Effects of auto-adaptive localization on a model calibration using ensemble methods

Abstract

Simulations of the natural systems for environmental decision-making typically benefit from a highly parameterized approach (Hunt et al. 2007; Doherty and Hunt 2010), which enhances the flow of information contained in state observations to the parameters and improves application to decision support. However, parameter estimation (PE) with highly parameterized environmental models using traditional approaches (e.g., Doherty and Hunt 2010) is computationally intensive. Attempts at addressing the computational burden include improved computing approaches (e.g., Schreüder 2009; Hunt et al. 2010) and advances in algorithmic approaches (e.g., Tonkin and Doherty 2005; Welter et al. 2012, 2015). Recently, the iterative ensemble smoother (IES) approach (Chen and Oliver 2013; White 2018; White et al. 2020a) has greatly improved the efficiency of the PE calibration process compared to previous algorithms while concurrently providing nonlinear estimates of uncertainty (Hunt et al. 2021).