Model structure and ensemble size: Implications for predictions of groundwater age - Kitlasten, Wesley; Moore, Catherine R.; Hemmings, Brioch (Frontiers in Earth Science, 2022)
This paper examines the influence of simplified vertical discretization alternatives (layering) and ensemble size on history matching and predictions of groundwater age for a national scale model of New Zealand (approximately 265,000 km2). A reproducible workflow using a combination of opensource tools and custom python scripts is used to generate three models that use the same model domain and underlying data with only the vertical discretization changing between the models. The iterative ensemble smoother approach is used for history matching each model to the same synthetic dataset. The results show that: 1) the ensemble based mean objective function is not a good indicator of model predictive ability, 2) predictive failure from model structural errors are compounded by history matching, especially when small ensembles are used, 3) predictive failure rates increase with iteration when small ensembles are used but stabilize at relatively low values (10%) with larger ensembles, 4) small ensembles contribute to predictive failure even in structurally “perfect” models, 5) correlation-based localization methods can help mitigate some of impacts of using small ensembles, 6) the deleterious effects of model simplification and ensemble size are problem specific. Systematic investigation of these issues is an important part of the model design, and this investigation process benefits greatly from a scripted, reproducible workflow using flexible, opensource tools.