Demonstrating correspondence between decision-support models and dynamics of real-world environmental systems. (September 2016)

• Record Type: Journal Article
• Authors: Huffaker, Ray; Muñoz-Carpena, Rafael; Campo-Bescós, Miguel A.; Southworth, Jane
• Is Part Of: Environmental modelling & software. Volume 83(2016:Sep.)
• Page Start: 74
• Page End: 87
• Publication Date: 2016-09
• Subjects: Model evaluation -- Nonlinear dynamics -- Phase space reconstruction -- Extreme value statistics; 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.2016.04.024
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: There are increasing calls to audit decision-support models used for environmental policy to ensure that they correspond with the reality facing policy makers. Modelers can establish correspondence by providing empirical evidence of real-world behavior that their models skillfully simulate. Since real-world behavior—especially in environmental systems—is often complex, credibly modeling underlying dynamics is essential. We present a pre-modeling diagnostic framework based on Nonlinear Time Series ( NLTS ) methods for reconstructing real-world environmental dynamics from observed data. The framework is illustrated with a case study of saltwater intrusion into coastal wetlands in Everglades National Park, Florida, USA. We propose that environmental modelers test for systematic dynamic behavior in observed data before resorting to conventional stochastic exploratory approaches unable to detect this valuable information. Reconstructed data dynamics can be used, along with other expert information, as a rigorous benchmark to guide specification and testing of environmental decision-support models corresponding with real-world behavior. Highlights: A pre-modeling Nonlinear Time Series (NLTS) framework presented. Framework reconstructs real-world system dynamics from observed data. Reconstructed dynamics inform model specification and auditing.