Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 1099

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2019)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2019)

    This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 3 in 2019. The site (115.7880° E, 40.3491° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    2021-01-08 879 16

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-large aperture scintillometer, 2018)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-large aperture scintillometer, 2018)

    This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2018. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.

    2021-01-08 943 11

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2018)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2018)

    This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2018. The site (115.7923° E, 40.3574°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    2021-01-08 872 14

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2018)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-10m tower, 2018)

    This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2018. The site (115.7880° E, 40.3491° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    2021-01-08 922 20

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2019)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2019)

    This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to October 24 in 2019. The site (115.7923° E, 40.3574°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.

    2021-01-08 684 12

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin: Huailai station-lysimeters (2018)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin: Huailai station-lysimeters (2018)

    This dataset includes the observation data from 01 Jan. 2019 through 31 Dec. 2018, collected by lysimeters, which are located at 115.788 E, 40.349 N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost data were replaced by -6999; 2) deleting the abnormal data; 3) deleting the outlier data; 4) keeping the consistent date and time format (e.g.2018-6-10 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xls.

    2021-01-07 972 11

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin: Huailai station-lysimeters (2019)

    Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin: Huailai station-lysimeters (2019)

    This dataset includes the observation data from 01 Jan. 2019 through 31 Dec. 2019, collected by lysimeters, which are located at 115.788E, 40.349N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost data were replaced by -6999; 2) deleting the abnormal data; 3) deleting the outlier data; 4) keeping the consistent date and time format (e.g. 2019-01-01 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xls.

    2021-01-07 894 13

  • China's investment on BRI countries (2003-2019)

    China's investment on BRI countries (2003-2019)

    1) China's investment on BRI countries from 2003 to 2019. 2) The Data comes from UNCTAD database. 3) The data quality is good. However, the data of Syria, Tajikistan, Nepal, Myanmar, Brunei and Maldives are missing. 4) The data could reveal China's investment on BRI countries since 2003.

    2021-01-06 634 10

  • Multi-scale surface flux and meteorological elements observation dataset in the Hai River Basin (Huailai station-eddy covariance system-40m tower, 2018)
  • Dataset of land  resources vulnerability from 1995 to 2015 in Central Asia (V1.0)

    Dataset of land resources vulnerability from 1995 to 2015 in Central Asia (V1.0)

    Aiming at sustainable agriculture and food production in Central Asia, the vulnerability of land resources is investigated from the view of exploitation risk of land resources. The evaluation indices of land resources for farmland include topographic factors (such as elevation and slope), land use type, soil texture, etc. The evaluation indices of sustainable agriculture include GDP per capita, grain production per capita, growth rate of agricultural economy, urbanization rate, natural growth rate of population, soil organic matter content, etc. The evaluation indices above which can indicate the properties of land resources directly are used as the evaluation indices of land resources vulnerability. Further, the weighted average of these indices is taken as the land resources vulnerability. The land resources vulnerability is one element of land resources exploitation risk, and the weights of land resources vulnerability evaluation indices are determined with multiple linear regression when the land resources exploitation risk is evaluated. The datasets include land resources vulnerabilities in 1995s (1992-1996), 2000s (1997-2001), 2005s (2002-2006), 2010s (2007-2011), 2015s (2012-2017) and 1995-2015 with a spatial resolution of 0.5°×0.5°. It is expected to provide basic information for agricultural production and land resources exploitation in five countries in Central Asia.

    2021-01-05 680 8

  • Multi band median reflectance dataset of Gwadar Port, Pakistan (2014-2015)

    Multi band median reflectance dataset of Gwadar Port, Pakistan (2014-2015)

    Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data includes the median values of 343 landsat8 data in each 30 meter grid of Gwadar Port Area and its surrounding area from 2014 to 2015. The data includes 12 bands with a spatial resolution of 30 meters, of which the thermal infrared band is 100 meters and the resampling resolution is 30 meters.

    2021-01-05 700 16

  • Archaeological site investigation and plant and animal resource utilization in the Tibet Plateau ( Paleolithic-Epipaleolithic Age)

    Archaeological site investigation and plant and animal resource utilization in the Tibet Plateau ( Paleolithic-Epipaleolithic Age)

    By archaeological investigation and excavation in Tibetan Plateau, we discovered 8 Paleolithic-Epipaleolithic sites, including Baishiya karst cave, Jiangjunfu site, Meilongdapu cave, Xiaqulong site and so on. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of uranium series, radiocarbon and optically stimulated luminescence. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Paleolithic.

    2021-01-04 271 6

  • Genome sequencing data of equine (2020)

    Genome sequencing data of equine (2020)

    In order to study the population evolution history and local adaptive genetic mechanism of main domesticated equine animals in Qinghai Tibet Plateau and its surrounding areas, and to establish the corresponding germplasm genetic resource bank. We have sequenced 236 horse samples collected in Qinghai Province, Tibet Autonomous Region and Xinjiang Autonomous Region by the end of 2018, including Tibetan horse, Tibetan donkey, plain domestic donkey and Jiama plain local breed. Seventy five samples (including 73 donkey samples and two horse samples) were sequenced for mitochondrial genome and D-loop sequencing. A number of genomic data were generated by sequencing, which provided data for tracing the domestication, migration, expansion and other historical events of horse domesticated animals in this area, and further exploring the adaptation mechanism of equine animals to the harsh environment such as hypoxia, high temperature and dryness.

    2021-01-04 691 1

  • High resolution land cover dataset of Gwadar Port, Pakistan (v2010)

    High resolution land cover dataset of Gwadar Port, Pakistan (v2010)

    Gwadar deep water port is located in the south of Gwadar city in the southwest of Balochistan province, Pakistan. It is 460km away from Karachi in the East and 120km away from Pakistan Iran border in the West. It is adjacent to the Arabian Sea in the Indian Ocean in the South and the Strait of Hormuz and Red Sea in the West. It is a port with strategic position far away from Muscat, capital of Oman. This data is the land cover data of Gwadar and its surrounding areas. The data is from globeland30 with a spatial resolution of 30 meters and a data format of TIFF. The classification images used in the development of globeland30 data set mainly include Landsat's TM5, ETM +, oli multispectral images and HJ-1 multispectral images. Using the Pok based classification method, the total volume accuracy is 83.50%, and the kappa coefficient is 0.78.

    2021-01-04 733 9

  • Landslides and debris flows in Bangladesh-China-India-Myanmar Economic Corridor(2010-2020)

    Landslides and debris flows in Bangladesh-China-India-Myanmar Economic Corridor(2010-2020)

    There are 428 large and medium-sized landslides in the Bangladesh China India Myanmar economic corridor. The number of landslides in Myanmar is the largest, reaching 304, accounting for 71% of the total landslides, followed by China and India. The number of landslides is 71 and 52, accounting for 17% and 12% of the total landslides, respectively. There is only one landslide in Bangladesh. According to the material composition of landslide, it can be divided into rock landslide and soil landslide. There are 343 rock landslides in this area, accounting for 80% of the total number of landslides, and 85 soil landslides, accounting for 20% of the total number of landslides. Rock landslides are mainly distributed in the north of China, India and Myanmar, while soil landslides are mainly distributed in the middle and south of Myanmar. A total of 1569 debris flows were interpreted in the Bangladesh China India Myanmar corridor, including 574 gully debris flows and 995 slope debris flows. In the eastern part of the study area, debris flows are mainly distributed on both sides of Lancang River, Nujiang River, Mojiang River and Honghe River, and they are distributed in the north-south direction along these rivers. In the central part of the study area, debris flows are distributed in the ruokai mountain area. Compared with the gully type debris flow, the scale and harm of slope debris flow are much smaller. In this study, the correlation analysis of debris flow is mainly aimed at the gully type debris flow.

    2021-01-04 398 14

  • Landslides and debris flows in China-Mongolia-Russia Economic Corridor(2010-2020)

    Landslides and debris flows in China-Mongolia-Russia Economic Corridor(2010-2020)

    The China Mongolia Russia economic corridor starts from China in the East, passes through Mongolia in the west to Russia, and crosses the Mongolian Plateau, West Siberian plain and Eastern European Plain. There are great differences in natural environment and complex geological conditions in the region. Driven by regional differences in structure, earthquake, meteorology, hydrology and ecology, landslides are widely distributed in China Mongolia Russia economic corridor. Based on remote sensing images, the landslide and debris flow disasters in China Mongolia Russia economic corridor are interpreted. Statistics show that there are 396 landslide disasters in China Mongolia Russia economic corridor, and the landslide disaster area is between 0.0006km2 ~ 8.57km2. The watershed area within 100km on both sides of the railway line, with a total area of 1.43 × 106km2, has identified 1336 debris flow gullies in the China Mongolia Russia economic corridor.

    2021-01-04 331 4

  • Spatial distribution data set of economic development degree in Yangon deepwater port area (2019)

    Spatial distribution data set of economic development degree in Yangon deepwater port area (2019)

    Gross domestic product (GDP) refers to the sum of the value of all the final products and services produced by all the resident units of a country (or region) in a certain period of time. It is an important indicator to measure the overall economic situation of a country. The source data of economic development degree comes from Matti. After cutting the data and obtaining the data of the main urban area of Yangon deep water port, downscaling processing is carried out to reduce the resolution of the data to 10m level. The data of economic development degree can be used to measure the development degree of Yangon deep water port area, and can be used in urban planning, election, risk assessment, disaster relief, disease prevention and control, poverty alleviation and other fields.

    2021-01-01 92 18

  • Phenotypic data of main domestic animals in Xinjiang and surrounding areas (2020)

    Phenotypic data of main domestic animals in Xinjiang and surrounding areas (2020)

    In order to describe the distribution pattern of genetic diversity of main domesticated animals in Qinghai Tibet Plateau and its surrounding areas, clarify their genetic background and establish corresponding genetic resource bank. In 2020, a total of 707 blood, tissue and fecal samples from 209 domesticated animals were collected in Ili area of Xinjiang Uygur Autonomous Region, including RNA samples of heart, liver, spleen, lung and kidney of 12 horses. This data set contains the species, varieties, detailed sampling places, sample types, collection time, collectors, storage methods and other basic sample information of sheep, horses, cattle, dogs, chickens, geese, goats and other species in Yili area of Xinjiang, and is stored in Excel form. This data set also contains the appearance photos of individuals sampled and stored in JPG format.

    2020-12-31 758 1

  • Archaeological site investigation and plant and animal resource utilization in the Northeast Tibet Plateau (Neolithic and Bronze Age)

    Archaeological site investigation and plant and animal resource utilization in the Northeast Tibet Plateau (Neolithic and Bronze Age)

    By archaeological investigation and excavation in the Tibet Plateau and neighbouring areas, we discovered Jinchankou site, Zongri site, Xinancheng cemetery, bangga site and Tshem gzhung kha thog. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of radiocarbon and optically stimulated luminescence dating;identification and isotopic composition and quality indicators of animal remains and plant fossil. At the same time, the relevant animal and plant remains and isotopes in the Tibet Plateau and neighbouring areas are sorted out. This dataset provide important basic data for understanding when and how human lived in the Tibet Plateau and neighbouring areas during the Neolithic Age and historical period.

    2020-12-30 351 16

  • High-resolution extreme climate change dataset (air temperature and precipitation) during 10 years under 1.5-2.0℃ global warming

    High-resolution extreme climate change dataset (air temperature and precipitation) during 10 years under 1.5-2.0℃ global warming

    This dataset is the high-resolution downscaled results of three global circulation models (CCSM4, HadGEM2-ES, and MPI-ESM-MR) from CMIP5. The regional climate model applied is the WRF model. The domain of this dataset covers the five countries of Central Asia. Its horizontal resolution is 9km. The future (reference) period is 2031-2050 (1986-2005), which includes the 10 years under 1.5-2℃ global warming. The carbon emission scenario is RCP4.5. The variances are annual mean temperature at 2m and precipitation (cumulus and grid-scale precipitation). This dataset can be used to project the climate in Central Asia.

    2020-12-30 847 1