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

AbstractHierarchical 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 simulat...