Details

Autor(en) / Beteiligte

• Migliavacca, Mirco
• Reichstein, Markus
• Richardson, Andrew D
• Mahecha, Miguel D
• Cremonese, Edoardo
• Delpierre, Nicolas
• Galvagno, Marta
• Law, Beverly E
• Wohlfahrt, Georg
• Andrew Black, T
• Carvalhais, Nuno
• Ceccherini, Guido
• Chen, Jiquan
• Gobron, Nadine
• Koffi, Ernest
• William Munger, J
• Perez‐Priego, Oscar
• Robustelli, Monica
• Tomelleri, Enrico
• Cescatti, Alessandro

Titel

Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests

Ist Teil von

• Global change biology, 2015-01, Vol.21 (1), p.363-376

Ort / Verlag

England: Blackwell Science

Erscheinungsjahr

2015

Quelle

Wiley Online Library Journals Frontfile Complete

Beschreibungen/Notizen

• Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECOmodels can be substantially improved by considering the biotic dependency of RECOon the short‐term productivity (e.g., daily gross primary production, GPP) in addition to the well‐known environmental controls of temperature and water availability. Here, we use a model‐data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECOat 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data‐oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECOwhen compared to a version of the model that does not consider the physiological phenology. The reduction of the model‐observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model‐error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECOin deciduous forests by including the phenological cycle of the canopy.