Comparing the structural uncertainty and uncertainty management in four common Land Use Cover Change (LUCC) model software packages. (July 2022)

• Record Type: Journal Article
• Authors: García-Álvarez, David; Camacho Olmedo, María Teresa; Van Delden, Hedwig; Mas, Jean-François; Paegelow, Martin
• Is Part Of: Environmental modelling & software. Volume 153(2022)
• Publication Date: 2022-07
• Subjects: Uncertainty -- LUCC Modeling -- CA_Markov -- Dinamica EGO -- Land change modeler -- Metronamica; 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.2022.105411
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Research on the uncertainty of Land Use Cover Change (LUCC) models is still limited. Through this paper, we aim to globally characterize the structural uncertainty of four common software packages (CA_Markov, Dinamica EGO, Land Change Modeler, Metronamica) and analyse the options that they offer for uncertainty management. The models have been compared qualitatively, based on their structures and tools, and quantitatively, through a study case for the city of Cape Town. Results proved how each model conceptualised the modelled system in a different way, which led to different outputs. Statistical or automatic approaches did not provide higher repeatability or validation scores than user-driven approaches. The available options for uncertainty management vary depending on the model. Communication of uncertainties is poor across all models. Highlights: Each model software package conceptualised the modelled system in a different way. There is not a best modelling approach, but each model entails different uncertainties and limitations. User intervention and the provision of several options for each modelling step allows to manage the structural uncertainty. Statistical or automatic models did not provide more repeatability or better simulation scores than user-driven models. Model software packages do not pay enough attention to uncertainty management and communication.