China meteorological assimilation driving datasets for the SWAT model Version 1.1 (2008-2016)
CMADS V1.1(The China Meteorological Assimilation Driving Datasets for the SWAT model Version 1.1) Version of the data set introduced the 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 (2m), air pressure, humidity, and wind speed data (10m) 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 (2m), average pressure, maximum and minimum temperature (2m), specific humidity, cumulative precipitation, and average wind speed (10m).
The CMADS comprises other variables for any hydrological model(under 'For-other-model' folder): Daily Average Temperature (2m), Daily Maximum Temperature (2m), Daily Minimum Temperature (2m), Daily cumulative precipitation (20-20h), Daily average Relative Humidity, Daily average Specific Humidity, Daily average Solar Radiation, Daily average Wind (10m), 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 )
1).Station
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 2m temperature(℃)
Wind-58500 Daily average 10m 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 2m temperature(℃)
Maximum-Temperature-txt Daily maximum 2m temperature(℃)
Minimum-Temperature-txt Daily minimum 2m 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 10m wind speed(m/s)
Data storage information: data set storage format is .dbf and .txt
Other data information:
Total data:45GB
Occupied space: 50GB
Time: From year 2008 to year 2014
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
The Swat sub data drive set of cmads v1.1 (in the for other model folder), including all weather input data required by the regular model (day by day). The above input data are located in the following directories:
Atmospheric pressure TXT \ daily average atmospheric pressure (HPA)
Average temperature TXT \ daily average temperature of 2 meters (℃)
Maximum temperature-txt \ daily maximum temperature of 2m (℃)
Minimum-Temperature-txt daily minimum temperature of 2 meters (℃)
Precision-txt \ daily accumulated 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 of 10m (M / s)
Data storage information
Storage format and read: the data set storage format is divided into SWAT subset file (DBF file) and other schema data set (txt file).
Meng, X., Wang, H. (2018). China meteorological assimilation driving datasets for the SWAT model Version 1.1 (2008-2016). National Tibetan Plateau Data Center, DOI: 10.3972/westdc.002.2016.db. CSTR: 18406.11.westdc.002.2016.db. (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. 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 | Bibtex)
3. 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 | Bibtex)
Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.
1.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 )
2.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 )
3.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 )
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 )
(No:2015BAB07BO3)
(No:2011DFG23150)
(No:GYHY201306045)
(No:201301103)
(No:41130531)
To respect the intellectual property rights, protect the rights of data authors,expand servglacials of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.
Example of acknowledgement statement is included below: The data set is provided by National Tibetan Plateau Data Center (http://data.tpdc.ac.cn).
License：
This work is licensed under an
Attribution 4.0 International (CC BY 4.0)
Current page automatically show English comments Show comments in all languages
| East: 160.00 | West: 60.00 |
|---|---|
| South: 0.00 | North: 65.00 |
