Predictive groundwater modeling and uncertainty estimation in practice

The traditional groundwater modeling approach of manual calibration with a handful of parameters and ad hoc one-at-a-time sensitivity analysis is giving way to formal data assimilation and uncertainty estimation, where the natural very-high dimensionality of the inverse problem is embraced.  In theory, this is an improvement for applied groundwater modeling, and, more importantly, the management of groundwater resources.  However, this transition is not without hardship.  Many new concepts, skills, and techniques must be learned to effectively and efficiently assimilate many kinds of information and to ultimately provide robust estimates of predictive uncertainty in an applied groundwater modeling setting, where time and budget pressures are real. 

This talk will present some foundational concepts surrounding predictive groundwater modeling, including the roles of model complexity, data, and uncertainty. The talk will include discussion of apparent trends in the groundwater modeling industry, with a few examples of modern applied predictive groundwater modeling.