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

0 2020-04-02

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\) 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 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

0 2020-04-02

China meteorological forcing dataset (1979-2018)

The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.

0 2020-04-01

Precipitation during the growing season in Pailougou watershed (2011-2013)

Precipitation is one of the elements of meteorological monitoring and a measurement basis of regional precipitation. Precipitation is the only source of water for plants’ survival in mountain areas. Therefore, precipitation is the main link of the forest hydrological cycle. This data only provides precipitation of the Pailugou watershed during the growing season.

0 2020-03-31

Meteorological data of surface environment and observation network in China's cold region (2018)

1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station( Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.

0 2020-03-25

1-km monthly precipitation dataset for China (1901-2017)

This dataset includes the monthly precipitation data with 0.0083333 arc degree (~1km) for China from Jan 1901 to Dec 2017. The data form belongs to NETCDF, namely .nc file. The unit of the data is 0.1 mm. The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on Delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. The dataset covers the main land area of China, including Hong Kong, Macao and Taiwan regions, and excluding islands and reefs in South China Sea.

0 2020-03-22

Dynamic downscaled daily 9 km precipitation and near-surface air temperature over the Pan Third Pole region (2000-2010)

The data include daily precipitation (Precip) amount and daily mean near-surface air temperature (T2M) over the Pan Third Pole region. The data is downscaled by using the Weather Research and Forecasting (WRF) model (3.7.1). The boundary and initial condition come from the fifth-generation global reanalysis product by the European Centre for Medium-Range Weather Forecasts (ECMWF), ERA5. The seasonal cycle and summer mean of precipitation over Tibet is well reproduced in comparison to the in situ observations.

0 2020-03-18

HiWATER: WATERNET observation dataset in the upper reaches of the Heihe River Basin (2014)

This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since January 2014. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. Please refer to "waternet data document 20141206. Docx" for details

0 2020-03-13

HiWATER:WATERNET observation dataset in the upper reaches of the Heihe River Basin (2013)

This data set includes the observation data of 40 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River since the end of June 2013. Soil moisture of 4cm, 10cm and 20cm is the basic observation of each node; 19 nodes include the observation of soil moisture and surface infrared radiation temperature; 11 nodes include the observation of soil moisture, surface infrared radiation temperature, snow depth and precipitation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification.

0 2020-03-13

HiWATER: WATERNET observation dataset in the upper of Heihe River Basin (2015)

This data set includes the observation data of 25 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River from January 2015 to December 2015. 4cm and 20cm soil moisture / temperature is the basic observation of each node; some nodes also include 10cm soil moisture / temperature, surface infrared radiation temperature, snow depth and precipitation observation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. For details, please refer to "2015 data document 20160501. Docx of water net of Babao River in the upper reaches of Heihe River"

0 2020-03-13