Estimation of Cloud Water Resources in China - Jie Yu; Yuquan Zhou; Miao Cai; Jianjun Ou (Earth, 2025)
With the increasing scarcity of global water resources, the exploitation of atmospheric water resources has emerged as a crucial strategy for mitigating water shortages. However, the development of regional atmospheric water resources remains constrained by the lack of precise atmospheric water resource assessments. Existing studies primarily focus on historical evaluations of atmospheric water resources in China, while future changes in cloud water resources across target regions have yet to be comprehensively investigated. In this study, projections of cloud water resources over China for the next 30 years are conducted based on CMIP6 global climate model simulations, in conjunction with observationally diagnosed cloud water resources datasets from 2000 to 2019. A random forest model, coupled with a fuzzy logic approach, is employed to estimate future cloud water resources, as well as their spatial distribution and temporal trends. The results indicate that the random forest model effectively captures the relationship between atmospheric physical variables and cloud water resources, demonstrating strong agreement with historical data. Over the next three decades, cloud water resources in China are projected to exhibit an overall increasing trend, with the most pronounced enhancement occurring under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5). The spatial distribution pattern of cloud water resources is expected to remain largely consistent with that of the past two decades, while inter-model differences are primarily observed in southeastern China and the southern Tibetan Plateau. Further analysis using fuzzy logic inference reveals that the most significant increases in cloud water resources are anticipated in northwestern China, with the potential for an expansion of these increases toward the north under the high-emission scenario. This study provides a scientific framework for predicting future variations in cloud water resources across China, offering critical theoretical and data-driven support for the sustainable development and utilization of atmospheric water resources.