The Effect of Available Data on the Worth of Future Observations for Groundwater Modelling
Year: 2025
Type: preprint
Abstract: Models for groundwater flow and solute transport are employed for many problems in research and practice, to guide decisions, and to predict quantities of interest. Following initial model development, Bayesian statistical methods are often used to infer distributions of model parameters from observation data. Reducing the resulting uncertainty in model outputs is of high importance in many contexts and Bayesian experimental design can be used for this purpose. This approach is computationally expensive, which hampers practical applicability. To solve the computational challenges, we use a novel combination of Bayesian inversion, accelerated via multilevel methods, and Bayesian experimental design. For a synthetic aquifer, we then analyze the effect of environmental tracer observations (besides groundwater head observations) in the initial observation dataset on the distributions of various quantities of interest after Bayesian inversion and subsequent experimental design. Our results demonstrate that posterior samples from Bayesian inversion can be re-used to perform experimental design without additional model evaluations. In both scenarios, uncertainties with respect to flux-related and groundwater age-related quantities are substantially reduced through experimental design and complementary observations of tracer concentrations are associated with the highest individual utility. The presence of environmental tracer data in the initial observation dataset leads to reduced uncertainty and bias in model outputs after Bayesian inversion and reduced overestimation in complementary observation utility.
Institutions: Technische Universität Dresden, University of Potsdam
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413526100