China meteorological assimilation driving datasets for the SWAT model Version 1.0 (2008-2016)

China meteorological assimilation driving datasets for the SWAT model Version 1.0 (2008-2016)

CMADS V1.0(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.0)Version of the data set introduces the technology of STMAS assimilation algorithm . It was constructed using multiple technologies and scientific methods, including loop nesting of data, projection of resampling models, and bilinear interpolation. The CMADS series of datasets can be used to drive various hydrological models, such as SWAT, the Variable Infiltration Capacity (VIC) model, and the Storm Water Management model (SWMM). It also allows users to conveniently extract a wide range of meteorological elements for detailed climatic analyses. Data sources for the CMADS series include nearly 40,000 regional automatic stations under China’s 2,421 national automatic and business assessment centres. This ensures that the CMADS datasets have wide applicability within the country, and that data accuracy was vastly improved.

The CMADS series of datasets has undergone finishing and correction to match the specific format of input and driving data of SWAT models. This reduces the volume of complex work that model builders have to deal with. An index table of the various elements encompassing all of East Asia was also established for SWAT models. This allows the models to utilize the datasets directly, thus eliminating the need for any format conversion or calculations using weather generators. Consequently, significant improvements to the modelling speed and output accuracy of SWAT models were achieved.

Most of the source data in the CMADS datasets are derived from CLDAS in China and other reanalysis data in the world. The integration of air temperature, air pressure, humidity, and wind velocity data was mainly achieved through the LAPS/STMAS system. Precipitation data were stitched using CMORPH’s global precipitation products and the National Meteorological Information Center’s data of China (which is based on CMORPH’s integrated precipitation products). The latter contains daily precipitation records observed at 2,400 national meteorological stations and the CMORPH satellite’s inversion precipitation products.The inversion algorithm for incoming solar radiation at the ground surface makes use of the discrete longitudinal method by Stamnes et al.(1988)to calculate radiation transmission. The resolutions for CMADS V1.0, V1.1, V1.2, and V1.3 were 1/3°, 1/4°, 1/8°, and 1/16°, respectively.

In CMADS V1.0 (at a spatial resolution of 1/3°), East Asia was spatially divided into 195 × 300 grid points containing 58,500 stations. Despite being at the same spatial resolution as CMADS V1.0, CMADS V1.1 contains more data, with 260 × 400 grid points containing 104,000 stations. For both versions, the stations’ daily data include average solar radiation, average temperature, average pressure, maximum and minimum temperature, specific humidity, cumulative precipitation, and average wind velocity.

The CMADS comprises other variables for any hydrological model(under 'For-other-model' folder ): Daily Average Temperature, Daily Maximum Temperature, Daily Minimum Temperature, Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind, and Daily average Atmospheric Pressure.

Introduction to metadata of CMADS

CMADS storage path description:(CMADS was divided into two datesets)

1.CMADS-V1.0\For-swat\ --specifically driving the SWAT model

2.CMADS-V1.0\For-other-model\ --specifically driving the other hydrological model(VIC,SWMM,etc.)

CMADS--\For-swat-2009\ folder contain:(Station\ and Fork\)


Relative-Humidity-58500\ Daily average relative humidity(fraction)

Precipitation-58500\ Daily accumulated 24-hour precipitation(mm)

Solar radiation-58500\ Daily average solar radiation(MJ/m2)

Tmperature-58500\ Daily maximum and minimum temperature(℃)

Wind-58500\ Daily average wind speed(m/s)

Where R, P, S, T, W+ dimensional grid number - the number of longitude grid is the station in the above five

folders respectively.(Where R,P,S,T,W respective Daily average relative humidity,Daily cumulative

precipitation(24h),Daily mean solar radiation(MJ/m2),Daily maximum and minimum temperature(℃) and Daily mean

wind speed (m/s)) respectively.Data format is (.dbf)

2).Fork\ (Station index table over East Asia)

PCPFORK.txt (Precipitation index table)

RHFORK.txt (Relative humidity index table)

SORFORK.txt (Solar radiation index table)

TMPFORK.txt (Temperature index table)

WINDFORK.txt (Wind speed index)

CMADS--\For-swat-2012\ folder contain:(Station\ and Fork\) Storage structure is consistency with \For-swat-

2009\.However, all the data in this directory are only available in TXT format and can be readed by SWAT2012.

3)\For-other-model\ (Includes all weather input data required by the any hydrologic model (daily).)

Atmospheric-Pressure-txt\ Daily average atmospheric pressure(hPa)

Average-Temperature-txt\ Daily average temperature(℃)

Maximum-Temperature-txt\ Daily maximum temperature(℃)

Minimum-Temperature-txt\ Daily minimum temperature(℃)

Precipitation-txt\ Daily accumulated 24-hour precipitation (mm)

Relative-Humidity-txt\ Daily average relative humidity(fraction)

Solar-Radiation-txt\ Daily average solar radiation(MJ/m2)

Specific-Humidity-txt\ Daily average Specific Humidity(g/kg)

Wind-txt\ Daily average wind speed(m/s)

Data storage information: data set storage format is .dbf and .txt

Other data information:

Total data: 33.6GB

Occupied space: 35.2GB

Time: From year 2008 to year 2016

Time resolution: Daily

Geographical scope description: East Asia

Longitude: 60°E

The most east longitude: 160°E

North latitude: 65°N

Most southern latitude: 0°N

Number of stations: 58500 stations

Spatial resolution: 1/3 * 1/3 * grid points

Vertical range: None

File naming and required software

Cmads -- file path and name description of each subset of SWAT drive data
Cmads -- SWAT drive data subset path
1) The Swat sub data drive set of cmads (in the for SWAT folder), which contains the station \ and fork \ subdirectories.
The station \ directory is all the input data (day by day) required by SWAT model. The above input data are located in the following directories:
Relative humidity-58500 \ daily average relative humidity (fraction)
Precipitation-58500 \ daily accumulated precipitation (mm)
Solar radiation-58500 \ daily average solar radiation (MJ / m2)
Temperature-58500 \ daily maximum and minimum temperature of 2m (℃)
Wind-58500 \ daily average 10m wind speed (M / s)
Cmads -- SWAT drive data subset naming format
Name of SWAT subset file of China's air data assimilation SWAT model data set (cmads):
The data set code consists of element code: R, P, s, t, W + dimension grid number - longitude grid number (for the extraction of longitude and latitude grid number, please refer to cmads data set user manual. PDF).

Data Citations Data citation guideline What's data citation?
Cite as:

Meng, X., Wang, H. (2018). China meteorological assimilation driving datasets for the SWAT model Version 1.0 (2008-2016). National Tibetan Plateau Data Center, DOI: 10.11888/Meteoro.tpdc.270543. CSTR: 18406.11.Meteoro.tpdc.270543. (Download the reference: RIS | Bibtex )

Related Literatures:

1. Meng, X.Y., Shi, C.X., Liu, S.Y., Wang, H., Lei, X.H., Liu, Z.H., Ji, X.N., Cai, S.Y., Zhao, Q.D. (2016). CMADS Datasets and Its Application in Watershed Hydrological Simulation: A Case Study of the Heihe River Basin. Pearl River, 37(7), 1-19.( View Details | Bibtex)

2. Shi C X, Xie Z H, Qian H, et al. China land soil moisture EnKF data assimilation based on satellite remote sensing data. Sci China Earth Sci, 2011, doi: 10.1007/s11430-010-4160-3( View Details | Bibtex)

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

References literature

1.Meng, X.Y., Ji, X.N., Liu, Z.H., et al. (2014). Research on Improvement and Application of Snowmelt Module in SWAT[J]. Journal of Natural Resources, 29(3), 528-539. (View Details )

2.Shi, C.X., Xie, Z.H. (2008). A Time Downscaling Scheme of Precipitation by Using Geostationary Meteorological Satellite Data, Progress in Geography, 27(4), 15-22. (View Details )

3.Ren, Z.H., Xiong, A.Y. (2007). Operational system development on three-step quality control of observations from AWS, Meteorological monthly, (01), 19-24. (View Details )

4.Zhang, T. (2013). Multi-source data fusion and application research base on LAPS/STMAS, Master Thesis, Nanjing: Nanjing university of information science and technology. (View Details )

5.Meng, X.Y., Wang, H., Wu, Y.P., Long, A.H., Wang, J.H., Shi, C.X. et al. (2017). Investigating spatiotemporal changes of the land surface processes in Xinjiang using high-resolution CLM3.5 and CLDAS: Soil temperature. Scientific Reports. 7. doi:10.1038/s41598-017-10665-8. (View Details )

6.Meng, X.Y., Wang, H., Liu, Z.H., Shi, C.X., Liu, S.Y., Chen, X., Gong, W.W. (2017). Simulation and verification ofland surface soil temperatures in the Xinjiang Region by the CLM3.5 model forced by CLDAS. Acta Ecologica Sinica, 37(3), 979-995. (View Details )

7.Meng, X.Y., Wang, H., et al. (2017). Simulation, validation, and analysis of the Hydrological components of Jing and Bo River Basin based on the SWAT model driven by CMADS. ACTA ECOLOGICA SINICA. 39(3). DOI:10.5846/stxb201608231719. (View Details )

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License: This work is licensed under an Attribution 4.0 International (CC BY 4.0)

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Geographic coverage
East: 160.00 West: 60.00
South: 0.00 North: 65.00
  • Temporal resolution: Daily
  • Spatial resolution: 0.1º - 0.25º
  • File size: 35,200 MB
  • Views: 44,289
  • Downloads: 1,177
  • Access: Open Access
  • Temporal coverage: 2008-02-12 To 2017-02-11
  • Updated time: 2021-04-19
: Meng Xianyong   Wang Hao  

Distributor: National Tibetan Plateau Data Center


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