青海湖流域草地植被生物量空间分布格局研究

doi:10.12118/j.issn.1000-6060.2025.204

Abstract

Abstract:

Grassland vegetation biomass is a key indicator of grassland ecosystem productivity and carbon storage. Its spatial distribution pattern and driving mechanisms are of great scientific significance for understanding the maintenance of regional grassland ecosystem structure and function and their responses to climate change. Taking the Qinghai Lake Basin as the study area, this research integrates field sampling data (collected in July-August 2023) and remote sensing data to analyze the spatial distribution characteristics of grassland vegetation biomass (including aboveground and belowground components) and to explore its driving pathways using statistical analysis, correlation analysis, and structural equation modeling. The results reveal (1) Significant differences in biomass among different vegetation types, with meadow types demonstrating higher values than steppe types. Aboveground biomass is highest in mountain shrub meadows (311.54 g·m^-2) and lowest in mountain solifluction meadows (64.67 g·m^-2), whereas belowground biomass is highest in dwarf kobresia meadows (3534.05 g·m^-2) and lowest in rhodiola desert (339.12 g·m^-2). (2) High aboveground biomass values are primarily concentrated in the middle and lower reaches of the Shaliu River Basin and the southern region surrounding the lake, whereas the high-value areas for belowground and total biomass are primarily located in the middle reaches of the Buha River, the Quanji River, the Qiadangqu River Basin and middle reaches of the Shaliu River. Lower altitude areas provide more suitable temperatures and fertile soil, thereby promoting the growth of aboveground parts. Conversely, due to colder conditions and poorer soils, higher altitude regions drive plants to enhance root system development and improve resource acquisition capacity. (3) Structural equation modeling revealed that ecosystem carbon use efficiency (total effect: -0.44) and soil bulk density (total effect: -0.59) were direct factors affecting both aboveground and belowground biomass of grassland vegetation. In conclusion, vegetation type, and regional environment collectively affected grassland vegetation in the Qinghai Lake Basin, identifying ecosystem carbon use efficiency and soil bulk density as the primary determinants. This study provides critical data and scientific support for understanding vegetation biomass spatial patterns and for guiding grassland conservation and restoration in the Qinghai Lake Basin.

Key words: root-shoot ratio, spatial distribution, above-ground biomass, Qinghai Lake Basin

PEI Ruoying, CAO Shengkui, HOU Yaofang, LEI Yizhen, WANG Jiang, LIU Zhenmei, DING Chenshen. Spatial distribution pattern of grassland vegetation biomass in the Qinghai Lake Basin[J].Arid Land Geography, 2026, 49(2): 301-315.

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指标	最大值	最小值	平均值	标准差	中值
地上生物量/g·m^-2	489.20	28.53	141.41	96.82	109.47
地下生物量/g·m^-2	7690.49	212.31	2787.19	1649.18	2465.20
总生物量/g·m^-2	7778.89	314.09	2928.53	1661.50	2729.98
根冠比	119.66	1.55	29.93	25.23	20.75

影响因素	总效益		直接效益		间接效益
影响因素	地上生物量	地下生物量	地上生物量	地下生物量	地上生物量	地下生物量
地表温度	-0.05	-0.28	-	-0.22	-0.05	-0.06
降水量	0.36	0.07	0.22	-	0.15	0.07
土壤含水量	0.22	0.21	-	-0.20	0.22	0.41
土壤容重	-0.31	-0.59	-0.10	-0.59	-0.21	-
生态系统碳利用效率	-0.44	-	-0.44	-	-	-

Spatial distribution pattern of grassland vegetation biomass in the Qinghai Lake Basin

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