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Article: Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS)
Title | Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS) |
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Authors | |
Keywords | CMADS Hydrological modeling Impact SWAT |
Issue Date | 2019 |
Publisher | MDPI 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? |
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. © 2019 by the authors. |
Persistent Identifier | http://hdl.handle.net/10722/274849 |
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 0.724 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Meng, X | - |
dc.contributor.author | Wang, H | - |
dc.contributor.author | Chen, J | - |
dc.date.accessioned | 2019-09-10T02:30:09Z | - |
dc.date.available | 2019-09-10T02:30:09Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Water, 2019, v. 11 n. 4, article no. 832 | - |
dc.identifier.issn | 2073-4441 | - |
dc.identifier.uri | http://hdl.handle.net/10722/274849 | - |
dc.description.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. © 2019 by the authors. | - |
dc.language | eng | - |
dc.publisher | MDPI AG. The Journal's web site is located at http://www.mdpi.com/journal/water | - |
dc.relation.ispartof | Water | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | CMADS | - |
dc.subject | Hydrological modeling | - |
dc.subject | Impact | - |
dc.subject | SWAT | - |
dc.title | Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS) | - |
dc.type | Article | - |
dc.identifier.email | Chen, J: jichen@hku.hk | - |
dc.identifier.authority | Chen, J=rp00098 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/w11040832 | - |
dc.identifier.scopus | eid_2-s2.0-85065030703 | - |
dc.identifier.hkuros | 303064 | - |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | article no. 832 | - |
dc.identifier.epage | article no. 832 | - |
dc.identifier.isi | WOS:000473105700202 | - |
dc.publisher.place | Switzerland | - |
dc.identifier.issnl | 2073-4441 | - |