribution analysis. First, in consideration of the geological and hydrogeological setting of the karst region in Guangxi, a relatively large dataset of measured groundwater DIC concentrations was compiled. High-resolution remote sensing data were also selected to characterize environmental conditions relevant to karst carbon processes. Based on these multi-source datasets, machine learning models were trained and validated to reconstruct the spatiotemporal distribution of DIC concentrations in karst groundwater across Guangxi from 2002 to 2022. Compared with conventional point-based observational approaches, this method enhances the regional representativeness of the estimates and helps compensate for the limited spatial and temporal coverage of measured data. Additionally, the incorporation of remotely sensed variables provides feasible proxy indicators for certain environmental conditions that are challenging to monitor continuously over extended periods. Second, the reconstructed DIC concentration fields were combined with long-term discharge data, and the karst inorganic carbon sink generated by carbonate weathering was recalculated using the classical and widely applied hydrochemical flux method, thereby providing a more accurate representation of both temporal fluctuations and spatial variability in dissolved carbon export. Finally, the Geodetector model was employed to quantify the explanatory power of potential influencing factors through both factor detection and interaction detection, with the aim of identifying the dominant controls on the spatial differentiation of the karst carbon sink in Guangxi. The results show that: (1) From 2002 to 2022, the cumulative karst inorganic carbon sink in Guangxi reached 5.03×107 tCO2, with an annual average of 2.40 ×106 tCO2·a−1. (2) Interannual variability in the karst inorganic carbon sink was pronounced, with a marked increasing trend from 2003 to 2006, followed by an overall fluctuating upward trend thereafter. The maximum annual sink occurred in 2008, reaching 3.56×106 tCO2·a−1, whereas the minimum was recorded in 2009 at 1.44×106 tCO2·a−1, the former being approximately 2.5 times the latter. (3) The mean karst inorganic carbon sink intensity during the study period was 30.24 tCO2·km−2·a−1, with clear spatial heterogeneity. Higher values were mainly concentrated in central and northeastern Guangxi, whereas lower values occurred in the southwestern and southeastern parts of the region. (4) Geodetector analysis indicates that precipitation is the primary factor controlling the spatiotemporal variation of the karst inorganic carbon sink in Guangxi, consistent with the strong dependence of karst process on water availability. Further interaction detection reveals that the explanatory power of the interaction between any two factors is greater than that of any single factor, indicating that the formation and differentiation of the karst carbon sink exhibit significant nonlinear coupling characteristics. Among these interactions, the interaction between temperature and precipitation shows the highest explanatory power. -
图 1 研究区概况与取样点分布图
Figure 1. Overview of the study area and distribution of sampling points
图 2 2002—2022年广西岩溶区溶解无机碳时空变化特征
Figure 2. Temporal and spatial variation characteristics of dissolved inorganic carbon in the karst region of Guangxi from 2002 to 2022
图 3 广西岩溶区21年来年均径流模数（a）和径流量（b）
Figure 3. Annual average runoff modulus(a) and runoff volume(b) in the karst region of Guangxi over 21 years
图 4 2002—2022年广西岩溶碳汇年际变化及变化率
Figure 4. Interannual variation and change rate of karst carbon sink in Guangxi from 2002 to 2022
图 5 广西岩溶无机碳汇强度空间分布及其演变趋势
Figure 5. Spatial distribution and evolution trend of karst inorganic carbon sink intensity in Guangxi
图 6 广西气温、降水、Q、DIC与碳汇量变化图
Figure 6. Temporal variations of air temperature, precipitation, Q, DIC, and carbon sink flux in Guangxi
图 7 广西岩溶无机碳汇量驱动因子交互探测结果
Figure 7. Interactive detection results of driving factors of karst inorganic carbon sink in Guangxi
表 1 广西岩溶区碳汇量研究
Table 1. Study on carbon sink capacity in karst region of Guangxi
来源 时间 方法 年均碳汇量/（tCO2·a−1） 汤颖颖等[45] 2005—2020年 GEM-CO2 3.12×106 Zhou[16] 2003—2012年 GEM-CO2 3.30×106 曾思博[46] 1970—2013年 入渗−碳酸盐平衡化学法 1.60×106 陶晓东[47] 2003—2012年 GEM-CO2 3.30×106 Du等[48] 1991—2020 年 经典热力学溶蚀模型 5.07×106 李汇文等[11] 2000—2014年 随机森林、最大潜在溶蚀模型 2.32×106 曾思博等[49] 2010年 碳酸盐溶蚀平衡模型 5.86×106 本研究 2002—2022年 水化学径流法 2.40×106 
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表 2 单因子探测结果
Table 2. Results of single factor detection
因子 2002年 2007年 2012年 2017年 2022年 q值 p值 q值 p值 q值 p值 q值 p值 q值 p值 X1 0.275 0.000 0.431 0.000 0.154 0.000 0.168 0.000 0.257 0.000 X2 0.661 0.000 0.504 0.000 0.592 0.000 0.264 0.000 0.621 0.000 X3 0.067 0.000 0.162 0.000 0.066 0.000 0.161 0.000 0.043 0.000 X4 0.001 0.009 0.022 0.000 0.002 0.004 0.019 0.000 0.001 0.048 X5 0.002 0.000 0.002 0.000 0.001 0.000 0.008 0.000 0.001 0.000 X6 0.496 0.000 0.249 0.000 0.376 0.000 0.084 0.000 0.344 0.000 X7 0.029 0.000 0.284 0.000 0.046 0.000 0.109 0.000 0.311 0.000 X8 0.132 0.000 0.075 0.000 0.025 0.000 0.080 0.000 0.363 0.000 X9 0.015 0.000 0.089 0.000 0.009 0.000 0.070 0.000 0.011 0.000 X10 0.021 0.000 0.097 0.000 0.010 0.000 0.088 0.000 0.003 0.000 X11 0.180 0.000 0.404 0.000 0.077 0.000 0.182 0.000 0.125 0.000
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