skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Bayesian calibration of groundwater models with input data uncertainty: CALIBRATING WITH INPUT DATA UNCERTAINTY

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3];  [4]; ORCiD logo [5]
  1. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana Illinois USA, Now at Department of Earth and Environmental Sciences, Michigan State University, East Lansing Michigan USA
  2. Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana Illinois USA
  3. Department of Scientific Computing, Florida State University, Tallahassee Florida USA
  4. Department of Statistics, University of Illinois at Urbana-Champaign, Urbana Illinois USA
  5. Illinois State Geological Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Urbana Illinois USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1402401
Grant/Contract Number:
SC0002687
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal Issue: 4; Related Information: CHORUS Timestamp: 2017-10-23 18:12:37; Journal ID: ISSN 0043-1397
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Xu, Tianfang, Valocchi, Albert J., Ye, Ming, Liang, Feng, and Lin, Yu-Feng. Bayesian calibration of groundwater models with input data uncertainty: CALIBRATING WITH INPUT DATA UNCERTAINTY. United States: N. p., 2017. 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: CALIBRATING WITH INPUT DATA UNCERTAINTY. United States. doi:10.1002/2016WR019512.
Xu, Tianfang, Valocchi, Albert J., Ye, Ming, Liang, Feng, and Lin, Yu-Feng. Wed . "Bayesian calibration of groundwater models with input data uncertainty: CALIBRATING WITH INPUT DATA UNCERTAINTY". United States. doi:10.1002/2016WR019512.
@article{osti_1402401,
title = {Bayesian calibration of groundwater models with input data uncertainty: CALIBRATING WITH INPUT DATA UNCERTAINTY},
author = {Xu, Tianfang and Valocchi, Albert J. and Ye, Ming and Liang, Feng and Lin, Yu-Feng},
abstractNote = {},
doi = {10.1002/2016WR019512},
journal = {Water Resources Research},
number = 4,
volume = 53,
place = {United States},
year = {Wed Apr 19 00:00:00 EDT 2017},
month = {Wed Apr 19 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1002/2016WR019512

Save / Share:
  • While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less
  • This paper presents a new technique--Integrated Bayesian Uncertainty Estimator (IBUNE) to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. The uncertainties from the input (forcing) data--mainly the precipitation observations and from the model parameters are reduced through a Monte Carlo Markov Chain (MCMC) scheme named Shuffled Complex Evolution Metropolis (SCEM) algorithm which has been extended to include a precipitation error model. Afterwards, the Bayesian Model Averaging (BMA) scheme is employed to further improve the prediction skill and uncertainty estimation using multiple model output. A series of case studies using three rainfall-runoff models to predict the streamflow in themore » Leaf River basin, Mississippi are used to examine the necessity and usefulness of this technique. The results suggests that ignoring either input forcings error or model structural uncertainty will lead to unrealistic model simulations and their associated uncertainty bounds which does not consistently capture and represent the real-world behavior of the watershed.« less
  • Calibration data (observed values corresponding to model-computed values of dependent variables) are incorporated into a general method of computing exact Scheff-type confidence intervals analogous to the confidence intervals developed in part I (Cooley, this issue) for a function of parameters derived from a groundwater flow model. Parameter uncertainty is specified by a distribution of parameters conditioned on the calibration data. This distribution was obtained as a posterior distribution by applying Bayes theorem to the hydrogeologically derived prior distribution of parameters from part I and a distribution of differences between the calibration data and corresponding model-computed dependent variables. Tests show thatmore » the new confidence intervals can be much smaller than the intervals of part I because the prior parameter variance-covariance structure is altered so that combinations of parameters that give poor model fit to the data are unlikely. The confidence intervals of part I and the new confidence intervals can be effectively employed in a sequential method of model construction whereby new information is used to reduce confidence interval widths at each stage.« less
  • The genetic algorithms (GA) and Bayesian Model Averaging (BMA) were used to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT).