Data Overview: Zhangye's channels are divided into five levels: dry, branch, bucket, agricultural and Mao channels, of which the agricultural channels are generally unlined. Mao channels are field projects, so the three levels of dry, branch and bucket channels and a small part of agricultural channels are mainly collected. The irrigation canal system data includes 2 main canals (involving multiple irrigation districts), 157 main canals (within a single irrigation district), 782 branch canals and 5315 dou canals, with a total length of 8, 745.0km. Data acquisition process: remote sensing interpretation and GPS field measurement are adopted for data acquisition of irrigation canal system. Direct GPS acquisition channel is the most effective method, but the workload of GPS acquisition channel is too large, and we only verify the measurement in some irrigation areas. The main method is to first collect the manual maps of irrigation districts drawn by each water pipe. Most of these maps have no location, only some irrigation districts such as Daman and Shangsan have been located based on topographic maps, and some irrigation districts in Gaotai County have used GPS to locate some channels. Referring to the schematic diagram of the irrigation district, channel spatial positioning is carried out based on Quikbird, ASTER, TM remote sensing images and 1: 50000 topographic maps. For the main canal and branch canal, due to the obvious linear features on remote sensing images and the general signs on topographic maps, it can be located more accurately. For Douqu, areas with high-resolution images can be located more accurately, while other areas can only be roughly located according to fuzzy linear features of images and prompt information of irrigation district staff, with low positioning accuracy. Each water management office simultaneously provides channel attribute data, which is one-to-one corresponding to spatial data. After the first draft of the channel distribution map is completed, it is submitted twice to the personnel familiar with the channel distribution of each water pipe for correction. The first time is mainly to eliminate duplication and leak, and the second time is mainly to correct the position and perfect the attribute data. Description of data content: The fields in the attribute table include code, district and county name, irrigation area name, channel whole process, channel name, channel type, location, total length, lined, design flow, design farmland, design forest and grass, real irrigation farmland, real irrigation forest and grass, water right area, and remarks. Code example: G06G02Z15D01, where the first letter represents the county name, the 2nd and 3rd numbers represent the county (district) number, the 4th to 6th characters represent the trunk canal code, the 7th to 9th characters represent the branch canal code, and the 10th to 12th characters represent the dou canal code.
MA Mingguo
The research project on land surface data assimilation system in western China belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation. the person in charge is Li Xin, researcher of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. One of the data collected in this project is the reanalysis data of surface climate factors in western China in 2002. This data set is generated based on the daily 1 × 1 provided by the National Environmental Prediction Center (NCEP). However, the re-analysis of the data has the following problems: (1) the temporal and spatial resolution is not high enough (the horizontal resolution is 1 degree and the time is 6 hours); (2) The low-level errors in plateau areas are large; (3) The data are standard isosurface data and need interpolation. The 2002 reanalysis data set of surface climate elements in western China was generated by combining NCEP reanalysis data and MM5 model by Dr. Longxiao and Professor Qiu Chongjian of Lanzhou University using Newton relaxation data assimilation method (Nudging), including 10m horizontal and vertical wind speed (m/s), 2m air temperature (k), 2m mixing ratio, surface pressure (Pa), upstream and downstream short wave and long wave radiation (w/m2), convective precipitation and large scale precipitation (mm/s) at 0.25 degree per hour throughout 2002. I. preparation background The quality of the driving data seriously affects the ability of the land surface model to simulate the land surface state, so a very important component of the land surface modeling research is the driving data used to drive the land surface model. No matter how realistic these models are in describing the surface process, no matter how accurate the boundary and initial conditions they input, if the driving data are not accurate, they cannot get the results close to reality. Land surface models are so dependent on the quality of externally provided data that any error in these externally provided data will seriously affect the ability of land surface models to simulate soil moisture, runoff, snow cover and latent heat flux. These externally provided data include: precipitation, radiation, temperature, wind field, humidity and pressure. The 2002 reanalysis data set of surface climate elements in western China uses Newton relaxation data assimilation method (Nudging) to combine NCEP reanalysis data and MM5 model to generate driving data with higher spatial and temporal resolution suitable for complex terrain in western China. Second, the basic parameters of the operation mode 1. Using the US PSU/NCAR mesoscale model MM5 as a simulation model; The selection of simulation grid domain: center (32°N, 90°E), grid distance of 36km, number of horizontal grid points of 131*151, vertical resolution of 25 layers, and mode top of 100hPa；; 2. The data used for initialization are 1 * 1 GRIB grid data of NCEP in the United States. 3. The time step is 120s. Third, the physical process 1. physical process treatment of cloud and precipitation: Grell cumulus cloud parameterization scheme is adopted for sub-grid scale precipitation, and Reisner mixed phase microphysical explicit scheme is adopted for distinguishable scale precipitation; 2. MRF parameterization scheme is adopted for planetary boundary layer process. 3. the radiation process adopts CCM2 radiation scheme. IV. File Format and Naming It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: 2002***&.forc, where * * * is Julian day and 2002***& is time (in hours), where. forc is the file extension. V. data format Stored in binary floating point type, each data takes up 4 bytes.
LONG Xiao, QIU Chongjian
The experimental project of vegetation degradation mechanism and reconstruction in Yuanjiang dry-hot valley in Yunnan belongs to the major research program of "Environmental and Ecological Science in Western China" of the National Natural Science Foundation. The principal is researcher Cao Kunfang of Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. The project runs from January 2004 to December 2007. Data collected for this project include: 1. Excel table of multi-year average temperature and rainfall in Yuanjiang dry-hot valley (1961-2004), with attribute fields including monthly average temperature and monthly average rainfall. 2. excel table of annual average temperature (1750-2006) in the middle of Hengduan Mountain in China based on tree ring, with attribute fields including year and reconstructed average temperature. 3. excel table of summer temperatures (1750-2006) in the central Hengduan Mountains in southern China based on tree rings. The attribute fields include the year and the reconstructed average temperature in summer (April-September). 4. excel table of drought index (1655-2005) in central Hengduan Mountains of China based on tree rotation, with attribute fields including year and reconstruction of drought index in spring (March-May). 5. pdf file of growth dynamic graph of leaves and branches. it records the growth dynamic trend line and leaf dynamic trend graph of plants with s-type, f-type, intermediate-type and S+SD-type branches from March 22, 2004 to April 8, 2005. 6.32 Phenological Summary Tables of Woody Plants (word Document: Specific Name, Number of Observed Plants/Branches, Type of Branch Extension, Leaf Phenology, Length of Current Year Branches (cm), Total Leaves on Branches, Leaf Area (cm2), Non-leaf Period (Months), Flowering Period, Fruit Ripening Period and Fruit Type) 7. Seasonal Changes of Relative Water Content of Plant Leaves in Yuanjiang Dry-hot Valley (March 2003-February 2004) Excel Table 8. Seasonal Changes of Photosynthesis of 6 Representative Plants in Yuanjiang Dry-hot Valley (Maximum Photosynthetic Rate, Stomatal Conductance, Water Use Efficiency, Maximum Subefficiency of photosystem II) excle Table (2003-2005) 9. excle Table of Long-term Water Use Efficiency (Isotope) Data of Representative Plants in Yuanjiang Dry-hot Valley (Water Use Efficiency in Dry and Wet Seasons of Shrimp Flower, Red-skin Water Brocade Tree, Three-leaf Lacquer, Phyllanthus emblica, Pearl Tree, Dried Sky Fruit, Cyclobalanopsis glauca, West China Small Stone Accumulation, Geranium, Tiger thorn, Willow and Pigexcrement Bean) 10. word Document of List of Plants in Mandan Qianshan, Yuanjiang
CAO Kunfang
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
This data is 2002.07.04-2010.12.31 MODIS daily cloudless snow products in the Tibetan Plateau. Due to the snow and cloud reflection characteristics, the use of optical remote sensing to monitor snow is severely disturbed by the weather. This product is based on the most commonly used cloud removal algorithm, using the MODIS daily snow product and passive microwave data AMSR-E snow water equivalent product, and the daily cloudless snow product in the Tibetan Plateau is developed. The accuracy is relatively high. This product has important value for real-time monitoring of snow cover dynamic changes on the Tibetan Plateau. Projection method: Albers Conical Equal Area Datum: D_Krasovsky_1940 Spatial resolution: 500 m Data format: tif Naming rules: maYYMMDD.tif, where ma represents the data name; YY represents the year (01 represents 2001, 02 represents 2002 ...); MM represents the month (01 represents January, 02 represents February ...); DD represents the day (01 Means 1st, 02 means 2nd ...).
HUANG Xiaodong
I. Overview The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. Ⅱ. Data processing description The VEGETATION sensor was launched by SPOT-4 in March 1998, and has received SP0T VGT data for global vegetation coverage observation since April 1998. It has a very complete and efficient image ground processing mechanism system. The VEGETATION data is mainly received by the Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides related parameters (such as calibration coefficients). Finally, the image processing and archiving center of VITO Institute in Belgium Global VEGETATION data archiving and user orders. Among them, VGT-P (prototype) data products mainly provide scientific researchers with high-quality physical quantity prototype data in order to facilitate their research and development of algorithms and application models. The data undergoes strict systematic error correction and resampling into a longitude and latitude network projection, the pixel resolution is lkm, and the pixel brightness value is the reflectivity of the ground features on the top layer of the atmosphere. In addition to providing four bands of raw data, relevant auxiliary parameters such as atmospheric conditions, system information (solar zenith angle, azimuth, field of view, and reception time) and terrain data are also provided according to user needs. VGT-S (synthesis) products provide atmospheric-corrected surface reflectance data, and use multi-band synthesis techniques to obtain a normalized vegetation index (w) data set with lkm resolution. VGI-S products include the spectral reflectance and NDVI data set (s1) of four bands synthesized daily, the spectral reflectance of four bands synthesized every 10 days, and the maximum NDVI data set (S10) every 10 days to reduce cloud and The impact of BRDF, while S10 was also resampled into 4km resolution (S10.4) and 8km resolution (S10.8) datasets. VGT-S products are widely used for their high time resolution. This data set contains the spectral reflectance of four bands synthesized every 10 days and the 10-day maximized NDVI data set (S10). The pre-processing of SPOT source data includes atmospheric correction, radiation correction, and geometric correction. NDVI data with a maximum of 10 days of synthesis is generated, and the values of -1 to -0.1 are set to -0.1, and then formula YDN = (JNDVI +0.1) /0.004 Convert to a YDN value from 0 to 250. Ⅲ. Data content description The long-term sequence China Vegetation Index dataset is mainly for the normalized vegetation index (NDVI), based on four bands synthesized every 10 days from 1 April 1998 to 31 December 2011 with a spatial resolution of 1 km. Spectral reflectance and 10-day maximized NDVI dataset. The SPOT-VEGETATION-NDVI data set contains .zip compressed files with time resolution from April 1, 1998 to December 31, 2011. After decompression, it is an ESRI-GRID file with a scene every 10 days. The SPO-VEGETATION-NDVI data set naming rules are: v-yymmdd, where v is the abbreviation of vegetation, yymmdd represents the date of the file, and is the main identifier that distinguishes other files. Ⅳ. Data usage description An important feature of the Vegetation Index product is that it can be converted into leaf crown biophysical parameters. Vegetation index (VI) also plays an "intermediate variable" in the acquisition of vegetation biophysical parameters (such as foliar index LAI, green shade, fAPAR, etc.). The relationship between vegetation indices and vegetation biophysical parameters is currently being studied using globally representative ground, aircraft and satellite observation datasets. These data can be used to evaluate the performance of the VI algorithm before satellite launch, and also provide the conversion coefficient between the vegetation index product and the biophysical characteristics of the leaf crown. The use of biophysical data is part of the Vegetation Index Verification Program. Vegetation index products will play a major role in several Earth Observation System (EOS) studies and are also part of global and regional biosphere model products in recent years.
XUE Xian, DU Heqiang
The glacial change trend in the Tarim River Basin and its impact on water resources change belong to the National Natural Science Foundation of China's Western Environment and Ecological Science major research project. The time is 2003.1-2005.12. The project submitted data: Kochikarbachi Glacier Observation Data (excel): Including precipitation, wind direction, wind speed and temperature data 1.3300a_climate (2003.6.29-2004.6.22): 4 hours data during the day, including field date, time, wind speed, wind up, temperature. 2.4200b_climate (2004.1.29-2004.5.12): 6:00, 8:00, 9:00, 10:00, 12:00, 14:00, 16:00, 18:00, 20:00, 22: 00, 23:00 observation data, including field date, time, wind speed, wind up, temperature. 3.3700_Precipitation: 13 days daily precipitation from 2003.7 to 2005.9 4.4200_Precipitation: 18-day daily precipitation between 2003.7 and 2006. 6
LIU Shiyin
This dataset includes passive microwave remote sensing brightness temperatures data for longitude and latitude projections and 0.25 degree resolution from 2002 to 2008 in China. 1. Data processing process: NSIDC produces AMSR-E gridded brightness temperature data by interpolating AMSR-E data (6.9 GHz, 10.7 GHz, 18.7 GHz, 23.8 GHz, 36.5 GHz, and 89.0 GHz) to the output grids from swath space using an Inverse Distance Squared (ID2) method. 2. Data format: Brightness temperature files: two-byte unsigned integers, little-endian byte order Time files: two-byte signed integers, little-endian byte order 3. Data naming: ID2rx-AMSRE-aayyyydddp.vnn.ccc (China-ID2r1-AMSRE-D.252002170A.v03.06V) ID2 Inverse Distance Squared r1 Resolution 1 swath input data AMSRE Identifies this an AMSR-E file D.25 Identifies this as a quarter degree file yyyy Four-digit year ddd Three-digit day of year p Pass direction (A = ascending, D = descending) vnn Gridded data version number (for example, v01, v02, v03) ccc AMSR-E channel indicator: numeric frequency (06, 10, 18, 23, 36, or 89) followed by polarization (H or V) 4. Cutting range: Corner Coordinates: Upper Left (60.0000000, 55.0000000) (60d 0'0.00 "E, 55d 0'0.00" N) Lower Left (60.0000000, 15.0000000) (60d 0'0.00 "E, 15d 0'0.00" N) Upper Right (140.0000000, 55.0000000) (140d 0'0.00 "E, 55d 0'0.00" N) Lower Right (140.0000000, 15.0000000) (140d 0'0.00 "E, 15d 0'0.00" N) Center (100.0000000, 35.0000000) (100d 0'0.00 "E, 35d 0'0.00" N) Origin = (60.000000000000000, 55.000000000000000) 5. Data projection: GEOGCS ["WGS 84", DATUM ["WGS_1984", SPHEROID ["WGS 84", 6378137,298.257223563, AUTHORITY ["EPSG", "7030"]], TOWGS84 [0,0,0,0,0,0,0], AUTHORITY ["EPSG", "6326"]], PRIMEM ["Greenwich", 0, AUTHORITY ["EPSG", "8901"]], UNIT ["degree", 0.0174532925199433, AUTHORITY ["EPSG", "9108"]], AUTHORITY ["EPSG", "4326"]]
Mary Jo Brodzik, Matthew Savoie, Richard Armstrong, Ken Knowles
China long-sequence surface freeze-thaw dataset——decision tree algorithm (1987-2009), is derived from the decision tree classification using passive microwave remote sensing SSM / I brightness temperature data. This data set uses the EASE-Grid projection method (equal cut cylindrical projection, standard latitude is ± 30 °), with a spatial resolution of 25.067525km, and provides daily classification results of the surface freeze-thaw state of the main part of mainland China. The data set is stored by year and consists of 23 folders, from 1987 to 2009. Each folder contains the day-to-day surface freeze-thaw classification results for the current year. It is an ASCII file with the naming rule: SSMI-frozenYYYY ***. Txt, where YYYY represents the year and *** represents the Julian date (001 ~ 365 / 366). The freeze-thaw classification result txt file can be opened and viewed directly with a text program, and can also be opened with ArcView + Spatial Analyst extension module or Arcinfo's Asciigrid command. The original frozen and thawed surface data was derived from daily passive microwave data processed by the National Snow and Ice Data Center (NSIDC) since 1987. This data set uses EASE-Grid (equivalent area expandable earth grid) as a standard format . China's surface freeze-thaw long-term sequence data set-The decision tree algorithm (1987-2009) attributes consist of the spatial-temporal resolution, projection information, and data format of the data set. Spatio-temporal resolution: the time resolution is day by day, the spatial resolution is 25.067525km, the longitude range is 60 ° ~ 140 ° E, and the latitude is 15 ° ~ 55 ° N. Projection information: Global equal-area cylindrical EASE-Grid projection. For more information about EASE-Grid projection, see the description of this projection in data preparation. Data format: The data set consists of 23 folders from 1987 to 2009. Each folder contains the results of the day-to-day surface freeze-thaw classification of the year, and is stored as a txt file on a daily basis. File naming rules: For example, SMI-frozen1994001.txt represents the surface freeze-thaw classification results on the first day of 1994. The ASCII file of the data set is composed of a header file and a body content. The header file consists of 6 lines of description information such as the number of rows, the number of columns, the coordinates of the lower left point of the x-axis, the coordinates of the lower left point of the y-axis, the grid size, and the value of the data-less area. Array, with columns as the priority. The values are integers, from 1 to 4, 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. Because the space described by all ASCII files in this data set is nationwide, the header files of these files are unchanged. The header files are extracted as follows (where xllcenter, yllcenter and cellsize are in m): ncols 308 nrows 166 xllcorner 5778060 yllcorner 1880060 cellsize 25067.525 nodata_value 0 All ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the header file, the main content is a numerical representation of the surface freeze-thaw state: 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. If you want to display it with an icon, we recommend using ArcView + 3D or Spatial Analyst extension module to read it. During the reading process, a grid format file will be generated. The displayed grid file is the graphic representation of the ASCII code file. Reading method: [1] Add 3D or Spatial Analyst extension module in ArcView software, and then create a new View; [2] Activate View, click the File menu, select the Import Data Source option, the Import Data Source selection box pops up, select ASCII Raster in Select import file type: in this box, and a dialog box for selecting the source ASCII file automatically pops up Find any ASCII file in the data set and press OK; [3] Type the name of the Grid file in the Output Grid dialog box (a meaningful file name is recommended for later viewing), and click the path where the Grid file is stored, press Ok again, and then press Yes (to select an integer) Data), Yes (call the generated grid file into the current view). The generated file can be edited according to the Grid file standard. This completes the process of displaying the ASCII file as a Grid file. [4] During batch processing, you can use ARCINFO's ASCIIGRID command to write an AML file, and then use the Run command to complete in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}
LI Xin
This data is digitized from the "Zhangye Land Use Status Map" of the drawing. This map is a key scientific and technological research project of the "Seventh Five-Year Plan" of the country: "Three North" Shelter Forest Remote Sensing Comprehensive Survey, and one of the series maps of Ganqingning Type Area. The information is as follows: * Chief Editor: Wang Yimou * Deputy Editors: Feng Yushun, You Xianxiang, Shen Yuancun * Editors: Wang Xian, Wang Jingquan, Qiu Mingxin, Quan Zhijie, Mou Xindai, Qu Chunning, Yao Fafen, Qian Tianjiu, Huang Autonomy, Mei Chengrui, Han Xichun, Li Yujiu, Hu Shuangxi * Responsible Editor: Huang Meihua * Manuscript: Mou Xin-shi, Cui Sai-hua, Wang Xian. He Shouhua * Compiling: He Shouhua, Wang Xian, Quan Zhijie, Cui Saihua, Long Yaping, Mu Xinshi, He Shouhua, Mao Xiaoli, Cui Saihua, Wang Changhan * Editors: Feng Yushun and Wang Yimou * Qing Hua: Feng Yushun, Zhang Jingqiu, Yang Ping * Cartography: Feng Yushun, Yao Fafen, Wang Jianhua, Zhao Yanhua, Li Weimin * Cartographic unit: compiled by Desert Research Office of Chinese Academy of Sciences * Publishing House: Xi 'an Map Publishing House * Scale: 1: 500000 * Publication time: not yet available 1. File Format and Naming Data is stored in ESRI Shapefile format, including the following layers: Zhang Ye's landuse Map, River, Road, 2. Data Fields and Attributes Type number type face desert Paddy field 12 Irrigated field 13 dryland Non-irrigated field 131 Plain non-irrigated field Valley non-irrigated field Slope non-irrigated field, 133 slope dryland 134 dryland Terrace non-irrigated field ................. Please refer to the data document for details. 3. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
WANG Yimou, YOU Xianxiang, SHEN Yuancun, FENG Yusun, WANG Xian, YAO Fafen, SHEN Yuancun, FENG Yusun, WANG Jianhua
This dataset: Editor-in-Chief: Hou Xueyu Drawing: Hou Xueyu, Sun Shizhou, Zhang Jingwei, He Miaoguang. Wang Yifeng, Kong Dezhen, Wang Shaoqing Publishing: Map Press Issue: Xinhua Bookstore Year: 1979 Scale: 1: 4,000,000 It took five years to complete from May 1972 to July 1976. In the process of drawing legends and mapping, referring to the vast majority of vegetation survey data (including maps and texts) after 1949 in China, we held more than a dozen mapping seminars involving researchers from inside and outside the institute. During the layout after the mapping work was completed, many new survey data were added, especially vegetation data in western Tibet. The nature of this map basically belongs to the current vegetation map, including two parts of natural vegetation and agricultural vegetation. The legend of natural vegetation is arranged according to the seven vegetation groups. They are mainly divided according to the appearance of plant communities and certain ecological characteristics. The concept of agricultural vegetation community, like the natural vegetation community, also has a certain life form (appearance, structure, layer), species composition and a certain ecological location. In 1990, the State Key Laboratory of Resources and Environmental Information Systems of the Institute of Geographical Sciences and Resources, Chinese Academy of Sciences completed the digitization of this map, and wrote relevant data description documents. The digitized data also adopt equal product cone projection and can be converted into other projections by GIS software. This data includes a vector file in e00 format, a Chinese vegetation coding design description, a dataset description, a vegetation data layer attribute data table, and a scanned "People's Republic of China Vegetation Map-Brief Description" and other files. Data projection: Projection: Albers false_easting: 0.000000 false_northing: 0.000000 central_meridian: 110.000000 standard_parallel_1: 25.000000 standard_parallel_2: 47.000000 latitude_of_origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: Unknown Angular Unit: Degree (0.017453292519943299) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Unknown Spheroid: Clarke_1866 Semimajor Axis: 6378206.400000000400000000 Semiminor Axis: 6356583.799999999800000000 Inverse Flattening: 294.978698213901000000
HOU Xueyu, SUN Shizhou, ZHANG Jingwei, HE Miaoguang, WANG Yifeng, KONG Dezhen, WANG Shaoqing
In the mid-latitude region of Asia, the southeastern region is humid and affected by monsoon circulation (thus, it is referred to as the monsoon region), and the inland region is arid and controlled by the other circulation patterns (these areas include the cold and arid regions in the northern Tibetan Plateau, referred to as the westerly region). Based on the generalization of the climate change records published in recent years, the westerly region was humid in the mid-late Holocene, which was significantly different from the pattern of the Asian monsoon in the early-middle Holocene. In the past few millennia, the westerly region was arid during the Medieval Warm Period but relatively humid during the Little Ice Age. In contrast, the oxygen isotope records derived from a stalagmite in the Wanxiang Karst Cave showed that the monsoon precipitation was high in the Medieval Warm Period and low during the Little Ice Age. In the last century, especially in the last 50 years, the humidity of the arid regions in the northwest has increased, while the eastern areas of northwestern and northern China affected by the monsoon have become more arid. Moreover, in the northern and southern parts of the Tibetan Plateau, which are affected by the westerlies and the monsoon, respectively, the precipitation changes on the interdecadal and century scales have also shown an inverse phase. Based on these findings, we propose that the control zone of the westerly belt in central Asia has different humidity (precipitation) variation patterns than the monsoon region on every time scale (from millennial to interdecadal) in the modern interglacial period. The integrated research project on Holocene climate change in the arid and semi-arid regions of western China was a major research component of the project Environmental and Ecological Science for West China, which was funded by the National Natural Science Foundation of China. The leading executive of the project was Professor Fahu Chen from Lanzhou University. The project ran from January 2006 to December 2009. The data collected by the project include the following: 1. The integrate humidity data over the Holocene in the arid regions of Central-East Asia and 12 lakes (11000-0 cal yr BP): including Lake Van, Aral Sea, Issyk-Kul, Ulunguhai Lake, Bosten Lake, Barkol Lake, Bayan Nuur, Telmen Lake, Hovsgol Nuur, Juyan Lake, Gun Nuur and Hulun Nuur. 2. The integrated humidity data over the past millennium in the arid regions of Central-East Asia and at five research sites (1000-2000): including Aral Sea, Guliya, Bosten Lake, Sugan Lake, and the Badain Juran desert. Data format: excel table.
CHEN Fahu
This dataset is TM remote sensing data covers western China, around the 1980s. Data attributes: Pixel Size: 30-meter reflective: Bands 1-5 and 7 60-meter thermal: Band 6 Output Format: GeoTIFF Resampling method: cubic convolution (CC) Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica. Image Orientation: Map (North Up) The data was partially downloaded from the USGS http://eros.usgs.gov/ website, and partly collected from various projects. The data folder is named the row and column number where the image is located. The folder contains TM 7 bands images (* .tif), header files (* .met, * .hdr) and thumbnails (jpg). The naming format of image files is row and column number _TM image mark (5t), and image acquisition time _ band number.
EROS DATA CENTER
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
