Why models underestimate West African tropical forest primary productivity

Area of Science:

• Ecology
• Earth System Science
• Forest Science

Background:

• Tropical forests are crucial for global photosynthesis.
• Existing models often underestimate West African forest productivity compared to field data.
• Data-model mismatches hinder accurate carbon cycle assessments.

Purpose of the Study:

• Investigate discrepancies between field measurements and model estimations of tropical forest gross primary productivity (GPP).
• Identify reasons for the underestimation of GPP in West African forests by global models.
• Propose improvements for GPP estimation models and input data.

Main Methods:

• Biometric GPP measurements were compared with multiple global GPP products at West African study sites.
• A standard photosynthesis model was updated with local field-measured fractional absorbed photosynthetic radiation (fAPAR) and photosynthetic traits.
• Systematic underestimation of fAPAR by remote sensing products due to cloud contamination was analyzed.

Main Results:

• Biometric GPP measurements were 56.3% higher on average than global GPP products at the study sites.
• Model underestimation of GPP was significantly reduced when incorporating local fAPAR and trait data.
• Remote sensing products underestimated fAPAR by 33.9% on average, primarily due to cloud cover.

Conclusions:

• Global GPP models may systematically underestimate the productivity of tropical forests, particularly in regions like West Africa.
• Accurate field-derived data, especially for fAPAR and photosynthetic traits, are critical for improving photosynthesis models.
• Addressing cloud contamination in remote sensing and integrating local measurements are key to advancing tropical forest carbon cycling research.