Integrated Bayesian Multi-model approach to quantify input, parameter and conceptual model structure uncertainty in groundwater modeling

Syed Md Touhidul Mustafa*, Jiri Nossent, Gert Ghysels, Marijke Huysmans

*Corresponding author for this work

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

A flexible Integrated Bayesian Multi-model Uncertainty Estimation Framework (IBMUEF) is presented to simultaneously quantify conceptual model structure, input and parameter uncertainty of a groundwater flow model. In this fully Bayesian framework, the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm with a novel likelihood function is combined with Bayesian Model Averaging (BMA). Four alternative conceptual models, representing different geological representations of an overexploited aquifer, have been developed. The uncertainty of the input of the model is represented by multipliers. A novel likelihood function based on a new heteroscedastic error model is included to extend the applicability of the framework. The results of the study confirm that neglecting conceptual model structure uncertainty results in unreliable prediction. Consideration of both model structure and input uncertainty are important to obtain confident parameter sets and better model predictions. This study shows that the IBMUEF provides more reliable model predictions and accurate uncertainty bounds.
Original languageEnglish
Article number104654
Number of pages17
JournalEnvironmental Modelling and Software
Volume126
Early online date13 Feb 2020
DOIs
Publication statusPublished - Apr 2020

Keywords

  • Conceptual model structure uncertainty
  • Bayesian approach
  • input uncertainty
  • Bayesian model averaging
  • uncertainty quantification
  • groundwater flow model
  • ENSEMBLE
  • MANAGEMENT
  • Uncertainty quantification
  • DROUGHT
  • FLOW
  • PREDICTION
  • Groundwater flow model
  • Input uncertainty
  • FRAMEWORK
  • BASIN
  • MONTE-CARLO-SIMULATION
  • SELECTION
  • WATER

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