Brief Introduction: Pan-third-polar environmental change and green silk road construction
Number of Datasets: 627
1) The data includes the soil erosion modulus of 11 watersheds with a resolution of 5 m in the year of 2017 in Tibet. 2）Based on the surface layer of rainfall erosivity R, soil erodibility K, slope length factor LS, vegetation coverage FVC, and rotation sampling survey unit, the Chinese soil erosion model (CSLE) was used to calculate soil erosin modulus in 11 watersheds respectively. Through spatial data processing (including chart linking and transformation, vector-grid conversion, and resampling), R, K, LS factors were calculated from the regional thematic map of rainfall erosivity, soil erodibility, and DEM. By half-month FVC, NPV, half-month rainfall erosivity data, we calculated the value of B factors in each sampling watershed. The value of E factor was calculated based on the remote sensing interpretation results and engineering measure factor table. The value of tillage factor T was obtained from tillage zoning map and tillage measure table. And then the soil erosion modulus in each sampling watershed was calculated by the equation: A=R•K•LS•B•E•T. The selection of 11 watersheds was based on the layout of sampling survey in pan-third polar region. 3) Compared with the data of soil erosion intensity in the same region in the same year, there is no significant difference and the data quality is good.4) the data of soil erosion modulus is of great significance for studying the present situation of soil erosion in Pan third polar region, and it is also crucial for the implementation of the development policy of the Silk Road Economic Belt and the 21st-Century Maritime Silk Road.
2019-10-18 247 0 View Details
Data description: This dataset includes the grid data of annual temperature and annual precipitation on the Tibetan Plateau from 1998 to 2017. It is the basic data for study of climate change and its impact on the ecological environment. Data source and processing: The meta data was aquired from the temperature and precipitation daily data of China's ground high-density stations (above 2,400 national meteorological stations) based on the latest compilation of the National Meteorological Information Center's basic data. After removing the missing stations, the software's thin plate spline method in ANUSPLIN was used to perform spatial interpolation, in order to generate grid data with spactial resolution of 1 km on the Tibetan Plateau . Data application: This data can be used to indentify the impact of climate change on the ecological environment.
2019-10-16 819 74 View Details
Using the Landsat8 OLI images at the summerof 2015, the spectral characteristics of satellite sensors were extracted in the Belt and Road's region. The bands included the band (0.45 - 0.51μm)、band (0.53 - 0.59μm)、band (0.64 - 0.67μm)、band (0.85 - 0.88μm)、band (1.57 - 1.65μm)、band (2.11 - 2.29 μm)、band (10.60 - 11.19 μm)和band (11.50 - 12.51 μm). And the Land cover data of the Belt and Road's region (Version 1.0) (2015) was used to extract the land cover/use at each location. Data includes the format of excel and shp. The data of shp format includes the spatial distribuition and the spectral characteristics of each sampling point.
2019-10-15 266 0 View Details
Precipitation and temperature are essential input variables for hydrological models. There are few meteorological stations in the big Naryn Basin of the Syr Darya, which cannot meet the needs of hydrological simulation. Precipitation data in the Syr Darya were collected through online resources and field research. The precipitation gradient in the study area is obtained. Based on the precipitation gradient, the precipitation and temperature grid products (PGMFD) (http://hydrology.princeton.edu/data.pgf.php)were then corrected to get this set of data sets. The year covered by this data is 1951-2016, the spatial precision is 10km, and the time resolution is daily. The more detail information about the correction method can be found in (Generation of High Mountain Precipitation and Temperature Data for a Quantitative Assessment of Flow Regime in the Upper Yarkant Basin in the Karakoram, Kan et al., 2018)
2019-10-12 281 6 View Details
This data set is collected from the supplementary information part of the paper: Pei, S.P., Niu, F.L., Ben-Zion, Y., Sun, Q., Liu, Y.B., Xue, X.T., Su,J.R., & Shao, Z.G. (2019). Seismic velocity reduction and accelerated recovery due to earthquakes on the Longmenshan fault. Nature Geoscience. 12. 387-392. doi:10.1038/s41561-019-0347-1. This paper studies the structural evolution process of The Longmenshan fault zone located at a pronounced topographic boundary between the eastern margin of the Tibetan plateau and the western Sichuan basin. With the observations on coseismic velocity reductions and the healing phases, it is found that the healing phase of Wenchuan earthquake fracture zone accelerated significantly in response to the Lushan earthquake. This data set contains 3 tables, table names and content are as follows: Data list: The data name list of the rest tables; t1: Data of the four periods (befor Wenchuan earthquake, after Wenchuan earthquake, before Lushan earthquake, after Lushan earthquake); t2: The average velocities with error in Figure 2 in the paper for Wenchuan earthquake (WCEQ) and Lushan earthquake (LSEQ) area. See attachments for data details: Supplementary information.pdf, Seismic velocity reduction and accelerated recovery due to earthquakes on the Longmenshan fault.pdf.
2019-10-09 484 2 View Details
This data set is collected from the supplementary information part of the paper: Yao, T. , Thompson, L. , & Yang, W. . (2012). Different glacier status with atmospheric circulations in tibetan plateau and surroundings. Nature Climate Change, 1580, 1-5. This paper report on the glacier status over the past 30 years by investigating the glacial retreat of 82 glaciers, area reductionof 7,090 glaciers and mass-balance change of 15 glaciers. This data set contains 8 tables, the names and content are as follows: Data list: The data name list of the rest tables; t1: Distribution of Glaciers in the TP and surroundings; t2: Data and method for analyzing glacial area reduction in each basin; t3: Glacial area reduction during the past three decades from remote sensing images in the TP and surroundings; t4: Glacial length fluctuationin the TP and surroundings in the past three decades; t5: Detailed information on the glaciers for recent mass balance measurement in the TP and surroundings; t6: Recent annual mass balances in different regions in the TP; t7: Mass balance of Long-time series for the Qiyi, Xiaodongkemadi and Kangwure Glaciers in the TP. See attachments for data details: Supplementary information.pdf, Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings.pdf.
2019-10-09 952 44 View Details
This data set is provided by the author of the paper: Huang, R., Zhu, H.F., Liang, E.Y., Liu, B., Shi, J.F., Zhang, R.B., Yuan, Y.J., & Grießinger, J. (2019). A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau since 1340 CE. Climate Dynamics, 53(5-6), 3221-3233. In this paper, in order to understand the past few hundred years of winter temperature change history and its driving factors, the researcher of Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences. Prof. Eryuan Liang and his research team, reconstructed the minimum winter (November – February) temperature since 1340 A.D. on southeastern Tibetan Plateau based on the tree-ring samples taken from 2007-2016. The data set contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf
2019-10-03 514 15 View Details
Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.
2019-09-30 546 21 View Details
Vegetation functional type (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of vegetation-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited vegetative functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied vegetation-functional map (Bonan et al., 2002).Functional vegetation has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of vegetation 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km vegetation function map is based on the climate rules of land cover and vegetation function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
2019-09-16 1085 76 View Details
Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the tsinghua university global land cover data (FROM GLC, 30 m grid)、NASA MODIS global land cover data (MCD12Q1, 300 m grid)、the United States Geological Survey (USGS global land data (GFSAD30, 30 m)、Japanese global forest data (PALSAR/PALSAR - 2, 25 m),we build the LUC classification system in the Belt and Road’s region and the rest of the data transformation rules of the classification system.We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Belt and Road’s region V1.0(64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).
2019-09-16 924 45 View Details
1)The data content includes three stages of soil erosion intensity in Qinghai-Tibet Plateau in 1992, 2005 and 2015, and the grid resolution is 300m. 2) China soil erosion prediction model (CSLE) was used to calculate the soil erosion amount of more than 4,000 investigation units on the Qinghai-Tibet Plateau. Soil erosion was interpolated according to land use on Qinghai-Tibet Plateau. According to the soil erosion classification standard, the soil erosion intensity map of Qinghai-Tibet Plateau was obtained. 3) By comparing the differences of three-stage soil erosion intensity data, it conforms to the actual change law and the data quality is good. 4) The data of soil erosion intensity are of great significance to the study of soil erosion in the Qinghai-Tibet Plateau and the sustainable development of local ecosystems. In the attribute table, "Value" represents the erosion intensity level, from 1 to 6, the value represents slight, mild, moderate, intense, extremely intense and severe. "BL" represents the percentage of echa erosion intensity in the total area.
2019-09-16 550 21 View Details
The dataset includes vector map of the lakes larger than 1k㎡ on Tibetan Plateau in 1970s, 1990, 2000, 2010. The lake boundry data was extracted from remote sensing image like Landsat MSS, TM, ETM+, by means of visual interpretation. The data type is vector data, and it's attribute class includes Area (km²). The Projected Coordinate System is Albers Conical Equal Area. It is mainly used in the study of changes in lakes, hydrological and meteorological on the Tibetan Plateau.
2019-09-15 680 36 View Details
Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.
2019-09-15 583 5 View Details
The data set contains series data of populations of major cities and counties on the Tibetan Plateau from 1970 to 2006. It is used to study social and economic changes on the Tibetan Plateau. The table has six fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: Population (10,000) Interpretation: Population Field 6: Data Sources Interpretation: Source of Data Extraction The data comes from the statistical yearbook and county annals of Tibet Autonomous Region, Qinghai, Sichuan, Gansu, Yunnan and Xinjiang. Some are listed as follows:  Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009  Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009  Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009  Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009  Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009  Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008.  County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993  Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994  Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993  Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996  Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995  Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003  Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993  Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005  Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004  Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995  Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006  Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996  County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993  Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995  Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995  Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006  Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997  Kangle County Annals Editorial Committee. Kangle County Annals [J]. Shanghai: Sanlian Bookstore. 1995  County Annals Editorial Committee of Jishishan (Baoan, Dongxiang, Sala) Autonomous County. County Annals of Jishishan (Baoan, Dongxiang, Sala) Autonomous County[J], Gansu: Gansu Cultural Publishing House, 1998  Luqu County Annals Editorial Committee. Luqu County Annals [J]. Gansu: Gansu People's Publishing House, 2006  Zhouqu County Annals Editorial Committee. Zhouqu County Annals [J]. Shanghai: Sanlian Bookstore. 1996  Xiahe County Annals Editorial Committee. Xiahe County Annals [J]. Gansu: Gansu Cultural Publishing House, 1999  Zhuoni County Annals Editorial Committee. Zhuoni County Annals [J]. Gansu: Gansu Nationality Publishing House, 1994  Diebu County Annals Editorial Committee. Diebu County Annals [J]. Gansu: Lanzhou University Press, 1998  Pengxian County Annals Editorial Committee. Pengxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1989  Guanxian County Annals Editorial Committee. Guanxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1991  Wenjiang County Annals Editorial Committee. Wenjiang County Annals [J]. Sichuan: Sichuan People's Publishing House, 1990  Shifang County Annals Editorial Committee. Shifang County Annals [J]. Sichuan: Sichuan University Press, 1988  Tianquan County Annals Editorial Committee. Tianquan County Annals [J]. Sichuan: Sichuan Science and Technology Press, 1997  Shimian County Annals Editorial Committee. Shimian County Annals [J]. Sichuan: Sichuan Cishu Publishing House, 1999  Lushan County Annals Editorial Committee. Lushan County Annals [J]. Sichuan: Fangzhi Publishing House, 2000  Hongyuan County Annals Editorial Committee. Hongyuan County Annals [J]. Sichuan: Sichuan People's Publishing House, 1996  Wenchuan County Annals Editorial Committee. Wenchuan County Annals [J]. Sichuan: Bayu Shushe, 2007  Derong County Annals Editorial Committee. Derong County Annals [J]. Sichuan: Sichuan University, 2000  Baiyu County Annals Editorial Committee. Baiyu County Annals [J]. Sichuan: Sichuan University Press, 1996  Batang County Annals Editorial Committee. Batang County Annals [J]. Sichuan: Sichuan Nationality Publishing House, 1993  Jiulong County Annals Editorial Committee. Jiulong County Annals(Sequel) (1986-2000) [J]. Sichuan: Sichuan Science and Technology Press, 2007  County Annals Editorial Committee of Derung-Nu Autonomous County Gongshan. County Annals of Derung-Nu Autonomous County Gongshan [J]. Beijing: Nationality Publishing House, 2006  Lushui County Annals Editorial Committee. Lushui County Annals [J]. Yunnan: Yunnan People's Publishing House, 1995  Deqin County Annals Editorial Committee. Deqin County Annals [J]. Yunnan: Yunnan Nationality Publishing House, 1997  Yutian County Annals Editorial Committee. Yutian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006  Cele County Annals Editorial Committee. Cele County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2005  Hetian County Annals Editorial Committee. Hetian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006  Qiemo County Local Chronicles Editorial Committee. Qiemo County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996  Shache County Annals Editorial Committee. Shache County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996  Yecheng County Annals Editorial Committee. Yecheng County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1999  Akto County Local Chronicles Editorial Committee. Akto County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996  Wuqia County Local Chronicles Editorial Committee. Wuqia County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1995
2019-09-15 360 20 View Details
The Map of Permafrost on the Qinghai-Tibet Plateau (1:3,000,000) (Shude Li and Guodong Cheng, 1996) was made by the State Key Laboratory of Frozen Soil Engineering, LIGG, CAS (currently called the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences). It was based on first-hand information from the study of frozen soil and previous research papers and literature. By detailed study and consultation of aerial photographs, satellite images, the Permafrost Map along the Qinghai-Tibet Highway (1:600,000) (Boliang Tong, et al., 1983), Geomorphological Map of the Qilian Mountains (1:1,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1985), Natural Landscape Map of Qinghai-Tibetan Plateau (1:3,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1990), Quaternary Glacial Distribution Map of the Qinghai-Tibetan Plateau (1:3,000,000) (Bingyuan Li and Jijun Li, 1991), Frozen Soil Remote Sensing Map of the Western Channel Project of the South-North Water Diversion in the Region of the Tongtian-Yalong Rivers (1:500,000) (Lanzhou Institute of Glaciology and Cryopedology, Chinese Academy of Sciences, 1995), and Map of Snow, Ice, Frozen Ground in China (1:4,000,000) (Yafeng Shi and Desheng Mi, 1988), with editing on 1,000,000 aerial survey topographic maps, and the 1:3,000,000 Map of Permafrost on the Qinghai-Tibetan Plateau was then generated. It was later digitized by Zhuotong Nan of the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The data include: 1) Digitized distribution map of frozen soil on the Qinghai-Tibetan Plateau 2) Scanned map of frozen soil map on the Qinghai-Tibetan Plateau The types of frozen soil in the digitized frozen soil map include: 0. Seasonally frozen ground; seasonal frozen soil 1. Permafrost 2. Island permafrost; 3. Continuous permafrost;
2019-09-15 26670 20 View Details
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from August 31 to December 24, 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 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 (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). 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_30m, 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 IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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. 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
2019-09-15 697 38 View Details
This data set contains 5 data entities. The spatial distribution data of the lakes on the Tibetan Plateau extracted from Landsat images are used, and the time range is from September to November in 1995, 2000, 2005, 2010 and 2015. The data are made jointly by the Third Pole Environment Database and the Geospatial Data Cloud. The object-oriented method is used for lake extraction. (1) Data preprocessing occurs first. Because this paper only uses the first 7 wave bands of Landsat 8 data, band synthesis is carried out for 1-7 wave bands, and the influence of cloud cover on lake extraction is examined. The data are replaced if the influence is too great. (2) Multiscale segmentation of images is performed in eCognition. Because the spectral characteristics of the lake are uniform, spectral segmentation is used again on this basis. (3) The average characteristics of the 5, 6, and 7 bands (Brigh-567) are used for preliminary water extraction, and for some unextracted lakes, NDWI is used for supplementary extraction. (4) At this time, some shadows remain mistakenly extracted, and most of them can be excluded using NDWI<0.05. At the same time, based on the actual situation, the shadows can be eliminated using the value of the first band. (5) To ensure the accuracy, manually check the unextracted independent lake and the mistakenly extracted object and manually modify it. (6) On this basis, use NDWI>0 for the second extraction around the extracted water body and define the edge of the lake. (7) Check again, then merge the objects and export the results. The data effectively remove the influence of mountain shadows, clouds and cloud shadows, snow cover, glaciers and other non-water objects. The lake boundary is accurate and clear, and the error is controlled within a pixel. The accuracy requirement is 30 meters, which is a pixel.
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This dataset contains the flux measurements from the Sidaoqiao superstation eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (101.1374° E, 42.0012° N) was located in the Ejina Banner in Inner Mongolia Autonomous Region . The elevation is 873 m. The EC was installed at a height of 3.2 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 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Latent heat flux during November 9 to 21, 2018 were missing due to the sensor malfunction of CO2/H2O gas analyzer. 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 (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 Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
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Statistical Yearbook of Xinjiang 2006-2015， data from China statistical database: www.shujuku.org.The xinjiang statistical Yearbook system collects the economic and social statistical data of the whole region, regions and counties (cities) from 2006 to 2015. It is an annual publication that comprehensively reflects the economic and social development of xinjiang uygur autonomous region.The Yearbook includes: national economic accounting;Population and employment;Investment in fixed assets;Foreign economic trade and tourism;Resources and environment;Energy;Prices;Agriculture;Industry;The construction industry;Transportation and postal services;Wholesale and retail, accommodation and catering;The financial sector.Education, technology and culture;Statistics on health and environmental conditions.This data source is based on several important sectors of the country and has high credibility. It is the basis for understanding the impact of cryosphere changes on socio-ecology-economy, and also the basis for offering advice on how to deal with adverse changes.
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According to the schedule of the project implementation plan and commissioned by the project team management committee, the Xinjiang Institute of Ecology and Geography of the Chinese Academy of Sciences organized a team of 6 people to carry out a field investigation in the Aral Sea from November 15 to November 26 in view of the desertification, salinization and vegetation construction of the surrounding land. The history, current situation, harnessing work, achievements and pressing problems of soil desertification and salinization around the Aral Sea were preliminarily understood. Sampling of vegetation and soil was carried out, and the technical idea to solve the problem was put forward, that is, planting Halophytes in brackish and saline groundwater to realize rapid vegetation construction in saline-alkali land. Through on-the-spot investigation, the investigation group believed that the implementation of vegetation construction in saline-alkali land should mainly focus on halophytes and local tree species. According to the distribution law of local halophytes and the characteristics of main constructive species in saline-alkali land, combined with the climatic conditions of the implementation site, seven halophytes, such as Salt-eared Tree, should be selected for planting demonstration. After the investigation, the investigation group put forward three specific suggestions on vegetation construction in saline-alkali land.
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