Sequential and batch data assimilation approaches to cope with groundwater model error: An empirical evaluation. (October 2022)

• Record Type: Journal Article
• Authors: Markovich, Katherine H.; White, Jeremy T.; Knowling, Matthew J.
• Is Part Of: Environmental modelling & software. Volume 156(2022)
• Publication Date: 2022-10
• Subjects: Groundwater decision support modeling -- Data assimilation -- Model error; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105498
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Groundwater model data assimilation (DA) aims to reduce uncertainty in simulated outcomes of interest to resource management while minimizing the potential for predictive bias. Sequential DA, which can estimate model states along with properties and stresses dynamically in time, offers a potentially powerful alternative to batch DA (i.e., history matching) for reducing bias in decision-relevant predictions in the presence of incorrect model structure and/or processes. This study evaluates the ability of batch and sequential DA approaches to history match and forecast simulated quantities in the presence of model error using a novel ensemble-based paired complex–simple approach that enables the incorporation of stochastic uncertainty and a statistical evaluation of predictive bias. Our findings have implications for groundwater decision support modeling as they underscore the pitfalls of fixing parameters and forcing variables a priori and present a proof of concept for using adjustable model states to cope with model error. Highlights: Batch and sequential data assimilation are evaluated in the presence of model error. We develop an enhanced paired complex–simple approach to statistically evaluate bias. We present a proof of concept for using adjustable states to cope with model error.