1. Glacial lake data sets (1960s−2020) This data set contains glacial lake data for the 1960s, 2016, 2017, 2018, 2019, and 2020, mapped from Korona KH-4, Sentinel-2, and Sentinel-1 imagery. 2. Potential Outburst Flood Hazard level of Bhutanese glacial lakes This data contains the Potential Outburst Flood Hazard level of Bhutanese glacial lakes with an area greater than 0.05 km2 (n=278). The value for each hazard assessment criteria is also provided in the data attributes.
RINZIN Sonam, ZHANG Guoqing
The data contains the grid-scale future water resources forecast data (2010-2100) of the five South Asian countries (Myanmar, Thailand, Laos, Vietnam, and Cambodia). The data comes from the output of the DBH model in the Inter-Domain International Impact Model Comparison Program (ISIMIP), which takes meteorological data from multiple climate models as input, and finally obtains the average value of each model under the high emission scenario (RCP8.5) . The spatial interpolation method is used to downscale the 0.5 degree water resource data to 0.25 degree water resource estimate data. The data provided by ISIMIP has undergone good data quality testing and control, and there is no further verification after data interpolation. The data can be used for water resource assessment in five countries in South Asia.
LIU Xingcai
1) Data content: daily water level change data of Nam Co in 2019. The coordinates of observation points are 90.96 ° E, 30.77 ° N, 4730m above sea level, and the underlying surface is alpine grassland. (2) Data source and processing method: measure by manually reading the water level gauge. The original observation data shall be processed and quality controlled by a specially assigned person according to the observation records. (3) Data quality description: because the data is obtained by manual reading of water gauge, it is greatly affected by the harsh environment, and the data is missing and discontinuous in some periods. (4) Data application prospect: the data can be applied to scientific research fields such as Lake hydrology and hydrological process in high and cold areas.
WANG Junbo
1. The data content includes: year, month, day, hour, longitude, latitude, altitude, meridional (UQ) and latitudinal (VQ) components of water vapor flux; 2. Data source and processing method: GPS meteorological sounding data of voyages in the eastern Indian Ocean, and calculate water vapor flux through relative humidity, wind field, air pressure and altitude; 3. Data quality description: vertical continuous observation with 1 second vertical resolution; 4. Data application achievements and prospects: Study on the changes of water vapor transport in the tropical Indian Ocean;
LIU Zhaofei, YAO Zhijun
Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.43 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.
CHEN Yongzhe, FENG Xiaoming, FU Bojie
The data set mainly includes the ice observation frequency (ICO) of north temperate lakes in four periods from 1985 to 2020, as well as the location, area and elevation of the lakes. Among them, the four time periods are 1985-1998 (P1), 1999-2006 (P2), 2007-2014 (P3) and 2015-2020 (P4) respectively, in order to improve the "valid observation" times in the calculation period and improve the accuracy. The ICO of the four periods is calculated by the ratio of "icing" times and "valid observation" times counted by all Landsat images in each period. Other lake information corresponds to the HydroLAKEs data set through the "hylak_id" column in the table. In addition, the data only retains about 30000 lakes with an area of more than 1 square kilometer, which are valid for P1-P4 observation. The data set can reflect the response of Lake icing to climate change in recent decades.
WANG Xinchi
The data set contains the variations of water level, area, and volume for ten lakes in Jiangsu Province (Taihu Lake, Hongze Lake, Gaoyou Lake, Luoma Lake, Shijiu Lake, Gehu Lake, Yangcheng Lake, Baima Lake, Shaobo Lake and Dianshan Lake) from 2003 to 2019, which provides important parameters for the study of lake hydrological ecosystem balance in Jiangsu Province in recent years. The water level data of the ten lakes were obtained from altimetry satellites Envisat/RA-2, Cryosat-2, ICESat, and ICESat-2. The water area data were obtained from Landsat TM/OLI images bsed on Modified Normalized Difference Water Index. For the four lakes with complete water level data (Hongze Lake, Gaoyou Lake, Gehu Lake and Taihu Lake), the water volume changes from 2003 to 2019 were estimated according to the water level and area results. Compared with the in-situ water level data, the water level extracted from altimetry data showed significantly consistent (α = 0.05) for all the ten lakes, with the average absolute error of 0.168 m. The data set provides the variations of water level, area, and volume for the ten lakes in Jiangsu Province from 2003 to 2019, which can provide data support for the management and dispatching of water resources in Jiangsu Province.
KE Changqing, CHANG Xiangyu, CAI Yu, XIA Wentao
China's high-quality natural gauge-based streamflow dataset (CNRD_gauge) was developed from a well-trained and tested land surface model (VIC) that coupled to a routing model with flow direction correction. The dataset currently covers multiple hydrological stations for the period 1961–2018 , and will continue to update. The land surface model was trained by a comprehensive parameter uncertainty framework, including parameter sensitivity, optimization, and regionalization. The rooting model was corrected based on high-resolution river flowlines, as well the ascertained gauge locations and catchment areas. Supported by a well-trained model system, about 83% of the catchments across China exhibited NSE > 0.7, and about 56% of the catchments exhibited KGE > 0.7. The systematic bias of estimated natural streamflow from a calibrated land surface model was reduced by the statistical post-processing technique with the Pbias metric decreased from 17.13% to 2.27%. The reconstructed gauge-based streamflow dataset provides a reliable representation of natural hydrological processes in regions affected by intensive human activity.
MIAO Chiyuan, GOU Jiaojiao
This data set was collected in 2018 during the ground-based microwave radiometry and radar cooperative experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Zhenglan Banner, Inner Mongolia (115.93° E, 42.04° N, at 1362 m in altitude). The data set contains four parts, namely brightness temperature data, radar backscatter coefficient, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data was acquired every 30 minutes from 30° to 65° with an interval of 2.5°. The active microwave data is obtained by ground-based synthetic aperture radar (GBSAR), including the L- and C-band backscattering coefficients under four polarization modes (VV, VH, HH, HV), and the incidence varies from 30° to 65° (2.5° interval). The soil data contains the surface roughness, soil moisture and temperature at six depths of layer (1 cm, 3 cm, 5 cm, 10 cm, 20 cm, 50 cm). The vegetation data is mainly the vegetation water content of the grassland. The experimental period lasted from August 18 to September 25, 2018, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.
ZHAO Tianjie, ZHAO Tianjie, ZHAO Tianjie, HU Lu, GENG Deyuan, SHI Jiancheng
The data set is the seasonal hydrological observation data of the Yellow River from the hydrological station of the Qinghai Tibet Plateau. There are two hydrological stations: 1. Longmen hydrological station in the middle reaches of the Yellow River, which is the weekly hydrological data in 2013, including water temperature (T), runoff (QW), physical erosion rate (per) and pH. 2. Tangnaihai hydrological station of the Yellow River is monthly data from July 2012 to June 2014, including runoff (QW), sediment (salt), pH and EC. The data set was commissioned to be observed by the staff of the hydrological station of the Yellow River Water Conservancy Commission to provide basic hydrological data for the study of hydrology, hydrochemistry and hydrosphere cycle under the background of Qinghai Tibet Plateau uplift.
JIN Zhangdong, ZHAO Zhiqi
The study of chemical weathering is of great significance to understand how the plateau uplift regulates the mechanism of climate change and the circulation of elements and materials in the sphere. The data set is the seasonal major element concentration and stable isotope data of the river water at the hydrological station of the Yellow River Basin originating from the Qinghai Tibet Plateau. There are two hydrological stations in total: 1. Longmen hydrological station in the middle reaches of the Yellow River is the high-resolution (weekly) sample data collected in 2013, and the element concentrations include K, CA, Na, Mg, SO4, HCO3, Cl, etc. The cation data of collected water samples are tested on ICP-AES of Institute of earth environment, Chinese Academy of Sciences, and the anion data are tested on ion chromatograph (ics1200) of Nanjing Institute of geography and lakes, Chinese Academy of Sciences. The uncertainty is within 5%, and HCO3 is tested by titration. The high-resolution (weekly) Li isotope data of river water was tested in MC-ICP-MS of Institute of earth environment, Chinese Academy of Sciences in 2017, and the test accuracy 2sd is better than 5 ‰; 2. Tangnaihai hydrological station on the Yellow River is the river water (month by month) data set collected from July 2012 to June 2014. The major element concentrations include K, CA, Na, Mg, SO4, HCO3, Cl, etc., and the stable isotope data include s, O and H. The data set can be used to study the modern weathering process under the background of the uplift of the Qinghai Tibet Plateau, and provides the first-hand reliable data for the study of physical erosion and chemical weathering in the basin.
JIN Zhangdong, ZHAO Zhiqi
The data set contains the data of rivers and lakes in Sichuan Tibet transportation corridor. The river is divided into 1-4 grades. The rivers are numbered and geocoded. The data can be used not only as the basic elements of regional geographic base map, but also as the basic conditions of hydrological regional division. The data source is not the national 1:1 million basic geographic data, covering the national land area and the main islands including Taiwan Island, Hainan Island, Diaoyu Island, South China Sea Islands and their adjacent waters, with a total of 77 1:1 million maps. The overall current situation of the data is 2015. 2000 national geodetic coordinate system, 1985 National elevation datum, longitude and latitude coordinates are used.
WANG Lixuan
This data set was collected in summer 2017 during the ground-based microwave radiometry experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Duolun County, Inner Mongolia (116.47°E, 42.18°N, at 1269 m in altitude). The data set contains three parts, namely brightness temperature data, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data were acquired from 30° to 65° with an interval of 2.5°, and the time resolution is 0.5 hours. Soil data contains 5 layers of soil moisture and soil temperature (2.5 cm, 10 cm, 20 cm, 30 cm, 50 cm) over three croplands (corn, oats, and buckwheat), with sampling intervals of 10 minutes. The soil data also contains soil surface roughness, rainfall, irrigation flags, and soil texture. Vegetation data contains leaf area index, plant height, vegetation water content, etc. The experimental period lasted from July 19 to August 30, 2017, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.
ZHAO Tianjie, HU Lu, LI Shangnan, FAN Dong, WANG Pingkai, GENG Deyuan, SHI Jiancheng
This data set contains in-situ measurements of soil moisture, soil temperature and precipitation at 34 stations from a wireless Soil Moisture Network within the ShanDian River basin (referred to as the SMN-SDR hereafter). The coverage of entire network is about 10,000 km2 (115.5-116.5°E, 41.5-42.5°N). The topography of the SMN-SDR is relatively flat, and land surfaces are typically dominated by grasslands and croplands. A total of 34 stations are set up in the network with three sampling scales including 100 km (large scale), 50 km (medium scale), and 10 km (small scale). The soil moisture sensors used are Decagon 5TM with five measuring depths (3, 5, 10, 20, and 50 cm) installed for each station. Of the 34 station, there are 20 stations equipped with HOBO rain gauges. Undisturbed soil samples at each layer of soil for each station was taken to analyze the gravimetric/volumetric water content, bulk density, and soil texture for a further specific calibration. The power supply is provided by solar panels and all data can be transmitted wirelessly to a server. The sampling interval of the data recording time is 10 (before June 2019) or 15 minutes (after June 2019). This network can improve the comprehensive observation capabilities of key water cycle parameters in the ShanDian River basin and provide long-term ground reference data for satellite- and model-based soil moisture products.
ZHAO Tianjie, JI Dabin, JIANG Lingmei, CUI Qian, CHEN Deqinq, ZHENG Jingyao, ZHANG Ziqian, HU Lu, SHI Jiancheng
This data set contains in-situ measurements of soil moisture, soil temperature and precipitation at 34 stations from a wireless Soil Moisture Network within the ShanDian River basin (referred to as the SMN-SDR hereafter). The coverage of entire network is about 10,000 km2 (115.5-116.5°E, 41.5-42.5°N). The topography of the SMN-SDR is relatively flat, and land surfaces are typically dominated by grasslands and croplands. A total of 34 stations are set up in the network with three sampling scales including 100 km (large scale), 50 km (medium scale), and 10 km (small scale). The soil moisture sensors used are Decagon 5TM with five measuring depths (3, 5, 10, 20, and 50 cm) installed for each station. Of the 34 station, there are 20 stations equipped with HOBO rain gauges. Undisturbed soil samples at each layer of soil for each station was taken to analyze the gravimetric/volumetric water content, bulk density, and soil texture for a further specific calibration. The power supply is provided by solar panels and all data can be transmitted wirelessly to a server. The sampling interval of the data recording time is 10 (before June 2019) or 15 minutes (after June 2019). This network can improve the comprehensive observation capabilities of key water cycle parameters in the ShanDian River basin and provide long-term ground reference data for satellite- and model-based soil moisture products.
ZHAO Tianjie, JI Dabin, JIANG Lingmei, CUI Qian, CHEN Deqing, ZHENG Jingyao, ZHANG Ziqian, HU Lu, SHI Jiancheng
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of Yulei station on Qinghai lake from Janurary 1 to December 31, 2020. The site (100° 29' 59.726'' E, 36° 35' 27.337'' N) was located on the Yulei Platform in Erlangjian scenic area, Qinghai Province. The elevation is 3209m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 12 and 12.5 m above the water surface, towards north), wind speed and direction profile (windsonic; 14 m above the water surface, towards north) , rain gauge (TE525M; 10m above the water surface in the eastern part of the Yulei platform ), four-component radiometer (NR01; 10 m above the water surface, towards south), one infrared temperature sensors (SI-111; 10 m above the water surface, towards south, vertically downward), photosynthetically active radiation (LI190SB; 10 m above the water surface, towards south), water temperature profile (109, -0.2, -0.5, -1.0, -2.0, and -3.0 m). The observations included the following: air temperature and humidity (Ta_12 m, Ta_12.5 m; RH_12 m, RH_12.5 m) (℃ and %, respectively), wind speed (Ws_14 m) (m/s), wind direction (WD_14 m) (°) , 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), water temperature (Tw_20cm、Tw_50cm、Tw_100cm、Tw_200cm、Tw_300cm) (℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. As the lake water freezes in winter, the water temperature probe is withdrawn, so there is no water temperature data record during October 19, 2020 to December 31, 2020. The missing data were denoted by -6999. (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: 2018-1-1 10:30. Moreover, suspicious data were marked in red.
LI Xiaoyan
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Suganhu Station from November 27 to December 31, 2020. The site (93.708° E, 40.348° N) was located on a wetland in the Suganhu west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.1m, Ts_0.2m, Ts_0.4m) (℃), soil moisture (Ms_0.1m, Ms_0.2m, Ms_0.4m) (%, volumetric water content), soil conductivity (Ec_0.1m, Ec_0.2m, Ec_0.4m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day and missing records were denoted by -6999. (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.
ZHAO Changming, ZHANG Renyi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Sidalong Station from January 1 to April 12, 2020. The site (38.430°E, 99.931°N) was located on a forest in the Kangle Sunan, which is near Zhangye city, Gansu Province. The elevation is 3059 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (0.5, 3, 13, 24, and 48 m), wind speed and direction profile (windsonic; 0.5, 3, 13, 24, and 48 m), air pressure (1.5 m), rain gauge (24 m), infrared temperature sensors (4 m and 24m, vertically downward), photosynthetically active radiation (4 m and 24m), soil heat flux (-0.05 m and -0.1m), soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m and -0.6mr), four-component radiometer (24 m, towards south), sunshine duration sensor(24 m, towards south). The observations included the following: air temperature and humidity (Ta_0.5 m, Ta_3 m, Ta_13 m, Ta_24 m, and Ta_48 m; RH_0.5 m, RH_3 m, RH_13 m, RH_24 m, and RH_48 m) (℃ and %, respectively), wind speed (Ws_0.5 m, Ws_3 m, Ws_13 m, Ws_24 m, and Ws_48 m) (m/s), wind direction (WD_0.5 m, WD_3 m, WD_13 m, WD_24 m, and WD_48 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_A, IRT_B) (℃), photosynthetically active radiation (PAR_A, PAR_B) (μmol/ (s m^2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, and Ts_60 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, and Ms_60 cm) (%, volumetric water content),soil water potential (SWP_5cm, SWP_10cm, SWP_20cm, SWP_40cm, and SWP_60cm)(kpa), soil conductivity (Ec_5cm, Ec_10cm, Ec_20cm, Ec_40cm, and Ec_60cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day and missing records were denoted by -6999; (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.
ZHAO Changming, ZHANG Renyi
The data are phytoplankton data of 61 sites in 23 lakes in Tibetan in 2019. The sampling time is from August to September in 2019. The sampling method is conventional phytoplankton sampling method. During the sampling process, 1 liter of water sample is collected, fixed by Lugo's solution, static sedimentation, siphon concentration, and inverted microscope is used for identification. The data include 6 phyla, such as Bacillariophyta, Chlorophyta, cyanobacteria, Pyrrophyta, Euglenophyta and Cryptophyta, and different phytoplankton species. The data is the original data, which has not been processed, and the unit is individuals / L. The data can be used to characterize the composition and abundance of phytoplankton in the open water area of these lakes, and can also be used to calculate the diversity of phytoplankton community in these lakes.
ZHANG Min
The data set consists of four sub tables, which are remote sensing monitoring of Lake area from 2000 to 2019, total lake water storage based on underwater 3D simulation model, Lake area volume equation based on underwater 3D simulation model, and key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province. The first sub table is the time series Lake area data from 2000 to 2019 from remote sensing image data monitoring. The third sub table stores the area storage capacity equation of the lake based on the underwater three-dimensional simulation model of the lake. The second sub table is the estimation result by combining the time series Lake area data and the area storage capacity equation, Finally, the key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province from 2000 to 2019 are obtained, including simulated water depth, maximum water depth, simulated reference water level and corresponding Lake area of each lake, which are stored in the fourth sub table.
FANG Chun, LU Shanlong, JU Jianting, TANG Hailong
