Multimodel Validity Assessment of Groundwater Flow Simulation Models Using Area Metric Approach. Issue 2 (5th October 2016)

• Record Type: Journal Article
• Authors: Aphale, Omkar; Tonjes, David J.
• Is Part Of: Ground water. Volume 55:Issue 2(2017)
• Page Start: 219
• Page End: 226
• Publication Date: 2016-10-05
• Subjects: Groundwater -- Periodicals; Wells -- Periodicals; Eau souterraine -- Périodiques; Puits -- Périodiques; Grondwater; Eau souterraine; Puits; Electronic journals; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 551.49
• DOI: 10.1111/gwat.12470
• Languages: English
• ISSNs: 0017-467X

Abstract

Abstract: We demonstrate the application of the Area Metric developed by Ferson et al. (2008) for multimodel validity assessment. The Area Metric quantified the degree of models' replicative validity: the degree of agreement between the observed data and the corresponding simulated outputs represented as their empirical cumulative distribution functions (ECDFs). This approach was used to rank multiple representations of a case study groundwater flow model of a landfill by their Area Metric scores. A multimodel approach allows to account for uncertainties that may either be epistemic (from lack of knowledge) or aleatory (from variability inherent in the system). The Area Metric approach enables explicit incorporation of model uncertainties, epistemic as well as aleatory, into validation assessment. The proposed approach informs understanding of the collected data and that of the model domain. It avoids model overfitting to a particular system state, and in fact is a blind assessment of the models' validity: models are not adjusted, or updated, to achieve a better numerical fit. This approach assesses the degree of models' validity, in place of the typical binary model validation/invalidation process. Collectively, this increases confidence in the model's representativeness that, in turn, reduces risk to model users. Abstract : Article impact statement : Area Metric approach incorporates epistemic and aleatory uncertainties and assesses multiple models' validity over a range of observed data.