## 2 Materials and Methods
### Data Sources  
#### 2.2.1 Driving Data for the Biome-BGC Model  
The Biome-BGC model requires the input of three key data types: geographical data, meteorological data, and ecophysiological parameters. Geographical data included the annual CO\({}_{2}\) concentration, soil texture, elevation, slope, and aspect. The CO\({}_{2}\) data required for the model were obtained from the publicly available annual monitoring data provided by the NOAA Global Monitoring Laboratory, located at the Mauna Loa Observatory in Hawaii. The data were accessed at [[https://www.co2.earth](https://www.co2.earth)]([https://www.co2.earth](https://www.co2.earth)) on 13 November 2022 [20]. Soil texture data were sourced from the China Soil Texture Spatial Distribution Dataset published by the Resource and Environment Science and Data Center, Chinese Academy of Sciences ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 1 July 2024) [21]. Elevation, slope, and aspect data were obtained from the Chinese DEM distribution dataset also provided by the Resource and Environment Science and Data Center, Chinese Academy of Sciences ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)), accessed on 1 July 2024) [21].  
The meteorological data required to drive the Biome-BGC model included the daily maximum temperature (\({}^{\circ}\)C), daily minimum temperature (\({}^{\circ}\)C), average daily air temperature, daily total precipitation (cm), daily average water vapor pressure difference (Pa), and daily average global radiation (W/m\({}^{2}\)) [22]. These daily-scale meteorological data were derived using the MTCLIM model v4.3 at [[https://www.umt.edu/numerical-terradynamic-simulation-group/project/mt-clim.php](https://www.umt.edu/numerical-terradynamic-simulation-group/project/mt-clim.php)]([https://www.umt.edu/numerical-terradynamic-simulation-group/project/mt-clim.php](https://www.umt.edu/numerical-terradynamic-simulation-group/project/mt-clim.php)), accessed on 23 August 2024 [23]. For this study, we selected data from nine national meteorological stations closest to the research sites to obtain daily-scale meteorological data from 1989 to 2008. These data were then used to drive the MTCLIM model to simulate the meteorological conditions for the ten study areas. The station numbers of the nine national meteorological stations are 54049, 54285, 54102, 54208, 52866, 55493, 57799, 59278, and 56969. The data from these stations were sourced from the China National Surface Weather Station Basic Meteorological Elements Daily Dataset (V3.0), provided by the China National Meteorological Information Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 13 November 2022) [24].  
Ecophysiological parameters are also essential for driving the Biome-BGC model and typically include key plant functional traits such as the leaf carbon-to-nitrogen ratio and specific leaf area. In this study, the types of ecophysiological parameters that require assignment were determined by the results of a parameter sensitivity analysis based on the EFAST method. The values for sensitive ecophysiological parameters were derived from literature reviews and parameter optimization using PEST, while non-sensitive parameters were assigned the default values provided by the model. The parameters subjected to sensitivity analysis and their value ranges are detailed in Supplementary Materials S1, and the assigned values for the sensitive ecophysiological parameters for the ten study sites, along with their sources, are provided in Supplementary Materials S2.