基于Biome-BGC模型的浙江凤阳山针阔混交林碳动态模拟

黄璐瑶, 杜珊凤, 纪小芳, 管鑫, 刘胜龙, 叶丽敏, 姜姜

南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (5) : 11-20.

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南京林业大学学报（自然科学版）
PDF(4196 KB)
南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (5) : 11-20. DOI: 10.12302/j.issn.1000-2006.202211005
专题报道:自然保护地森林生态系统研究

基于Biome-BGC模型的浙江凤阳山针阔混交林碳动态模拟

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Carbon dynamic simulation based on Biome-BGC model in mixed coniferous and broadleaved forest of Fengyang Mountain, Zhejiang Province

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摘要

【目的】探究浙江省凤阳山亚热带针阔混交林的碳动态变化规律及其对气候变化的响应。【方法】运用Biome- BGC模型模拟了1979—2018年凤阳山的净初级生产力(NPP)、总初级生产力(GPP)和净生态系统生产力(NEP),对不同时间尺度的气候因子和NPP之间做皮尔逊相关性分析与二次函数拟合,探究NPP与主要气候因子的关系及响应模式,最后设定不同气候情景预测凤阳山未来100 a的碳动态变化趋势。【结果】过去40年凤阳山针阔混交林GPP、NPP、NEP的平均值分别为1 392.94、451.25、16.21 g/(m2·a),除了1984、2002、2005、2008及2010年,其余年份为碳汇,且呈现“碳源—碳汇”季节交替的特征。NPP对气温变化的敏感程度最高,夏季气温的上升对NPP的增加起积极作用,而冬季气温的升高却对NPP起到反作用。一定程度内,冬季降水对NPP有促进作用,而夏季降水对NPP为负作用。RCP2.6、RCP4.5、RCP6.0情景下凤阳山森林总初级生产力在21世纪均呈现上升趋势,至2100年,RCP2.6、RCP4.5和RCP6.0情景下凤阳山GPP分别达到1 552.73、1 660.30及1 960.41 g/(m2·a),相对于2018年GPP分别增加1.38%、8.41%和28.00%。【结论】凤阳山森林生态系统在正常情况下表现为碳汇,但山区夏季阴雨天气一定程度上抑制了气温对碳汇的增加作用。未来增温、降水量增加、CO2浓度升高同时作用下,将有利于凤阳山针阔混交林的生长。

Abstract

【Objective】This study aims to investigate the carbon dynamics of subtropical mixed coniferous and broadleaved forests in Fengyang Mountain, Zhejiang Province and their response to climate change. 【Method】The Biome-BGC model was used to simulate the net primary productivity (NPP), gross primary productivity (GPP), and net ecosystem productivity (NEP) in Fengyang Mountain from 1979 to 2018, to investigate the relationships between climate factors and NPP at different time scales. Pearson correlation analysis and quadratic function fitting were performed between climate factors and NPP at different temporal scales to explore the relationship and response patterns between NPP and major climate factors, and finally, different climate scenarios were applied to predict the carbon cycling trends in Fengyang Mountain in the next 100 years. 【Result】The average values of GPP, NPP and NEP of mixed coniferous and broadleaved forests in Fengyang Mountain for 40 years were 1 392.94, 451.25 and 16.21 g/(m2·a), respectively. Except for 1984, 2002, 2005, 2008 and 2010, which were carbon sinks and showed that the sensitivity of NPP to temperature change was the highest, and the increase of temperature in summer had a positive effect on the increase of NPP, while the increase of temperature in winter had a negative effect on NPP. To a certain extent, winter rainfall showed a positive effect on NPP, while summer precipitation showed a negative effect on NPP. The gross primary productivity of Fengyang Mountain forests in RCP2.6, RCP4.5 and RCP6.0 scenarios will keep increasing in the 21st century, and by 2100, the GPP of the studied forests in Fengyang Mountain under RCP2.6, RCP4.5 and RCP6.0 scenarios will reach 1 552.73, 1 660.30 and 1 960.41 g/(m2·a), respectively, and get increased 1.38%, 8.41% and 28.00% relative to the GPP in 2018. 【Conclusion】Overall, the forest ecosystem of Fengyang Mountain exhibited carbon sinks under normal conditions, but the cloudy and rainy summer weather in the mountainous area inhibited the increasing effect of temperature on carbon sinks to some extent. The future warming, increased rainfall and higher CO2 concentration simultaneously will favor the vegetation growth of mixed coniferous forests in Fengyang Mountain.

关键词

针阔混交林 / Biome-BGC模型 / 碳动态 / 气候变化 / 浙江凤阳山

Key words

mixed coniferous and broadleaved forest / Biome-BGC model / carbon dynamics / climate change / Fengyang Mountain of Zhejiang Province

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黄璐瑶, 杜珊凤, 纪小芳, 等. 基于Biome-BGC模型的浙江凤阳山针阔混交林碳动态模拟[J]. 南京林业大学学报（自然科学版）. 2024, 48(5): 11-20 https://doi.org/10.12302/j.issn.1000-2006.202211005
HUANG Luyao, DU Shanfeng, JI Xiaofang, et al. Carbon dynamic simulation based on Biome-BGC model in mixed coniferous and broadleaved forest of Fengyang Mountain, Zhejiang Province[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2024, 48(5): 11-20 https://doi.org/10.12302/j.issn.1000-2006.202211005
中图分类号： S718.5   

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基金

百山祖国家公园科学研究项目(2022JBGS03)
百山祖国家公园科学研究项目(2021ZDLY01)
国家自然科学基金项目(32071612)
江苏省碳达峰碳中和科技创新专项(BE2022307)

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