###### Abstract
The statistical models, including the Miami model, SIF correlation model, NDVI correlation model, and so on, establish a correlation equation between the vegetation GPP and meteorological factors or other elements and then utilize the statistical relationship to estimate the GPP [10; 11]. The light use efficiency (LUE) models estimate the GPP by taking solar radiation as the main driving variable. Next, meteorological factors, such as temperature factors and precipitation factors, are used to adjust LUE models. For instance, models like the CASA model, PEM model, VPM model, and so forth [12; 13]. The process models are constructed based on the physiological processes of vegetation, taking into account the characteristics and eco-physiological parameters of different ecosystems comprehensively. They can accurately simulate the carbon, water, and nitrogen fluxes of different vegetation types under various environmental conditions. Currently, widely applied process models include the Biome-BGC model, BEPS model, TEM model, Century model, etc. [14; 15] Among them, Biome-BGC (BioGeoChemical Cycles) is a process-based biogeochemical model that can estimate the water and carbon fluxes of ecosystems [16]. It has the advantages of strong flexibility and portability and performs better in explaining mechanisms [17]. Therefore, the Biome-BGC model is widely used for simulating ecosystem GPP due to its detailed description of vegetation ecology and physiological processes. However, there are 43 parameters in the eco-physiological constant file of the Biome-BGC model. It is the numerous parameters that result in great uncertainty during the simulation process [18]. Therefore, parameter calibration and validation are essential for accurate simulation in Biome-BGC. In the most current research, eco-physiological parameters are often partially substituted with empirical or measured values and optimized to target levels using the manual adjustment method. However, these approaches are not only inefficient but also lack reliability [19; 20]. While some studies have attempted to optimize parameters by coupling PEST with Biome-BGC [21], there are problems such as complex operation, easily obtaining the local optima, and low efficiency. How to efficiently optimize the eco-physiological parameters of Biome-BGC for different vegetation types is a primary challenge when using the model. For regional-scale application of the Biome-BGC model, researchers commonly employed eddy covariance flux data to calibrate model parameters. Subsequently, they conducted flux simulations for the region [22; 23]. Although the Biome-BGC model performs well at the single-point level, its simulation performance at the regional scale, where it faces diverse environmental conditions, remains uncertain when using the same parameters. In previous studies at the regional scale, the majority have only focused their subject on a single vegetation type in the study area, such as studies of forests [24], crops [25], and grasses [26]. For the study of flux simulation in a large-scaleregion, if there is no comparison among different vegetation types, it cannot show the difference among them. The Yellow River Basin constitutes a crucial ecological barrier and a significant economic zone in China. However, the fragile ecosystems have caused many questions about the YRB. It is of great significance to strengthen protection of the eco-environment and promote high-quality development in the YRB. Consequently, we firstly used the DE algorithm to calibrate the parameters of different vegetation ecosystems, and then we simulated the GPP of representative vegetation ecosystems within the gridded YRB from 2000 to 2021. Lastly, this study tested the model's adaptability and analyzed the spatiotemporal trends of the GPP for various typical vegetation types in the YRB. Two main questions were solved through this article. (1) Can the Biome-BGC model be applied to the Yellow River Basin with complex vegetation types by DE parameter optimization?