关键词: 土壤呼吸, CO₂扩散梯度, CO₂扩散速率, 线性回归, 指数回归, 日变化

Abstract:

Aims Soil CO₂ flux is driven primarily by the CO₂ diffusion gradient across the soil surface. Ideally, the soil CO₂ flux measurement should be made without affecting the diffusion gradient across the soil surface. With the closed chamber system, the soil CO₂ diffusion rate (∂c/∂t) is required to estimate the soil CO₂ flux. To obtain the ∂c/∂t, the chamber CO₂ concentration must be allowed to rise. Consequently, the ∂c/∂t will be affected by the CO₂ diffusion gradient across the soil surface because of the decreased CO₂ diffusion gradient in the soil chamber. Additionally, the ∂c/∂t will also be affected by the diurnal variation of the CO₂ concentration across the soil surface. Our objective was to compare linear and exponential fitting methods to estimate ∂c/∂t.
Methods Currently, the ∂c/∂t is commonly estimated using linear fitting regression. Instead of using the linear fitting method, an exponential fitting method is used to fit the time series of chamber CO₂ concentration adopted in the LI-8100 automated soil CO₂ flux system.
Important findings The ∂c/∂t estimated from the linear slopes was consistently underestimated as compared to that from exponential initial slopes. Nighttime ∂c/∂t was significantly negatively correlated with soil surface CO₂ concentration, suggesting that the decreased CO₂ diffusion gradient across the soil surface strongly influences the ∂c/∂t. For the closed-chamber method, linear curve fitting significantly underestimated the ∂c/∂t rate during the nighttime. These results demonstrated the importance of estimating the ∂c/∂t at ambient soil surface CO₂ concentration. The response of the ∂c/∂t to air temperature exhibited significant asymmetry characteristic, showing that it is a better way for exponential fitting to make long-term and continuous soil CO₂ flux measurement to elucidate the magnitudes and processes of soil CO₂ flux in the typical terrestrial ecosystem.

Key words: soil respiration, CO₂ diffusion gradient, CO₂ diffusion rate, linear fit, exponential fit, diurnal variation