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.
YANG Kun, HE Jie, WENJUN TANG , LU Hui, QIN Jun , CHEN Yingying, LI Xin
The Chinese regional surface meteorological element data set is a set of near-surface meteorological and environmental element reanalysis data set developed by the Qinghai-Tibet Plateau Research Institute of the Chinese Academy of Sciences. The data set is based on the existing Princeton reanalysis data, GLDAS data, GEWEX-SRB radiation data and TRMM precipitation data in the world, and is made by combining the conventional meteorological observation data of China Meteorological Administration. The temporal resolution is 3 hours and the horizontal spatial resolution is 0.1, including 7 factors (variables) including near-surface air temperature, near-surface air pressure, near-surface air specific humidity, near-surface full wind speed, ground downward short wave radiation, ground downward long wave radiation and ground precipitation rate. The physical meaning of each variable: | Meteorological Element || Variable Name || Unit || Physical Meaning | near-surface temperature ||temp|| K || instantaneous near-surface (2m) temperature | surface pressure || pres|| Pa || instantaneous surface pressure | specific humidity of near-surface air || shum || kg/ kg || instantaneous specific humidity of near-surface air | near ground full wind speed || wind || m /s || instantaneous near ground (anemometer height) full wind speed | downward short wave radiation || srad || W/m2 || 3-hour average (-1.5 HR ~+1.5 HR) downward short wave radiation | Downward Long Wave Radiation ||lrad ||W/m2 ||3-hour Average (-1.5 hr ~+1.5 hr) Downward Long Wave Radiation | precipitation rate ||prec||mm/hr ||3-hour average (-3.0 HR ~ 0.0 HR) precipitation rate For more information, please refer to the "User's Guide for China Meteorological Al Forcing Dataset" published with the data. The main changes in the latest version (01.06.0014) are: 1. Extend the data to December 2015 (except for short-wave and long-wave data, only until October 2015; the data from November to December 2015 are interpolated based on GLDAS data, and the error may be too large); 2. Set the minimum wind speed at 0.05 m/s; 3. Fixed a bug in the previous radiation algorithm to make our short wave and long wave data more reasonable in the morning and evening periods. 4. bug of precipitation data has been corrected, and the period involved in the change is 2011-2015.
YANG Kun, HE Jie
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
Meng Xianyong, Wang Hao
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
Meng Xianyong, Wang Hao
1) The data set is composed of global atmospheric reanalysis data jointly produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). These grid data are generated by reanalysing the global meteorological data from 1948 to present by applying observation data, forecasting models and assimilation systems. The data variables include surface, near-surface (.995 sigma layer) and multiple meteorological variables in different barospheres, such as precipitation, temperature, relative humidity, sea level pressure, geopotential height, wind field, heat flux, etc. 2) The coverage time is from 1948 to 2018, and the data from 1948 to 1957 are non-Gaussian grid data. The data cover the whole world. The spatial resolution is a 2.5° latitude by 2.5° longitude grid. The vertical resolution is a 17-layer standard pressure barosphere, with layer boundaries at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa, and 28 sigma levels. Some variables are calculated for 8 layers (omega) or 12 layers (humidity), with temporal resolutions of 6 hours, daily, monthly or a long-term monthly average (from 1981 to 2010). The daily data are obtained by averaging the daily values of 0Z, 6Z, 12Z and 18Z. 3) Missing values are assigned a value of -9.99691e+36f. The data are stored in the .nc format with the file name var.time.stat.nc, and each file includes data on latitude, longitude, time, and atmospheric variables. For detailed data specifications, please visit http://www.esrl.noaa.gov/pad/data.
National Oceanic and Atmospheric Administration, National Center for Atmospheric Research
The dataset of GPS radiosonde observations was obtained at an interval of 2 seconds in the cold region hydrology experimental area in March, 2008 and the arid region hydrology experimental area from May to July, 2008. The items were the air temperature, relative humidity, air pressure, the dew temperature, the water vapor mixing ratio, latitudinal and longitudinal wind speeds, the wind speed and direction. Simultaneous with the satellite/airplane overpass, GPS radiosonde observations were carried out: Binggou watershed on Mar. 14, A'rou on Mar. 15, Binggou watershed on Mar. 15, Biandukou on Mar. 17, Binggou watershed on Mar. 22, Binggou watershed on Mar. 29, and A'rou on Apr. 1 for the upper stream experiments; Linze grassland station on May 30, Yingke oasis on Jun.1, Huazhaizi desert station on Jun. 4, Linze grassland station on Jun. 5, Linze grassland station on Jun. 6, Huazhaizi desert station on Jun. 16, Yingke oasis on Jun. 29, Binggou watershed on Jul. 5, Yingke oasis on Jul. 7, Linze grassland station on Jul. 11, and Yingke oasis at 0, 4:10, 8:09, and 12:09 on Jul. 14 for middle stream experiments.
GU Lianglei, HU Zeyong, LI Maoshan, MA Weiqiang, SUN Fanglei
This dataset contains the flux observation matrix measurements obtained from the automatic weather station (AWS) at the Daman superstation between 10 May and 26 September, 2012. The site (100.37223° E, 38.85551° N) was located in a cropland (maize surface) in the Daman irrigation, which is near Zhangye, Gansu Province. The elevation is 1556.06 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m), four-component radiometer (PSP&PIR; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, vertically downward), photosynthetically active radiation (LI-190SB; 12 m, towards south), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil heat flux (HFP01SC; 3 duplicates with one below the vegetation; and the other between plants, -0.06 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m, m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30 m, and WD_40 m, °), air pressure (press, hpa), precipitation (rain, mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), photosynthetically active radiation (PAR, μmol/ (s m^-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm, ℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
This dataset contains the automatic weather station (AWS) measurements from site No.12 in the flux observation matrix from 10 May to 21 September, 2012. The site (100.36631° E, 38.86515° N) was located in a cropland (maize surface) in Daman irrigation district, which is near Zhangye, Gansu Province. The elevation is 1559.25 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45D; 5 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), wind speed and direction (034B; 10 m, towards north), a four-component radiometer (CNR4; 6 m, towards south), two infrared temperature sensors (IRTC3; 6 m, vertically downward), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (ECh2o-5; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFT3; 3 duplicates with one below the vegetation and the other between plants, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and RH_5 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_10 m, m/s), wind direction (WD_10 m, °), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_100 cm, ℃), and soil moisture profile (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_100 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
This dataset contains the automatic weather station (AWS) measurements from site No.10 in the flux observation matrix from 1 June to 17 September, 2012. The site (100.39572° E, 38.87567° N) was located in a cropland (maize surface) in Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1534.73 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP155; 5 m, towards north), rain gauge (TE525M; 10 m), wind speed (03001; 10 m, towards north), a four-component radiometer (CNR1; 6 m, towards south), two infrared temperature sensors (SI-111; 6 m, vertically downward), soil temperature profile (109ss-L; 0, -0.02, and -0.04 m), soil moisture profile (CS616; 0.02, 0.04 m), and soil heat flux (HFP01; 3 duplicates with one below the vegetation and the other between plants, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and RH_5 m) (℃ and %, respectively), precipitation (rain, mm), wind speed (Ws_10 m, m/s), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, and Ts_4 cm, ℃), and soil moisture profile (Ms_2 cm and Ms_4 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
In the mid-latitude region of Asia, the southeastern region is humid and affected by monsoon circulation (thus, it is referred to as the monsoon region), and the inland region is arid and controlled by the other circulation patterns (these areas include the cold and arid regions in the northern Tibetan Plateau, referred to as the westerly region). Based on the generalization of the climate change records published in recent years, the westerly region was humid in the mid-late Holocene, which was significantly different from the pattern of the Asian monsoon in the early-middle Holocene. In the past few millennia, the westerly region was arid during the Medieval Warm Period but relatively humid during the Little Ice Age. In contrast, the oxygen isotope records derived from a stalagmite in the Wanxiang Karst Cave showed that the monsoon precipitation was high in the Medieval Warm Period and low during the Little Ice Age. In the last century, especially in the last 50 years, the humidity of the arid regions in the northwest has increased, while the eastern areas of northwestern and northern China affected by the monsoon have become more arid. Moreover, in the northern and southern parts of the Tibetan Plateau, which are affected by the westerlies and the monsoon, respectively, the precipitation changes on the interdecadal and century scales have also shown an inverse phase. Based on these findings, we propose that the control zone of the westerly belt in central Asia has different humidity (precipitation) variation patterns than the monsoon region on every time scale (from millennial to interdecadal) in the modern interglacial period. The integrated research project on Holocene climate change in the arid and semi-arid regions of western China was a major research component of the project Environmental and Ecological Science for West China, which was funded by the National Natural Science Foundation of China. The leading executive of the project was Professor Fahu Chen from Lanzhou University. The project ran from January 2006 to December 2009. The data collected by the project include the following: 1. The integrate humidity data over the Holocene in the arid regions of Central-East Asia and 12 lakes (11000-0 cal yr BP): including Lake Van, Aral Sea, Issyk-Kul, Ulunguhai Lake, Bosten Lake, Barkol Lake, Bayan Nuur, Telmen Lake, Hovsgol Nuur, Juyan Lake, Gun Nuur and Hulun Nuur. 2. The integrated humidity data over the past millennium in the arid regions of Central-East Asia and at five research sites (1000-2000): including Aral Sea, Guliya, Bosten Lake, Sugan Lake, and the Badain Juran desert. Data format: excel table.
CHEN Fahu
This dataset contains the automatic weather station (AWS) measurements from site No.3 in the flux observation matrix from 3 June to 18 September, 2012. The site (100.37634° E, 38.89053° N) was located in a cropland (maize surface) in Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1543.05 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP155; 5 m, towards north), rain gauge (TR525; 10 m), wind speed (010C; 10 m, towards north), a four-component radiometer (NR01; 6 m, towards south), two infrared temperature sensors (SI-111; 6 m, vertically downward), soil temperature profile (AV-10T; 0, -0.02, -0.04 m), soil moisture profile (CS616; -0.02, -0.04 m), and soil heat flux (HFP01; 3 duplicates with one below the vegetation and the other between plants, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and RH_5 m) (℃ and %, respectively), precipitation (rain, mm), wind speed (Ws_10 m, m/s), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, ℃), soil moisture profile (Ms_2 cm, Ms_4 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
This data set contains the observation data of Zhangye National Climate Observatory from 2008 to 2009. The station is located in Zhangye, Gansu Province, with longitude and latitude of 100 ° 17 ′ e, 39 ° 05 ′ N and altitude of 1456m. The observation items include: atmospheric wind temperature and humidity gradient observation (2cm, 4cm, 10cm, 20m and 30m), wind direction, air pressure, photosynthesis effective radiation, precipitation, radiation four components, surface temperature, multi-layer soil temperature (5cm, 10cm, 15cm, 20cm and 40cm), soil moisture (10cm, 20cm, 50cm, 100cm and 180cm) and soil heat flux (5cm, 10cm and 15cm). Please refer to the instruction document published with the data for specific header and other information.
Zhangye city meteorological bureau
This dataset contains the automatic weather station (AWS) measurements from Bajitan Gobi station in the flux observation matrix from 13 May to 21 September, 2012. The site (100.30420° E, 38.91496° N) was located in a Gobi surface, which is near Zhangye city, Gansu Province. The elevation is 1562 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45AC; 5 m and 10 m, towards north), air pressure (PTB110; 2 m), rain gauge (TE525M; 10 m), wind speed (03001; 5 m and 10 m, towards north), wind direction (03001; 10 m, towards north), a four-component radiometer (CNR1; 6 m, towards south), two infrared temperature sensors (IRTC3; 6 m, vertically downward), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (ECh2o-5; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFT3; 3 duplicates, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m, RH_5 m and RH_10 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_5 m and Ws_10 m, m/s), wind direction (WD_10 m, °), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_100 cm, ℃), and soil moisture profile (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_100 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
SPAC system is a comprehensive platform for observation of plant transpiration water consumption and environmental factors. In this project, a set of SPAC system is set up in the Alxa Desert eco hydrological experimental study. The main observation data include temperature, relative humidity, precipitation, photosynthetic effective radiation, etc. the sampling frequency is one hour. This data provides basic data support for the study of plant transpiration water environmental response mechanism.
SI Jianhua
The project of ecological security evaluation and landscape planning in the inner flow area of hexi corridor belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation, led by researcher xiao duning of the institute of cold and dry environment and engineering, Chinese academy of sciences. The project runs from Jan. 2002 to Dec. 2004. The data of the project is the ecological data of the inner flow area of hexi corridor, including heihe basin, shiyang river basin, shule river basin and river runoff. Investigation and analysis data of ejin banner in heihe river area 1. Soil moisture TDR data The data is stored in Excel format and includes both tubular and well 2002 soil moisture survey data. Tube TDR data Tubular soil moisture survey data with 1.8m underground intervals of 0.2 m on June 1, June 11, June 21, July 1, July 11, July 21, July 31, August 11 and August 21, 2002, including erdaqiao, gobi, forest farm, qidaqiao and tseng forest. Well TDR data Data of well soil moisture survey on June 21, July 1, July 11, July 21, July 31, August 11 and August 21, 2002, which included willows, gobi, populus euphratica and weeds, with intervals of more than 5 meters and 0.2 meters underground. Groundwater GPS data In Excel format, the TDR observation points were measured by GPS, including basic information such as longitude, latitude and elevation, plus information such as water level, logging type and remarks. 2. Soil nutrient salinity data To Excel format, 42 samples containing "total oxygen N %", "total phosphorus P %", "% organic matter", "hydrolysis N N mg/kg", "organic P P mg/kg", "available K K mg/kg", "% calcium carbonate", "PH", "the % of salt" and "total potassium % K" nutrient investigation and analysis of data, such as 42 samples containing "conductance value (%) computing the salt", CO3, HCO3, CI, SO4, Ca, mg, Na + K salt investigation and analysis of data, etc. 3. Soil mechanical composition In Excel format, 42 sample points contained soil particle composition information analysis tables of depth (cm), percentage of particle content at each level (sieve analysis method) (>2mm, 2-1mm, 1-0.5mm, 0.5-0.25mm and 0.25-0.1mm) and percentage of particle content at each level (straw method) (<0.1mm, 0.1-0.05mm, 0.05-0.02mm, 0.02-0.002mm and <0.002mm). 4. Meteorological data of erqi station Is the Excel sheet, including rainfall data from 1957 to 1998, evaporation data from 1957 to 1998, temperature data from 1957 to 1991, wind speed data from 1972 to 1992, maximum temperature data from 1972 to 1992, minimum temperature data from 1972 to 1992, sunshine data from 1972 to 1992 and relative humidity data from 1972 to 1992. Scan copy of jiuquan area The scanning copy of the general map of land use status in jiuquan 1:300,000, the scanning copy of the evaluation map of the distribution of cultivated land reserve resources in jiuquan 1:300,000 and the scanning copy of the district map of jiuquan 1:300,000 Zhang ye water protection information It contains the statistics of water and soil conservation in the regions of ganzhou district, gaotai district, linze county, minle county, shandan county, sunan county and zhangye city in zhangye region (stored in Excel format) and the planning report of each region (stored in Word format). Shiyang river basin Jinchang water resources survey data It includes the scan of 1:50000 water resource distribution map of jinchang city in 1997, the average decline degree of groundwater level in qinghe and jinchuan irrigation areas in jinchang city from 81 to 2000, the statistical table of annual groundwater supply in 1986, 1995 and 2001, and the survey and evaluation report of cultivated land reserve resources in jinchang city. Survey data of water resources in minqin Includes detailed minqin county area typical Wells status per acre crops irrigation water use questionnaire, irrigation, industrial and agricultural water use questionnaire, seeded area of villages and towns questionnaire, the survey data of groundwater hardness index, minqin county of surface runoff and the runoff change situation report, irrigation water quota formulation of evaluation report, minqin county water resources development and utilization of report and opinion polls irrigation works report, etc. Zoning map of soil improvement and utilization in wuwei area For the scanning part of water and soil conservation planning map of wuwei city, the scanning part of the location map of wuwei irrigation area, the scanning part of the scanning part of the administrative map of wuwei city, the scanning part of the water source and water conservancy project construction map of wuwei city, the scanning part of the planning map of wuwei sanbei phase ii shelterbelt project and the scanning part of the administrative map of liangzhou district. Yongchang county water protection information It is the scanning copy of the soil and water conservation supervision, prevention and control plan of 1994 in yongchang county at 1:20000. Shule river basin Distribution map of water resources development and utilization in yumen city It consists of four jpeg images, a 1:250,000 general scanning map of yumen's water resources development and utilization in 2002, and three high-resolution sub-maps. River runoff This data set is stored in Excel format, mainly including the total flow of three basins from 1949 to 2002, the annual runoff of each tributary of the basin, the annual runoff of detailed investigation areas such as jiuquan and the upstream inflow of yuanyang pond reservoir. Total basin Is the annual runoff data of heihe river basin, shiyang river basin and shule river basin from 1949 to 2002. Annual runoff of black river Is the annual runoff data of heihe river, liyuan river, taolai river, hongshui river, qingshui river, fengle river and hongsha river from 1949 to 2002. Annual runoff of shiyang river Is the annual runoff data of xidahe river, dongdahe river, xiying river, jinta river, zama river, huangyang river, gulang river, dajing river and other tributaries from 1949 to 2002. Annual runoff of shule river Is the annual runoff data of dang river, shule river and harten river from 1950 to 2002. Annual river runoff in jiuquan area For the annual flow data of changma gorge of shule river, dangcheng bay of danghe river, junmiao of shule river, baiyang river, icegou of toulai river, yuanyang pond of toulai river, xindi of hongshui river, fengle river, hongsha river of maying river and suang river of yulin river in jiuquan region from 1950 to 2002. Statistics of upstream inflow of yuanyang pond reservoir The data are the upstream inflow data of yuanyang pond reservoir from 1959 to 2001.
Xiao Duning
This dataset contains the automatic weather station (AWS) measurements from Zhangye wetland station in the flux observation matrix from 25 June to 21 September, 2012. The site (100.44640° E, 38.97514° N) was located in a wetland surface, which is near Zhangye city, Gansu Province. The elevation is 1460 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45AC; 5 m and 10 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), wind speed (03002; 5 m and 10 m, towards north), wind direction (03002; 10 m, towards north), a four-component radiometer (NR01; 6 m, towards south), two infrared temperature sensors (SI-111; 6 m, vertically downward), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, and -0.4 m), and soil heat flux (HFP01; 3 duplicates, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m, RH_5 m and RH_10 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_5 m and Ws_10 m, m/s), wind direction (WD_10 m, °), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3, W/m^2), and soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, ℃). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
The dataset of CMA operational meteorological stations observations in the Heihe river basin were provided by Gansu Meteorological Administration and Qinghai Meteorological Administration. It included: (1) Diurnal precipitation, sunshine, evaporation, the wind speed, the air temperature and air humidity (2, 8, 14 and 20 o'clock) in Mazongshan, Yumen touwnship, Dingxin, Jinta, Jiuquan, Gaotai, Linze, Sunan, Zhangye, Mingle, Shandan and Yongchang in Gansu province (2) the wind direction and speed, the temperature and the dew-point spread (8 and 20 o'clock; 850, 700, 600, 500, 400, 300, 250, 200, 150, 100 and 50hpa) in Jiuquan, Zhangye and Mingqin in Gansu province and Golmud, Doulan and Xining in Qinghai province (3) the surface temperature, the dew point, the air pressure, the voltage transformation (3 hours and 24 hours), the weather phenomena (the present and the past), variable temperatures, visibility, cloudage, the wind direction and speed, precipitation within six hours and unusual weather in Jiuquan, Sunan, Jinta, Dingxin, Mingle, Zhangye, Gaotai, Shandan, Linze, Yongchang and Mingqin in Gansu province and Tuole, Yeniugao, Qilian, Menyuan, Xining, Gangcha and Huangyuan in Qinhai province.
Gansu meteorological bureau, Qinghai Meteorological Bureau
The dataset generated from the radiosonde observations in middle basin of Heihe River during 2012. The instrument type are RS92-SGP (Vaisala inc., Finland) or CF-06-A (Changfeng Micro-Electroinics, CHINA). Radiosondes were released during aerospace experiment, such as CASI/SAI, TASI, WIDAS sensors. Atmospheric parameters: pressure, temperature, relative humidity, wind speed and wind direction are measured or calculated at different altitude. This atmospheric parameter profiles can back up atmospheric correction in remote sensing. It can support meteorology research. Observation Site: 1. Wuxing Village: Latitude: 38°51′11.9″N，Longitude: 100°21′48.8″E，Altitude: 1563 m 2. Gaoya Hydrological Station Latitude: 39°8′7.2″N，Longitude: 100°23′59.0″E，Altitude: 1418 m 3. A’Rou Super Station Latitude: 38°03′17.9″N，Longitude: 100°27′28.1″E，Altitude: 2991 m Observation Instrument Type: RS92-SGP manufacture by Vaisala inc., Finland CF-06-A manufacture by Beijing Changfeng Micro-Electronics Technology Co., LTD, CHINA. Observation Time: Simultaneous observation time from 29 June, 2012 to 29 July, 2012 (UTC+8). Accessory data: Pressure, temperature, relative humidity, wind speed and wind direction profiles data.
TAN Junlei, MA Mingguo, Han Huibang, YU Wenping, Hu Ronghai, Zhao Jing, Wang Yan
This dataset contains the automatic weather station (AWS) measurements from Huazhaizi desert steppe station in the flux observation matrix from 2 June to 21 September, 2012. The site (100.31860° E, 38.76519° N) was located in a desert steppe surface, which is near Zhangye city, Gansu Province. The elevation is 1731 m. There are two equipment in the site, and installed by Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences (CAREERI) and Beijing Normal University (BNU), respectively. The installation heights and orientations of BNU were as follows: two infrared temperature sensors (SI-111; 2.65 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (AV-10T; 0, -0.02, -0.04 m), and soil moisture profile (CS616; -0.02, -0.04 m). For the CAREERI installation: air temperature and humidity profile (HMP45A; 1, 1.99 and 2.99 m, north), wind speed profile (03102; 0.48, 0.98, 1.99 and 2.99 m, north), wind direction (03302; 4 m, north), air pressure (PTB210; in waterproof box), rain gauge (CTK-15PC; 0.7 m), four-component radiometer (CNR1; 2.5 m, south), soil temperature profile (107; -0.04, -0.1, -0.18, -0.26, -0.34, -0.42 and -0.5 m), soil moisture profile (ML2X; -0.02, -0.1, -0.18, -0.26, -0.34, -0.42, -0.5, and -0.58 m, 3 duplicates in -0.02 m). The observations included the following: (1) infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm) (%). (2) air temperature and humidity (Ta_1 m, Ta_1.99 m and Ta_2.99 m; RH_1 m, RH_1.99 m and RH_2.99 m) (℃ and %, respectively), wind speed (Ws_0.48 m, Ws_0.98 m, Ws_1.99 m and Ws_2.99 m) (m/s), wind direction (WD_4 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), soil temperature (Ts_4 cm, Ts_10 cm, Ts_18 cm, Ts_26 cm, Ts_34 cm, Ts_42 cm and Ts_50 cm) (℃), soil moisture (Ms_2 cm_1, Ms_2 cm_2, Ms_2 cm_3, Ms_10 cm, Ms_18 cm, Ms_26 cm, Ms_34 cm, Ms_42 cm, Ms_50 cm and Ms_58 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The BNU data were averaged over intervals of 10 min, The CAREERI data were averaged over intervals of 30 min. A total of 144 runs per day were recorded in BNU data and 48 records per day in CAREERI data. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2012-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
This dataset contains the automatic weather station (AWS) measurements from site No.1 in the flux observation matrix from 10 June to 17 September, 2012. The site (100.3582° E, 38.8932° N) was located in a cropland (vegetable surface) in the Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1552.75 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP155; 5 m, towards north), air pressure (PTB110; 2 m), rain gauge (TR525M; 10 m), wind speed and direction (03002; 10 m, towards north), a four-component radiometer (CNR4; 6 m, towards south), two infrared temperature sensors (SI-111; 6 m, vertically downward), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (SM300; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFP01; 3 duplicates with one below the vegetation and the other between plants, 0.06 m). One of the infrared temperature sensors (IRT_2) was adjusted to a zenith angle of 50° after 6 August. The observations included the following: air temperature and humidity (Ta_5 m and RH_5 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_10 m, m/s), wind direction (WD_10 m, °), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_100 cm, ℃), and soil moisture profile (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_100 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, LI Xin, XU Ziwei
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