Local and global parameter sensitivity within an ecophysiologically based forest landscape model. (July 2019)

• Record Type: Journal Article
• Authors: McKenzie, Patrick F.; Duveneck, Matthew J.; Morreale, Luca L.; Thompson, Jonathan R.
• Is Part Of: Environmental modelling & software. Volume 117(2019)
• Page Start: 1
• Page End: 13
• Publication Date: 2019-07
• Subjects: Uncertainty -- LANDIS-II -- PnET -- Forest landscape model -- Fourier amplitude sensitivity test -- Regression tree; 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.2019.03.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Forest landscape models (FLM) are widely used for simulating forest ecosystems. As FLMs have become more mechanistic, more input parameters are required, which increases model parameter uncertainty. To better understand the increased mechanistic detail provided by LANDIS-II/PnET-Succession, we studied the effects of parameter uncertainty on model outputs based on three different approaches. Global sensitivity analyses summarized the influence of each parameter, a local sensitivity analysis determined the magnitude of and degree of nonlinearity of variation in model outputs alongside variation in individual parameters, and a regression tree analysis identified hierarchical relationships among and interaction effects between parameters. Foliar nitrogen, maintenance respiration, and atmospheric carbon dioxide concentration were the most influential parameters in the global analysis. Knowing where parameter influence is concentrated will help model users interpret results from LANDIS-II/PnET-Succession to address ecological questions and should guide priorities for data acquisition. Highlights: Mechanistic forest landscape models increasingly require more input parameters. We quantified LANDIS-II/PnET-Succession global and local parameter uncertainty. Foliar nitrogen, maintenance respiration, and carbon dioxide was highly influential.