A Hierarchical Bayesian Model Averaging Framework for Groundwater Prediction under Uncertainty

Groundwater prediction models are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate and prioritize sources of uncertainty in a hierarchical structure and conduct BMA for concentration prediction. A BMA tree of models...

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Bibliographic Details
Published in:Ground water 2015-03, Vol.53 (2), p.305-316
Main Authors: Chitsazan, Nima, Tsai, Frank T.-C.
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Calibration
Chlorides
Chlorides - analysis
Geologic Sediments
Groundwater
Groundwater - chemistry
Louisiana
Mathematical models
Models, Theoretical
Predictions
Sand
Seawater - chemistry
Transport
Trees
Uncertainty
Water Movements
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Summary:Groundwater prediction models are subjected to various sources of uncertainty. This study introduces a hierarchical Bayesian model averaging (HBMA) method to segregate and prioritize sources of uncertainty in a hierarchical structure and conduct BMA for concentration prediction. A BMA tree of models is developed to understand the impact of individual sources of uncertainty and uncertainty propagation to model predictions. HBMA evaluates the relative importance of different modeling propositions at each level in the BMA tree of model weights. The HBMA method is applied to chloride concentration prediction for the “1,500‐foot” sand of the Baton Rouge area, Louisiana from 2005 to 2029. The groundwater head data from 1990 to 2004 is used for model calibration. Four sources of uncertainty are considered and resulted in 180 flow and transport models for concentration prediction. The results show that prediction variances of concentration from uncertain model elements are much higher than the prediction variance from uncertain model parameters. The HBMA method is able to quantify the contributions of individual sources of uncertainty to the total uncertainty.
ISSN:0017-467X
1745-6584
DOI:10.1111/gwat.12207