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Editorial

Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia

by *,† and *,†
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin & China Institute of Water Resources and Hydropower Research, Beijing 100038, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Water 2017, 9(10), 765; https://doi.org/10.3390/w9100765
Received: 26 August 2017 / Revised: 16 September 2017 / Accepted: 30 September 2017 / Published: 8 October 2017
The high degree of spatial variability in climate conditions, and a lack of meteorological data for East Asia, present challenges to conducting surface water research in the context of the hydrological cycle. In addition, East Asia is facing pressure from both water resource scarcity and water pollution. The consequences of water pollution have attracted public concern in recent years. The low frequency and difficulty of monitoring water quality present challenges to understanding the continuous spatial distributions of non-point source pollution mechanisms in East Asia. The China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) was developed to provide high-resolution, high-quality meteorological data for use by the scientific community. Applying CMADS can significantly reduce the meteorological input uncertainty and improve the performance of non-point source pollution models, since water resources and non-point source pollution can be more accurately localised. In addition, researchers can make use of high-resolution time series data from CMADS to conduct spatial- and temporal-scale analyses of meteorological data. This Special Issue, “Application of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in East Asia”, provides a platform to introduce recent advances in the modelling of water quality and quantity in watersheds using CMADS and hydrological models, and underscores its application to a wide range of topics. View Full-Text
Keywords: East Asia; CMADS; meteorological input uncertainty; hydrological modelling; SWAT; non-point source pollution models East Asia; CMADS; meteorological input uncertainty; hydrological modelling; SWAT; non-point source pollution models
MDPI and ACS Style

Meng, X.; Wang, H. Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia. Water 2017, 9, 765. https://doi.org/10.3390/w9100765

AMA Style

Meng X, Wang H. Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia. Water. 2017; 9(10):765. https://doi.org/10.3390/w9100765

Chicago/Turabian Style

Meng, Xianyong, and Hao Wang. 2017. "Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia" Water 9, no. 10: 765. https://doi.org/10.3390/w9100765

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