1990－2020年黄河流域碳储量时空演变及驱动因素研究

doi:10.16258/j.cnki.1674-5906.2025.03.001

生态环境学报 ›› 2025, Vol. 34 ›› Issue (3): 333-344.DOI: 10.16258/j.cnki.1674-5906.2025.03.001

• 碳循环与碳减排专栏 • 下一篇

1990－2020年黄河流域碳储量时空演变及驱动因素研究

李曼¹(), 吴东丽², 何昊¹, 余慧婕¹, 赵琳¹, 刘聪², 胡正华¹^,^*(), 李琪¹^,^*()

1.南京信息工程大学气象灾害预报预警与评估协同创新中心/生态与应用气象学院，江苏南京 210044
2.中国气象局气象探测中心，北京 100081

收稿日期:2024-06-17 出版日期:2025-03-18 发布日期:2025-03-24
通讯作者: 李琪。E-mail: liqix123@sina.com
*胡正华。E-mail: zhhu@nuist.edu.cn;
作者简介:李曼（1999年生），女，硕士研究生，研究方向为碳汇与生态遥感。E-mail: liman66622@163.com
基金资助:
中国气象局气候变化专题项目(QBZ202309)

Spatio-temporal Evolution and Driving Factors of Carbon Storage in the Yellow River Basin from 1990 to 2020

LI Man¹(), WU Dongli², HE Hao¹, YU Huijie¹, ZHAO Lin¹, LIU Cong², HU Zhenghua¹^,^*(), LI Qi¹^,^*()

1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters/School of Ecology and Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, P. R. China
2. Meteorological Observation Centre of China Meteorological Administration, Beijing 100081, P. R. China

Received:2024-06-17 Online:2025-03-18 Published:2025-03-24

摘要/Abstract

摘要：

准确估算区域尺度的陆地生态系统碳储量及其驱动因素，对于制定科学合理的土地利用政策具有重要意义。基于土地利用/覆被数据和气象站点数据，运用InVEST（Integrated Valuation of Ecosystem Services and Tradeoffs）模型定量估算了1990－2020年黄河流域碳储量的时空分布。通过土地利用转移矩阵和碳储量贡献率分析土地利用变化对碳储量的影响，并采用最优参数地理探测器（OPGD）识别碳储量空间分异性的主要驱动因素。结果表明，1990－2020年间，黄河流域耕地面积减少，而林地、草地、建设用地面积增加，碳储量值呈现波动上升趋势，增加了0.549×10⁸ t，增幅为0.37%，经历了1990－1995年和2005－2010年两个增加阶段，以及1995－2005年和2010－2020年两个减少阶段。碳储量的空间分布具有明显的异质性，碳储量变化呈现零散分布，增减不一的特点。极显著热点区集中在青海、陕西、内蒙古等森林覆盖较广泛的山区，冷点分布在经济发达地区。草地是主要碳储存类型，未利用地转为草地对碳储量贡献最大（73.3%），耕地转为建设用地对碳储量产生最大负效应（−20.8%）。在5 km最优空间尺度和因子最佳空间离散化参数下，单因子和交互探测分别显示，归一化植被指数（NDVI）是碳储量空间分异性的主要驱动因素（20.7%），坡度、降水和日照等因素也具有显著影响；归一化植被指数与高程的组合解释力最强，达到29.0%。综合考虑自然地理和气候因素，因地制宜地制定土地利用政策，平衡城市扩张、农业发展与生态保护，是实现区域碳储量增加的关键。

关键词: 碳储量, InVEST模型, 土地利用变化, 最优参数地理探测器, 驱动因素, 黄河流域

Abstract:

In the context of climate change, accurately estimating regional-scale terrestrial ecosystem carbon storage and identifying its driving factors are crucial for developing scientifically sound land use policies and achieving sustainable development. Using land use/cover and meteorological station data, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model was employed to quantitatively estimate the spatial and temporal distributions of carbon storage in the Yellow River Basin from 1990 to 2020. The impact of land use change on carbon storage was analyzed using land use transition matrices and carbon storage contribution rates. Additionally, an optimal parameter-based geographical detector (OPGD) was used to identify the primary driving factors behind the spatial heterogeneity of carbon storage. The results indicated that cropland area decreased between 1990 and 2020, whereas forest, grassland, and construction land increased in the Yellow River Basin. The carbon storage exhibited a fluctuating upward trend, increasing by 0.549×10⁸ t, with a growth rate of 0.37%. Two periods of increase were noted, from 1990 to 1995 and from 2005 to 2010, whereas two periods of decline occurred between 1995 and 2005, and from 2010 to 2020. The spatial distribution of carbon storage displayed significant heterogeneity with scattered and uneven changes. Highly significant hotspots of carbon storage were found in forest-covered mountainous regions such as Qinghai, Shaanxi, and Inner Mongolia, whereas cold spots were identified in economically developed areas. Grasslands were identified as the primary carbon storage type, with the conversion of unused land to grassland contributing the most to carbon storage (73.3%), whereas the conversion of cropland to construction land had the most negative impact (−20.8%). At an optimal spatial scale of 5 km with the best spatial discretization parameters, single-factor and interaction detections were conducted, revealing that the Normalized Difference Vegetation Index (NDVI) was the main driving factor of spatial differentiation in carbon storage (20.7%), with slope, precipitation, and sunshine duration also having significant effects. The combination of NDVI and elevation had the strongest explanatory power, reaching 29.0%. Therefore, it is essential to comprehensively consider the natural geographical and climatic factors and formulate land-use policies tailored to local conditions. Balancing urban expansion, agricultural development, and ecological protection is crucial for enhancing regional carbon storage.

Key words: carbon storage, InVEST model, land-use change, optimal parameters-based geographical detector model, driving factor, the Yellow River Basin

中图分类号:

X144

李曼, 吴东丽, 何昊, 余慧婕, 赵琳, 刘聪, 胡正华, 李琪. 1990－2020年黄河流域碳储量时空演变及驱动因素研究[J]. 生态环境学报, 2025, 34(3): 333-344.

LI Man, WU Dongli, HE Hao, YU Huijie, ZHAO Lin, LIU Cong, HU Zhenghua, LI Qi. Spatio-temporal Evolution and Driving Factors of Carbon Storage in the Yellow River Basin from 1990 to 2020[J]. Ecology and Environment, 2025, 34(3): 333-344.

图/表 11

参考文献 56

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土地利用类型	地上碳密度	地下碳密度	土壤碳密度
耕地	17.0	80.7	108
林地	42.4	116	159
草地	35.3	86.5	99.9
水域	0.30	0	0
建设用地	2.50	27.5	78.0
未利用地	1.30	0	21.6

土地利用类型	地上碳密度	地下碳密度	土壤碳密度
耕地	17.0	80.7	108
林地	42.4	116	159
草地	35.3	86.5	99.9
水域	0.30	0	0
建设用地	2.50	27.5	78.0
未利用地	1.30	0	21.6

土地利用类型	地上碳密度	地下碳密度	土壤碳密度
耕地	14.5	68.7	99.7
林地	36.1	98.7	147
草地	30.0	73.6	92.2
水域	0.26	0	0
建设用地	2.13	23.4	72.0
未利用地	1.11	0	19.9

土地利用类型	地上碳密度	地下碳密度	土壤碳密度
耕地	14.5	68.7	99.7
林地	36.1	98.7	147
草地	30.0	73.6	92.2
水域	0.26	0	0
建设用地	2.13	23.4	72.0
未利用地	1.11	0	19.9

土地利用类型	碳储量/10⁸ t
土地利用类型	1990	1995	2000	2005	2010	2015	2020
耕地	39.8	39.8	40.1	39.3	38.9	38.7	37.8
林地	29.1	27.5	29.0	29.8	29.8	29.8	30.0
草地	75.1	76.9	74.8	74.4	75.4	75.3	75.6
水域	0.004	0.003	0.004	0.004	0.004	0.004	0.004
建设用地	1.72	1.76	1.86	2.01	2.51	2.70	3.02
未利用地	1.54	1.48	1.52	1.56	1.38	1.37	1.35

1990－2020年黄河流域碳储量时空演变及驱动因素研究

Spatio-temporal Evolution and Driving Factors of Carbon Storage in the Yellow River Basin from 1990 to 2020

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土地利用类型转换	碳储量贡献率/%
土地利用类型转换	1990‒1995	1995‒2000	2000‒2005	2005‒2010	2010‒2015	2015‒2020	1990‒2020
耕地→林地	1.05	0.830	0.595	0.406	0.010	0.265	2.51
耕地→草地	2.87	2.23	0.403	1.27	0.050	1.27	3.78
耕地→水域	−0.220	−0.309	−0.144	−0.296	−0.025	−0.159	−0.716
耕地→建设用地	−0.885	−1.05	−0.333	−5.90	−0.324	−2.24	−20.8
耕地→未利用地	−0.106	−0.185	−0.059	−0.022	−0.002	−0.382	−0.178
林地→耕地	−0.832	−1.04	−0.014	−0.222	−0.017	−0.143	−0.380
林地→草地	−21.0	−4.43	−0.047	−0.404	−0.047	−0.654	−2.05
林地→水域	−0.005	−0.003	−0.001	−0.008	0.000	−0.003	−0.016
林地→建设用地	−0.003	−0.002	−0.002	−0.041	−0.007	−0.028	−0.137
林地→未利用地	−0.081	−0.008	−0.002	−0.029	0.000	−0.013	−0.013
草地→耕地	−2.82	−3.51	−0.214	−1.31	−0.052	−0.527	−3.07
草地→林地	4.65	20.1	0.519	0.660	0.052	0.834	4.36
草地→水域	−0.107	−0.408	−0.030	−0.074	−0.007	−0.150	−0.610
草地→建设用地	−0.018	−0.038	−0.017	−0.441	−0.111	−0.746	−2.48
草地→未利用地	−15.0	−31.1	−2.51	−11.7	−0.130	−5.73	−17.6
水域→耕地	0.629	0.363	0.025	0.382	0.003	0.050	0.721
水域→林地	0.005	0.005	0.000	0.001	0.000	0.002	0.004
水域→草地	0.406	0.077	0.008	0.061	0.007	0.049	0.116
水域→建设用地	0.001	0.000	0.000	0.011	0.001	0.002	0.019
水域→未利用地	0.015	0.005	0.003	0.001	0.000	0.002	0.008
建设用地→耕地	0.588	0.428	0.001	1.05	0.010	0.419	0.593
建设用地→林地	0.001	0.002	0.000	0.001	0.000	0.009	0.001
建设用地→草地	0.023	0.012	0.000	0.020	0.005	0.127	0.006
建设用地→水域	0.000	−0.001	0.000	−0.002	0.000	−0.014	−0.005
建设用地→未利用地	0.000	−0.001	0.000	0.000	0.000	−0.010	0.000
未利用地→耕地	0.193	0.229	0.016	0.539	0.010	0.087	0.644
未利用地→林地	0.016	0.076	0.007	0.010	0.001	0.152	0.195
未利用地→草地	35.4	16.0	0.256	60.4	0.154	8.94	73.3
未利用地→水域	−0.005	−0.014	−0.001	−0.009	−0.001	−0.002	−0.024
未利用地→建设用地	0.002	0.000	0.000	0.024	0.006	0.061	0.139

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