Augmentation of groundwater monitoring networks using information theory and ensemble modeling with pedotransfer functions

Improving understanding of chemical transport in the subsurface commonly employs evolving groundwater monitoring networks. The objective of this work was to apply the information theory to propose an objective algorithm for augmenting a subsurface monitoring network (SMN) with the purpose of discrimination of conceptually different subsurface flow and transport models. This method determines new monitoring locations where the Kullback–Leibler total information gain is maximized. The latter is computed based on estimates of the uncertainty in modeling results and uncertainty in observations. The method was applied to discriminate models in (1) a synthetic case of groundwater contamination from a point source; (2) the tracer experiment conducted at the USDA-ARS OPE3 research site where a pulse of KCL solution was applied with irrigation water and CL− concentrations were subsequently monitored. Models were compared that included or ignored the effect of subsurface soil lenses on chemical transport. Pedotransfer functions were used to develop the ensemble of models for estimating the uncertainty in modeling results obtained with the numerical 3D flow and transport model. Peak tracer breakthrough concentrations were used to define the information gains. The determination of the new locations to augment existing ones was conducted on a 2-D grid. The information gain peaked in small area, and additional observation locations were very well spatially defined. Well-calibrated models provided a single optimal location, whereas, if models were not calibrated well, the Bayesian estimates of the new observation location depended on the activation sequence assumed for existing locations. The information gain maximization can suggest data collection locations to reduce uncertainties in the conceptual models of subsurface flow and transport.