Incorporating water availability into autumn phenological model improved China's terrestrial gross primary productivity (GPP) simulation

Ecosystem models provide an effective approach to quantify the terrestrial carbon cycle, but the lack of accurate phenological information prevents them from better simulations of the physical processes. Compared with spring phenology (i.e. the start of the growing season, SOS), the vegetation phenology in autumn (the end of the growing season, EOS) is not well-simulated and it is challenging to incorporate vegetation phenology into ecosystem models. The simulation of EOS based on temperature and photoperiod was widely accepted, such as Delpierre et al (2009 Agric. For. Meteorol. 149 938-48)'s model (DM), yet its accuracy has not been fully discussed at a regional scale. Here, we developed a regional autumn phenological model (DMS) with inputs of temperature, photoperiod, and water availability for China's terrestrial ecosystems. The new DMS model significantly improved the representation of EOS in terms of the lower root mean square error (RMSE), higher model efficiency, and a higher percentage of significant correlation with the referenced EOS. We observed widespread delaying trends of EOS with an average rate of 0.1 d yr(-1) for vegetated areas over 2001-2018. We further incorporated the improved EOS into the boreal ecosystem productivity simulator (BEPS) and found that the phenology-modified BEPS model had better performances in predicting annual gross primary productivity (GPP) with similar to 28% lower RMSE than the original model when testing against GPP measurements from flux tower sites. From 2001 to 2017, the interannual GPP simulated by the modified BEPS model showed an increasing trend with a rate of 6.0 g C m(-2) yr(-2). In conclusion, our study proves that water availability is of great significance for modeling autumn phenology, and the incorporation of phenological dates into an ecosystem model is helpful for productivity simulation.

Automated summary

The study developed a regional autumn phenological model (DMS) for China's terrestrial ecosystems, significantly improving the representation of EOS and predicting GPP with better performance. Incorporating phenological dates into ecosystem models could be helpful for productivity simulation and potentially lead to more accurate carbon cycle quantification.