Uncertainty quantification in coastal aquifers using the multilevel Monte Carlo method

TL;DR

This paper applies the multilevel Monte Carlo method to quantify uncertainty in nonlinear, time-dependent coastal aquifer salinization problems, demonstrating cost reduction and parallelization in physical and stochastic spaces.

Contribution

It introduces the application of MLMC to complex density-driven flow problems with uncertain parameters, improving computational efficiency and scalability.

Findings

MLMC reduces computational costs compared to traditional methods.

Parallelization enhances efficiency in both physical and stochastic spaces.

MLMC provides accurate mean estimates for salt mass fraction in uncertain aquifer models.

Abstract

We consider a class of density-driven flow problems. We are particularly interested in the problem of the salinization of coastal aquifers. We consider the Henry saltwater intrusion problem with uncertain porosity, permeability, and recharge parameters as a test case. The reason for the presence of uncertainties is the lack of knowledge, inaccurate measurements, and inability to measure parameters at each spatial or time location. This problem is nonlinear and time-dependent. The solution is the salt mass fraction, which is uncertain and changes in time. Uncertainties in porosity, permeability, recharge, and mass fraction are modeled using random fields. This work investigates the applicability of the well-known multilevel Monte Carlo (MLMC) method for such problems. The MLMC method can reduce the total computational and storage costs. Moreover, the MLMC method runs multiple scenarios on different spatial and time meshes and then estimates the mean value of the mass fraction. The parallelization is performed in both the physical space and stochastic space. To solve every deterministic scenario, we run the parallel multigrid solver ug4 in a black-box fashion. We use the solution obtained from the quasi-Monte Carlo method as a reference solution.