A new efficient Bayesian parameter inference strategy: Application to flow and pesticide transport through unsaturated porous media

Two challenging test cases are investigated to assess efficiency and accuracy of the new strategy. The first test case considers estimation of flow and pesticide transport parameters from a synthetic infiltration experiment. The second test case deals with the assessment of unsaturated hydraulic soil parameters from a real-word laboratory drainage experiment. The results of the proposed strategy are compared to those of FOA, of the standard MCMC method and of an improved MCMC method in which the sampler is preconditioned with draws from the FOA posterior distribution. For both test cases, the new strategy provides accurate mean estimated parameter values and uncertainty regions and is much more efficient than the other MCMC methods. It is up to 50 times more efficient than the standard MCMC method.