宁夏生态系统碳汇时空变化及潜力诊断分区

doi:10.12118/j.issn.1000-6060.2025.460

摘要/Abstract

摘要：

准确量化分析陆地生态系统碳汇与时空特征，是促进区域生态碳汇格局优化和低碳可持续发展的基础。基于长时序遥感产品、地形和气象等数据，构建“样地清查-遥感反演-机器学习-线性趋势分析”等融合研究方法，估算了长时序宁夏陆地生态系统碳储量，完成了多视角生态系统碳汇时空分析与诊断分区。结果表明：（1） 2001—2024年宁夏碳储量呈显著上升趋势，年总碳储量和年平均碳储量增加速率分别为256.86×10⁴ t·a^-1和0.49 t·hm^-2·a^-1。（2） 2001—2024年宁夏生态系统碳汇贡献以草地和农田为主，贡献率分别为41.49%和33.43%，农田、草地和森林之间的相互转化，共增加碳汇1093.19×10⁴ t，贡献率为17.31%。（3） 2001—2024年宁夏生态系统碳汇显著增加的面积占比达78.7%，未来暖湿化趋势下，宁夏92.75%的区域碳储量趋于持续增加趋势，碳汇潜力较大。Moran’s I “高-高”值显著区域主要分布在宁南地区，面积占比达25.1%，且呈现上升趋势，是宁夏碳汇的高度优先区。研究结果可为宁夏生态系统管理、土地利用结构优化和“双碳”目标路径探索提供参考。

关键词: 陆地生态系统, 生态碳汇, 随机森林模型, 空间自相关

Abstract:

Accurate quantification and analysis of terrestrial ecosystem carbon sinks and their spatiotemporal characteristics are fundamental for optimizing regional ecological carbon sink patterns and promoting low-carbon, sustainable development. Based on long-term remote sensing products, topographic data, and meteorological data, this study constructed an integrated research methodology combining sample plot inventory, remote sensing inversion, machine learning, and linear trend analysis to estimate the long-term carbon storage of terrestrial ecosystems in Ningxia, China. A multiperspective spatiotemporal analysis and diagnostic zoning of ecosystem carbon sinks were conducted. The results revealed the following: (1) From 2001 to 2024, Ningxia’s carbon storage showed a significant upward trend, with annual total carbon storage and average carbon storage increasing at rates of 256.86×10⁴ t·a^-1 and 0.49 t·hm^-2·a^-1, respectively. The total carbon storage increased by 6323.08×10⁴ t, reaching 1.67×10⁸ t in 2024. Guyuan City, with an annual carbon storage increase rate of 0.75 t·hm^-2·a^-1, emerged as the core carbon sink area in Ningxia. (2) Grassland and farmland were the primary contributors to Ningxia’s ecosystem carbon sinks from 2001 to 2024, with carbon storage increasing by 4732.87×10⁴ t, accounting for 41.49% and 33.43% of the total contribution, respectively. Through two phases of ecological restoration (2000—2012 and 2012—2024), including the Grain for Green Program, grazing prohibition, and the Three-North Shelterbelt Project, the conversion among farmland, grassland, and forests increased carbon sinks by 1093.19×10⁴ t, contributing 17.31% to the total increase. Optimizing land-use structure and enhancing vegetation coverage can effectively improve ecosystem carbon sink capacity. (3) From 2001 to 2024, 78.7% of Ningxia’s ecosystem carbon sinks exhibited significant increases, with all cities showing increases of over 66.3%, and Guyuan City reaching 96.0%. Areas with decreases were mainly urbanized regions, accounting for only 3.4%. Areas with high variability (C_v≥0.3) covered 41.0%, predominantly distributed in the arid, semi-arid, and desertified regions of central and northern Ningxia, driven primarily by precipitation. Under future warm-wet trends, 92.75% of Ningxia is projected to sustain increasing carbon storage, indicating its substantial carbon sink potential. The “high-high” clusters identified by Moran’s I index were concentrated in southern Ningxia, expanding from 18.7% in 2001 to 25.1% in 2024, marking these areas as high-priority zones for carbon sink enhancement. These findings provide a scientific basis for ecosystem management, land-use optimization, and the pursuit of “dual-carbon” goals in Ningxia.

Key words: terrestrial ecosystem, ecological carbon sink, random forest model, spatial autocorrelation

包玉斌, 张慧娟, 杨雪茹, 王耀宗, 李樵民, 王科, 胡胜. 宁夏生态系统碳汇时空变化及潜力诊断分区[J]. 干旱区地理, 2026, 49(4): 740-755.

BAO Yubin, ZHANG Huijuan, YANG Xueru, WANG Yaozong, LI Qiaomin, WANG Ke, HU Sheng. Spatiotemporal variation and potential zoning diagnosis of ecosystem carbon sinks in Ningxia[J]. Arid Land Geography, 2026, 49(4): 740-755.

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时空分析方法	公式	基本原理及计算公式
Theil-Sen	$\beta =\frac{{C}_{j}-{C}_{i}}{j-i},1<i<j<n$	n为研究年数；C_i和C_j分别为第i年和第j年的碳储量值；β为趋势度，β>0时，表示呈上升趋势，β<0时，表示呈现下降趋势
Mann-Kendall	$S=\stackrel{n}{\sum _{j=1}}\stackrel{n}{\sum _{i=j+1}}\mathrm{s}\mathrm{g}\mathrm{n}({C}_{j}-{C}_{i})$$\mathrm{s}\mathrm{g}\mathrm{n}\left({C}_{j}-{C}_{i}\right)=\left\{\begin{array}{l}1,{C}_{j}-{C}_{i}>0\\ 0,{C}_{j}-{C}_{i}=0\\ -1,{C}_{j}-{C}_{i}<0\end{array}\right.$$Z=\left\{\begin{array}{cc}\frac{S-1}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(S\right)}},& S>0\\ 0,& S=0\\ \frac{S+1}{\sqrt{\mathrm{V}\mathrm{a}\mathrm{r}\left(S\right)}},& S<0\end{array}\right.$	S为检验统计量；Z为标准化后的检验统计量；Var(S)为方差；sgn(C_j-C_i)为逻辑判别函数；当$\left\|Z\right\|$>1.65、$\left\|Z\right\|$>1.96和$\left\|Z\right\|$>2.58时，分别表示在置信度90%、95%、99%下通过了碳储量趋势的显著性检验，本研究采用95%置信度双边趋势检验
Hurst指数	$\mathrm{l}\mathrm{o}\mathrm{g}\left(\frac{R\left(t\right)}{S\left(t\right)}\right)=H\times \mathrm{l}\mathrm{o}\mathrm{g}\left(t\right)+C$ R(t)=max(X_l,t)-min(X_l,t) $S\left(t\right)=\sqrt{\frac{1}{t}\stackrel{t}{\sum _{i=1}}({x}_{i}-{\stackrel{-}{x}}_{t}{)}^{2}}$${X}_{l,t}=\stackrel{t}{\sum _{i=1}}\left({x}_{i}-{\stackrel{-}{x}}_{t}\right),l=\mathrm{1,2},\cdots,t$	设时间序列长度为N，将其划分为长度为t的连续子区间（通常t取N/2, N/4, …）；X_l_,_n为累计离差；${\stackrel{-}{x}}_{t}$为子区间的均值；R(t)为极差；S(t)为标准差；R(t)/S(t)为重标极差；斜率H为Hurst指数；C为线性回归的截距项（常数项）；x_i为原始时间序列的第i个观测值（i=1,2,$\cdots $,N）
C_v	${C}_{\mathrm{v}}=\frac{\sqrt{\frac{\stackrel{k}{\sum _{i=1}}\left({x}_{i}-\stackrel{-}{x}\right)}{k-1}}}{\stackrel{-}{x}}$	C_v为时序数据变异系数；$\stackrel{-}{x}$为时序数据均值；k为时序年数；x_i为第i年的空间栅格值
空间偏相关分析	${R}_{xy,z}=\frac{{R}_{xy}-{R}_{xz}{R}_{yz}}{\sqrt{\left(1-{R}_{xy}^{2}\right)\left(1-{R}_{yz}^{2}\right)}}$$t=\frac{{R}_{xy,z}}{\sqrt{1-{R}_{xy,z}^{2}}}\sqrt{f-m-1}$${R}_{xyz}=\sqrt{1-\left(1-{R}_{xy}^{2}\right)\left(1-{R}_{xz,y}^{2}\right)}$$F=\frac{{R}_{xyz}^{2}}{1-{R}_{xyz}^{2}}\times \frac{f-m-1}{m}$	R_xy、R_xz、R_yz分别为x，y，z变量之间的相关系数，采用t显著性检验方法；R_xy,z为控制变量z后、x与y之间的偏相关系数；${R}_{xz,y}^{}$为控制变量y、变量x与z之间的偏相关系数，采用F显著性检验方法；R_xyz为因变量x与自变量y、z的复相关系数；f为年数；m为自变量个数
莫兰指数空间自相关分析	$I=\frac{\stackrel{e}{\sum _{i=1}}\stackrel{e}{\sum _{j=1}}\left[{W}_{pq}\times \left(Yp-\stackrel{-}{Y}\right)\times \left({Y}_{q}-\stackrel{-}{Y}\right)\right]}{{S}^{2}\times \stackrel{e}{\sum _{p=1}}\stackrel{e}{\sum _{q=1}}{W}_{pq}}$${S}^{2}=\frac{1}{e}\stackrel{e}{\sum _{p=1}}{\left(Yp-\stackrel{-}{Y}\right)}^{2}$	I为莫兰指数；S²为方差值；e为研究对象数目；Yp、Y_q分别为p、q栅格单元值；$\stackrel{-}{Y}$为所有栅格单元的全局均值；W_pq为p、q之间的空间邻接矩阵，是p与q之间的邻近关系

驱动因素类型	分类依据
驱动因素类型	R_CAR-_T	R_CAR-_P	R_CAR-_T_-_P
气温和降水量共同驱动	t≤t_0.05	t≤t_0.05	F>F_0.05
降水量驱动	t>t_0.05		F>F_0.05
气温驱动		t>t_0.05	F>F_0.05
非气候因素驱动			F≤F_0.05

变化类型	生态系统类型	面积/km²	碳储量变化/10⁴ t	碳储量斜率/t·hm^-2·a^-1	决定系数（R²）	贡献率/%
碳汇	森林	3433.3	372.23	0.58	0.64	5.89
	草地	24868.7	2621.04	0.41	0.60	41.49
	农田	14685.1	2111.83	0.57	0.77	33.43
	湿地	303.0	23.80	0.31	0.81	0.38
	城镇	977.2	75.24	0.35	0.78	1.19
	草地→森林	363.9	50.48	0.60	0.76	0.80
	草地→农田	1614.7	342.80	0.96	0.91	5.43
	草地→湿地	180.1	4.81	0.03	0.04	0.08
	农田→森林	297.5	58.15	0.79	0.73	0.92
	农田→草地	3839.1	641.76	0.68	0.66	10.16
	湿地→农田	68.9	13.03	0.83	0.85	0.21
	城镇→草地	23.8	2.45	0.41	0.75	0.04
碳源	草地→城镇	725.1	-50.97	-0.25	0.56	62.20
	农田→城镇	568.6	-24.69	-0.26	0.37	30.13
	农田→湿地	137.0	-5.52	-0.34	0.56	6.74
	森林→城镇	15.7	-0.74	-0.15	0.16	0.90
	湿地→城镇	9.1	-0.03	-0.07	0.04	0.03