A python framework for environmental model uncertainty analysis. (November 2016)

• Record Type: Journal Article
• Authors: White, Jeremy T.; Fienen, Michael N.; Doherty, John E.
• Is Part Of: Environmental modelling & software. Volume 85(2016:Nov.)
• Page Start: 217
• Page End: 228
• Publication Date: 2016-11
• Subjects: Uncertainty analysis -- FOSM -- Python -- Model independent -- Highly-parameterized -- Data worth analysis; 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.2016.08.017
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We have developed pyEMU, a python framework for Environmental Modeling Uncertainty analyses, open-source tool that is non-intrusive, easy-to-use, computationally efficient, and scalable to highly-parameterized inverse problems. The framework implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses. The FOSM-based analyses can also be completed prior to parameter estimation to help inform important modeling decisions, such as parameterization and objective function formulation. Complete workflows for several types of FOSM-based and non-linear analyses are documented in example notebooks implemented using Jupyter that are available in the online pyEMU repository. Example workflows include basic parameter and forecast analyses, data worth analyses, and error-variance analyses, as well as usage of parameter ensemble generation and management capabilities. These workflows document the necessary steps and provides insights into the results, with the goal of educating users not only in how to apply pyEMU, but also in the underlying theory of applied uncertainty quantification. Highlights: pyEMU is a python framework for model-independent uncertainty analysis and supports highly-parameterized inversion. pyEMU exposes several methods for data-worth analysis for designing observation networks and data collection activities. pyEMU can be used to estimate parameter and forecast uncertainty before inversion. pyEMU can be used to design objective functions and parameterizations.