DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters. (August 2021)

• Record Type: Journal Article
• Authors: Jiang, Peishi; Chen, Xingyuan; Chen, Kewei; Anderson, Jeffrey; Collins, Nancy; Gharamti, Mohamad EL.
• Is Part Of: Environmental modelling & software. Volume 142(2021)
• Publication Date: 2021-08
• Subjects: Data assimilation -- Ensemble smoother -- DART -- PFLOTRAN -- Inverse modeling -- Subsurface flow and transport; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105074
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: A new data assimilation software was developed by linking DART with PFLOTRAN. Iterative ensemble smoother was enabled in the software for static and dynamic parameter estimations. The software was verified using synthetic cases that estimate permeability and exchange flux from groundwater temperature.