基于PLUS-InVEST模型的天山北坡生态系统碳储量时空变化与预测

doi:10.13866/j.azr.2024.07.14

Abstract

Abstract:

As land use change is a critical factor affecting carbon storage in terrestrial ecosystems, investigating the correlations between the two is of great significance for maintaining the regional carbon balance and achieving carbon peaking and carbon neutrality goals. Based on the Third Comprehensive Scientific Expedition of Xinjiang, this study analyzes the quantitative relationship between land use change and carbon storage on the northern slope of Tianshan Mountains in 1990, 2000, 2010, and 2020. It also simulates the probable spatial distribution of regional carbon storage in 2030 under the three scenarios of historical trend, water resource conservation, and economic development by combining the PLUS and InVEST models. The findings revealed the following: (1) Grassland and barren constitute the main land cover types on the northern slope of Tianshan Mountains; the area of cropland, forest, construction land, and wetland, as well as water, increased between 1990 and 2020; cropland increased most significantly, while the others decreased, most considerably in grassland. (2) Carbon storage on the northern slope of Tianshan Mountains first enhanced and then declined from 1990 to 2020, with conversions among the grassland, barren, cropland, and forest being the primary drivers of alterations in total carbon storage. (3) The carbon storage under all three scenarios was lower than the 2020 level, with the most remarkable reduction in the economic development scenario. A decrease in grassland and an increase in barren are the leading causes of carbon loss.

Key words: land use change, carbon storage, PLUS model, InVEST model, northern slope of Tianshan Mountains

ZHANG Shunxin, WU Zihao, YAN Qingwu, LI Gui’e, MU Shouguo. Spatiotemporal changes in the ecosystem carbon storage on the northern slope of the Tianshan Mountains and simulations based on the PLUS-InVEST model[J].Arid Zone Research, 2024, 41(7): 1228-1237.

Figures/Tables 11

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References 40

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数据类型	数据名称	分辨率	数据来源
地形	DEM	30 m	地理空间数据云（https://www.gscloud.cn）
地形	坡度	30 m	在ArcGIS中基于DEM数据，采用“坡度”工具获取
气候	年平均气温	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn）
	年平均降水	1 km	国家青藏高原科学数据中心（https://data.tpdc.ac.cn）
	NDVI	30 m	科学数据银行（https://www.scidb.cn）
经济	人口密度	1 km	WorldPop数据集（https://hub.worldpop.org）
经济	GDP	1 km	中国科学院资源环境科学与数据中心（https://www.resdc.cn)
区位	到高速公路的距离	30 m	OSM数据经ArcGIS“欧氏距离”工具获取（https://www.openstreetmap.org）
	到国道的距离
	到省道的距离
	到县道的距离
	到铁路的距离
	到河流的距离
土地利用数据			中国年度土地覆盖数据集（https://zenodo.org）

土地利用类型	地上碳密度	地下碳密度	土壤碳密度	死亡有机物碳密度
耕地	51.79	61.49	7073.94	124.00
林地	551.67	162.86	9956.53	248.00
灌木	78.30	51.42	5895.74	80.00
草地	8.71	76.58	6975.63	22.00
水域	11.41	8.10	0.00	0.00
冰川/积雪	0.00	0.00	0.00	0.00
裸地	8.10	15.46	3559.78	0.00
建设用地	40.38	25.90	0.00	0.00
湿地	33.01	26.39	19640.14	0.00

土地利用类型	1990年	2000年	2010年	2020年	土地利用类型	1990年	2000年	2010年	2020年
耕地	10442.75	11510.24	15543.89	16453.72	冰川/积雪	0.00	0.00	0.00	0.00
林地	2921.97	3667.97	4114.96	4492.31	裸地	22373.26	22179.95	20260.68	21359.94
灌木	0.30	0.04	0.02	0.18	建设用地	1.53	3.71	7.39	10.85
草地	46347.76	44970.54	43872.70	40359.76	湿地	0.87	0.84	0.85	1.06
水域	2.69	3.33	3.92	3.93	总计	82091.13	82336.62	83804.41	82681.75

土地利用类型	历史趋势情景	水资源保护情景	经济发展情景
耕地	23376.13	23360.71	23357.68
林地	4433.79	4566.95	4433.28
灌木	0.36	0.45	0.44
草地	52969.71	52790.99	52898.10
水域	2019.50	2212.21	2013.50
冰川/积雪	2196.84	2118.95	2202.76
裸地	62061.04	61945.84	61998.42
建设用地	2078.90	2140.16	2232.11
湿地	0.64	0.64	0.64

土地利用类型	历史趋势情景	变化（较2020年）	水资源保护情景	变化（较2020年）	经济发展情景	变化（较2020年）
耕地	17090.80	637.09	17079.53	625.81	17077.31	623.59
林地	4841.28	348.96	4986.68	494.37	4840.72	348.41
灌木	0.22	0.05	0.28	0.10	0.27	0.09
草地	37518.04	-2841.72	37391.45	-2968.31	37467.32	-2892.44
水域	3.94	0.01	4.32	0.38	3.93	0.00
冰川/积雪	0.00	0.00	0.00	0.00	0.00	0.00
裸地	22238.60	878.67	22197.32	837.39	22216.16	856.23
建设用地	13.78	2.93	14.18	3.34	14.79	3.95
湿地	1.26	0.20	1.26	0.20	1.26	0.20
总计	81707.92	-973.81	81675.02	-1006.72	81621.76	-1059.97

Spatiotemporal changes in the ecosystem carbon storage on the northern slope of the Tianshan Mountains and simulations based on the PLUS-InVEST model

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