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Project ID: FIKR-CT-2000-00025
Finanziato nell'ambito di: FP5-EAECTP C
Paese: Belgium

Bridging the gap between model predictions and observations for nuclear emergency response

There is an urgent need for developing a methodology that enables radiological advisors to use observations to update the predictions of models; hence reducing the uncertainty associated with model-based dose and consequence assessments, in the light of available monitoring data. Such a methodology is known as data assimilation. Application of data assimilation in a model spreads the information obtained from point measurements to the entire domain in a consistent manner, according to the model dynamics (interpolation in space). Furthermore, by updating the modelling system whenever measurements are available ensures that the model will not drift away from the measurements (interpolation in time).

In the 4th Framework Programme, a considerable amount of effort has been devoted to integrating a suite of computer codes, with different degrees of complexity, into a European real-time, on-line decision support system for off-site management of nuclear emergencies (the RODOS system). The modelling system describes the transport and dispersion of radionuclides in both atmospheric and aquatic systems, as well as their impact on the food chain.

RODOS predicts the values of many quantities that are likely to be of interest to decision makers after an accident (e.g. air concentration, deposition, concentration in foods, external dose rates, concentrations in water bodies). The predictions will not exactly reflect the situation after an accident, as the models use a number of assumptions that are appropriate to the average situation across large areas of Europe, rather than to the particular conditions of the area affected by the accident. In the period immediately after the accident there will be a limited amount of information available from monitoring programmes. To make the best use of this information, it is necessary to correct the RODOS predictions in light of the available measurements.

In general, in off-site emergency management, data assimilation will prove useful throughout the different stages of the accident. In the assessment of the consequences during the early phase, in the improvement of prior assumptions based solely on expert judgement, and when there is a clear need for longer-term predictions to assess the radiological impact on the food chain.

The core of the proposed work was to develop data assimilation tools for off-site nuclear emergency management, and integrate these tools into the real-time, on-line decision support system RODOS. The work consisted of three main components, which are associated with the main pathways through which radioactivity will reach the biosphere.

These include:
- Data assimilation in the early phase of a nuclear accident, including atmospheric transport and dispersion.

- Data assimilation in the late phase after the deposition of radionuclides from the atmosphere has ceased, including food chain and dose assessment.

- Data assimilation in the hydrological model chain, including runoff of radionuclides from watersheds and transport and dispersion in rivers.

For all three components, a common methodology based on Bayes¿ theorem and Kalman filtering has been adopted for assimilation of data. The use of a common data assimilation approach in the different pathways and the subsequent implementation in the RODOS decision support system guaranteed a harmonised and coherent methodology to handle and to propagate uncertainties from one model chain into the other. However, due to the different nature and complexity of the models involved, the Kalman filter needed to be adapted differently in the various models, and hence different methodological developments are required in each of the three components.

Informazioni correlate

Reported by

Belgian Nuclear Research Centre (SCK-CEN)
200 Boeretang
B-2400 Mol
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