Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox:facilitating the communication of epistemic uncertainty

There is a general trend toward the increasing inclusion of uncertainty estimation in the environmental modelling domain. We present the Consortium on Risk in the Environment: Diagnostics, Integration, Benchmarking, Learning and Elicitation (CREDIBLE) Uncertainty Estimation (CURE) toolbox, an open-source MATLAB (TM) toolbox for uncertainty estimation aimed at scientists and practitioners who are not necessarily experts in uncertainty estimation. The toolbox focusses on environmental simulation models and, hence, employs a range of different Monte Carlo methods for forward and conditioned uncertainty estimation. The methods included span both formal statistical and informal approaches, which are demonstrated using a range of modelling applications set up as workflow scripts. The workflow scripts provide examples of how to utilize toolbox functions for a variety of modelling applications and, hence, aid the user in defining their own workflow; additional help is provided by extensively commented code. The toolbox implementation aims to increase the uptake of uncertainty estimation methods within a framework designed to be open and explicit in a way that tries to represent best practice with respect to applying the methods included. Best practice with respect to the evaluation of modelling assumptions and choices, specifically including epistemic uncertainties, is also included by the incorporation of a condition tree that allows users to record assumptions and choices made as an audit trail log.

Automated summary

The CREDIBLE Uncertainty Estimation (CURE) toolbox is an open-source MATLAB(TM) toolbox that provides a range of Monte Carlo methods for uncertainty estimation in environmental simulation models. It aims to help scientists and practitioners without expertise in uncertainty estimation by providing examples of modelling applications and workflow scripts.