A coupled modeling framework for predicting ecosystem carbon dynamics in boreal forests. (July 2017)

• Record Type: Journal Article
• Authors: Huang, Chao; He, Hong S.; Hawbaker, Todd J.; Liang, Yu; Gong, Peng; Wu, Zhiwei; Zhu, Zhiliang
• Is Part Of: Environmental modelling & software. Volume 93(2017)
• Page Start: 332
• Page End: 343
• Publication Date: 2017-07
• Subjects: Model coupling -- LINKAGES v2.2 -- LANDIS PRO -- Fire -- Harvest -- Carbon stocks; 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.2017.03.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Carbon stocks in boreal forests play an important role in global carbon balance but are sensitive to climate change and disturbances. Ecological models offer valuable insights into the effects of climate change and disturbances on boreal forests carbon stocks. However, the current pixel-based model coupling approaches are challenging to apply over large spatial extents because high computational loads and model parameterizations. Therefore, we developed a new framework for coupling a forest ecosystem and a landscape model to predict aboveground and soil organic carbon stocks at the ecoregion level. Our results indicated that the new model-coupling framework has some advantages on computation efficiency and model validation. The model results showed that carbon stocks and its spatial distribution were significantly influenced by fire, harvest, and their interactions. Simulation results showed that boreal forests carbon stocks are vulnerable to loss because of future potential disturbances, complicating efforts to offset greenhouse gas emissions through forest management. Highlights: We developed a new framework for coupling a forest ecosystem and a landscape model to predict carbon stocks at the ecoregion level. Results indicate that the new model coupling framework has a lot of advantages in model validation and computational efficiency compared with the current pixel-level model coupling framework. Fire, harvest and their interactions strongly influence the spatial distribution and amount of boreal forest carbon stocks.