Model testing of radioactive contamination by 90Sr, 137Cs and 239,240Pu of water and bottom sediments in the Techa River (Southern Urals, Russia).

This paper presents results of testing models for the radioactive contamination of river water and bottom sediments by (90)Sr, (137)Cs and (239,240)Pu. The scenario for the model testing was based on data from the Techa River (Southern Urals, Russia), which was contaminated as a result of discharges of liquid radioactive waste into the river. The endpoints of the scenario were model predictions of the activity concentrations of (90)Sr, (137)Cs and (239,240)Pu in water and bottom sediments along the Techa River in 1996. Calculations for the Techa scenario were performed by six participant teams from France (model CASTEAUR), Italy (model MARTE), Russia (models TRANSFER-2, CASSANDRA, GIDRO-W) and Ukraine (model RIVTOX), all using different models. As a whole, the radionuclide predictions for (90)Sr in water for all considered models, (137)Cs for MARTE and TRANSFER-2, and (239,240)Pu for TRANSFER-2 and CASSANDRA can be considered sufficiently reliable, whereas the prediction for sediments should be considered cautiously. At the same time the CASTEAUR and RIVTOX models estimate the activity concentrations of (137)Cs and (239,240)Pu in water more reliably than in bottom sediments. The models MARTE ((239,240)Pu) and CASSANDRA ((137)Cs) evaluated the activity concentrations of radionuclides in sediments with about the same agreement with observations as for water. For (90)Sr and (137)Cs the agreement between empirical data and model predictions was good, but not for all the observations of (239,240)Pu in the river water-bottom sediment system. The modelling of (239,240)Pu distribution proved difficult because, in contrast to (137)Cs and (90)Sr, most of models have not been previously tested or validated for plutonium.

Mathematical modeling of radionuclide dispersion in the Pripyat-Dnieper aquatic system after the Chernobyl accident.

The Chernobyl accident heavily contaminated the largest aquatic system in the Ukraine, requiring the development of a model-based decision support system in the field of aquatic radioecology. The main objectives of the system were to simulate and predict radionuclide dispersion in the Pripyat-Dnieper River-reservoir system, assess the effectiveness of special hydraulic countermeasures designed to decrease the rate of radionuclide dispersion in the water bodies, and support the Dnieper reservoirs' management operations. A hierarchy of mathematical models was developed. A two-dimensional (2-D) vertical-longitudinal model, a 2-D lateral-longitudinal model, a one-dimensional (1-D) channel model and a box-type model are briefly presented. These models describe the main features of radionuclide dispersion, including the processes governing radionuclide-sediment interactions. Examples of the models' applications are presented to show the peculiarities of radionuclide dispersion in this aquatic system.

Is there a need for hydrological modelling in decision support systems for nuclear emergencies.

This paper discusses the role of hydrological modelling in decision support systems for nuclear emergencies. In particular, most recent developments such as, the radionuclide transport models integrated in to the decision support system RODOS will be explored. Recent progress in the implementation of physically-based distributed hydrological models for operational forecasting in national and supranational centres, may support a closer cooperation between national hydrological services and therefore, strengthen the use of hydrological and radiological models implemented in decision support systems.

Data assimilation in the decision support system rodos.

Model predictions for a rapid assessment and prognosis of possible radiological consequences after an accidental release of radionuclides play an important role in nuclear emergency management. Radiological observations, e.g. dose rate measurements, can be used to improve such model predictions. The process of combining model predictions and observations, usually referred to as data assimilation, is described in this article within the framework of the real time on-line decision support system (RODOS) for off-site nuclear emergency management in Europe. Data assimilation capabilities, based on Kalman filters, are under development for several modules of the RODOS system, including the atmospheric dispersion, deposition, food chain and hydrological models. The use of such a generic data assimilation methodology enables the propagation of uncertainties throughout the various modules of the system. This would in turn provide decision makers with uncertainty estimates taking into account both model and observation errors. This paper describes the methodology employed as well as results of some preliminary studies based on simulated data.

Computerised Decision Support Systems for the management of freshwater radioecological emergencies: assessment of the state-of-the-art with respect to the experiences and needs of end-users.

Assessment of the environmental and radiological consequences of a nuclear accident requires the management of a great deal of data and information as well as the use of predictive models. Computerised Decision Support Systems (CDSS) are essential tools for this kind of complex assessment and for assisting experts with a rational decision process. The present work focuses on the assessment of the main features of selected state-of-the-art CDSS for off-site management of freshwater ecosystems contaminated by radionuclides. This study involved both developers and end-users of the assessed CDSS and was based on practical customisation exercises, installation and application of the decision systems. Potential end-users can benefit from the availability of several ready-to-use CDSS that allow one to run different kinds of models aimed at predicting the behaviour of radionuclides in aquatic ecosystems, evaluating doses to humans, assessing the effectiveness of different kinds of environmental management interventions and ranking these interventions, accounting for their social, economic and environmental impacts. As a result of the present assessment, the importance of CDSS "integration" became apparent: in many circumstances, different CDSS can be used as complementary tools for the decision-making process. The results of this assessment can also be useful for the future development and improvement of the CDSS.