Chunying Wang

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.

Simulation of stream nitrate-nitrogen export using the Soil and Water Assessment Tool model in a dairy farming watershed with an external water source

Quantitative assessment of stream nitrate-nitrogen (NO3-N) export in a watershed with an external underground water source is complex because the underground flux for both the water discharge and NO3-N cannot be directly measured. A modeling approach, using the Soil and Water Assessment Tool (SWAT), was tested to simulate the NO3-N export in the 672 km2 (417 mi2) Shibetsu Watershed with external water source (EXT) in eastern Hokkaido, Japan. The EXT and its NO3-N loading contents were added as the assumed point source discharge and loading in the SWAT model. The results yielded high Nash-Suttcliffe coefficient (Ens) values for daily streamflow (over 0.5) and monthly NO3-N loading (over 0.7) for the simulation during the calibration and validation periods. The simulated seasonal patterns of discharge and NO3-N loading were also well matched with the measured data during 2004 to 2008. However, the spatial patterns of NO3-N concentrations showed a poor r2 correlation (r2 = 0.34) with the measured data in 2004. These results showed that, although the model had limitations for the simulation of spatial patterns, the method of adding EXT and NO3-N loading as assumed points in SWAT was reasonable for the assessment of the stream NO3-N export. Therefore, the calibrated model was repeated without adding EXT and NO3-N loading as assumed points. Approximately 66% of the NO3-N export was from the watershed itself, and the NO3-N export was high in the grasslands. These results indicated that most of the NO3-N export was related to dairy farming in the Shibetsu Watershed, and the best management practices for controlling nonpoint source pollution should focus on manure applications on grasslands in the future. Meanwhile, the EXT (47% of streamflow) contributed to 34% of the annual NO3-N loading, which may cause the overestimation of the stream NO3-N export when using only the measured data. Therefore, the EXT should not be ignored when estimating the annual NO3-N loading.