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Bayesian calibration of groundwater models with input data uncertainty

Journal Article · · Water Resources Research
DOI:https://doi.org/10.1002/2016WR019512· OSTI ID:1466031
 [1];  [2];  [3];  [2];  [2]
  1. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States); Michigan State Univ., East Lansing, MI (United States); Univ. of Illinois at Urgana-Champaign, IL (United States)
  2. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
  3. Florida State Univ., Tallahassee, FL (United States)
+ Show Author Affiliations
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.
Research Organization:
Florida State Univ., Tallahassee, FL (United States)
Sponsoring Organization:
USDOE; USDOE Office of Science (SC)
Grant/Contract Number:
SC0002687
OSTI ID:
1466031
Alternate ID(s):
OSTI ID: 1402401
Journal Information:
Water Resources Research, Journal Name: Water Resources Research Journal Issue: 4 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 (4)

Toward a combined Bayesian frameworks to quantify parameter uncertainty in a large mountainous catchment with high spatial variability journal December 2018
RETRACTED ARTICLE: Uncertainty Analysis of a Continuous Hydrological Model Using DREAM-ZS Algorithm
  • Aghakhani Afshar, Amirhosein; Hassanzadeh, Yousef; Pourreza-Bilondi, Mohsen
  • Iranian Journal of Science and Technology, Transactions of Civil Engineering, Vol. 44, Issue 4 https://doi.org/10.1007/s40996-019-00287-7
journal June 2019
Estimation and Impact Assessment of Input and Parameter Uncertainty in Predicting Groundwater Flow With a Fully Distributed Model journal September 2018
Addressing Challenges for Mapping Irrigated Fields in Subhumid Temperate Regions by Integrating Remote Sensing and Hydroclimatic Data journal February 2019

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Related Subjects

54 ENVIRONMENTAL SCIENCES
Bayesian
calibration
input uncertainty
uncertainty quanitfication