The data are collected from the automatic weather station (AWS, Campbell company) in the moraine area of the 24K glacier in the Southeast Tibet Plateau, Chinese Academy of Sciences. The geographic coordinates are 29.765 ° n, 95.712 ° E and 3950 m above sea level. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), net radiation (w / m2), water vapor pressure (kPa) and air pressure (mbar). In the original data, an average value was recorded every 30 minutes before October 2018, and then an average value was recorded every 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The net radiation probe is nr01, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Data quality: the data has undergone strict quality control. The original abnormal data of 10 minutes and 30 minutes are removed first, and then the arithmetic mean of each hour is calculated. Finally, the daily value is calculated. If the number of hourly data is less than 24, the data is removed, and the corresponding date data in the data table is empty. In addition to the lack of some parameter data due to the thick snow and low temperature in winter and spring, the data can be used by scientific researchers who study climate, glacier and hydrology through strict quality control.
LUO Lun
In the past 50 years, under the background of global climate change, with the increase of population and economic development, Eurasian grassland has been seriously degraded. One belt, one road surface, is a key indicator of grassland quality. Its spatial temporal pattern and distribution can directly reflect the degradation of grassland. Effective assessment of grassland quality is of great significance for the sustainable development of the countries along the border and the promotion of China's "one belt and one road" strategy. In previous studies, there is room for improvement in accuracy and accuracy of spatial and temporal distribution of soil properties. With the development of geographic information system, global positioning system, various sensors and soil mapping technology, digital soil mapping has gradually become an efficient method to express the spatial distribution of soil. Based on soil landscape science and spatial autocorrelation theory, this study combined multi-source sample data and environmental covariate data, and used machine learning model to predict the spatial distribution of surface soil attributes of warm grassland in Eurasia around 2000. In order to solve the problem of soil sample standardization, the equal area spline function was used to fit the soil properties of different profiles to the soil properties of 20 cm in the surface layer, and the soil particle distribution parameter model was used to transform the classification standards of different soil textures into the United States system. In order to solve the problem of insufficient number of soil samples, pseudo expert observation points were used to supplement soil organic matter and sand content samples in under sampling area; stepwise regression combined with support vector machine model was used, and effective soil bulk density simulation samples were screened by calculating threshold. According to the characteristics of complex terrain and climate conditions, combined with multi-source remote sensing data, ngboost model is applied to mine the relationship between soil attributes and environmental landscape factors (topography, climate, vegetation, soil type, etc.) and spatial location based on sample points, and to predict soil organic matter, sand content and bulk density in the study area from 1980 to 1999 and 2000 to 2019 respectively, and the uncertainty of corresponding indicators is given Spatial distribution of sex. The spatial distribution trend of the simulated soil attribute indexes is consistent with the actual situation. Before 2000, the soil organic matter content, bulk density and sand content were 0.64, 0.35 and 0.44 respectively, and the RMSE were 0.25, 0.07 and 13.94 respectively; after 2000, the RMSE were 0.79, 0.77 and 0.86 respectively, and the RMSE were 0.2, 0.13 and 6.61 respectively. The results show that this method can effectively retrieve the soil physical and chemical properties of temperate grassland in Eurasia, and provide a basis for the evaluation of grassland degradation and the construction of grassland quality evaluation system.
ZHANG Na
This data set includes daily average data on soil temperature, humidity and carbon flux obtained from a station in Southeast Tibet from 2007 to December 2019. The data collection site is the atmospheric environment observation site of the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet, which is run by the Chinese Academy of Sciences. The site is located at longitude 94°44'18", latitude 29°45'56" and is at an elevation of 3326 m. The observation instrument models are as follows: Soil temperature: Campbell Co 107; Soil humidity: Campbell Co CS616; Carbon flux: Collector model: C3000, Measurement interval: 10 seconds; The observations and data collection were performed in strict accordance with the instrument operating specifications, and the data have been published in relevant academic journals. Data with obvious errors were removed, and missing data were replaced with null values. Observation of the soil thermal flux was stopped in 2013. In 2015, due to damage to the station probe, soil temperature and humidity data were recorded only for the first two months, the probe was repaired in April 2016.
LUO Lun
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
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
Temporal aliasing caused by the incomplete reduction of high frequency atmosphere and ocean variability contributes as a major error source in the time-variable gravity field products recovered from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO), and likely future gravity missions. The current state-of-the-art of satellite gravity data processing makes use of de-aliasing products to reduce high-frequency mass anomalies, for example, the most recent official Atmosphere and Ocean De-aliasing products (AOD1B-RL06) are applied to model non-tidal mass changes in the ocean and atmosphere. The products already achieved a temporal resolution of 3 hours that greatly improved the quality of gravity inversion compared to the previous releases. In this study, we explore a refined mass integration approach of the atmosphere that considers geometrical, physical, and numerical modifications of the current AOD1B method. Then, the newly available ERA-5 global climate data of 31 km spatial and 1-hour temporal resolution are used to produce a new set of non-tidal atmosphere de-aliasing product (HUST-ERA5) that is computed in terms of spherical harmonics up to degree/order 100 covering 2002 onwards. Despite of an overall agreement with the AOD1B-RL06 (correlation of low-degree coefficients are all greater than 0.99), discrepancy is still distinguished for spatial-temporal analysis, i.e., a better consistency of HUST-ERA5 from 2007 to 2010. The factors contributing the differences, including the input data, method and temporal resolution, are therefore respectively analyzed and quantified through extensive assessments. We find the difference of HUST-ERA5 and AOD1B-RL06 has led to a mean variation of 7.34 nm/s on the the LRI (Laser Ranging Interferometry) range-rate residual on Jan 2019, which is close to the LRI precision already. This impact is invisible for GRACE(-FO) gravity inversion because of the less accurate onboard KBR(K-band ranging) instrument, however, it will be nonnegligible and should be considered when the LRI completely replaces KBR in the future gravity mission. In addition, HUST-ERA5 can also be widely used in LEO satellite orbit determination and superconducting gravimeter atmospheric correction.
YANG Fan LUO Zhicai
This data includes the general layout of the reconstruction project of the middle reaches of the Heihe River, and describes in detail the water diversion flow, irrigation area and other data of each diversion outlet in the middle reaches of the Heihe River. It is attached with the statistical table of the current situation of the diversion portal (listing the diversion form, bank type, irrigation area name, irrigation area name and diversion flow of all diversion portal), the statistical table of the relative distance of the reconstructed diversion portal in the middle reaches (including the relative distance between the reconstructed diversion portal and Zhengyi gorge, bank type and the distance from the previous one), and the general layout plan of the combined reconstruction of the diversion portal (including the combined one Water diversion type, bank type, irrigation area name, irrigation area and water diversion flow) There is no vector format for the data, we only collect JPG format, with a diversion channel table.
XU Zongxue
Based on the meteorological data of 105 meteorological stations in and around the Qinghai Tibet Plateau from 1980 to 2019, the National Meteorological Science Data Center of China Meteorological Administration (CMA) was established. By calculating the oxygen content, it is found that there is a significant linear correlation between oxygen content and altitude, y = - 0.0263x + 283.8, R2 = 0.9819. Therefore, the oxygen content distribution map can be calculated based on DEM data grid. Due to the limitation of the natural environment in the Qinghai Tibet Plateau, there are few related fixed-point observation institutions. This data can reflect the distribution of oxygen content in the Qinghai Tibet Plateau to a certain extent, and has certain reference significance for the research of human living environment in the Qinghai Tibet Plateau.
HE Xiaobo ZHANG Jian NING Tianxiang HUANG Xiaoming JIANG Heng LIU Shaomin LI Xin
The railway data of 34 key areas along the Belt and Road is collected from the Internet and reprocessed. First, we download the linear railway data from the country where the key node areas along the One Belt One Road are located from the OpenStreetMap, and cut and extracted them by region. Meanwhile, it is compared and analyzed with the railway extraction result based on high resolution remote sensing images, and then updated with data from regional statistical bureaus. It is finally integrated into a railway infrastructure element data product. The format of data is linear shapefile data. The spatial coordinate system of the railway data is WGS84, and it contains attribute fields such as name, class and so on. This data can be used to calculate the length of railways and analyze the distribution of railways in cities. The railway data can provide important basic data for the development of socio-economic infrastructure and transportation in key area and regions along the Belt and Road.
GE Yong LING Feng
The EPMA data set of single mineral of magmatic rocks in the Qinghai Tibet Plateau is mainly based on the main data of single mineral in some areas of the Hoh Xil Lhasa plate, and the single mineral test points are more than 1000. The samples were distributed in Hoh Xil lake, Baohu Lake in South Qiangtang and Narusongduo area in Gangdise. Cameca sxlivefe electron microprobe was used for single mineral electron probe. The data comes from published articles or in the acceptance stage. The data were published in SCI or Ni journals, including American mineralogist and Journal of petroleum. The main testing units are Guangzhou Institute of geochemistry, Chinese Academy of Sciences and Institute of mineral resources, Chinese Academy of Geological Sciences. The data set can be used to study the petrogenesis of magmatic rocks in different areas of the Qinghai Tibet Plateau.
TANG Gongjian QI Yue WANG Jun ZHOU Jinsheng
These datasets fill the data gap between GRACE and GRACE-FO, they contain CSR RL06 Mascon and JPL RL06 Mascon. They take China as the study area, and the dataset includes "Decimal_time”, "lat”, "lon”, "time”, "time_bounds”, "TWSA_REC" and "Uncertainty" 7 parameters in total. Among them, "Decimal_time” corresponds to decimal time. There are 191 months from April 2002 to December 2019 (163 months for GRACE data, 17 months for GRACE-FO data, and 11 months for the gap between GRACE and GRACE-FO. We have not filled the missing data of individual months between GRACE or GRACE-FO data). "lat" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the cumulative day of the data from January 1, 2002. And "time_bounds" corresponding to the cumulative day at the start date and end date of each month. “TWSA_REC" represents the monthly terrestrial water storage anomalies from April 2002 to December 2019 in China; "Uncertainty" is the uncertainty between the data and CSR RL06 Mascon products. We use GRACE satellite data from CSR GRACE/GRACE-FO RL06 Mascon solutions (version 02), China Gauge-based Daily Precipitation Analysis (CGDPA, version 1.0) data, and CN05.1 temperature dataset. The precipitation reconstruction model was established, and the seasonal and trend terms of CSR RL06 Mascon products were considered to obtain the dataset of terrestrial water storage anomalies in China. The data quality is good as a whole, and the uncertainty of most regions in China is within 5cm. This dataset complements the nearly one-year data gap between GRACE and GRACE-FO satellites, and provides a full time series for long-term land water storage change analysis in China. As the CSR RL06 Mascon product, the average value between 2004.0000 and 2009.999 is deducted from this dataset. Therefore, the 164-174 months (i.e., July 2017 to May 2018) of this dataset can be directly extracted as the estimation of terrestrial water storage anomalies during the gap period. The reconstruction method for the gap of JPL RL06 Mascon is consistent with that of CSR RL06 Mascon.
ZHONG Yulong FENG Wei ZHONG Min MING Zutao
Lithofacies analysis is an important research method to explore the source region, background, and nature of sedimentary basins. Through the systematic investigation of several late Cretaceous strata in Nepal, situated on the south flank of the Himalayas, the Tulsipur and Butwal sections conducted detailed lithology and sedimentary facies analysis. Continuous strata include the Taltang Fm. , Amile Fm. , Bhainskati Fm. and Dumri Fm. from bottom to top. The lithology contains terrigenous clastic rocks such as conglomerate, sandstone, siltstone and mudstone, chemical rocks such as limestone and siliceous rock, as well as special lithology such as coal seam, carbonaceous layer and oxidation crust. Both sections have various colors and sedimentary structures, which are good materials for the analysis of lithofacies evolution. According to the characteristics of lithofacies and sedimentary assemblage revealed that the Nepal sedimentary environment evolution during the late Cretaceous, which experienced the marine, fluvial, lacustrine, and delta evolution process.
MENG Qingquan
This data set mainly includes the whole rock SR Nd isotopic data of 83 magmatic rocks from the Hoh Xil- basin to Lhasa block in the Qinghai Tibet Plateau. The samples are mainly distributed in Hoh Xil- lake, Guoganjianian in the South Qiangtang, Dugur, Nasongduo and Saga counties in the Gangdise. Rock samples include olivine leucite, quartz monzonite, diorite and granite. The data mainly come from published articles or articles in the acceptance stage. MC-ICP-MS was used to measure SR Nd isotopes in Guangzhou Institute of geochemistry, Chinese Academy of Sciences and other key laboratories. The published articles of the data set have been included in high-level SCI or Ni journals, and the data results are true and reliable. In the future, it can be used to study the lithospheric evolution and magmatic genesis of the Qinghai Tibet Plateau.
TANG Gongjian DAN Wei QI Yue WANG Jun ZHOU Jinsheng
The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes（92% coverage on TP）in 1972-79，including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s（1981,1986-89,which covers 6.5% of TP） and 2 scenes in 1994（by 1.5% coverage on TP）.Among all satellite data，77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.
YE Qinghua WU Yuwei
The Tibetan Plateau Glacier Data –TPG2017 is a glacial coverage data on the Tibetan Plateau from selected 210 scenes of Landsat 8 Operational Land Imager (OLI) images with 30-m spatial resolution from 2013 to 2018, among of which 90% was in 2017 and 85% in winter. Therefore, 2017 was defined as the reference year for the mosaic image. Glacier outlines were digitized on-screen manually from the 2017 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2017. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2017 if they were identifiable on images in all other three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
YE Qinghua
The Tibetan Plateau Glacier Data –TPG2013 is a glacial coverage data on the Tibetan Plateau around 2013. 128 Landsat 8 Operational Land Imager (OLI) images were selected with 30-m spatial resolution, for comparability with previous and current glacier inventories. Besides, about 20 images acquired in 2014 were used to complete the full coverage of the TP. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2013. Glacier outlines were digitized on-screen manually from the 2013 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. [To minimize the effects of snow or cloud cover on glacierized areas, high-resolution (30 m spatial resolution and 4-day repetition cycle) images were also used for reference in glacier delineation from the Chinese satellites HJ-1A and HJ-1B, which were launched on Sep.6th 2008. Both carried as payload two 4-band CCD cameras with swath width 700 km (360 km per camera). All HJ-1A/1B data in 2012, 2013 and 2014 (65 scenes, Fig.S1, Table S1) were from China Centre for Resources Satellite Data and Application (CRESDA; http://www.cresda.com/n16/n92006/n92066/n98627/index.html). Each scene was orthorectified with respect to the 30m-resolution digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) and Landsat images.] The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery and HJ-1A/1B satellite data) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2013. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2013 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
YE Qinghua
The Tibetan Plateau Glacial Data –TPG2001 is a glacial coverage data on the Tibetan Plateau in around 2000 from 150 scenes of Landsat7 TM/ETM+ images by 30 m spatial resolution. The selected Landsat7 TM/ETM+ images were within the period between 1999 and 2002, including 61 scenes (41%) in 2001 and 47 scenes (31%) in 2000. Among all the images, 71% was taken in winter. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2001. Glacier outlines were digitized on-screen manually from the 2001 image mosaic, relying on false-colour image composites (RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2001. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2001 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.8%.
YE Qinghua Yuwei Wu
Soil moisture is one of the core variables in the water cycle. Although its variation is very small, for a precipitation process, soil moisture directly determines the transformation of precipitation into evaporation, runoff and groundwater, which is very important to finely simulate spatial-temporal dynamics of various variables in hydrological process and to accurately estimate water inflow in the upper reaches of Heihe River. This dataset includes soil moisture and temperature data observed by 40 nodes from July 2013 to December 2017. Each node in Babao River Basin has soil moisture observation at depth of 4cm and 20cm; some nodes also include observations at depth of 10 cm. The data observation frequency is 1 hour. The dataset can provide ground -based observations for hydrological simulation, data assimilation and remote sensing verification.
JIN Rui KANG Jian
The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
YAO Junqiang CHEN Jing LI Jiangang
In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.
ZHANG Chi Geping Luo
