## 1 Introduction  
Because of fossil fuel combustion and changes in land use and land cover, the increase in the atmospheric CO\({}_{2}\) concentration has become a common problem in recent years [1]. Forests are important carbon sinks for terrestrial ecosystems, and rational use of the carbon sequestration function of forest ecosystems can effectively alleviate the aggravation of the greenhouse effect [2]. Rubber plantations widely planted in Southeast Asia and tropical China can produce natural rubber, provide timber, and also have a considerable carbon sink capacity [3]. At present, the rubber planting area on Hainan Island has reached \(5.43\times 10^{5}\) hectares, accounting for more than \(1/4\) of the total forest area of the island [4]. Therefore, rubber plantations may contribute significantly to global carbon sequestration, and it is of great significance to accurately monitor the carbon sink capacity of rubber plantations and their long-term responses to meteorological factors. Currently, methods for monitoring forest carbon sinks include eddy covariance measurements, remote sensing techniques, and model simulation. However, due to differences in specific research objectives, these methods have certain shortcomings [5]. For example, eddy covariance measurements can monitor precise exchanges of CO\({}_{2}\), water, and energy between forests and the atmosphere on smaller timescales, but are expensive and limited in scope [6]. Remote sensing techniques can provide continuous observations on a larger scale, but the monitoring results are strongly influenced by clouds and rain and have low accuracy [7]. Model simulation can describe the production of plants at different stages and has good scalability, but there are general problems of low simulation accuracy and large simulation uncertainty under different vegetation types and climatic conditions [8]. A common approach today is to combine models with multiple sources of observations to achieve accurate simulations [9]. Process-based models treat ecosystems as an atmosphere-vegetation-soil continuum to describe the dynamics of carbon, nitrogen, and water stocks and fluxes between the different modules [10; 11]. Among them, the Biome-BioGeochemical Cycles (Biome-BGC) model is widely used because it is driven by traditional meteorological data [12]. The model has good scalability and can not only simulate the carbon sink changes of biological communities under different environmental factors and human management modes through structural adjustment [13; 14], but also can be combined with remote sensing data to explore the spatiotemporal dynamics of vegetation at the regional scale [15; 16; 17]. However, this model was originally designed for temperate forests, and the phenological differences of different vegetation types were ignored in its structure, so the application of the model in tropical regions has been relatively rare and shown some simulation errors. The model uses more than 40 electrophysiological parameters, introducing high uncertainty in the simulation results [18]. The commonly used model calibration methods include model parameter optimization based on observation data and data assimilation methods [19]. Sensitivity analysis methods can quantify the contribution of parameters to the model output, which can be used to explain the functional pattern of the model and its parameter optimization [20]. The variance-based global sensitivity analysis method can analyze nonlinear models and is widely used in various process models to identify sensitive parameters for different outputs or the coupling effects of different parameters on the outputs [21]. However, the range of the parameter values used for analysis is usually defined by the model itself or the perturbation of a certain value [22; 23]; therefore, the influence of different parameter value ranges on the analysis results is still uncertain.