Parametrization of homogeneous forested areas and effect on simulated dose rates near a nuclear research reactor

Abstract

One of the major uncertainties in dispersion-based simulations at the local scale is the representation of terrain effects. The aim of the current study is to quantify this type of uncertainty for dose-rate predictions over a homogeneous forest cover. At the investigated nuclear reactor, situated in a forested environment, ambient gamma-dose-rate data from routine Ar-41 releases are available in the first 300 m from the release point. We develop a forest parameterization that meets the site-specific needs, and integrate it in different dispersion models. Using different terrain-roughness parameterizations, we compare three types of models: a dispersion model driven by a Langevin equation, an advection-diffusion model, and a Gaussian plume model as a special case of the latter one. We find that all models are biased up to a factor of four, partly due to an uncertain source strength. The dose-rate uncertainty due to the model choice is a factor of 2.2 for a stack release and a factor of 14 for a ground release.

A Fast-Converging Kernel Density Estimator for Dispersion in Horizontally Homogeneous Meteorological Conditions

“…In Figure 1a,b, one can see that the predictions from the PI (path integral-based) estimator, Equation (24), and KS (kernel smoother) estimator, Equation ( 6), coincide well. In particular, the prediction from the PI estimator and the analytical solution (39) are indistinguishable, while the KS estimator shows a slight deviation for the maximum.…”

“…Other parameterizations of homogeneous terrain that can be relevant may, e.g., be the forest. See, for instance, [39].…”