A two-leaf daily GPP model based on a rectangular hyperbolic model adjusted for air temperature and vegetation type.

Area of Science:

• Earth and Environmental Sciences
• Ecology
• Climate Science

Background:

• Accurate Gross Primary Productivity (GPP) modeling is crucial for understanding global terrestrial carbon cycle dynamics.
• Current GPP models, including Light Use Efficiency (LUE) and process-based approaches, have limitations in simplicity or accuracy.
• A need exists for accessible yet accurate GPP models for large-scale, long-term studies.

Purpose of the Study:

• To introduce and evaluate a novel two-leaf GPP model (TL-RHM) designed for daily temporal resolution.
• To provide a user-friendly and accurate tool for simulating GPP across various vegetation types.
• To assess the model's performance against eddy-covariance flux data.

Main Methods:

• Developed a two-leaf GPP model (TL-RHM) with two expression forms.
• Integrated a modified rectangular hyperbolic model accounting for temperature effects on GPP.
• Validated the model using CO2 eddy-covariance flux data from 21 sites representing four major vegetation types.

Main Results:

• The TL-RHM demonstrated strong agreement between simulated and measured daily GPP.
• Model performance was consistent across calibration and validation datasets for all tested vegetation types.
• The model effectively captures GPP variations influenced by temperature across different ecosystems.

Conclusions:

• The TL-RHM offers a valuable tool for accurate, long-term GPP simulations at regional and global scales.
• Its relatively simple structure combined with high accuracy makes it suitable for broad ecological and climate research.
• The model advances GPP estimation, aiding in the study of terrestrial carbon dynamics.