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

نویسندگانAbolfazl Akbarpour,Mohsen Pourreza-Bilondi,Abbas Khashei Siuki,
نشریهJournal of Hydroinformatics
شماره صفحات1-16
شماره سریال3
شماره مجلد2
ضریب تاثیر (IF)1.2
نوع مقالهFull Paper
تاریخ انتشار2021
رتبه نشریهISI
نوع نشریهچاپی
کشور محل چاپایران
نمایه نشریهJCR،Scopus

چکیده مقاله

Growing demands in arid regions have increased groundwater vulnerabilities necessitating appropriate modeling and management strategiesto understand and sustain aquifer system behaviors. Sustainable management of aquifer systems, however, requires a proper understandingof groundwater dynamics and accurate estimates of recharge rates which often cause error and uncertainty in simulation. This study aims toquantify the uncertainty and error associated with groundwater simulation using various multi-model ensemble averaging (MEA) techniquessuch as simple model averaging, weighted averaging model, multi-model super ensemble, and modified MMSE. Two numerical solutions, i.e.,finite difference andfinite element (FE), werefirst 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 aqui-fer in Iran) case studies. The MEA approach was considerably successful in calibrating a complex arid aquifer in a way that honors complexgeological heterogeneity and stress configurations. Specifically, the MEA techniques skillfully reduced the error and uncertainties in simu-lation, particularly those errors associated with water table variability andfluctuation. Furthermore, a coupled FE-ES methodoutperformed other approaches and generated the best groundwater-level simulation for the synthetic case study, while stand-alone FEwas particularly successful for the Birjand aquifer simulation as a real-world case study.

لینک ثابت مقاله

tags: arid regions, groundwater simulation, multi-collinearity, multi-model ensemble averaging techniques, uncertainty assessmen