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Uncertainty estimation is an important part of 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 endeavour, numerical modeling frameworks support an unparalleled degree of subsurface complexity and its associated uncertainty. When boundary uncertainty is concerned, however, the numerical framework can be restrictive. The interdependence of grid discretization and the enclosing boundaries make exploring uncertainties in their extent or nature difficult. 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 comprehensive 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 versatile, uncertain regional flow more easily, we propose a new element based on conformal mapping. We then include this element in a simple Python-based AEM toolbox and benchmark it against MODFLOW. Coupling AEM with a Markov Chain Monte Carlo (MCMC) 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 modelling or can support advanced model design by informing the placement of numerical model boundaries.

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