Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations

•Polynomial chaos expansions (PCEs) are applied to seawater intrusion modeling.•PCEs can be accurate and fast surrogate models in Monte Carlo simulations.•PCEs can be reliably used in the estimation of moment-independent sensitivity indices.

SummaryReal world models of seawater intrusion (SWI) require high computational efforts. This creates computational difficulties for the uncertainty propagation (UP) analysis of these models due the need for repeated numerical simulations in order to adequately capture the underlying statistics that describe the uncertainty in model outputs. Moreover, despite the obvious advantages of moment-independent global sensitivity analysis (SA) methods, these methods have rarely been employed for SWI and other complex groundwater models. The reason is that moment-independent global SA methods involve repeated UP analysis which further becomes computationally demanding. This study proposes the use of non-intrusive polynomial chaos expansions (PCEs) as a means to significantly accelerate UP analysis in SWI numerical modeling studies and shows that despite the highly non-linear and non-smooth input/output relationship that exists in SWI models, non-intrusive PCEs provide a reliable and yet computationally efficient surrogate of the original numerical model. The study illustrates that for the considered two and six dimensional UP problems, PCEs offer a more accurate estimation of the statistics describing the uncertainty in model outputs compared to Monte Carlo simulations based on the original numerical model. This study also shows that the use of non-intrusive PCEs in the estimation of the moment-independent sensitivity indices (i.e. delta indices) decreases the computational time by several orders of magnitude without causing significant loss of accuracy. The use of non-intrusive PCEs for the generation of SWI hazard maps is proposed to extend the practical applications of UP analysis in coastal aquifer management studies.