Towards dynamic and process-based modelling of radionuclides cycling in terrestrial radioecology

Mathematical models are frequently used in terrestrial radioecology to interpret observations and to assess the detrimental impacts of radioactive releases to the environment. Conventional radioecological models are largely based on equilibrium and empirical relationships with reasonable data requirements, making them practical tools for long-term assessments. But conventional models may be inadequate to simulate radionuclide dynamics in terrestrial environments realistically. Specifically, the structure of such models seldom conforms to the physics of water flow and solute transport in soils. The equilibrium relationships may fail to predict seasonality in radionuclide transfer between environmental compartments; model transferability between sites is often hampered by its empirical nature. Numerous studies have highlighted the need to circumvent these limitations. In this paper, we introduce dynamic and process-based modelling to a conventional radioecological model by coupling an empirical plant module to a process-based soil module that simulates water flow, solute transport and root uptake in the soil column. Illustrative simulations are presented using the coupled model and stable chlorine cycling in a temperate Scots pine (Pinus sylvestris L.) stand as an example. The model satisfactorily reproduced soil moisture dynamics and the inventory of inorganic chlorine in the tree and forest floor compartments. The inventory of organic chlorine in the stand, however, was overestimated, indicating that processes pertinent to organochlorine cycling at the stand were missing from the model. The approach proposed in this paper is a step towards dynamic and process-based modelling in terrestrial radioecology and impact assessment. It can be particularly useful for modelling transfer of elements, such as redox-sensitive radionuclides, whose behaviour in soil-plant systems is moisture-dependent.

Keywords: Chlorine; Compartmental model; Process–based model; Scots pine (Pinus sylvestris L.); Terrestrial radioecology.