Sensitivity and Uncertainty Analysis of China's Terrestrial Carbon-Water Cycle Using a Dynamic Global Vegetation Model

Abstract. Parameter uncertainty in Dynamic Global Vegetation Models (DGVMs) substantially impacts the reliability of carbon-water cycle simulations. Using the LPJ-GUESS model at 13 sites across China's diverse ecosystems, this study employed a multi-method (Morris, eFAST, Sobol') sensitivity analysis on 39 key parameters to assess their impacts on nine carbon-water cycle variables. Our results revealed that the model's behavior is co-dominated by both core physiological parameters, often hard-coded in the source, and plant functional type-specific traits. This finding suggests limitations in the common practice of focusing calibration solely on user-adjusted files. Furthermore, these parameter controls are highly context-dependent, shifting based on both the target process (e.g., carbon uptake as opposed to water flux) and the regional climate, where arid ecosystems respond most strongly to water-use parameters. The multi-method approach also highlighted that the influence of many parameters is mediated through complex interactions rather than direct effects alone. Consequently, this complex web of sensitivities propagates into contrasting patterns of model uncertainty: arid ecosystems exhibit the highest relative uncertainty, making predictions more uncertain, while humid, productive ecosystems show the largest absolute uncertainty, posing a challenge for carbon budgeting. These findings provide a scientific basis for developing targeted, region-specific parameterization strategies to reduce model uncertainty and improve assessments of terrestrial carbon sink functions.