Are more complex physiological models of forest ecosystems better choices for plot and regional predictions?. (January 2016)

• Record Type: Journal Article
• Authors: Jin, Wenchi; He, Hong S.; Thompson, Frank R.
• Is Part Of: Environmental modelling & software. Volume 75(2016:Jan.)
• Page Start: 1
• Page End: 14
• Publication Date: 2016-01
• Subjects: Process-based forest ecosystem models -- Carbon -- Model comparison -- Decadal scale -- Plot scale -- Regional scale; 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.2015.10.004
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Process-based forest ecosystem models vary from simple physiological, complex physiological, to hybrid empirical-physiological models. Previous studies indicate that complex models provide the best prediction at plot scale with a temporal extent of less than 10 years, however, it is largely untested as to whether complex models outperform the other two types of models at plot and regional scale in longer timeframe (i.e. decades). We compared model predictions of aboveground carbon by one representative model of each model type (PnET-II, ED2 and LINKAGES v2.2, respectively) with field data (19–77 years) at both scales in the Central Hardwood Forests of the United States. At plot scale, predictions by complex physiological model were the most concordant with field data, suggesting that physiological processes are more influential than forest composition and structure. Hybrid model provided the best predictions at regional scale, suggesting that forest composition and structure may be more influential than physiological processes. Highlights: We evaluated performance of process-based forest ecosystem models. A complex physiological model performed best at the plot scale. A hybrid empirical-physiological model performed best at the regional scale.