文献类型：期刊文献

英文题名：Application of Climate Sensitivity Transfer Matrix Growth Model in Qinghai Province

作者：Chen, Keyi[1] Yan, Ni[2] He, Youjun[1] Wang, Jianjun[1]

第一作者：陈科屹

通信作者：Wang, JJ[1]

机构：[1]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Dept Planning & Finance, Beijing 100714, Peoples R China

年份：2025

卷号：16

期号：11

外文期刊名：FORESTS

收录：;EI(收录号：20254819610522);Scopus(收录号：2-s2.0-105023099282);WOS:【SCI-EXPANDED(收录号:WOS:001624352700001)】；

基金：Research on Quantification of Forest Ecological Compensation in Three Gorges Reservoir Area Based on the Value of Carbon Sinks, Funded by the Research Institute of Forestry Policy and Information, Chinese Academy of Forestry.

语种：英文

外文关键词：climate sensitivity; transfer matrix growth models; natural forests; Qinghai Province; stock volume

摘要：This study utilizes data from the eighth and ninth Chinese National Forest Inventories of Qinghai Province to establish a climate-sensitive transfer matrix growth model for natural forests in Qinghai Province. The model considers tree species diversity (Sd), size diversity (Dc), mean annual temperature (MAT), and mean annual precipitation (MAP) and their impacts on tree growth, mortality, and recruitment. Additionally, the forest stand growth and development were predicted under different climate scenarios (RCP2.6, RCP4.5, RCP8.5) for the next 50 years. The results show that the number of Qinghai spruce (Picea crassifolia Kom.) and White birch (Betula platyphylla Sukaczev) trees per hectare gradually decreases, but the stock volume continues to increase. The number of trees per hectare remains relatively stable (from 2235 to 855), with stock volume increasing annually for the first 30 years of the simulation and then stabilizing (from 76.96 to 798.02). Other tree species groups exhibit a continuous annual increase. Comparing the changes in stock volume and tree numbers under three different climate scenarios, there was no significant difference, and the overall trend remained similar. The finding fills a gap in the research on climate-sensitive transfer matrix growth models for natural forests in Qinghai Province. Compared to single-tree and whole-stand models, this model can predict forest stand growth more quickly and effectively, providing a reliable reference for future forest management. It helps formulate policies to address climate change and promote the sustainable development of forest health. This achievement will contribute to a better understanding of future forest stand growth trends, offering valuable insights for sustainable forest management.