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中国抚河流域碳储量的时空变化及其驱动因素

Spatiotemporal variation and driving forces of carbon storage in the Fuhe river basin, China.

作者信息

Chen Ziwei, Wei Kai, Hu Longsong, Yi Caiqiong, Liu Shiyu

机构信息

Jiangxi Agricultural University, Nanchang, 330045, China.

Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin, Nanchang, 330029, Jiangxi, China.

出版信息

Sci Rep. 2025 Aug 18;15(1):30224. doi: 10.1038/s41598-025-14518-7.

DOI:10.1038/s41598-025-14518-7
PMID:40825802
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12361439/
Abstract

The relationship between land use change and regional carbon storage is closely linked. Understanding how land use changes affect regional carbon storage is crucial for maintaining the carbon balance of ecosystems. This study integrated the advantages of the PLUS model, the InVEST model, and the optimal parameter-based geographic detector (OPGD) models to analyze the spatiotemporal variation in land use patterns and carbon storage in the Fuhe River Basin under three scenarios in 2030, and analyze the driving forces of the spatial differentiation of carbon storage. The results show that the following: (1) From 1980 to 2020, the areas of water and construction land in the Fuhe River Basin increased by 28.08 km² and 217.65 km², respectively, while the areas of cultivated land, woodland, grassland, and unused land decreased by 115.33 km², 112.79 km², 88.79 km², and 1.36 km², respectively. (2) Between 1980 and 2020, the total carbon storage in the Fuhe River Basin showed a decreasing trend, with a reduction of 20.39 × 10 t. The main drivers of this decline were the reduction in woodland area and the expansion of construction land. (3) By 2030, carbon storage is projected to continue decreasing under all three scenarios, with the ecological protection scenario showing the most pronounced mitigating effect on the reduction of carbon storage. (4) The spatial differentiation of carbon storage in the Fuhe River Basin is influenced by various factors, including land use intensity, NDVI, elevation, and slope, with land use intensity having the strongest explanatory power, reaching 0.24. This study offers policymakers valuable insights for optimizing ecosystem carbon storage and provides essential guidance for achieving the "dual carbon" goals.

摘要

土地利用变化与区域碳储量之间的关系紧密相连。了解土地利用变化如何影响区域碳储量对于维持生态系统的碳平衡至关重要。本研究综合了PLUS模型、InVEST模型和基于最优参数的地理探测器(OPGD)模型的优势,分析了2030年三种情景下抚河流域土地利用格局和碳储量的时空变化,并分析了碳储量空间分异的驱动因素。结果表明:(1)1980年至2020年,抚河流域水域和建设用地面积分别增加了28.08平方千米和217.65平方千米,而耕地、林地、草地和未利用地面积分别减少了115.33平方千米、112.79平方千米、88.79平方千米和1.36平方千米。(2)1980年至2020年,抚河流域碳储量总量呈下降趋势,减少了20.39×10吨。这种下降的主要驱动因素是林地面积的减少和建设用地的扩张。(3)到2030年,在所有三种情景下碳储量预计将继续下降,其中生态保护情景对碳储量减少的缓解作用最为明显。(4)抚河流域碳储量的空间分异受多种因素影响,包括土地利用强度、归一化植被指数(NDVI)、海拔和坡度,其中土地利用强度的解释力最强,达到0.24。本研究为政策制定者优化生态系统碳储量提供了有价值的见解,并为实现“双碳”目标提供了重要指导。

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Sci Rep. 2025 Feb 22;15(1):6468. doi: 10.1038/s41598-025-89407-0.
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Spatiotemporal variation and dynamic simulation of carbon stock based on PLUS and InVEST models in the Li River Basin, China.
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Sci Rep. 2025 Feb 19;15(1):6060. doi: 10.1038/s41598-025-86226-1.
4
Response of carbon storage to land use change and multi-scenario predictions in Zunyi, China.中国遵义碳储量对土地利用变化的响应及多情景预测
Sci Rep. 2025 Jan 2;15(1):236. doi: 10.1038/s41598-024-81444-5.
5
[Temporal and Spatial Evolution and Prediction of Ecosystem Carbon Storage in Jiangxi Province Based on PLUS-InVEST Model].基于PLUS-InVEST模型的江西省生态系统碳储量时空演变及预测
Huan Jing Ke Xue. 2024 Jun 8;45(6):3284-3296. doi: 10.13227/j.hjkx.202305239.
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J Environ Manage. 2022 Jun 15;312:114911. doi: 10.1016/j.jenvman.2022.114911. Epub 2022 Mar 16.
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