Addressing model bias and uncertainty in three dimensional groundwater transport forecasts for a physical aquifer experiment

摘要

This work contributes a combination of laboratory-based aquifer tracer experimentation and bias-aware Ensemble Kalman Filtering (EnKF) to demonstrate that systematic modeling errors ( or bias) in source loading dynamics and the spatial distribution of hydraulic conductivity pose severe challenges for groundwater transport forecasting under uncertainty. The impacts of model bias were evaluated using an ammonium chloride tracer experiment conducted in a three dimensional laboratory tank aquifer with 105 near real-time sampling locations. This study contributes a bias-aware EnKF framework that (i) dramatically enhances the accuracy of concentration breakthrough forecasts in the presence of systematic, spatio-temporally correlated modeling errors, (ii) clarifies in space and time where transport gradients are maximally impacted by model bias, and (iii) expands the size and scope of flow- and-transport problems that can be considered in the future.