Utilization of China Meteorological Assimilation Driving Datasets for the SWAT Model Long Series (CMADS-L) in East Asia

Dear Colleagues,

Constrained by the scarcity of meteorological observational data and the distinctive geographical features inherent to East Asia, in-depth investigations into meteorology, hydrology, and associated disciplines continue to encounter numerous challenges within this region. Furthermore, East Asia is presently contending with a confluence of pressures stemming from water resource scarcity, frequent occurrences of water-related disasters, and the pervasive issue of water pollution. In recent years, concerted efforts from both governmental and scientific sectors have been directed towards leveraging scientific methodologies such as runoff reconstruction, climate change and drought/flood prediction, attribution analysis, and water environment simulation forecasting to ameliorate and address sudden-onset disasters prevalent in the region. In response to pronounced demands from the scientific community and society at large, a comprehensive dataset spanning 40 years (1979–2018)—China Meteorological Assimilation Driving Datasets for the SWAT model long series (CMADS-L)—has been made publicly available. This dataset equips the scientific community with a robust toolset for conducting a broad spectrum of activities, including climate–hydrological cycle analysis, runoff reconstruction, model calibration, flood and sediment simulation, and the simulation and prediction of non-point source pollution. Notably, peer-reviewed studies have indicated that approximately 35% of international reanalysis, data-driven research in hydrology and environmental modeling is centered in East Asia, with CMADS, TRMM, and CFSR emerging as the most favored products in such investigations. CMADS significantly mitigates uncertainties associated with scientific research in the East Asian region, thereby enhancing the efficiency of scientific inquiry.

This Special Issue, titled "Utilization of China Meteorological Assimilation Driving Datasets for the SWAT Model Long Series (CMADS-L) in East Asia", seeks to invite submissions that present the latest advancements in utilizing CMADS-L or employing this dataset to propel various hydrological and environmental models. Encompassing themes such as water resource modeling and runoff reconstruction, simulation and prediction of water-related disasters, water environment analysis, water resource management, non-point source pollution, long series analysis of meteorological changes, validation of meteorological data, atmospheric–hydrological coupling, and atmospheric–hydrological response studies in arid and cold regions, we encourage contributions that are underpinned by theoretical rigor, computational precision, and empirical robustness, thereby showcasing innovative applications.

We eagerly anticipate your esteemed contributions.

Prof. Dr. Xianyong Meng
Prof. Dr. Hao Wang
Prof. Dr. Chengbin Chu
Guest Editors

Dr. Wei Ding
Guest Editor Assistant

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