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引用本文:张婷,周军志,李建柱,陈伏龙,冯平.2022.陆地生态系统碳水通量特征研究进展[J].地球环境学报,13(6):645-666
ZHANG Ting, ZHOU Junzhi, LI Jianzhu, CHEN Fulong, FENG Ping.2022.Review on research and development of carbon and water fluxes characteristics in terrestrial ecosystem[J].Journal of Earth Environment,13(6):645-666
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陆地生态系统碳水通量特征研究进展
张婷,周军志,李建柱,陈伏龙,冯平
1.天津大学 水利工程仿真与安全国家重点实验室,天津 300072
2.石河子大学 水利建筑工程学院,石河子 832003
摘要:
CO2/H2O通量特征对于揭示微尺度生态系统的区域碳循环规律及机制具有重要意义。陆地生态系统作为全球生态系统重要组成部分,其CO2/H2O通量变化深刻影响着全球碳循环和水循环平衡。本文综述了碳通量、水汽通量的研究方法,归纳了各类研究方法的研究进展,评述了各类方法的优势与不足。总结了我国包括森林、草地、农田、荒漠等不同陆地生态系统碳通量和水汽通量的最新研究进展,对碳通量和水汽通量的影响环境因子进行了总结分析,最后从模拟尺度以及影响因子角度,分析了目前碳水通量研究存在的问题难点及未来发展趋势。
关键词:  陆地生态系统  碳通量  水通量  涡度相关  环境因子
DOI:10.7515/JEE221006
CSTR:32259.14.JEE221006
分类号:
基金项目:国家自然科学基金项目(51779165)
英文基金项目:National Natural Science Foundation of China (51779165)
Review on research and development of carbon and water fluxes characteristics in terrestrial ecosystem
ZHANG Ting, ZHOU Junzhi, LI Jianzhu, CHEN Fulong, FENG Ping
1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China
2. College of Water & Architectural Engineering, Shihezi University, Shihezi 832003, China
Abstract:
Background, aim, and scope Understanding the characteristics of carbon-water fluxes is highly significant for revealing regional carbon cycle patterns and mechanisms in microscale ecosystems. As an important part of the global ecosystem, changes in the carbon and water fluxes of terrestrial ecosystems have a profound impact on the global carbon cycle and water cycle balance. To summarize the current literature on terrestrial ecosystem carbon and water fluxes, this paper reviews the technical means, research methods and research progress of ecosystem carbon and water flux observations and simulations and proposes future research trends and challenges. Materials and methods Previous research progress on carbon and water flux observations and simulations in terrestrial ecosystems and the influence mechanism of carbon and water fluxes in typical ecosystems were summarized. Results Eddy covariance flux measurements are the main observation method and technical means for long-term continuous observation of carbon and water fluxes in the world. A combination of flux observations and model simulations is the primary way to comprehensively evaluate regional and global carbon and water cycles. At present, the carbon budget of China’s major ecosystems is being disturbed by changes in environmental factors and biological factors. Discussion In the context of China’s realization of the “dual-carbon” goal, China’s terrestrial ecosystems play an important role as carbon sinks. Among them, forest ecosystems are the most powerful carbon sink. The farmland ecosystem is most affected by human factors, and its carbon source and sink functions are deeply affected by human management methods. In desert and grassland ecosystems, the carbon source and sink functions are very fragile. The transformation of desert and grassland source and sink functions are comprehensively affected by environmental factors. It is worth noting that the water condition of a desert ecosystem may be the key to determining its source and sink function. Conclusions The lack of flux observation stations in the ecosystem transition zone, a large number of uncertainties in the ecological system simulation, and the interaction and feedback mechanism between meteorological factors and biological factors on the ecosystem present opportunities and challenges for accurately evaluating and predicting the global carbon and water cycle process under the future climate model. Recommendations and perspectives Consequently, increasing the number of global carbon and water flux observation stations and effectively reducing the uncertainty of simulations, ecological mechanism and parameter calculation will provide effective help to accurately assess the status and predict the carbon and water cycle process under future climate models.
Key words:  terrestrial ecosystem  carbon flux  water flux  eddy covariance  environmental factors
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