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土壤温湿度对北京大兴杨树人工林土壤呼吸的影响
引用本文:谭炯锐,查同刚,张志强,孙阁,戴伟,方显瑞,徐枫.土壤温湿度对北京大兴杨树人工林土壤呼吸的影响[J].生态环境,2009,18(6).
作者姓名:谭炯锐  查同刚  张志强  孙阁  戴伟  方显瑞  徐枫
作者单位:1. 北京林业大学水土保持学院//教育部水土保持与荒漠化防治重点实验室,北京,100083
2. Southern,Global,Change,Program,.USDA,Forest,Service,NC27606,USA
基金项目:北京市教育委员会共建项目,教育部重点项目,国家林业局"948"项目,高等学校博士专项科研基金项目,国家"十五"科技攻关课题,中美碳联盟USCCC国际合作项目 
摘    要:采用Li-cor-8150土壤呼吸测定系统,对北京大兴杨树人工林(欧美107,Populus×euramericana cv."74/76")土壤CO_2释放通量、土壤温度和水分进行了为期1年(2007)的定位连续观测,系统研究土壤温度(T_S)和土壤含水量(w)对土壤呼吸速率(R_s)的影响.结果表明:(1)土壤呼吸速率日变化呈单峰曲线,具有明显的白天高,夜间低的规律.非生长季土壤呼吸速率较低,自5月份土壤呼吸速率上升,8月份达到最大值.(2)土壤温度是影响土壤呼吸速率的主要因素,用指数模型解释全年过程中土壤温度对土壤呼吸速率变化的能力为69%.在低温段(<0℃)土壤呼吸速率随土壤温度升高而下降,而在土壤温度>0℃条件下土壤呼吸速率与土壤温度表现为正相关.土壤呼吸速率随土壤含水量上升表现出先升高后降低的趋势,三次方程模拟表明土壤水分的贡献率为33%,而当土壤含水量低于9.5%时,土壤水分的贡献率上升到51%.(3)土壤温、湿度共同作用于土壤呼吸,在不同含水量区间土壤呼吸对土壤温度的响应程度不同:在4%~10%土壤含水量范围内.土壤温度与土壤呼吸的指数模型的R~2达到0.86,而在土壤水分较高或较低时,其相关系数仅为0.6.土壤温度是影响土壤呼吸速率变化的主导因素,当土壤含水量过低或过高时,土壤温度的主导作用相对减弱,土壤含水量的影响作用相对加强.土壤呼吸的温度敏感性受土壤温度区间和水分区间的综合影响,用指数模型模拟土壤温湿度对土壤呼吸的影响不能很好的模拟土壤湿度的作用,所以单一模型并不是描述土壤温湿度对土壤呼吸的共同影响的最优模型,而多种模型复合的数学模型有待进一步研究.

关 键 词:杨树人工林  土壤呼吸  土壤温度  土壤水分

Effects of soil temperature and moisture on soil respiration in a poplar plantation in Daxing district, Beijing
TAN Jiongrui,ZHA Tonggang,ZHANG Zhiqiang,SUN Ge,DAI Wei,FANG Xianrui,XU Feng.Effects of soil temperature and moisture on soil respiration in a poplar plantation in Daxing district, Beijing[J].Ecology and Environmnet,2009,18(6).
Authors:TAN Jiongrui  ZHA Tonggang  ZHANG Zhiqiang  SUN Ge  DAI Wei  FANG Xianrui  XU Feng
Abstract:Continuous half-hourly measurements of soil CO_2 efflux with Li-Cor-8150 in a polar plantation in Daxing District of Beijing between January and December 2007 were made to investigate the seasonal and diurnal dependence of soil respiration(R_s) on soil temperature(T_s) and water content(w). The results showed:(1) The diurnal variation of R_s was described as a single-peak curve, high during daytime and low during night. R_s was low during the non-growing season, but it increased from May and reached the maximum in August.(2) T_s was the main factor that influences R_s. An exponential model explained 69% of the annual R_s variation. In a low temperature environment(<0℃), R_s decreased with the increase in T_s. There was a positive correlatation between R_s and T_s when Ts >0 ℃. R_s increased ed at first and then decreased with the increase of w. A cubic model indicated that w contributed 33% of the variation of Rs. The contribution increased to 51% when wwaslower than 9.5%. T_s was also an important factor to R_s.(3) The influences of w and T_s a on R_s acted together. R_s responded to T_s differently under different w conditions. The R~2 of the exponential model between R_s and T_s reached 0.86 with w was the range of 4%-10%,; when w was too high or too low, the influence of T_s was relatively weakened, while the effect of w was relatively strengthened. We conclude that the sensitivity of R_s was affected by w and T_s in a non-linear fashion and could not be described by a exponential function. This study suggest multiple functions are needed to describe the complex relationship between Rs and environmental factors.
Keywords:polar plantation  soil respiration  soil temperature  soil water content
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