Hierarchical Bayesian models for soil CO2 flux using soil texture: a case study in central Hokkaido, Japan

Abstract

Hierarchical Bayesian (HB) methods are useful tools for modeling multifaceted, nonlinear phenomena such as those encountered in ecology, and have been increasingly applied in environmental sciences, e.g., to estimate soil gas flux from different soil textures or sites. We have developed a model of soil carbon dioxide (CO2) flux based on soil temperature (T, 5 cm depth) and water-filled pore space (WFPS, 5 cm depth) using HB theory. The HB model was calibrated using a dataset of CO2 flux measured from bare soils belonging to four texture classes in 14 upland field sites in a watershed in central Hokkaido, Japan, in the nonsnow-cover season from 2003 to 2011. The numerical software HYDRUS-1D was used to simulate daily WFPS, and the estimated values were significantly correlated with the measured WFPS (R2 = 0.68, P < 0.001). Compared to a nonhierarchical Bayesian model (Bayesian pooled model), the CO2 predictions with the HB model more accurately represented texture-specific observations. The simulation–observation fit of the CO2 flux model was R2 = 0.64 (P < 0.001). More than 90% of the observed daily data were within the 95% confidence interval. The HB model exhibited high uncertainty for high CO2 flux values. The HB model calibration revealed differing sensitivity of CO2 flux to T and WFPS in different soil texture classes. CO2 flux increased with an increase in T, and it increased to a lesser degree with a finer texture, possibly because the clay and silt facilitated soil aggregation, thus reducing temperature fluctuations. WFPS values between 0.48 and 0.64 resulted in optimal conditions for CO2 flux. The minimum WFPS value increased with an increase in clay content (P < 0.05). Although only a small number of soil types were studied in only one season in this study, the HB model may provide a method for predicting how the effects of soil temperature and moisture on CO2 flux change with texture, and soil texture could be regarded as an upscaling factor in future research on regional extrapolation.