| Title: | Significance of the China meteorological assimilation driving datasets for the SWAT Model (CMADS) of east Asia. |
| Authors: | Meng, X. and H. Wang |
| Year: | 2017 |
| Journal: | Water |
| Volume (Issue): | 9(10) |
| Pages: | |
| Article ID: | 765 |
| DOI: | 10.3390/w9100765 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | editorial, preface, introduction or conclusion |
| Primary Application Category: | climate data effects |
| Secondary Application Category: | none |
| Watershed Description: | none |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | This is a lead-off article for a special issue that is planned to be published in Water (see http://www.mdpi.com/journal/water/special_issues/CMADS). |
| Abstract: | 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. |
| Language: | English |
| Keywords: | East Asia; CMADS; meteorological input uncertainty; hydrological modelling; SWAT;
non-point source pollution models |