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

Abstract

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

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.

Figures/Tables 13

Fig. 1

Fig. 2

Fig. 3

Tab. 1

Tab. 2

Fig. 4

Tab. 3

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

References 32

[1]	易丹, 欧名豪, 郭杰, 等. 土地利用碳排放及低碳优化研究进展与趋势展望[J]. 资源科学, 2022, 44(8): 1545-1559. doi: 10.18402/resci.2022.08.02
	[Yi Dan, Ou Minghao, Guo Jie, et al. Progress and prospect of research on land use carbon emissions and low-carbon optimization[J]. Resources Science, 2022, 44(8): 1545-1559.] doi: 10.18402/resci.2022.08.02
[2]	刘坤, 张慧, 孔令辉, 等. 陆地生态系统碳汇评估方法研究进展[J]. 生态学报, 2023, 43(10): 4294-4307.
	[Liu Kun, Zhang Hui, Kong Linghui, et al. An overview of terrestrial ecosystem carbon sink assessment methods towards achieving carbon neutrality in China[J]. Acta Ecologica Sinica, 2023, 43(10): 4294-4307.]
[3]	张一凡, 陈亚鹏, 陈亚宁, 等. 基于自然气候解决方案的新疆陆地生态系统固碳能力估算[J]. 干旱区地理, 2025, 48(7): 1198-1205. doi: 10.12118/j.issn.1000-6060.2024.053
	[Zhang Yifan, Chen Yapeng, Chen Yaning, et al. Estimation of carbon sequestration capacity of Xinjiang terrestrial ecosystem based on natural climate solutions[J]. Arid Land Geography, 2025, 48(7): 1198-1205.] doi: 10.12118/j.issn.1000-6060.2024.053
[4]	梁森, 张建军, 王柯, 等. 区域生态保护修复碳汇潜力评估方法与应用——基于第一批山水林田湖草生态保护修复工程的研究[J]. 生态学报, 2023, 43(9): 3517-3531.
	[Liang Sen, Zhang Jianjun, Wang Ke, et al. Methodology and application of carbon sink potential assessment for regional ecological conservation and restoration: Based on the research of the first batch of pilots for ecological protection and restoration project of mountains-rivers-forests-farmlands-lakes-grasslands system[J]. Acta Ecologica Sinica, 2023, 43(9): 3517-3531.]
[5]	黄贤金, 张秀英, 卢学鹤, 等. 面向碳中和的中国低碳国土开发利用[J]. 自然资源学报, 2021, 36(12): 2995-3006. doi: 10.31497/zrzyxb.20211201
	[Huang Xianjin, Zhang Xiuying, Lu Xuehe, et al. Land development and utilization for carbon neutralization[J]. Journal of Natural Resources, 2021, 36(12): 2995-3006.] doi: 10.31497/zrzyxb.20211201
[6]	Chen X, Yu L, Hou S, et al. Unraveling carbon stock dynamics and their determinants in China’s Loess Plateau over the past 40 years[J]. Ecological Indicators, 2024, 159: 111760, doi: 10.1016/j.ecolind.2024.111760.
[7]	Zhang J J, Yang J, Liu P F, et al. Effects of land use/cover change on terrestrial carbon stocks in the Yellow River Basin of China from 2000 to 2030[J]. Remote Sensing, 2024, 16: 1810, doi: 10.3390/rs16101810.
[8]	Ma Y Q, Li J H, Cao W, et al. Grain for green program to grassland might lead to carbon sink leakage in the Loess Plateau[J]. Earth’s Future, 2025, 13: e2024EF005261, doi: 10.1029/2024EF005261.
[9]	Yang Y, Liu L X, Zhang P P, et al. Large-scale ecosystem carbon stocks and their driving factors across Loess Plateau[J]. Carbon Neutrality, 2023, 2(1): 5, doi: 10.1007/s43979-023-00044-w.
[10]	王少剑, 周诗洁, 方创琳, 等. 1980—2020年中国陆地生态系统碳储量时空格局与演进规律[J]. 中国科学: 地球科学, 2024, 54(10): 3323-3339.
	[Wang Shaojian, Zhou Shijie, Fang Chuanglin, et al. Spatial-temporal patterns and evolution of carbon storage in China’s terrestrial ecosystems from 1980 to 2020[J]. Scientia Sinica (Terrae), 2024, 54(10): 3323-3339.]
[11]	谢立军, 白中科, 杨博宇, 等. 碳中和背景下国内外陆地生态系统碳汇评估方法研究进展[J]. 地学前缘, 2023, 30(2): 447-462. doi: 10.13745/j.esf.sf.2022.2.78
	[Xie Lijun, Bai Zhongke, Yang Boyu, et al. Carbon sequestration assessment methods at home and abroad for terrestrial ecosystems: Research progress in achieving carbon neutrality[J]. Earth Science Frontiers, 2023, 30(2): 447-462.] doi: 10.13745/j.esf.sf.2022.2.78
[12]	朱建华, 田宇, 李奇, 等. 中国森林生态系统碳汇现状与潜力[J]. 生态学报, 2023, 43(9): 3442-3457.
	[Zhu Jianhua, Tian Yu, Li Qi, et al. The current and potential carbon sink in forest ecosystem in China[J]. Acta Ecologica Sinica, 2023, 43(9): 3442-3457.]
[13]	戴尔阜, 黄宇, 吴卓, 等. 内蒙古草地生态系统碳源/汇时空格局及其与气候因子的关系[J]. 地理学报, 2016, 71(1): 21-34. doi: 10.11821/dlxb201601002
	[Dai Erfu, Huang Yu, Wu Zhuo, et al. Spatial-temporal features of carbon source-sink and its relationship with climate factors in Inner Mongolia grassland ecosystem[J]. Acta Geographica Sinica, 2016, 71(1): 21-34.] doi: 10.11821/dlxb201601002
[14]	王菲, 曹永强, 周姝含, 等. 黄河流域生态功能区植被碳汇估算及其气候影响要素[J]. 生态学报, 2023, 43(6): 2501-2514.
	[Wang Fei, Cao Yongqiang, Zhou Shuhan, et al. Estimation of vegetation carbon sink in the Yellow River Basin ecological function area and analysis of its main meteorological elements[J]. Acta Ecologica Sinica, 2023, 43(6): 2501-2514.]
[15]	毛永发, 周启刚, 王陶, 等. 耦合PLUS-InVEST-Geodector模型的三峡库区碳储量时空变化及其定量归因[J]. 长江流域资源与环境, 2023, 32(5): 1042-1057.
	[Mao Yongfa, Zhou Qigang, Wang Tao, et al. Spatial-temporal variation of carbon storage and its quantitative attribution in the Three Gorges Reservoir Area coupled with PLUS-InVEST Geodector model[J]. Resources and Environment in the Yangtze Basin, 2023, 32(5): 1042-1057.]
[16]	邓元杰, 姚顺波, 侯孟阳, 等. 退耕还林还草工程对生态系统碳储存服务的影响——以黄土高原丘陵沟壑区子长县为例[J]. 自然资源学报, 2020, 35(4): 826-844. doi: 10.31497/zrzyxb.20200407
	[Deng Yuanjie, Yao Shunbo, Hou Mengyang, et al. Assessing the effects of the green for grain program on ecosystem carbon storage service by linking the InVEST and FLUS models: A case study of Zichang County in hilly and gully region of Loess Plateau[J]. Journal of Natural Resources, 2020, 35(4): 826-844.] doi: 10.31497/zrzyxb.20200407
[17]	Wu Q, Wang L, Wang T Y, et al. Spatial-temporal evolution analysis of multi-scenario land use and carbon storage based on PLUS-InVEST model: A case study in Dalian, China[J]. Ecological Indicators, 2024, 166: 112448, doi: 10.1016/j.ecolind.2024.112448.
[18]	吴美玲, 邵彦川, 胡丽条, 等. 基于机器学习的中国陆地生态系统碳通量预测及其对土地利用变化的响应[J]. 环境科学, 2025, 46(8): 4733-4741.
	[Wu Meiling, Shao Yanchuan, Hu Litiao, et al. Machine learning-based prediction of carbon fluxes in terrestrial ecosystems in China and its response to land use change[J]. Environmental Science, 2025, 46(8): 4733-4741.]
[19]	王可月, 王轶夫, 陈馨, 等. 基于集成学习算法和Optuna调优的江西省森林碳储量遥感估测[J]. 生态学报, 2025, 45(2): 685-700.
	[Wang Keyue, Wang Yifu, Chen Xin, et al. Remote sensing estimation of forest carbon storage in Jiangxi Province based on ensemble learning algorithm and Optuna tuning[J]. Acta Ecologica Sinica, 2025, 45(2): 685-700.]
[20]	包玉斌, 王耀宗, 路锋, 等. 六盘山区国土空间生态安全格局构建与分区优化[J]. 干旱区研究, 2023, 40(7): 1172-1183. doi: 10.13866/j.azr.2023.07.14
	[Bao Yubin, Wang Yaozong, Lu Feng, et al. Construction of an ecological security pattern and zoning optimization for territorial space in the Liupan Mountain Area[J]. Arid Zone Research, 2023, 40(7): 1172-1183.] doi: 10.13866/j.azr.2023.07.14
[21]	包玉斌, 黄涛, 王耀宗, 等. 基于生态重要性与敏感性的六盘山区生态保护修复分区[J]. 干旱区地理, 2023, 46(11): 1778-1791. doi: 10.12118/j.issn.1000-6060.2023.053
	[Bao Yubin, Huang Tao, Wang Yaozong, et al. Zoning for ecological conservation and restoration in Liupan Mountain area based on ecological importance and sensitivity evaluation[J]. Arid Land Geography, 2023, 46(11): 1778-1791.] doi: 10.12118/j.issn.1000-6060.2023.053
[22]	赵慧, 李梦华, 杨建玲, 等. 2000—2020年宁夏植被净初级生产力时空变化及其驱动因子分析[J]. 气象与环境学报, 2024, 40(4): 107-115.
	[Zhao Hui, Li Menghua, Yang Jianling, et al. Spatial and temporal variations of vegetation net primary productivity and its driving factors in Ningxia from 2000 to 2020[J]. Journal of Meteorology and Environment, 2024, 40(4): 107-115.]
[23]	全国森林资源标准化技术委员会(SAC/TC 370). GB/T43648-2024. 主要树种立木生物量模型与碳计量参数[S]. 北京: 中国标准出版社, 2024.
	[National Technical Committee 370 on Forest Resources of Standardization Administration of China (SAC/TC 370). GB/T43648-2024. Tree biomass models and carbon accounting parameters for dominant species[S]. Beijing: Standards Press of China, 2024.]
[24]	林子琦, 温仲明, 刘洋洋, 等. 基于遥感数据的植被碳水利用效率时空变化和归因分析[J]. 生态学报, 2024, 44(1): 377-391.
	[Lin Ziqi, Wen Zhongming, Liu Yangyang, et al. Spatiotemporal variation and attribution of carbon and water use efficiency in the Yellow River Basin based on remote sensing data[J]. Acta Ecologica Sinica, 2024, 44(1): 377-391.]
[25]	李彬, 曹广超, 王苑, 等. 2001—2022年黄河流域青海段植被NPP时空变化及其对气候的响应[J/OL]. 环境科学. [2025-08-02]. https://doi.org/10.13227/j.hjkx.202412233..
	[Li Bin, Cao Guangchao, Wang Yuan, et al. Spatial and temporal changes of vegetation NPP and its response to climate in the Qinghai section of the Yellow River Basin from 2001 to 2022[J/OL]. Environmental Science. [2025-08-02]. https://doi.org/10.13227/j.hjkx.202412233..]
[26]	徐勇, 黄雯婷, 郭振东, 等. 2000—2020年我国西南地区植被NEP时空变化及其驱动因素的相对贡献[J]. 环境科学研究, 2023, 36(3): 557-570.
	[Xu Yong, Huang Wenting, Guo Zhendong, et al. Spatio-temporal variation of vegetation net ecosystem productivity and relative contribution of driving forces in southwest China from 2000 to 2020[J]. Research of Environmental Sciences, 2023, 36(3): 557-570.]
[27]	李美丽, 尹礼昌, 张园, 等. 基于MODIS-EVI的西南地区植被覆盖时空变化及驱动因素研究[J]. 生态学报, 2021, 41(3): 1138-1147.
	[Li Meili, Yin Lichang, Zhang Yuan, et al. Spatio-temporal dynamics of fractional vegetation coverage based on MODIS-EVI and its driving factors in southwest China[J]. Acta Ecologica Sinica, 2021, 41(3): 1138-1147.]
[28]	曾庆雨, 孙才志. 黄河流域陆地生态系统碳储量测算及其影响因素[J]. 生态学报, 2024, 44(13): 5476-5493.
	[Zeng Qingyu, Sun Caizhi. Estimation and influencing factors of carbon storage in terrestrial ecosystems in the Yellow River Basin[J]. Acta Ecologica Sinica, 2024, 44(13): 5476-5493.]
[29]	张怡, 王志慧, 卢小平, 等. 黄土高原生态系统碳汇时空变化及其影响因素[J]. 水土保持研究, 2025, 32(1): 266-274.
	[Zhang Yi, Wang Zhihui, Lu Xiaoping, et al. Evolution of ecosystem carbon sink and its driving factors in the Loess Plateau[J]. Research of Soil and Water Conservation, 2025, 32(1): 266-274.]
[30]	吕文宝, 徐占军, 郭琦, 等. 黄土高原陆地生态系统碳储量的时间演进与空间分异特征[J]. 水土保持研究, 2024, 31(2): 252-263.
	[Lü Wenbao, Xu Zhanjun, Guo Qi, et al. Research on the temporal evolution and spatial differentiation characteristics of carbon storage in terrestrial ecosystems on the Loess Plateau[J]. Research of Soil and Water Conservation, 2024, 31(2): 252-263.]
[31]	Zhang Y, Cheng C X, Wang Z H, et al. Spatiotemporal variation and driving factors of carbon sequestration rate in terrestrial ecosystems of Ningxia, China[J]. Land, 2025, 14(1): 94, doi: 10.3390/land14010094.
[32]	李妙宇, 上官周平, 邓蕾. 黄土高原地区生态系统碳储量空间分布及其影响因素[J]. 生态学报, 2021, 41(17): 6786-6799.
	[Li Miaoyu, Shangguan Zhouping, Deng Lei. Spatial distribution of carbon storages in the terrestrial ecosystems and its influencing factors on the Loess Plateau[J]. Acta Ecologica Sinica, 2021, 41(17): 6786-6799.]

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

Spatiotemporal variation and potential zoning diagnosis of ecosystem carbon sinks in Ningxia

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 32

Related Articles 15

Metrics

Comments

Recommended 0

[1]	CHEN Wenxuan, CHEN Yu, WANG Shenglong. Characteristics of spatial and temporal evolution of agricultural carbon emissions in the Yellow River Basin and analysis of its influencing factors [J]. Arid Land Geography, 2026, 49(4): 713-726.
[2]	RUI Dongsheng, MAO Lu, REN Yanxia, GONG Haoxuan, LI Yanping, FU Zhicong. Spatial distribution characteristics and obstacle factors of relative poverty in Xinjiang [J]. Arid Land Geography, 2025, 48(9): 1672-1682.
[3]	ZHANG Yiming, TANG Yulei, FENG Junbo. Predicting and analysing glaciers in the Qinghai-Xizang Plateau: A random forest model [J]. Arid Land Geography, 2025, 48(8): 1342-1352.
[4]	ZHANG Yifan, CHEN Yapeng, CHEN Yaning, LIANG Qixiang. Estimation of carbon sequestration capacity of Xinjiang terrestrial ecosystem based on natural climate solutions [J]. Arid Land Geography, 2025, 48(7): 1198-1205.
[5]	MIAO Yingfeng, YUAN Ye, ZHOU Zhengwei, ZHAO Jiayu, GUO Yuxi. Spatio-temporal pattern evolution characteristics of crop planting structure on Fenwei Plain [J]. Arid Land Geography, 2025, 48(6): 995-1005.
[6]	ZHANG Yin, SUN Congjian, LIU Geng, CHAO Jinlong, GENG Tianwei, LIU Honghong. Spatiotemporal changes and driving factors of ecological environment quality in the Shanxi section of the Yellow River Basin from 2000 to 2023 [J]. Arid Land Geography, 2025, 48(11): 1983-1994.
[7]	MA Xiaomin, ZHANG Zhibin, GUO Qianqian, ZHAO Xuewei, ZHANG Ning. Spatial and temporal evolution and driving factors of population in Lanzhou City from 2000 to 2020 [J]. Arid Land Geography, 2025, 48(1): 168-178.
[8]	LU Dongyan, ZHU Xiufang, TANG Mingxiu, GUO Chunhua, LIU Tingting. Assessment of drought risk changes in China under different temperature rise scenarios [J]. Arid Land Geography, 2024, 47(3): 369-379.
[9]	REN Guixiu, LIU Kai. Spatiotemporal evolution characteristics and influencing factors of green innovation in the Yellow River Basin [J]. Arid Land Geography, 2024, 47(1): 158-169.
[10]	YUAN Hongwei, CAI Jun, ZHANG Lei. Temporal and spatial changes of human activities and habitat quality in national key ecological function areas and their spatial effects [J]. Arid Land Geography, 2023, 46(6): 934-948.
[11]	HAN Yanli,YU Deyong,CHEN Kelong,YANG Haizhen. Spatial distribution characteristics of temperature and precipitation trend in Qinghai Lake Basin from 2000 to 2018 [J]. Arid Land Geography, 2022, 45(4): 999-1009.
[12]	PAN Qun,SHI Haiyang,ZHANG Wenqiang,LUO Geping,CHEN Chunbo. Spatiotemporal distribution of rat population density in grassland on the north slope of Tianshan Mountains based on Cubist model [J]. Arid Land Geography, 2022, 45(4): 1200-1211.
[13]	SUN Jianwu,GAO Junbo,MA Zhifei,YU Chao,ZHANG Xinyi. Comparison of spatial poverty trap formation mechanisms in different geographical environments: A case of Dabie Mountains and Loess Plateau [J]. Arid Land Geography, 2022, 45(2): 650-659.
[14]	JIANG Li,WEI Tianxing,LI Yiran,WEI Anqi. Effects of topographical factors on tree species distribution of shelter forest in Loess hilly region of northern Shaanxi [J]. Arid Land Geography, 2021, 44(6): 1763-1771.
[15]	HUANG Yue,CHENG Jing,WANG Peng. Spatiotemporal evolution pattern and driving factors of ecological vulnerability in agro-pastoral region in northern China: A case of Yanchi County in Ningxia [J]. Arid Land Geography, 2021, 44(4): 1175-1185.