环境和生物因子对黄河三角洲滨海湿地净生态系统CO2交换的影响

通过涡度相关和微气象观测技术,对黄河三角洲滨海湿地净生态系统CO₂交换(NEE)以及环境、生物因子进行了观测,探究湿地NEE变化规律及环境和生物因子对NEE的影响. 结果表明: 在日尺度上,生长季NEE呈明显“U”型曲线,非生长季变幅较小;在季节尺度上,NEE生长季波动较大,表现为碳汇,非生长季波动较小,表现为碳源;在年尺度上,滨海湿地生态系统表现为碳汇,总净固碳量为-247 g C·m^-2. 白天NEE主要受控于光合有效辐射(PAR),且生态系统表观量子产量(α)与白天生态系统呼吸(R_eco,d)均于8月达到最大值,最大光合速率(A_max)于7月达到最大值;夜间NEE随气温(T_a)呈指数增加趋势,生态系统的温度敏感系数(Q₁₀)为2.5,且土壤含水量(SWC)越高,Q₁₀值越大.非生长季NEE只与净辐射(R_n)呈显著的线性负相关,与其他环境因子无显著相关关系.生长季NEE与R_n、T_a、土壤10 cm温度(T_{s 10})等环境因子以及叶面积指数(LAI)呈显著的线性负相关,但与地上生物量(AGB)无显著相关关系.多元回归分析表明,R_n和LAI对生长季NEE的协同影响达到52%.

Effect of environmental and biotic factors on net ecosystem CO2 exchange over a coastal wetland in the Yellow River Delta.

Using the eddy covariance technique, we measured the net ecosystem CO₂ exchange (NEE) and its environmental and biotic factors over a coastal wetland in the Yellow River Delta to investigate the diurnal and seasonal variation in NEE and quantify the effect of environmental and biotic factors on NEE. The results showed that the diurnal change of NEE showed a distinct U-shaped curve during the growing season, but not with substantial variation in its amplitude during the non-growing season. During the growing season, the wetland acted as a significant net sink for CO₂, while it became carbon source during the non-growing season. On the scale of a whole year, the wetland functioned as a strong carbon sink of -247 g C·m^-2. Daytime NEE was mainly dominated by photosynthetically active radiation (PAR). Apparent quantum yield (α) and daytime respiration of ecosystem (R_eco,d) reached maximum in August, while maximum photosynthesis rate (A_max) reached its maximum in July. Nighttime NEE had an exponential relationship with air temperature (T_a). The mean value of temperature sensibility coefficient (Q₁₀) was 2.5, and it was positively related to soil water content (SWC). During the non-growing season, NEE was negatively correlated with net radiation (R_n), but not with other environmental factors significantly. However, during the growing season NEE was significantly correlated with R_n, T_a, soil temperature at 10 cm depth (T_{s 10}) and leaf area index (LAI), but not with aboveground biomass (AGB). Stepwise multiple regression analysis indicated that R_n and LAI explained 52% of the variation in NEE during the growing season.