Accounting for uncertainty in complex alluvial aquifer modeling by Bayesian multi-model approach

JOURNAL OF HYDROLOGY

卷:601

Article

Yin, Jina^1,2  Tsai, Frank T-C³  Kao, Shih-Chieh⁴ 

[1] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Peoples R China

[2] Hohai Univ, Yangtze Inst Conservat & Dev, Nanjing, Peoples R China

[3] Louisiana State Univ, Dept Civil & Environm Engn, Baton Rouge, LA 70803 USA

[4] Oak Ridge Natl Lab, Environm Sci Div, Oak Ridge, TN 37831 USA

关键词: Groundwater;    Modeling;    Alluvial aquifer;    Parameter uncertainty;    Bayesian model averaging;   

DOI  :  10.1016/j.jhydrol.2021.126682

来源: Elsevier

【 摘 要 】

Alluvial aquifers by nature are complex caused by varied depositional environments. Developing a reliable groundwater model to represent an alluvial aquifer is non-trivial. Also, relying on a single best calibrated model may not be sufficient because of an inadequate choice of model parameter values. To better understand groundwater dynamics and improve model prediction reliability, this study presents a Bayesian multi-model uncertainty quantification (BMMUQ) framework to account for model parameter uncertainty in complex alluvial groundwater modeling. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer (MRAA), Northeast Louisiana. An aquifer architecture was first constructed using 7,259 well logs in the MRAA area which covers three fluvial deposits (alluvium, braided-stream terrace, and braided-stream terrace-loess). A 12-layer MODFLOW model was then developed to address the alluvial aquifer complexity and well calibrated through a genetic algorithm. This study quantified model parameter uncertainty in hydraulic conductivity and specific storage of sand facies. Bayesian model averaging (BMA) with the Expectation Maximization (EM) algorithm was adopted to derive posterior model weights and head variances of 50 alternative conceptual groundwater flow models, and thereby obtains BMA ensemble model predictions instead of only relying on the best calibrated conceptual model. Results show that an estimated around 950 million m(3) of groundwater storage loss occurs in 2015 with respect to the beginning of 2004, due to high groundwater demand for irrigation in the MRAA area. Explicitly quantifying model uncertainty can produce more reliable groundwater level predictions from BMA ensemble model. The presented groundwater modeling framework improves our understanding of the MRAA and provides a valuable tool to assist agricultural water management.

【 授权许可】

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