A comparative study of 17 phenological models to predict the start of the growing season

Vegetation phenological models play a major role in terrestrial ecosystem modeling. However, substantial uncertainties still occur in phenology models because the mechanisms underlying spring phenological events are unclear. Taking into account the asymmetric effects of daytime and nighttime temperature on spring phenology, we analyzed the performance of 17 spring phenological models by combining the effects of photoperiod and precipitation. The global inventory modeling and mapping study third-generation normalized difference vegetation index data (1982-2014) were used to extract the start of the growing season (SOS) in the North-South Transect of Northeast Asia. The satellite-derived SOS of deciduous needleleaf forest (DNF), mixed forest (MF), open shrublands (OSL), and woody savannas (WS) showed high correlation coefficients (r) with the model-predicted SOS, with most exceeding 0.7. For all vegetation types studied, the models that considered the effect of photoperiod and precipitation did not significantly improve the model performance. For temperature-based models, the model using the growing-degree-day temperature response had a lower root mean square error compared with the models using the sigmoid temperature response Importantly, we found that daily maximum temperature was most suitable for the spring phenology prediction of DNF, OSL, and WS; daily mean temperature for MF; and daily minimum temperature for grasslands. These findings indicate that future spring phenological models should consider the asymmetric effect between daytime and nighttime temperature across different vegetation types.

Automated summary

Vegetation phenological models are essential in terrestrial ecosystem modeling, but uncertainties remain due to unclear mechanisms underlying spring phenological events. This study combined the effects of photoperiod and precipitation to analyze the performance of 17 spring phenological models. The results showed that temperature-based models incorporating the growing-degree-day temperature response had better performance than those using the sigmoid temperature response. Additionally, different vegetation types showed varying temperature preferences for spring phenology prediction. These findings emphasize the importance of considering the asymmetric effects of daytime and nighttime temperature in future spring phenological models across different vegetation types.