Robust discrimination between uncertain management alternatives by iterative reflection on crossover point scenarios: Principles, design and implementations. (September 2016)

• Record Type: Journal Article
• Authors: Guillaume, Joseph H.A.; Arshad, Muhammad; Jakeman, Anthony J.; Jalava, Mika; Kummu, Matti
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 326
• Page End: 343
• Publication Date: 2016-09
• Subjects: Uncertainty -- Crossover points -- Robustness -- Scenario analysis -- Fuzzy set theory -- Vulnerability 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.2016.04.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: When comparing environmental management alternatives, there is a need to assess the effect of uncertainty in the underlying model(s) and future conditions on robustness of recommendations. At times, it may be difficult or undesirable to specify the uncertainty in inputs and parameters a priori . An alternative approach instead generates crossover points, describing scenarios where the preferred alternative will change (i.e. alternatives are of equal value), and prompts the analyst to assess their plausibility a posteriori . This paper extends previous work by introducing principles, design and implementation of a new method to analyse crossover points. It reduces the complexity of dealing with many variables by identifying single crossover points of greatest concern, and progressively building understanding through three stages of analysis. We present three implementations using R, Excel and a web interface. They use two examples involving cost-benefit analysis of managed aquifer recharge and the water footprint impact of changing diets. Graphical abstract: Highlights: Crossover points describe scenarios where recommendations change. Emphasis is on when each alternative is better, not which alternative is better. A multi-stage approach builds understanding of the method and of the problem. All assumptions are considered uncertain; analyst is prompted to review them. Multiple variables are dealt with by identifying crossover points of greatest concern.