Using a Bayesian estimation algorithm for estimating parameter uncertainty and optimization of monitoring networks

Uncertainty of groundwater flow models is caused by the uncertainty of model parameters and model concept errors. Inverse models help to decrease the uncertainty of the model parameters and model errors, and consequently improve model results. Unfortunately, prior statistics of these parameters and errors are not known perfectly and are often guessed at. We introduce an algorithm that helps to identify the prior statistics of model parameters and model errors such that the resulting head statistics correspond with measurement residuals. We have also introduced an algorithm to optimize monitoring strategy in cases where the goal is minimization of the uncertainty of a groundwater flow model.