Modelling uncertainty in social–natural interactions. (January 2016)

• Record Type: Journal Article
• Authors: Ropero, R.F.; Rumí, R.; Aguilera, P.A.
• Is Part Of: Environmental modelling & software. Volume 75(2016:Jan.)
• Page Start: 362
• Page End: 372
• Publication Date: 2016-01
• Subjects: Systemic change -- Socio-ecological system -- Water flows -- Hybrid Bayesian networks -- Mixtures of truncated exponentials; 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.2014.07.008
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Socio-ecological systems can be represented as a complex network of causal interactions. Modelling such systems requires methodologies that are able to take uncertainty into account. Due to their probabilistic nature, Bayesian networks are a powerful tool for representing complex systems where interactions between variables are subject to uncertainty. In this paper, we study the interactions between social and natural subsystems (land use and water flow components) using hybrid Bayesian networks based on the Mixture of Truncated Exponentials model. This study aims to provide a new methodology to model systemic change in a socio-ecological context. Two endogenous changes – agricultural intensification and the maintenance of traditional cropland – are proposed. Intensification of the agricultural practices leads to a rise in the rate of immigration to the area, as well as to greater water losses through evaporation. By contrast, maintenance of traditional cropland hardly changes the social structure, while increasing evapotranspiration rates and improving the control over runoff water. These results indicate that hybrid Bayesian networks are an excellent tool for modelling social–natural interactions. Highlights: Uncertainty has to be taken into account in Socio-ecological system modelling. Socio-ecological system is modelled by hybrid BNs. Extreme-values probabilities are provided as a new tool to assess systemic change. Hybrid BNs can represent complex systems under conditions of uncertainty.