Mango-GPP: A Process-Based Model for Simulating Gross Primary Productivity of Mangrove Ecosystems

Mangrove ecosystems are becoming increasingly important in global climate mitigation. However, large gaps still exist in evaluating mangroves' gross primary productivity (GPP) due to reasons such as the specific influences, for example, temperature and salt stresses are poorly described in Earth System Models (ESMs). This study developed a process-based biogeochemical model (Mango-GPP) to improve the GPP simulation in natural and restored mangroves. The model integrates mangrove-specific physiological processes, including the response to salt and temperature stresses, as well as the light-use efficiency at different growing stages. Eddy covariance flux measurements at two natural sites and one restored site in China were used to calibrate and validate Mango-GPP. The model was calibrated by inverse analysis approach based on two cases and independently validated against the other cases. The validation results showed that it was generally capable of simulating the seasonal and interannual GPP variations at different sites. The simulated daily and annual GPPs agreed well with the observations and yielded R2 of 0.67 and 0.96, with model efficiency of 0.64 and 0.93, respectively. In comparison, Mango-GPP showed better performances than many current satellite-based GPP products and ESMs. The model was more sensitive to solar radiation, carbon dioxide concentration, and leaf traits. Future improvements should focus on enhancing Mango-GPP's descriptive power of key processes, and further simulating other carbon fluxes at regional scales. This work provides a model foundation for further simulating carbon exchanges between the atmosphere, mangrove, and ocean for studying the coastal wetland restoration on regional carbon neutrality. Mangroves are becoming increasingly important for climate mitigation, but are greatly under-represented in current ecosystem models. This study developed a new process-based model (Mango-GPP) to specifically estimate carbon taken up by mangrove ecosystems. The model requires few and easily accessible inputs, and showed good performance in both natural and restored mangroves. This work sets the stage for future studies on impacts of mangrove changes (e.g., restoration and reestablishment etc.) on carbon sequestration and climate mitigation. We developed a process-based model simulating daily gross primary productivity (GPP) for mangrovesThe model incorporates the effects of salinity and temperature, as well as light absorption and use efficiency on mangroves' GPPThe model requires only 13 easily accessible inputs and is validated well at both natural and restored mangroves in China