An evaluation of empirical regression models for predicting temporal variations in soil respiration in a cool-temperate deciduous broad-leaved forest
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Soil respiration (RS) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in RS. The principal objectives of this study were to evaluate the relationship between RS rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of RS using ST and SWC. We have been measuring RS, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of RS, we compared a simple exponential regression (flux = aebt: Eq. [1]) and two polynomial multiple-regression models (flux = aebt × (θν – c) × (d – θν)f: Eq. [2] and flux = aebt × (1 – (1 – (θν/c))2): Eq. [3]) that included two variables (ST:t and SWC: θν) and that utilized hourly data for RS. In general, daily mean RS rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and RS rates on periods of a day based on the hourly RS data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST × SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the R2 from 0.71 to 0.78.
Abstract
Soil respiration (RS) is a critical component of the annual carbon balance of forests, but few studies thus far have attempted to evaluate empirical regression models in RS. The principal objectives of this study were to evaluate the relationship between RS rates and soil temperature (ST) and soil water content (SWC) in soil from a cool-temperate deciduous broad-leaved forest, and to evaluate empirical regression models for the prediction of RS using ST and SWC. We have been measuring RS, using an open-flow gas-exchange system with an infrared gas analyzer during the snowfree season from 1999 to 2001 at the Takayama Forest, Japan. To evaluate the empirical regression models used for the prediction of RS, we compared a simple exponential regression (flux = aebt: Eq. [1]) and two polynomial multiple-regression models (flux = aebt × (θν – c) × (d – θν)f: Eq. [2] and flux = aebt × (1 – (1 – (θν/c))2): Eq. [3]) that included two variables (ST:t and SWC: θν) and that utilized hourly data for RS. In general, daily mean RS rates were positively well-correlated with ST, but no significant correlations were observed with any significant frequency between the ST and RS rates on periods of a day based on the hourly RS data. Eq. (2) has many more site-specific parameters than Eq. (3) and resulted in some significant underestimation. The empirical regression, Eq. (3) was best explained by temporal variations, as it provided a more unbiased fit to the data compared to Eq. (2). The Eq. (3) (ST × SWC function) also increased the predictive ability as compared to Eq. (1) (only ST exponential function), increasing the R2 from 0.71 to 0.78.
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