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Abstract. Topographic effects on Cs-137 concentrations in a forested area were quantitatively examined using 58 soil core samples collected in a village in Fukushima, Japan, which was directly impacted by the radioactive plume emitted during the 2011 Fukushima Daiichi Nuclear Power Plant (FDNPP) accident. In this study, five topographic parameters and two soil properties were evaluated as controls on the soil Cs-137 concentration using generalized additive models (GAMs), a flexible statistical method for evaluating the functional dependencies of multiple parameters. GAMs employing soil dry bulk density, mass water content, and elevation explained 54 % of the observed concentrations of Cs-137 within this landscape, whereas GAMs employing elevation, slope, and upslope distance explained 47 % of the observed concentrations, which provide strong evidence of topographic effects on Cs-137 concentrations in soils. The model fit analysis confirmed that the topographic effects are strongest when multiple topographic parameters and soil properties are included. Theability of each topographic feature to predict Cs-137 concentrations wasinfluenced by the resolution of the digital elevation models. The movementof Cs-137 into the subsurface in this area near Fukushima was faster incomparison to regions affected by the Chernobyl Nuclear Power Plant accident. These results suggest that the effects of topographic parametersshould be considered carefully in the use of anthropogenic radionuclides asenvironmental tracers and in the assessment of current and futureenvironmental risks due to nuclear power plant accidents. 

Abstract Calibration of agent‐based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well‐calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA‐CAL. Specifically, variance‐based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high‐performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak.