Multiple Model Analysis for Studying Groundwater Uncertainties

Multiple Model Analysis was applied to study the groundwater modelling uncertainties caused by the deviation of model structure and heterogeneity in aquifer media. According to different natural conditions, two hydrogeological conceptual models were established. Using a large number of model parameter data, obtained through hydrogeological tests, as a priori information and based on the two conceptual models, a series of seepage field models was constructed using the Adaptive Metropolis-Markov Chain Monte Carlo method that acceptance condition was adjusted. Uncertainties of modelling output data are analysed based on corrected Akaike's Information Criteron. Research indicates that the ergodicity and convergence of sample parameters will not be affected by changes in acceptance conditions. The model output data include the following effects:"same results with different parameters" and "same results with different models". Although these effects exist, the model structure is closer to the objective of improving the probability of obtaining a high precision model. The proportion of the primary conceptual model, with a variance between 1 and 2, is 65%. When the model with Delta values greater than 10 is excluded, the top 10 models are retained and the cumulative a posterior probability is 0.996. The proportion of the second conceptual model, with a variance between 1 and 2, is 46%. When the model with Delta values greater than 10 is excluded, the top 21 models are retained. The cumulative posterior probability of the top 10 models is only 0.884.