Modeling the Sensitivity of the Seasonal Cycle of GPP to Dynamic LAI and Soil Depths in Tropical Rainforests

The seasonality of pan-tropical wet forests has been highlighted by recent remote sensing and eddy flux measurements that have recorded both increased and sustained dry-season gross primary productivity (GPP). These observations suggest that wet tropical forests are primarily light limited and that the mechanisms for resilience to drought and projected climate change must be considered in ecosystem model development. Here we investigate two proposed mechanisms for drought resilience of tropical forests, deep soil water access and the seasonality of phenology, using the LPJmL Dynamic Global Vegetation Model. We parameterize a new seasonal phenology module for tropical evergreen trees using remotely sensed leaf area index (LAI) and incoming solar radiation data from the Terra Earth Observing System. Simulations are evaluated along a gradient of dry-season length (DSL) in South America against MODIS GPP estimates. We show that deep soil water access is critical for maintaining dry-season GPP, whereas implementing a seasonal LAI did not enhance simulated dry-season GPP. The Farquhar-Collatz photosynthesis scheme used in LPJmL optimizes leaf nitrogen allocation according to light conditions, causing maximum photosynthetic capacity in the dry season. High LAI, characteristic of tropical forests, also dampens the seasonal amplitude of the fraction of photosynthetically active radiation (FPAR). Given the relatively high uncertainty in tropical phenology observations and their corresponding proximate drivers, we recommend that ecosystem model development focus on belowground processes. An improved representation of soil depths and rooting distributions is necessary for modeling the dynamics of dry-season tropical forest functioning and may have important impacts for modeling tropical forest vulnerability to climate change.

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