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Title: Bayesian calibration of groundwater models with input data uncertainty

Effective water resources management typically relies on numerical models to analyze groundwater flow and solute transport processes. Groundwater models are often subject to input data uncertainty, as some inputs (such as recharge and well pumping rates) are estimated and subject to uncertainty. Current practices of groundwater model calibration often overlook uncertainties in input data; this can lead to biased parameter estimates and compromised predictions. Through a synthetic case study of surface–ground water interaction under changing pumping conditions and land use, we investigate the impacts of uncertain pumping and recharge rates on model calibration and uncertainty analysis. We then present a Bayesian framework of model calibration to handle uncertain input of groundwater models. The framework implements a marginalizing step to account for input data uncertainty when evaluating likelihood. It was found that not accounting for input uncertainty may lead to biased, overconfident parameter estimates because parameters could be over–adjusted to compensate for possible input data errors. Parameter compensation can have deleterious impacts when the calibrated model is used to make forecast under a scenario that is different from calibration conditions. By marginalizing input data uncertainty, the Bayesian calibration approach effectively alleviates parameter compensation and gives more accurate predictions in the syntheticmore » case study. The marginalizing Bayesian method also decomposes prediction uncertainty into uncertainties contributed by parameters, input data, and measurements. Furthermore, the results underscore the need to account for input uncertainty to better inform postmodeling decision making.« less
Authors:
ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [3] ;  [2] ; ORCiD logo [2]
  1. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States); Michigan State Univ., East Lansing, MI (United States)
  2. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
  3. Florida State Univ., Tallahassee, FL (United States)
Publication Date:
Grant/Contract Number:
SC0002687
Type:
Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal Issue: 4; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Research Org:
Florida State Univ., Tallahassee, FL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; input uncertainty; calibration; Bayesian; uncertainty quanitfication
OSTI Identifier:
1466031
Alternate Identifier(s):
OSTI ID: 1402401

Xu, Tianfang, Valocchi, Albert J., Ye, Ming, Liang, Feng, and Lin, Yu -Feng. Bayesian calibration of groundwater models with input data uncertainty. United States: N. p., Web. doi:10.1002/2016WR019512.
Xu, Tianfang, Valocchi, Albert J., Ye, Ming, Liang, Feng, & Lin, Yu -Feng. Bayesian calibration of groundwater models with input data uncertainty. United States. doi:10.1002/2016WR019512.
Xu, Tianfang, Valocchi, Albert J., Ye, Ming, Liang, Feng, and Lin, Yu -Feng. 2017. "Bayesian calibration of groundwater models with input data uncertainty". United States. doi:10.1002/2016WR019512. https://www.osti.gov/servlets/purl/1466031.
@article{osti_1466031,
title = {Bayesian calibration of groundwater models with input data uncertainty},
author = {Xu, Tianfang and Valocchi, Albert J. and Ye, Ming and Liang, Feng and Lin, Yu -Feng},
abstractNote = {Effective water resources management typically relies on numerical models to analyze groundwater flow and solute transport processes. Groundwater models are often subject to input data uncertainty, as some inputs (such as recharge and well pumping rates) are estimated and subject to uncertainty. Current practices of groundwater model calibration often overlook uncertainties in input data; this can lead to biased parameter estimates and compromised predictions. Through a synthetic case study of surface–ground water interaction under changing pumping conditions and land use, we investigate the impacts of uncertain pumping and recharge rates on model calibration and uncertainty analysis. We then present a Bayesian framework of model calibration to handle uncertain input of groundwater models. The framework implements a marginalizing step to account for input data uncertainty when evaluating likelihood. It was found that not accounting for input uncertainty may lead to biased, overconfident parameter estimates because parameters could be over–adjusted to compensate for possible input data errors. Parameter compensation can have deleterious impacts when the calibrated model is used to make forecast under a scenario that is different from calibration conditions. By marginalizing input data uncertainty, the Bayesian calibration approach effectively alleviates parameter compensation and gives more accurate predictions in the synthetic case study. The marginalizing Bayesian method also decomposes prediction uncertainty into uncertainties contributed by parameters, input data, and measurements. Furthermore, the results underscore the need to account for input uncertainty to better inform postmodeling decision making.},
doi = {10.1002/2016WR019512},
journal = {Water Resources Research},
number = 4,
volume = 53,
place = {United States},
year = {2017},
month = {4}
}