Wang Xinyun, Zhu Ji, Pan Peipei
School of Ecology and Environmental Sciences, Ningxia University, Yinchuan, Ningxia, China.
College of Land Science and Spatial Planning, Hebei Geo University, Shijiazhuang, Hebei, China.
PLoS One. 2024 Dec 16;19(12):e0315329. doi: 10.1371/journal.pone.0315329. eCollection 2024.
Grassland plays a crucial role in the global cycles of matter, energy, water and, climate regulation. Biomass serves as one of the fundamental indicators for evaluating the ecological status of grassland. This study utilized the Carnegie-Ames-Stanford Approach (CASA) model to estimate Net Primary Productivity (NPP) from meteorological data and the Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) remote sensing data for northern China's temperate and alpine grasslands from 1981 to 2015. NPP was subsequently converted into aboveground biomass (AGB). The dynamic changes in grassland AGB were analyzed, and the influence of climate change was examined. The results indicate strong agreement between AGB estimations from the CASA model and Gill method based on field-measured AGB, confirming the model's reliability for these regions. The dynamic changes in AGB exhibited a significant increasing trend of 1.31 g/m2. Grazing intensity (GI), soil moisture, and mean annual precipitation are identified as key factors influencing changes in grassland AGB. Our findings indicate that precipitation and soil moisture are the primary drivers of AGB accumulation during the growing season (spring, summer, and autumn), while temperature plays a critical role in supporting biomass accumulation during winter. Higher temperatures in winter contributes to increased AGB in the following spring, particularly in desert steppe and alpine meadow ecosystems. These insights highlight the complex interaction between climate factors and human activities in shaping grassland productivity across different seasons.
草原在全球物质、能量、水分循环以及气候调节中发挥着至关重要的作用。生物量是评估草原生态状况的基本指标之一。本研究利用卡内基-艾姆斯-斯坦福方法（CASA）模型，根据气象数据以及1981年至2015年中国北方温带和高寒草原的全球库存监测与建模系统（GIMMS）归一化植被指数（NDVI）遥感数据估算净初级生产力（NPP）。随后将NPP转换为地上生物量（AGB）。分析了草原AGB的动态变化，并考察了气候变化的影响。结果表明，CASA模型估算的AGB与基于实地测量AGB的吉尔方法之间具有高度一致性，证实了该模型在这些地区的可靠性。AGB的动态变化呈现出显著的增加趋势，增幅为1.31克/平方米。放牧强度（GI）、土壤湿度和年平均降水量被确定为影响草原AGB变化的关键因素。我们的研究结果表明，降水和土壤湿度是生长季节（春季、夏季和秋季）AGB积累的主要驱动因素，而温度在冬季生物量积累过程中起着关键作用。冬季较高的温度有助于次年春季AGB增加，特别是在荒漠草原和高寒草甸生态系统中。这些见解凸显了气候因素与人类活动在不同季节塑造草原生产力方面的复杂相互作用。
