China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) (Annual report – 2016)

Source:IWHRAuthor:Xianyong MengLink:http://www.cmads.orgViews:794times
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China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS)

(Annual report – 2016)






By Xianyong Meng

China Institute of Water Resources and Hydropower Research

Translate by Hongjing Wu & Xianyong Meng

Research associate, NRPOP Lab, Faculty of Engineering and AppliedScience, Memorial University of Newfoundland, Canada.

February 2017

CMADS group, Beijing, China






Preface


   

Due to the vast geographical extent and varied climate conditions, coupled with insufficient meteorological data coverage in China, researchers face numerous challenges in studying surface water dynamics within the hydrological cycle and its driving forces. Presently, China confronts dual pressures of water resource scarcity and water pollution, with the consequences of the latter becoming increasingly apparent in recent years. Limited monitoring frequencies and inadequate coverage for non-point source pollutants pose challenges in comprehending the continuous spatial distribution of such pollution across China.


The China Meteorological Assimilation Driving Datasets (CMADS), developed based on the China Land Data Assimilation System (CLDAS), offer researchers high-resolution and quality meteorological data. Utilizing CMADS can substantially mitigate the uncertainties associated with meteorological inputs for non-point source models and enhance the performance of non-point source modeling by providing more precise localization of water resources and pollution sources. Additionally, researchers can leverage the high-resolution time series data from CMADS for spatial and temporal scale analyses of meteorological data. CMADS establishes a fundamental and standardized meteorological data system, enabling researchers to conduct related studies using a consistent meteorological source for better and more comprehensive comparative analyses in the future. We anticipate that CMADS will provide researchers with reliable data, fostering confidence and convenience in their research endeavors.


XianyongMeng

CMADS group

February, 12, 2017




Revitalizing the nation through advancements in science and technology underscores the critical importance of essential data and information. I am pleased to witness the dissemination of our work among various colleagues for research purposes. The development of CMADS datasets has been completed within a year and has already found applications in various fields. I encourage all colleagues in the scientific and technological community to leverage these datasets for their research endeavors.






Chinese Academy of Engineering academician: Hao Wang         

February, 12, 2017

   



CMADS has been released for over 10 months. Since April 2016, we have been sharing these datasets on the "National Earth System Science Data Sharing Infrastructure" (http://westdc.westgis.ac.cn/). Up to February 12, 2017, we have received a total of 732 requests nationally and recorded 14,985 page views. According to the statistics, the number of requests peaked in May 2016 (138 requests per month). Around August 2016, coinciding with the summer vacation period, we observed the first peak in requests (30 requests per month), followed by fluctuating request numbers.

Figure 1 The number of applications in 2016

To understand the distribution of CMADS users in China, we randomly selected 708 application forms from researchers and conducted an analysis. The majority of requests originated from six provinces/cities, namely Beijing (130 requests), Shaanxi (76 requests), Hubei (72 requests), Guangdong (48 requests), Gansu (47 requests), and Jiangsu (28 requests). Other requests came from provinces and regions such as Xinjiang, Hunan, Henan, Shandong, Chongqing, Jilin, Inner Mongolia, Zhejiang, Jiangxi, Sichuan, Yunnan, Shanghai, Heilongjiang, Tianjin, Hebei, Liaoning, Shanxi, Anhui, Guizhou, Fujian, Guangxi, Qinghai, and Ningxia. Notably, there were no requests from Macao, Nanhai, Taiwan, Tibet, or Hong Kong (See Figure 3).

Figure 2 The distribution of CMADS Chinese users

 Figure3 The distribution of CMADS Chinese users

We further summarized the preferred study areas of Chinese CMADS users. The findings revealed that the northwestern, southwestern, northern, and northeastern regions garnered the highest interest. However, it's worth noting that the number of traditional meteorological stations in these areas is relatively fewer compared to the central, eastern, and southern regions of China.

Figure 4 The interested study areas in CMADS Chinese users

Figure 5 The distribution of CMADS Chinese users

The six most sought-after study areas include Gansu (63 requests), Qinghai (46 requests), Xinjiang (45 requests), Beijing (44 requests), and Tibet (38 requests). Additionally, other provinces generating interest are Heilongjiang, Shaanxi, Inner Mongolia, Hebei, Guangdong, Yunnan, Shandong, Anhui, Hunan, Jiangxi, Hubei, Tianjin, Chongqing, Liaoning, Henan, Ningxia, Zhejiang, Sichuan, Jilin, Shanghai, Shanxi, Jiangsu, Guangxi, Fujian, Guizhou, and Hainan. We have also documented various research areas where CMADS has been applied (Refer to Figure 6).

Figure 6 CMADS users research areas


The results indicate that CMADS has been primarily applied in various research areas, with the majority focusing on water resource modeling (23%), eco-hydrology studies (20%), research on nonpoint source pollution (19%), and climate change analysis (7%). Other research areas include supporting remote sensing monitoring (3%), collecting precipitation data (3%), educational purposes (3%), groundwater research (2%), hydrological modeling in colder regions (2%), drought index calculations (2%), assessing human activities' impact on surface runoff (2%), uncertainty analysis of hydrological model parameters (2%), mathematical modeling research (2%), other hydrological studies (2%), research on PM 2.5 concentration (1%), studying water and salt transport using SWAT (1%), monitoring and predicting hailstone disasters (1%), researching urban inland inundation (1%), conducting multiple factor analysis of meteorological data (1%), water quality modeling in cold regions (1%), troposphere delay modeling (1%), research on technological products (1%), studying solar radiation (1%), and investigating evapotranspiration (1%).

This report refrains from categorizing the distinct features of various research areas into major classifications. For instance, hydrological modeling and water quality modeling in colder regions were not further subcategorized into water resource modeling and nonpoint source pollution research, respectively.

CMADS Users from:


Anqing Normal University

BaoJi University of Arts and Sciences

Beijing Jingshui River (Beijing) Engineering Consulting Co. Ltd.

Beijing Normal University

Beijing University of Technology

Beijing Wright Cyber Technology Services Ltd.

Central South Electric Power Design Institute

Chang'an University

Changjiang Water Resources Commission

Changsha University of Science and Technology

Chengdu University of Technology

China Academy of Forestry Sciences

China Agricultural University

China Institute of water resources and Hydropower Research

China Three Gorges University

China University of Geosciences (Wuhan)

China University of Petroleum (Hua Dong)

Chinese Research Academy of Environmental Sciences

Chongqing University

East China Normal University

Gansu Agricultural University

Gansu Meteorological Bureau Public Service Center

Guangxi University

Guangzhou Institute of Geography

Guizhou Meteorological Bureau

Harbin Institute of Technology

Hebei university of engineering

Henan Polytechnic University

Hohai University

Huazhong University of Science and Technology

Hunan University of Science and Technology

Hunan University of Technology

Inner Mongolia Agricultural University

Inner Mongolia University

Institute of Geographical Sciences and resources

Jiangxi Institute of soil and water conservation

Jiangxi Normal University

Jobon garden Limited by Share Ltd

kunming university of science and technology

Langfang urban and rural planning and Design Institute

Lanzhou University

Nanchang Institute of Technology

Nanjing Normal University

Nanjing University

nanjing university of information science and technology

Ningxia University

North China Electric Power University

North China University of Water Resources and Electric Power

Northeast Agricultural University

Northeast Forestry University

Northeast Institute of geography and agricultural ecology, Chinese Academy of Sciences

Northeast Normal University

Northwest Agriculture and Forestry University

Northwestern University

Pearl River Water Resources Commission

Peking University

PLA 65061 unit

Qiingdao University

Research and development center of State Forestry Administration

Research Center for Eco Environmental Sciences; Chinese Academy of Sciences

Research Institute of water transport, Ministry of transport

Shaanxi Normal University

Shandong University

Shang Zheng (Beijing) Information Technology Co., Ltd.

Shenzhen Institute of advanced technology, Chinese Academy of Sciences

Sichuan University

Southern China Environmental Science Research Institute

Southwestern University

Sun Yat-sen University

The Yellow River survey planning and Design Co., Ltd.

Tianjin University

Tsinghua University

University of Electronic Science and technology of China

Water Resources Protection Bureau

WuHan University

Xi'an Jiao Tong University

Xi'an University

Xi'an University of technology

Xianyang Normal University

Xinjiang Institute of ecology and geography, Chinese Academy of Sciences

Xinjiang University

Yantai Institute of coastal zone, Chinese Academy of Sciences

Yuxi normal university

Zhejiang University

Zhengzhou University




     CMADSwebsite

http://www.cmads.org/


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