青海湖高寒湿地生态系统CO2通量和水汽通量间的耦合关系

曹生奎; 曹广超; 陈克龙; 冯起; 李忠勤; 张静; 汉光昭; 林阳阳

doi:10.7522/j.issn.1000-694X.2016.00029

中国沙漠 >

2016 , Vol. 36 >Issue 5: 1286 - 1295

DOI: https://doi.org/10.7522/j.issn.1000-694X.2016.00029

生物与土壤

青海湖高寒湿地生态系统CO₂通量和水汽通量间的耦合关系

曹生奎 ,
曹广超 ,
陈克龙 ,
冯起 ,
李忠勤 ,
张静 ,
汉光昭 ,
林阳阳

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1. 青海师范大学生命与地理科学学院/青海省自然地理与环境过程重点实验室, 青海西宁 810008;
2. 中国科学院寒区旱区环境与工程研究所, 甘肃兰州 730000

曹生奎(1979-),男,青海西宁人,博士,教授,主要从事全球变化生态学及生态水文与水资源学研究。E-mail:caoshengkui@163.com

收稿日期: 2015-12-23

修回日期: 2016-03-07

网络出版日期: 2016-09-20

基金资助

国家自然科学基金项目（31260130）；中国科学院“西部之光”计划项目（科发人教字[2012]179号）；中国博士后基金项目（2013M542400）；教育部科学技术重点研究项目和青海省重点实验室平台建设项目（2014-Z-Y24，2015-Z-Y01）

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Coupling Relationships Between Alpine Wetland Ecosystem CO₂ and Vapor Fluxes around the Qinghai Lake

Cao Shengkui ,
Cao Guangchao ,
Chen Kelong ,
Feng Qi ,
Li Zhongqing ,
Zhang Jing ,
Han Gaungzhao ,
Lin Yangyang

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1. College of Life and Geographical Science/Qinghai Province Key Laboratory of Physical Geography and Environmental Process, Qinghai Normal University, Xining 810008, China;
2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China

Received date: 2015-12-23

Revised date: 2016-03-07

Online published: 2016-09-20

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摘要

利用涡度相关技术对青海湖高寒湿地生态系统不同时间尺度CO₂通量和水汽通量间的耦合关系进行了研究。结果显示：不同天气条件下青海湖高寒湿地生态系统30 min净CO₂交换量（NEE）与水汽通量间均显示了极显著负相关关系（P<0.0001）；30 min总生态系统生产力（GEP）与水汽通量呈极显著线性正相关关系（P<0.0001）；阴天水汽通量参与生态系统净CO₂交换和生态系统总碳吸收的比例最高。月均30 min NEE与水汽通量呈极显著线性负相关（R²=0.71，P<0.0001）。从植物返青期、生长期至枯草期，月均30 min的GEP与水汽通量不仅呈极显著线性正相关（P<0.0001），且在生长期和枯黄期阶段表现出极显著一元二次多项式关系（P<0.0001）。在日尺度上，NEE日总量与日蒸散量呈极显著一元二次多项式负相关关系（R²=0.58，P<0.0001）；GEP日总量与日蒸散量呈极显著指数正相关（R²=0.42，P<0.0001）。在月尺度上，NEE月总量与月蒸散量呈极显著线性负相关（R²=0.60，P<0.0001），两者还表现为极显著一元二次多项式负相关关系（R²=0.63，P<0.0001）。GEP月总量与月蒸散量呈极显著线性正相关（R²=0.51，P<0.0001），且表现出极显著指数正相关关系（R²=0.64，P<0.0001）。

关键词： 生态系统净CO₂交换; 总生态系统生产力; 水汽通量; 耦合关系; 高寒湿地; 青海湖

本文引用格式

曹生奎 , 曹广超 , 陈克龙 , 冯起 , 李忠勤 , 张静 , 汉光昭 , 林阳阳 . 青海湖高寒湿地生态系统CO₂通量和水汽通量间的耦合关系[J]. 中国沙漠, 2016 , 36(5) : 1286 -1295 . DOI: 10.7522/j.issn.1000-694X.2016.00029

Abstract

This paper studied the coupling relationships between alpine wetland ecosystem CO₂ fluxes and vapor fluxes on the different time scales with the eddy covariance techniques in Qinghai Lake. Results showed that alpine wetland ecosystem half-hour CO₂ fluxes and vapor fluxes in Qinghai Lake existed in extreme significant negative relationships (P<0.0001), as well as extreme significant and positive linear relationships between half-hour gross ecosystem productivities (GEPs) and vapor fluxes, which the ratio of vapor fluxes participating in ecosystem net CO₂ exchanges (NEE) and ecosystem total carbon absorption in the cloudy days was highest. The alpine wetland ecosystem monthly average half-hour NEEs and vapor fluxes showed an extreme significant negative relationship (R²=0.71,P<0.0001). From vegetation green up stage, growing stage to wilt stage, monthly average half-hour GEPs and vapor fluxes not only showed extreme significant positive linear relationship(P<0.0001), but also a quadratic polynomial one(P<0.0001) at growing stage and withering stage. On the daily scale, the alpine wetland ecosystem daily total NEE and evapotranspiration in Qinghai Lake existed in an extreme significant negative a quadratic polynomial nonlinear relationship (R²=0.58,P<0.0001). The alpine wetland ecosystem daily total GEP and evapotranspiration showed an extreme significant positive exponential correlation (R²=0.42,P<0.0001). On the monthly scale, the alpine wetland ecosystem monthly total NEE and evapotranspiration in Qinghai Lake showed an extreme significant negative linear relationship (R²=0.60,P<0.0001), as well as a quadratic polynomial nonlinear negative relationship (R²=0.63,P<0.0001). The alpine wetland ecosystem monthly total GEPs and evapotranspiration showed an extreme significant positive linear correlation (R²=0.51,P<0.0001) as well as a positive exponential relationship (R²=0.64,P<0.0001).

Key words： ecosystem net CO₂ exchange; gross ecosystem productivity(GEP); vapor flux; coupling relationships; alpine wetland; Qinghai Lake

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