近30年岩溶地区碳储量时空变化分析及预测

晏红波; 曾金钊; 卢献健; 赵凤阳

doi:10.11932/karst20250107

近30年岩溶地区碳储量时空变化分析及预测——以红水河流域为例

doi: 10.11932/karst20250107

晏红波^{1, 2,},
曾金钊¹,
卢献健^1, ,,
赵凤阳¹

1.
桂林理工大学测绘地理信息学院, 广西桂林 541004
2.
广西空间信息与测绘重点实验室, 广西桂林 541004

基金项目: 国家自然科学基金项目（42361052）；广西自然科学基金项目（2022GXNSFBA035639）；国家自然科学基金项目（42064003）

详细信息

作者简介:
晏红波（1983－），女，博士，教授，主要从事遥感数据智能处理及地表参数变化监测研究。E-mail：2009019@glut.edu.cn

通讯作者:
卢献健（1982－），男，硕士，副教授，主要从事遥感影像智能处理与应用。E-mail：2008056@glut.edu.cn。

中图分类号: X171.1
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出版历程
- 收稿日期: 2024-04-30
- 录用日期: 2024-07-03
- 修回日期: 2024-07-03
- 刊出日期: 2025-02-25

Analysis and prediction of spatial and temporal variation of carbon storage in limestone area in recent 30 years:A case study of the Hongshui River Basin

1.
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin, Guangxi 541004
2.
Guangxi Key Laboratory of Spatial of Information and Geomatics, Guilin, Guangxi 541004 China

摘要

摘要: 岩溶地区具有生态脆弱、环境容量小、土地承载力低、抗干扰能力差等特征，了解岩溶地区生态系统碳储量变化的原因对于预防和控制生态系统退化和支持可持续发展至关重要。本研究以红水河流域为例，基于InVEST模型和PLUS模型评估其1990年至2020年的岩溶地区碳储量，并预测了2030年不同情景下岩溶地区碳储量的变化。研究结果表明：(1) 1990－2020年30年间红水河流域岩溶地区碳储量总体呈递增趋势，空间分布特征由东南向西北逐渐升高，总体的碳源/汇效应为汇大于源，总增加量为71.59×10⁶ t；(2) 相较于2020年，在自然发展情景和生态保护情景下，2030年红水河流域的岩溶地区碳储量分别将增加7.69×10⁶ t和10.74×10⁶ t；而城镇发展情景下碳储量将减少5.14×10⁶ t，城镇发展会导致岩溶地区固碳能力较强的林地减少，导致岩溶流域固碳能力失衡；(3) 根据地理探测器结果显示土地利用对碳储量的空间异质性解释力最强，解释力q值为0.833，以及土地利用与年均NDVI因子之间的相互作用对红水河流域碳储量的变化影响最显著，交互作用解释力为0.848，土地利用变化是使得岩溶碳储量升高主要原因。此研究结果可为红水河流域实现岩溶地区生态系统服务碳储量的可持续性发展、为土地利用管理优化提供科学指导提供理论和数据支持。
- 碳储量 /
- 驱动因素 /
- InVEST模型 /
- 岩溶地区 /
- 红水河流域
Abstract: The vulnerable ecology of karst areas is characterized by limited environmental capacity, poor soil quality, insufficient water resources, low land carrying capacity, and high yet vulnerable biodiversity. These factors contribute to low ecosystem productivity and a diminished ability to withstand disturbances. Therefore, it is crucial to understand the causes of changes in ecosystem carbon storage to prevent and mitigate ecosystem degradation and to support sustainable development in karst areas. The Hongshui River Basin is located in the northwest and central regions of Guangxi, characterized by high terrain in the northwest and low terrain in the southeast. It is the main tributary of the Pearl River Basin and encompasses the largest contiguous karst area in Guangxi, covering an area of 50,479.745 km². Of this, the karst landform area spans 33,942.048 km², accounting for 67% of the total area. The Hongshui River Basin is a typical karst basin located in Southwest China. It features widely distributed karst landforms, significant altitude variations, and generally thin soil with low fertility and poor water retention capacity. As a result, the ecological environment in this regions is extremely fragile. The vulnerability of natural attributes such as carbon storage function and soil erosion in karst basins, combined with human activities, has led to severe ecological degradation in karst areas.Based on the InVEST model, this study took the Hongshui River Basin as an example to evaluate changes in ecosystem carbon storage in the karst area for the years 1990, 2000, 2010 and 2020. At the same time, the PLUS model was employed to simulate the trend of carbon storage changes in the study area under three future scenarios: natural development, urban prioritization and ecological protection. A geographical detector was utilized to identify the main driving factors influencing land use, precipitation, temperature, population density, and other elements affecting the spatial heterogeneity of carbon storage in the karst region of the study area. The conclusions are as follows,(1) From 1990 to 2020, the spatial distribution characteristics of carbon storage in the Hongshui River Basin showed a gradual increase from the southeast to the northwest. Over the past 30 years, the total carbon storage in the basin gradually increased by 71.59×10⁶ t. The most significant increase in carbon storage occurred between 1990 and 2000, indicating that the carbon sink capacity of the Hongshui River Basin was stronger than its carbon source over this period. Overall, the carbon sink effect surpassed the carbon source effect in the basin.(2) In the year 2030, the carbon storage in the Hongshui River Basin is projected to be 927.67×10⁶ t, 914.84×10⁶ t and 930.71×10⁶ t under the scenarios of natural development, urban development and ecological protection, respectively. Compared with 2020, both the natural development scenario and ecological protection scenario for the Hongshui River Basin are expected to demonstrate a generally increasing trend in carbon storage. This indicates that the carbon sink capacity under these scenarios in the future will be stronger than that of the carbon source. In 2030, the carbon storage of the Hongshui River Basin will increase by 7.69×10⁶ t and 10.74×10⁶ t, respectively. Compared with the natural development scenario, the ecological protection scenario shows advantages in areas where the overall spatial distribution of carbon storage increases. Under the urban development scenario for the year 2030, the carbon storage in the Hongshui River Basin is projected to decrease by 5.14×10⁶ t. As urban socio-economic development necessitates the expansion of urban construction land, urban development will lead to a reduction in forest land, which has a significant capacity for carbon sequestration in karst areas. This reduction will disrupt the ecological balance in this regions, further diminishing the carbon sequestration capacity of the basin and gradually shifting its carbon source-sink effect from a carbon sink to a carbon source.(3) The single-factor detection results from the geographic detector indicate that land use is the main driving factor influencing the spatial heterogeneity of carbon storage in the Hongshui River Basin, with a q value of 0.833. Additionally, the average annual NDVI has been shown to explain the spatial heterogeneity of carbon storage, with a q value of 0.545. The interactive detection results show that the interaction between land use and the annual average NDVI factor have the most significant effect on the change in carbon storage within the Hongshui River Basin, with an explanatory power of 0.833. This indicates that the specific combination of the land use interactions, annual average NDVI and other factors—such as annual average temperature, annual average rainfall, digital elevation model, and population density—will influence the spatial distribution of carbon storage. The land use change factor is the main contributor to the increase of carbon storage in the Hongshui River Basin, followed by the annual average NDVI factor. The findings of this study may provide significant theoretical and data support for the sustainable development of carbon storage within ecosystem services in the Hongshui River Basin. Furthermore, they will assist in the formulation of more effective ecological protection and resource management policies aimed at enhancing the carbon sink capacity of the ecosystem and promoting environmental health and sustainable development.
- carbon storage /
- driving factors /
- inVEST model /
- karst area /
- Hongshui River Basin

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图 1 红水河流域地理位置及岩溶地貌类型分布图

Figure 1. Geographical location of the Hongshui River Basin and the distribution of its karst landform types

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图 2 1990-2020年碳储量含量及空间变化

Figure 2. Carbon storage content from 1990 to 2020

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图 3 2020-2030年不同情景下土地利用时空变化

Figure 3. Spatio-temporal changes in land use under different scenarios from 2020 to 2030

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图 4 2020-2030年三种情景土地利用类型转移图

Figure 4. Land use type transition under three scenarios from 2020 to 2030

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图 5 不同情景下2030年碳储量空间分布及变化图

Figure 5. Spatial distribution and variation of carbon storage in 2030 under different scenarios

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图 6 因子探测和交互作用结果图

Figure 6. Results of factor detection and interaction

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表 1 红水河流域各土地利用类型碳密度/·hm⁻²

Table 1. Carbon density of land use types in Hongshui River Basin

土地利用类型	地上碳密度	地下碳密度	土壤碳密度	死亡有机碳密度
耕地	13.50	2.70	35.00	1.00
草地	3.01	13.53	10.00	1.00
林地	105.90	67.50	59.40	3.50
湿地	37.00	11.80	56.71	3.00
建设用地	1.20	0.93	12.48	0
水域	1.02	0	0	0

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表 2 PLUS模型转移成本矩阵

Table 2. PLUS model transfer cost matrix

		自然发展情景						城镇优先情景						生态保护情景
		A	B	C	D	E	F	A	B	C	D	E	F	A	B	C	D	E	F
土地利用类型	A	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
	B	1	1	1	1	1	1	1	1	1	0	1	1	0	1	1	1	0	0
	C	1	1	1	1	1	0	1	0	1	0	1	1	0	0	1	0	0	0
	D	1	0	0	1	0	0	1	1	0	1	1	0	0	0	0	1	1	1
	E	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0
	F	1	1	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1
注:A-耕地 B-草地 C-林地 D-湿地 E-建设用地 F-水域

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表 3 两个因子对因变量的交互作用

Table 3. Interaction of two factors on dependent variables

判断依据	交互作用
q(X1∩X2) < Min(q1,q2)	非线性减弱
Min(q1,q2) < q(X1∩X2) < Max(q1,q2)	单因子非线性减弱
q(X1∩X2) > Max(q1,q2)	双因子增强
q(X1∩X2) = q1+q2	独立
q(X1∩X2) > q1+q2	非线性增强
*表中Min(q1,q2)表示取q1，q2中的最小值，Max(q1,q2)表示取q1，q2中的最大值，q1+q2表示取q1，q2的和。

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表 4 1990、2000、2010和2020年各土地类型面积及占比

Table 4. Area and proportion of each land type in 1990, 2000, 2010 and 2020

土地类型	1990年		2000年		2010年		2020年
土地类型	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%	面积/km²	占比/%
耕地	17751.434	35.165	14210.221	28.150	13505.526	26.754	12689.366	25.138
草地	12.246	0.024	54.542	0.108	56.851	0.113	61.225	0.121
林地	31947.882	63.289	35094.401	69.522	35514.595	70.354	36044.503	71.404
湿地	0.149	0.000	1.923	0.004	2.094	0.004	2.374	0.005
建设用地	500.090	0.991	684.471	1.356	930.969	1.844	1206.539	2.390
水域	267.944	0.531	434.187	0.860	469.709	0.930	475.737	0.942

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表 5 1990年~2020年土地利用转移矩阵

Table 5. Land use transfer matrix from 1990 to 2020

		2020年土地利用面积/km²
		耕地	草地	林地	湿地	建设用地	水域	总面积
1990年土地利用面积/km²	耕地	10896.853	13.354	6032.528	1.598	624.868	182.234	10896.853
	草地	0.439	9.961	1.803	0.002	0.030	0.012	0.439
	林地	1772.772	37.847	30002.547	0.074	80.328	54.315	1772.772
	湿地	0	0	0	0.149	0	0	0
	建设用地	0	0	0	0	500.090	0	0
	水域	19.301	0.063	7.626	0.553	1.224	239.177	19.301
	总面积	10896.853	13.354	6032.528	1.598	624.868	182.234	10896.853

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表 6 未来各情景土地利用面积

Table 6. Future land use area under different scenarios

	2020年	2030年自然发展情景	2030年城镇优先情景	2030年生态保护情景
耕地	12689.366	11956.148	12591.183	11887.438
草地	61.225	55.096	59.988	53.656
林地	36044.503	36516.677	35829.470	36665.501
湿地	2.374	2.093	2.238	2.348
建设用地	1206.539	1468.697	1521.123	1390.058
水域	475.737	481.033	475.743	480.744

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