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Bayesian model averaging assessment on groundwater management under model structure uncertainty

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Abstract

This study introduces Bayesian model averaging (BMA) to deal with model structure uncertainty in groundwater management decisions. A robust optimized policy should take into account model parameter uncertainty as well as uncertainty in imprecise model structure. Due to a limited amount of groundwater head data and hydraulic conductivity data, multiple simulation models are developed based on different head boundary condition values and semivariogram models of hydraulic conductivity. Instead of selecting the best simulation model, a variance-window-based BMA method is introduced to the management model to utilize all simulation models to predict chloride concentration. Given different semivariogram models, the spatially correlated hydraulic conductivity distributions are estimated by the generalized parameterization (GP) method that combines the Voronoi zones and the ordinary kriging (OK) estimates. The model weights of BMA are estimated by the Bayesian information criterion (BIC) and the variance window in the maximum likelihood estimation. The simulation models are then weighted to predict chloride concentrations within the constraints of the management model. The methodology is implemented to manage saltwater intrusion in the “1,500-foot” sand aquifer in the Baton Rouge area, Louisiana. The management model aims to obtain optimal joint operations of the hydraulic barrier system and the saltwater extraction system to mitigate saltwater intrusion. A genetic algorithm (GA) is used to obtain the optimal injection and extraction policies. Using the BMA predictions, higher injection rates and pumping rates are needed to cover more constraint violations, which do not occur if a single best model is used.

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Acknowledgements

The study was supported in part by the Department of the Interior, U.S. Geological Survey under Grant No. 05HQGR0142 and 06HQGR0088. The views and conclusions contained in the document are those of the author and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Government. The author acknowledges Xiaobao Li and Asheka Rahman for their prior input. The constructive reviews were provided by the guest editors Philip Meyer and Ming Ye, and one anonymous reviewer.

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  1. Department of Civil and Environmental Engineering, Louisiana State University, 3418G Patrick F. Taylor Hall, Baton Rouge, LA, 70803-6405, USA

    Frank T.-C. Tsai

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Tsai, F.TC. Bayesian model averaging assessment on groundwater management under model structure uncertainty. Stoch Environ Res Risk Assess 24, 845–861 (2010). https://doi.org/10.1007/s00477-010-0382-3

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