珠江流域内人口与经济体密集，下游粤港澳大湾区是中国最为重要的经济增长极之一。准确理解气候变化与人类活动共同影响下流域内碳、水和能量循环变化趋势及其归因分析具有重要的经济和社会意义。陆面过程模型是模拟和解释陆地下垫面上复杂循环过程的重要工具。然而，陆面过程模型在模拟过程中具有很大的参数不确定性。本研究以CABLE陆面过程模型在珠江流域对碳、水和能量循环相关变量的模拟为例，构造了一个多目标参数不确定性分析框架。研究通过多目标参数敏感性分析及基于替代模型的多目标参数优化过程解决复杂陆面过程模型的参数不确定性问题，从而提高模型对碳、水和能量循环各个变量的综合模拟能力。本研究旨在使用较少的模型模拟次数降低复杂陆面过程模型的模拟不确定性，提高其在区域尺度上对多变量的综合模拟能力，结果将为珠江流域碳、水和能量循环过程的模拟及归因分析提供重要的科学依据。

The Pearl River Basin is densely populated and economically intensive. The downstream Guangdong-Hong Kong-Macao Greater Bay Area is one of the most important economic growth poles in China. It is of great economic and social significance to accurately understand the changing trends of carbon, water and energy cycles and their attribution analysis under the influence of climate change and human activities. Land surface model is an important tool to simulate and explain the complex cycle on land surface. However, the land surface model has large parameter uncertainties in the simulation process. In this study, we take CABLE land surface model to simulate the carbon, water and energy cycle related variables in the Pearl River Basin as an example to constructing a multi-objective parameter uncertainty analysis framework. The parameter uncertainty in complex land surface process model is reduced by multi-objective parameter sensitivity analysis and multi-objective surrogate modeling-based parameter optimization process. The results will improve the model simulations on various variables of carbon, water and energy cycle. The purpose of this study is to reduce the simulation uncertainty of land surface process model and improve its comprehensive simulation ability at regional scale. The results will provide an important scientific basis for the simulation and attribution analysis of carbon and water cycle processes in the Pearl River Basin.