Abstract Process‐based ecophysiological forest models are essential for understanding the mechanisms underlying forest responses to climate change. The MAIDENiso ecophysiological model provides an excellent framework for systematically analyzing photosynthetic production, carbon allocation strategies, and stable isotope signals in forests. However, the model involves numerous parameters that are challenging to derive from direct observations, which makes calibration especially difficult. In this study, we developed an improved genetic algorithm (GA) and combined it with the Markov chain Monte Carlo (MCMC) method, proposing a novel GA‐based parameter calibration approach (MCMC‐GA). Results from algorithm evaluations at two sites with distinct climatic conditions in China (PMC and HLS) demonstrated that MCMC‐GA substantially improved calibration accuracy and efficiency—the weighted RMSE at convergence decreased by an average of 23% across the two sites compared with the classical GA, while calibration efficiency (the number of algorithm iterations to reach a target RMSE) increased by 65%. Based on the application of the novel GA‐based approach to parameterize MAIDENiso, tree‐ring widths and stable carbon isotopes (δ 13 C TRC ) at both sites were successfully simulated. Overall, the improvements of the GA and its integration with MCMC significantly enhanced the calibration efficiency and accuracy of process‐based forest models, providing a valuable tool for parameterizing other Earth system models and offering promising opportunities for advancing forest ecological research.
Xue et al. (Wed,) studied this question.
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