Calibration of forest 137Cs cycling model ”FoRothCs” via approximate Bayesian computation based on 6-year observations from plantation forests in Fukushima

Predicting the environmental fate of 137Cs in forest ecosystems along with the concentrations of 137Cs in tree parts are important for the managements of radioactively contaminated forests. In this study, we calibrate the Forest RothC and Cs model (FoRothCs), a forest ecosystem 137Cs dynamics model, using observational data obtained over six years from four forest sites with different levels of 137Cs contamination from Fukushima Prefecture. To this end, we applied an approximate Bayesian computation (ABC) technique based on the observed 137Cs concentrations (Bq kg−1) of five compartments (leaf, branch, stem, litter, and soil) in a Japanese cedar plantation. The environmental decay (increment) constants of the five compartments were used as the summary statistics (i.e., the metric for model performance) to infer the five parameters related to 137Cs transfer processes in FoRothCs. The ABC technique successfully reconciled the model outputs with the observed trends in 137Cs concentrations at all sites during the study period. Furthermore, the estimated parameters are in agreement with the literature values (e.g., the root uptake rates of 137Cs). Our study demonstrates that model calibration with ABC based on the trends in 137Cs concentrations of multi compartments is useful for reducing the prediction uncertainty of 137Cs dynamics in forest ecosystems.