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Profound Impacts of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS)

1,2,*, 3,* and 2,*
1
College of Resources and Environmental Science, China Agricultural University (CAU), Beijing 100094, China
2
Department of Civil Engineering, The University of Hong Kong (HKU), Pokfulam 999077, Hong Kong, China
3
China Institute of Water Resources and Hydropower Research (IWHR), Beijing 100038, China
*
Authors to whom correspondence should be addressed.
Water 2019, 11(4), 832; https://doi.org/10.3390/w11040832
Received: 5 April 2019 / Revised: 5 April 2019 / Accepted: 18 April 2019 / Published: 19 April 2019
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Abstract

As global warming continues to intensify, the problems of climate anomalies and deterioration of the water environment in East Asia are becoming increasingly prominent. In order to assist decision-making to tackle these problems, it is necessary to conduct in-depth research on the water environment and water resources through applying various hydrological and environmental models. To this end, the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) has been applied to East Asian regions where environmental issues are obvious, but the stations for monitoring meteorological variables are not uniformly distributed. The dataset contains all of the meteorological variables for SWAT, such as temperature, air pressure, humidity, wind, precipitation, and radiation. In addition, it includes a range of variables relevant to the Earth’s surface processes, such as soil temperature, soil moisture, and snowfall. Although the dataset is used mainly to drive the SWAT model, a large number of users worldwide for different models have employed CMADS and it is expected that users will not continue to limit the application of CMADS data to the SWAT model only. We believe that CMADS can assist all the users involved in the meteorological field in all aspects. In this paper, we introduce the research and development background, user group distribution, application area, application direction, and future development of CMADS. All of the articles published in this special issue will be mentioned in the contributions section of this article. View Full-Text
Keywords: CMADS; impact; hydrological modeling; SWAT CMADS; impact; hydrological modeling; SWAT
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Meng, X.; Wang, H.; Chen, J. Profound Impacts of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). Water 2019, 11, 832.

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