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

Abstract

Ensemble-based Data Assimilation (EDA), based on the Monte Carlo approach, has been ef-fectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. However, implementation of EDA approach involves a complicated workflow that include setting up and executing ensemble forward model simulations, processing observations and model simulation results for parameter updates, and repeat for sequential or it-erative EDA. To facilitate the management of such workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a generic software frame-work linking the Data Assimilation Research Testbed (DART) with a massively parallel subsur-face FLOw and TRANsport code PFLOTRAN. The new DART-PFLOTRAN leverages both the core data assimilation engines in DART and the computational power a?orded by PFLOTRAN. In addition to the standard smoother and filtering options, DART-PFLOTRAN enables an iter-ative 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 im-plementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed, respectively, from contin-uous temperature measurements made across a depth profile. One-dimensional hydro-thermal simulations are performed in both cases tomore » relate temperature responses with the parameters of interest. In the case of estimating dynamic parameters, we demonstrate the flexibility of DART-PFLOTRAN in automating sequential ES-MDA workflow, which will significantly reduce the time researchers spend on managing complex workflows in similar applications. Both studies yield accurate estimations of the parameters compared to their synthetic truth, while ES-MDA leads to more accurate estimation when a high level of nonlinearity exist between observed responses and unknown parameters. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for applications in large-scale subsurface inverse modeling by automating the complex workflow of sequential ES-MDA that can be executed on various computing platforms.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1];  [2];  [2];  [2]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  2. NCAR Data Assimilation Research Section, Boulder, CO (United States)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1809045
Alternate Identifier(s):
OSTI ID: 1809046
Report Number(s):
PNNL-SA-160802
Journal ID: ISSN 1364-8152
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Environmental Modelling and Software
Additional Journal Information:
Journal Volume: 142; Journal ID: ISSN 1364-8152
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Data assimilation; Ensemble smoother; DART; PFLOTRAN; Inverse modeling; Subsurface flow and transport

Citation Formats

Jiang, Peishi, Chen, Xingyuan, Chen, Kewei, Anderson, Jeffrey, Collins, Nancy, and Gharamti, Mohamad EL. DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters. United States: N. p., 2021. Web. doi:10.1016/j.envsoft.2021.105074.
Jiang, Peishi, Chen, Xingyuan, Chen, Kewei, Anderson, Jeffrey, Collins, Nancy, & Gharamti, Mohamad EL. DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters. United States. https://doi.org/10.1016/j.envsoft.2021.105074
Jiang, Peishi, Chen, Xingyuan, Chen, Kewei, Anderson, Jeffrey, Collins, Nancy, and Gharamti, Mohamad EL. 2021. "DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters". United States. https://doi.org/10.1016/j.envsoft.2021.105074. https://www.osti.gov/servlets/purl/1809045.
@article{osti_1809045,
title = {DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters},
author = {Jiang, Peishi and Chen, Xingyuan and Chen, Kewei and Anderson, Jeffrey and Collins, Nancy and Gharamti, Mohamad EL.},
abstractNote = {Ensemble-based Data Assimilation (EDA), based on the Monte Carlo approach, has been ef-fectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. However, implementation of EDA approach involves a complicated workflow that include setting up and executing ensemble forward model simulations, processing observations and model simulation results for parameter updates, and repeat for sequential or it-erative EDA. To facilitate the management of such workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a generic software frame-work linking the Data Assimilation Research Testbed (DART) with a massively parallel subsur-face FLOw and TRANsport code PFLOTRAN. The new DART-PFLOTRAN leverages both the core data assimilation engines in DART and the computational power a?orded by PFLOTRAN. In addition to the standard smoother and filtering options, DART-PFLOTRAN enables an iter-ative 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 im-plementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed, respectively, from contin-uous temperature measurements made across a depth profile. One-dimensional hydro-thermal simulations are performed in both cases to relate temperature responses with the parameters of interest. In the case of estimating dynamic parameters, we demonstrate the flexibility of DART-PFLOTRAN in automating sequential ES-MDA workflow, which will significantly reduce the time researchers spend on managing complex workflows in similar applications. Both studies yield accurate estimations of the parameters compared to their synthetic truth, while ES-MDA leads to more accurate estimation when a high level of nonlinearity exist between observed responses and unknown parameters. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for applications in large-scale subsurface inverse modeling by automating the complex workflow of sequential ES-MDA that can be executed on various computing platforms.},
doi = {10.1016/j.envsoft.2021.105074},
url = {https://www.osti.gov/biblio/1809045}, journal = {Environmental Modelling and Software},
issn = {1364-8152},
number = ,
volume = 142,
place = {United States},
year = {2021},
month = {5}
}

Works referenced in this record:

DART/CAM: An Ensemble Data Assimilation System for CESM Atmospheric Models
journal, September 2012


The Data Assimilation Research Testbed: A Community Facility
journal, September 2009


Evaluating Methods to Account for System Errors in Ensemble Data Assimilation
journal, September 2012


Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation
journal, December 1996


Ensemble smoother with multiple data assimilation
journal, June 2013


Dam Operations and Subsurface Hydrogeology Control Dynamics of Hydrologic Exchange Flows in a Regulated River Reach
journal, April 2019


A Local Least Squares Framework for Ensemble Filtering
journal, April 2003


Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother
journal, December 2011


Evaluation of a Data Assimilation System for Land Surface Models Using CLM4.5
journal, October 2018


Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation
journal, August 2007