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Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data-driven error model

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1002/2016wr019831· OSTI ID:1532997
 [1];  [2];  [3];  [4]
  1. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Civil and Environmental Engineering; Michigan State Univ., East Lansing, MI (United States). Dept. of Earth and Environmental Sciences; DOE/OSTI
  2. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Civil and Environmental Engineering
  3. Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing
  4. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States). Dept. of Statistics
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Groundwater model structural error is ubiquitous, due to simplification and/or misrepresentation of real aquifer systems. During model calibration, the basic hydrogeological parameters may be adjusted to compensate for structural error. This may result in biased predictions when such calibrated models are used to forecast aquifer responses to new forcing. Here, we investigate the impact of model structural error on calibration and prediction of a real-world groundwater flow model, using a Bayesian method with a data-driven error model to explicitly account for model structural error. The error-explicit Bayesian method jointly infers model parameters and structural error and thereby reduces parameter compensation. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models (based on machine learning techniques) as a substitute for the computationally expensive groundwater model. We demonstrate that with explicit treatment of model structural error, the Bayesian method yields parameter posterior distributions that are substantially different from those derived using classical Bayesian calibration that does not account for model structural error. We also found that the error-explicit Bayesian method gives signficantly more accurate prediction along with reasonable credible intervals. Finally, through variance decomposition, we provide a comprehensive assessment of prediction uncertainty contributed from parameter, model structure, and measurement uncertainty. The results suggest that the error-explicit Bayesian approach provides a solution to real-world modeling applications for which data support the presence of model structural error, yet model deficiency cannot be specifically identified or corrected.

Research Organization:
Florida State Univ., Tallahassee, FL (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
Grant/Contract Number:
SC0008272
OSTI ID:
1532997
Journal Information:
Water Resources Research, Journal Name: Water Resources Research Journal Issue: 5 Vol. 53; ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)Copyright Statement
Country of Publication:
United States
Language:
English

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Cited By (12)

Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation journal March 2018
Evaluation of Land Surface Subprocesses and Their Impacts on Model Performance With Global Flux Data journal May 2019
Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations journal July 2018
Surrogate‐Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error journal January 2020
What We Talk About When We Talk About Uncertainty. Toward a Unified, Data-Driven Framework for Uncertainty Characterization in Hydrogeology journal June 2019
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources journal April 2019
Estimating time-dependent vegetation biases in the SMAP soil moisture product text January 2018
Inverse modeling of hydrologic systems with adaptive multi-fidelity Markov chain Monte Carlo simulations preprint January 2017
Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error text January 2018
Evaluation of terrestrial pan-Arctic carbon cycling using a data-assimilation system journal January 2019
Estimating time-dependent vegetation biases in the SMAP soil moisture product posted_content February 2018
Estimating time-dependent vegetation biases in the SMAP soil moisture product journal January 2018

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Bayesian calibration
environmental sciences & ecology
groundwater
marine & freshwater biology
model structural error
surrogate modeling
uncertainty decomposition
water resources