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Article: Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS)

TitleProfound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS)
Authors
KeywordsCMADS
Hydrological modeling
Impact
SWAT
Issue Date2019
PublisherMDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/water
Citation
Water, 2019, v. 11 n. 4, article no. 832 How to Cite?
AbstractAs 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. © 2019 by the authors.
Persistent Identifierhttp://hdl.handle.net/10722/274849
ISSN
2018 Impact Factor: 2.524
2015 SCImago Journal Rankings: 0.536
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMeng, X-
dc.contributor.authorWang, H-
dc.contributor.authorChen, J-
dc.date.accessioned2019-09-10T02:30:09Z-
dc.date.available2019-09-10T02:30:09Z-
dc.date.issued2019-
dc.identifier.citationWater, 2019, v. 11 n. 4, article no. 832-
dc.identifier.issn2073-4441-
dc.identifier.urihttp://hdl.handle.net/10722/274849-
dc.description.abstractAs 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. © 2019 by the authors.-
dc.languageeng-
dc.publisherMDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/water-
dc.relation.ispartofWater-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCMADS-
dc.subjectHydrological modeling-
dc.subjectImpact-
dc.subjectSWAT-
dc.titleProfound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS)-
dc.typeArticle-
dc.identifier.emailChen, J: jichen@hku.hk-
dc.identifier.authorityChen, J=rp00098-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3390/w11040832-
dc.identifier.scopuseid_2-s2.0-85065030703-
dc.identifier.hkuros303064-
dc.identifier.volume11-
dc.identifier.issue4-
dc.identifier.spagearticle no. 832-
dc.identifier.epagearticle no. 832-
dc.identifier.isiWOS:000473105700202-
dc.publisher.placeSwitzerland-
dc.identifier.issnl2073-4441-

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