DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters

Ensemble-based Data Assimilation (EDA) has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. To facilitate the management of EDA workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a software framework linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. We verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed from continuous temperature measurements. Both cases yield accurate estimations of the parameters compared to their synthetic truth. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for large-scale inverse modeling using the sequential ES-MDA.

Automated summary

By linking DART with PFLOTRAN, the DART-PFLOTRAN software framework enables an iterative EDA workflow to improve estimation accuracy for nonlinear forward problems. Through validation using two synthetic cases, DART-PFLOTRAN paves the way for large-scale inverse modeling using the sequential ES-MDA.