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

: 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 di ff erent 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.

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[2]  Xianyong Meng,et al.  Assessing the Impact of Reservoir Parameters on Runoff in the Yalong River Basin using the SWAT Model , 2019, Water.

[3]  Xianbo Zhao,et al.  Moisture Distribution in Sloping Black Soil Farmland during the Freeze–Thaw Period in Northeastern China , 2019, Water.

[4]  Yun Li,et al.  Investigating Spatial and Temporal Variation of Hydrological Processes in Western China Driven by CMADS , 2019, Water.

[5]  X. Hong,et al.  Impact of Climate Variability on Blue and Green Water Flows in the Erhai Lake Basin of Southwest China , 2019, Water.

[6]  Guodong Liu,et al.  Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model , 2019, Water.

[7]  Gang Yin,et al.  Seasonal Characteristics and Particle-size Distributions of Particulate Air Pollutants in Urumqi , 2019, International journal of environmental research and public health.

[8]  Xuan Liu,et al.  CMADS-Driven Simulation and Analysis of Reservoir Impacts on the Streamflow with a Simple Statistical Approach , 2019, Water.

[9]  B. Croke,et al.  Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models , 2018, Water.

[10]  C. Shi,et al.  Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) , 2018, Water.

[11]  Shi-guo Xu,et al.  An Integrated Methodology to Analyze the Total Nitrogen Accumulation in a Drinking Water Reservoir Based on the SWAT Model Driven by CMADS: A Case Study of the Biliuhe Reservoir in Northeast China , 2018, Water.

[12]  Z. Yang,et al.  Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China , 2018, Water.

[13]  Jian-xia Chang,et al.  Investigating the Dynamic Influence of Hydrological Model Parameters on Runoff Simulation Using Sequential Uncertainty Fitting-2-Based Multilevel-Factorial-Analysis Method , 2018, Water.

[14]  Yue‐Ping Xu,et al.  Evaluation of Potential Evapotranspiration Based on CMADS Reanalysis Dataset over China , 2018, Water.

[15]  Y. Guan,et al.  The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin , 2018, Water.

[16]  Xiaohui Lei,et al.  Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China , 2018, Water.

[17]  Qinglan Li,et al.  Parameter Uncertainty Analysis of the SWAT Model in a Mountain-Loess Transitional Watershed on the Chinese Loess Plateau , 2018 .

[18]  Li Li,et al.  Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia , 2018 .

[19]  Yongjian Ding,et al.  Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the Qinghai-Tibet Plateau , 2018 .

[20]  Yongnan Zhu,et al.  Spring Flood Forecasting Based on the WRF-TSRM Mode , 2018 .

[21]  Changbing Yang,et al.  Study on the characteristics of future precipitation in response to external changes over arid and humid basins , 2017, Scientific Reports.

[22]  C. Shi,et al.  Investigating spatiotemporal changes of the land-surface processes in Xinjiang using high-resolution CLM3.5 and CLDAS: Soil temperature , 2017, Scientific Reports.

[23]  Xianyong Meng,et al.  Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia , 2017 .

[24]  X. Lei,et al.  The China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) Application in China: A Case Study in Heihe River Basin , 2017 .

[25]  X. Lei,et al.  Hydrological modeling in the Manas River Basin using soil and water assessment tool driven by CMADS Xianyong Meng , 2017 .

[26]  Xianyong Meng,et al.  Snowmelt Runoff Analysis under Generated Climate Change Scenarios for the Juntanghu River Basin, in Xinjiang, China , 2016 .

[27]  Danlin Yu,et al.  Energy balance-based SWAT model to simulate the mountain snowmelt and runoff — taking the application in Juntanghu watershed (China) as an example , 2015, Journal of Mountain Science.