###### Abstract  
In terrestrial ecosystems, the quantification of carbon absorption is primarily represented by the gross primary productivity (GPP), which signifies the initial substances and energy acquired by the ecosystem. The GPP also serves as the foundation for the carbon cycle within the entire terrestrial ecosystem. The Biome-BGC model is a widely used biogeochemical process model for simulating the stocks and fluxes of water, carbon, and nitrogen between ecosystems and the atmosphere. However, it is the abundance of eco-physiological parameters that lead to challenges in calibrating the model. The parameter optimization method of coupling the differential evolution algorithm (DE) with the Biome-BGC model was used to calibrate and validate the eco-physiological parameters of the seven typical vegetation types in the Yellow River Basin (YRB). And then we used the calibrated parameters to simulate the GPP by way of grid-based simulation. Finally, we conducted model adaptability testing and spatiotemporal analysis of GPP variations in the YRB. The results of the validation (R\({}^{2}\), RMSE) were: temperate grasses (0.94, 24.33 g C m\({}^{-2}\)), alpine meadows (0.94, 18.13 g C m\({}^{-2}\)), shrubs (0.94, 29.20 g C m\({}^{-2}\)), erggreen needle leaf forests (0.96, 27.88 g C m\({}^{-2}\)), deciduous broad leaf forests (0.94, 32.09 g C m\({}^{-2}\)), one crop a year (0.96, 16.19 g C m\({}^{-2}\)), and two crops a year (0.90, 38.15 g C m\({}^{-2}\)). After adaptability testing, the average R\({}^{2}\) value between the simulated GPP values and the GPP product values in the YRB was 0.85, and the average RMSE value was as low as 50.92 g C m\({}^{-2}\). Overall, the model exhibited strong simulation accuracy. Therefore, after calibrating the model with the DE algorithm, the Biome-BGC model could effectively adapt to the ecologically complex YRB. Moreover, it was able to accurately estimate the GPP, which establishes a foundation for analyzing the spatiotemporal trends of the GPP in the YRB. This study provides a reference for optimizing Biome-BGC model parameters and simulating diverse vegetation types on a large scale. terrestrial ecosystems; gross primary productivity; Yellow River Basin; Biome-BGC model; differential evolution algorithm +
Footnote †: journal: water  
an important role in the \"carbon neutrality\" strategy that provides a cost-effective carbon sequestration pathway in the short term to reduce pressure on emissions. [1]. The Gross Primary Productivity (GPP) is the quantity of organic carbon fixed by terrestrial plants through photosynthesis per unit area within a specific time period. It constitutes the largest carbon flux between the terrestrial biosphere and the atmosphere, having a significant influence on global carbon cycling and the climate system. The GPP drives various ecosystem functions, such as respiration and growth, and holds a pivotal role in maintaining the ecological balance [7]. The primary object for quantifying carbon flux within terrestrial ecosystems is the GPP, which serves as the initial material and energy acquisition for terrestrial ecosystems. Meanwhile, the GPP forms the foundation of the carbon cycle in terrestrial ecosystems [8]. There is no doubt that the GPP reflects the capacity of vegetation to absorb and fix atmospheric carbon dioxide. Not only is the accurate comprehension of the spatiotemporal variability of the GPP within terrestrial ecosystems crucial for the global carbon cycle and forecasting the impacts of future climate change, but the GPP serves as a vital indicator for precisely assessing the capacity of terrestrial ecosystems to support sustainable human societal development [9]. At present, the methods of estimating a terrestrial ecosystem's GPP are mainly divided into eddy covariance techniques and model simulations. Among these, model simulations include statistical models, light use efficiency models, and process-based models.