Hydrogeological Uncertainty Estimation With the Analytic Element Method

• Published in: Water resources research Vol. 57; no. 6
• Main Authors: Ramgraber, Maximilian, Schirmer, Mario
• Format: Journal Article
• Language: English
• Published: 01.06.2021
• Subjects: AEM; analytic element modeling; Bayesian statistics; conformal mapping; hydrogeology; MCMC
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2020WR029509

Uncertainty estimation plays an important part in practical hydrogeology. With most of the subsurface unobservable, attempts at system characterization will invariably be incomplete. Uncertainty estimation, then, must quantify the influence of unknown parameters, forcings, and structural deficiencies. In this endeavor, numerical modeling frameworks can resolve a high degree of subsurface complexity and its associated uncertainty. Where boundary uncertainty is concerned, however, numerical frameworks can be restrictive. The interdependence of grid discretization and its enclosing boundaries render exploration of uncertainties in their extent or nature challenging. The analytic element method (AEM) may be an interesting complement, as it is computationally efficient, economic with its parameter count, and does not require enclosure through finite boundaries. These properties make AEM well suited for uncertainty estimation, particularly in data‐scarce settings or exploratory studies. In this study, we explore the use of AEM for flow field uncertainty estimation, with a particular focus on boundary uncertainty. To induce diverse, uncertain regional flow more easily, we propose a new element based on a Möbius transformation. We include this element in a simple Python‐based AEM toolbox and benchmark it against MODFLOW. Coupling AEM with a Markov Chain Monte Carlo routine using adaptive proposals, we explore its use in a synthetic case study. We find that AEM permits efficient uncertainty estimation for groundwater flow fields, which may form a basis for stochastic Lagrangian transport modeling or can support advanced model design by informing the placement of numerical model boundaries. Key Points In this study, we explore the use of the analytic element method (AEM) for hydrogeological uncertainty estimation using a Markov Chain Monte Carlo algorithm We include a flexible element based on conformal mapping for the influence of uncertain regional flow in a simple Python‐based AEM toolbox We find that AEM can be a useful tool for direct steady‐state uncertainty estimation or act as a support tool during model creation