A python framework for environmental model uncertainty analysis

Environmental Modelling and Software
By: , and 

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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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title A python framework for environmental model uncertainty analysis
Series title Environmental Modelling and Software
DOI 10.1016/j.envsoft.2016.08.017
Volume 85
Year Published 2016
Language English
Publisher Elsevier
Contributing office(s) Texas Water Science Center
Description 12 p.
First page 217
Last page 228