Hydraulic conductivity and state estimation for stochastic flow and transport models

The main objective of groundwater monitoring-networks is to carry out assessments of groundwater hydraulic-heads or groundwater quality and their variation over time. A method that involves space and time in a combined form for the optimal design of this type of networks was proposed by Herrera (1998). The method uses a space-time ensemble Kalman filter coupled with a stochastic flow and transport model and a sequential optimization method. When applying this method, it is important that the stochastic characteristics of the model be congruent with field data. For this reason, the main objective of this work is to apply the space-time ensemble Kalman filter to achieve parameter estimation. A synthetic case study is presented. The results show that for both SGSim and LHS, for all variables lnK, H and C, in general the best results are obtained when both H and C data are used.