Data Assimilation and Uncertainty Analysis of Environmental Assessment Problems

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Romanowicz, Renata J.; Young, Peter C.
stochastic transfer function; monte carlo simulation analysis; generalised likelihood uncertainty estimation; marine dose assessment; predictive uncertainty; data assimilation
artifact description
Stochastic Transfer Function (STF) and Generalised Likelihood Uncertainty Estimation (GLUE) techniques are outlined and applied to an environmental problem concerned with marine pollution dose assessment. The methods are used to estimate the amount and associated probability distributions of radionuclides transferred to marine biota from a given source: the British Nuclear Fuel Ltd (BNFL) repository plant in Sellafield, U.K. The complexity of the processes involved, together with the large dispersion and scarcity of observations regarding radionuclide concentrations in the marine environment, require efficient data assimilation techniques. In this regard, the basic STF methodology searches for identifiable, linear, Gaussian model structures that capture the maximum amount of information contained in the data with an identified parsimonious parameterisation. The GLUE based-methods, on the other hand, formulate the problem of estimation using a more general Bayesian approach, usually without prior statistical identification of the model structure. As a result, they are applicable to almost any linear or nonlinear stochastic model, although they are much less efficient both computationally and in their use of the information contained in the observations. As expected in this particular environmental application, the STF approach yields much narrower confidence limits for the estimates due to their more efficient use of the information contained in the data. The STF and GLUE techniques are then used to combine information originating from different locations. A final aim of the paper is to use the results obtained in this particular example to explore the differences between the STF and GLUE methods.