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Investigating the effect of complexity on groundwater flow modeling uncertainty

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Abstract

Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with Model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity.

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Acknowledgements

This work was supported by the Tabriz University. The third author was supported in part by DOE Early Career Award DE-SC0008272 and NSF-EAR Grant 1552329. We thank K. Shashok (Author AID in the Eastern Mediterranean) for technial editing.

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Authors and Affiliations

  1. Department of Earth Sciences, University of Tabriz, Tabriz, Iran

    Saeideh Samani & Asghar Asghari Moghaddam

  2. Department of Scientific Computing, Florida State University, Tallahassee, FL, USA

    Ming Ye

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  1. Saeideh Samani
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Correspondence to Saeideh Samani.

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Samani, S., Moghaddam, A.A. & Ye, M. Investigating the effect of complexity on groundwater flow modeling uncertainty. Stoch Environ Res Risk Assess 32, 643–659 (2018). https://doi.org/10.1007/s00477-017-1436-6

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