On the site-level suitability of biomass models. (November 2015)

• Record Type: Journal Article
• Authors: Pérez-Cruzado, César; Fehrmann, Lutz; Magdon, Paul; Cañellas, Isabel; Sixto, Hortensia; Kleinn, Christoph
• Is Part Of: Environmental modelling & software. Volume 73(2015:Nov.)
• Page Start: 14
• Page End: 26
• Publication Date: 2015-11
• Subjects: Biomass and carbon inventory -- Accuracy -- Estimation uncertainty -- Model localization -- Monitoring; 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.2015.07.019
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Tree biomass estimates in environmental studies are based on allometric models, which are known to vary with species, site, and other forest characteristics. The UNFCCC published a guideline to evaluate the appropriateness of biomass models before application, but it misleads the concept of model suitability and does also allow the selection of models with systematic deviations in the predictions. Here we present an alternative approach based on non-parametric techniques. The approach was tested for pure stands, but this methodology is likewise applicable to mixed forests. The proposed tests perform well in rejecting a model if the predictions for the targeted population are systematically deviant. It is demonstrated that the suitability of an allometric model is a matter of accuracy. The proposed method also allows localizing the model. The presented approach can improve the transparency of global forest monitoring systems and can be implemented with relatively small effort. Highlights: Biomass model suitability is often judged by the fitting statistics of the model. Characteristics of the local population are usually ignored in suitability checking. We propose statistical test to evaluate the suitability of biomass models. Biomass model suitability is a matter of accuracy and not precision. A new approach to evaluate model suitability based on statistical tests is presented.