Application of multi-model ensemble averaging techniques for groundwater simulation: synthetic and real-world case studies

Abstract

Growing demands in arid regions have increased groundwater vulnerabilities necessitating appropriate modeling and management strategies to understand and sustain aquifer system behaviors. Sustainable management of aquifer systems, however, requires a proper understanding of groundwater dynamics and accurate estimates of recharge rates which often cause error and uncertainty in simulation. This study aims to quantify the uncertainty and error associated with groundwater simulation using various multi-model ensemble averaging (MEA) techniques such as simple model averaging, weighted averaging model, multi-model super ensemble, and modified MMSE. Two numerical solutions, i.e., finite difference and finite element (FE), were first coupled under three schemes such as explicit scheme (ES), implicit scheme, and Crank-Nicolson Scheme to numerically solve groundwater simulation problems across two case studies, synthetic and real-world (the Birjand aquifer in Iran) case studies. The MEA approach was considerably successful in calibrating a complex arid aquifer in a way that honors complex geological heterogeneity and stress configurations. Specifically, the MEA techniques skillfully reduced the error and uncertainties in simulation, particularly those errors associated with water table variability and fluctuation. Furthermore, a coupled FE-ES method outperformed other approaches and generated the best groundwater-level simulation for the synthetic case study, while stand-alone FE was particularly successful for the Birjand aquifer simulation as a real-world case study.

A New Approach Based on Deep Neural Networks and Multisource Geospatial Data for Spatial Prediction of Groundwater Spring Potential

“…In recent years, the development of information technology with integrated and ensemble algorithms (Morgan, Madani, Hussien, & Nassar, 2023) has offered many new solutions for the field of groundwater research, i.e., Bayesian ensemble (Yin, Medellín-Azuara, Escriva-Bou, & Liu, 2021), Naïve Bayes ensemble (B. T. Pham et al, 2021), multi-model ensemble (Jafarzadeh, Pourreza-Bilondi, Akbarpour, Khashei-Siuki, & Samadi, 2021), boosting and bagging (Mosavi et al, 2021), cluster ensemble (Sharghi, Nourani, Zhang, & Ghaneei, 2022), ensemble Kalman filter (Zibo Wang, Lu, Chang, & Wang, 2022), and iterative ensemble smoother (Pan, Lu, & Bai, 2023). Overall, the proposed integration and ensemble of multiple models have consistently improved performance in groundwater modeling tasks.…”