China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) (Annual report – 2016)
Source:IWHRAuthor:Xianyong MengLink:http://www.cmads.orgViews:79times



China MeteorologicalAssimilation 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 thelarge areas and differences of climate conditions and the lack ofmeteorological data in China, there are many challenges for research on surfacewater in hydrologic cycle and the associated driven force. Currently,China is facing the pressures from both water resource scarcity and waterpollutions. The consequences of water pollution problem have been obviouslyrevealed in recent years. Due to the low frequencies and small number of monitoringfor non-point source pollutions, it becomes the challenge to understand the continuousspatial distributions of non-point source pollution in China.

China MeteorologicalAssimilation Driving Datasets (CMADS) is developed based on China Land DataAssimilation System (CLDAS) and provide high resolution and quality meteorologicaldata for researchers. Applying CMADS can significantly reduce themeteorological input uncertainty for non-point source models and improve theperformance of non-point source modeling, since water resources and non-pointsource pollution can be more accurately localized. Besides, researchers canmake use of high resolution time series data from CMADS for spatial andtemporal scale analysis of meteorological data. CMADS present a basic andstandard meteorological data system, and researchers can conduct the related researchusing the same meteorological source for better and further comparison studiesin the future. We expect that CMADS can provide the reliable data forresearchers with confidence and convenience.

XianyongMeng

CMADS group

February, 12, 2017








Rejuvenating thecountry through science and technology, the essential data and information areessential. I am glad to see our work can be shared with different colleaguesfor the research purposes. CMADS datasets have been developed under a year, andhave been applied to different areas. I hope all the science and technology colleaguescan make use of this datasets for their research.






Chinese Academy of Engineering academician: Hao Wang        

February, 12, 2017

 



CMADS has been released for over 10 months.From April 2016, we began to share this datasets on “National Earth System ScienceData Sharing Infrastructure” (http://westdc.westgis.ac.cn/).We have received 732 requests nationally and 14985 page views till Feb 12,2017. From the statistics, the requests reached the peak on May, 2016 (138persons/month). Around August, 2016 (around summer vacation time), the requestshave reached the first peak (30 persons/month), and then the number of requestsis fluctuated.

Figure1 The number of applications in 2016

In order tounderstand the distribution of CMADS Chinese user, we have randomly picked 708application forms from researchers and analyzed them. The most requests arefrom six provinces/cities, including Beijing (130 requests), Shaanxi (76 requests),Hubei (72 requests), Guangdong (48 requests), Gansu (47 requests) and Jiangsu(28 requests). The other requests from: Xinjiang, Hunan, Henan, Shandong,Chongqing, Jilin, Inner Mongolia, Zhejiang, Jiangxi, Sichuan, Yunnan, Shanghai,Heilongjiang, Tianjin, Hebei, Liaoning, Shanxi, Anhui, Guizhou, Fujian,Guangxi, Qinghai, Ningxia. There are no requests from following provinces: Macao,Nanhai, Taiwan, Tibet, Hongkong (Figure 3).

Figure2 The distribution of CMADS Chinese users

 Figure3 The distribution of CMADS Chinese users

We also summarizedthe interested study areas of the Chinese CMADS users, the results showed thatnorthwestern area, southwestern area, north China, and northeastern areas aremost interested areas. However, the number of traditional meteorological stationsin these areas is less than that in Central China, Eastern China, and SouthernChina areas.

Figure4 The interested study areas in CMADS Chinese users

Figure5 The distribution of CMADS Chinese users

The six mostinterested study areas include Gansu (63 requests), Qinghai (46 requests),Xinjiang (45 requests), Beijing (44 requests), Tibet (38 requests). Other interestedprovinces include, 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, Hainan. We also record different research areas where CMADS wereapplied (Figure 6).

Figure6 CMADSusers research areas

The results showedthat the most research areas where CMADS was applied mainly include: waterresource modeling (23%), eco-hydrology study (20%), nonpoint source pollutionresearch (19%), and climate change (7%). Other research areas include:assistance for remote sensing monitoring (3%), precipitation data collection(3%), teaching purposes (3%), groundwater research (2%), hydrological modelingin cool regions (2%), calculations for drought index (2%), Human activitiesimpacts on surface runoff (2%), Parameters uncertainty analysis forhydrological models (2%), mathematical modeling research (2%), otherhydrological studies (2%), PM 2.5 concentration research (1%), SWAT water andsalt transport (1%), hailstone disaster monitoring and prediction (1%), urbaninland inundation research (1%), multiple factor analysis for meteorologicaldata (1%), water quality modeling in cold regions (1%), troposphere delaymodeling (1%), technological products research (1%), solar radiation research(1%), and evapotranspiration research (1%).

     In  order to show the differences and features of the research, this report doesnot classify characteristic features of different research into major classification.For example, the hydrological modeling and water quality modeling in coldregions were not differentiated into water resource modeling and nonpointsource pollution, separately.  


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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