Basin scale hydrological and ecological processes and their impacts on global climate change data in the Loess Region

The hydrological ecological process at the loess basin scale and its response to global climate change is a project of the Major Research plan of the National Natural Science Foundation of China - Environmental and Ecological Science in Western China. The project is led by liu wenzhao, a researcher from the institute of water and soil conservation, ministry of water resources, Chinese academy of sciences. The project runs from January 2003 to December 2005. The project submitted data: The CLIGEN parameter and output dataset of the Loess Plateau: It was generated during the evaluation and improvement of the practicality of the weather generator CLIGEN in the Loess Plateau. The dataset includes parameter data files for driving CLIGEN and 100-year daily weather data files generated by running CLIGEN from 71 meteorological stations on the Loess Plateau. The 71 sites are distributed in 7 provinces (Shanxi, Shanxi, Gansu, Inner Mongolia, Ningxia, Henan, and Qinghai). Each file is individually saved in ASCII format and can be opened for viewing with text programs. This data set is generated based on long-term serial daily meteorological data measured by 71 meteorological stations on the Loess Plateau. Daily meteorological parameters include: precipitation, maximum, minimum, and average temperature, solar radiation, relative humidity, wind speed and direction. The data comes from the China Meteorological Science Data Sharing Service Network and the Loess Plateau Soil and Water Conservation Database. Among them, solar radiation data is available at only 12 sites on the Loess Plateau. The solar radiation parameters at other sites are generated by kriging space interpolation. The dew point temperature is calculated using the average temperature and relative humidity.

The combined 1000 yr temperature reconstruction records derived from a stalagmite and tree rings (1000 A.D.-2000 A.D.)

The application of general circulation models (GCMs) can improve our understanding of climate forcing. In addition, longer climate records and a wider range of climate states can help assess the ability of the models to simulate climate differences from the present. First, we try to find a substitute index that combines the effects of temperature in different seasons and then combine it with the Beijing stalagmite layer sequence and the Qilian tree-ring sequence to carry out a large-scale temperature reconstruction of China over the past millennium. We then compare the results with the simulated temperature record based on a GCM and ECH-G for the past millennium. Based on the 31-year average, the correlation coefficient between the simulated and reconstructed temperature records was 0.61 (with P < 0.01). The asymmetric V-type low-frequency variation revealed by the combination of the substitute index and the simulation series is the main long-term model of China's millennium-scale temperature. Therefore, solar irradiance and greenhouse gases can account for most of the low-frequency variation. To preserve low-frequency information, conservative detrended methods were used to eliminate age-related growth trends in the experiment. Each tree-ring series has a negative exponential curve installed while retaining all changes. The four fields of the combined 1000-yr (1000 AD-2000 AD) reconstructed temperature records derived from stalagmite and tree-ring archives (excel table) are as follows: 1) Year 2) Annual average temperature reconstruction 3) Reconstructed temperature deviation 4) Simulated temperature deviation

Glacier inventory dataset of Nepal (2000)

This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nation Environment Programme/Regional Resources Centre, Asia and The Pacific (UNEP/RRC-AP)。 1、The glacier inventory uses the remote sensing data of Landsat,reflecting the current status of glaciers in Nepal in 2000. 2、The spatial coverage of the glacier inventory: Nepal 3、Contents of the glacier inventory: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4、Data Projection: Grid Zone IIA Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 23°09'28.17"N Standard parallel 2: 28°49'8.18"N Minimum X Value: 1920240 Maximum X Value: 2651760 Minimum Y Value: 914398 Maximum Y Value: 1188720 Grid Zone IIB Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 21°30'00"N Standard parallel 2: 30°00'00"N Minimum X Value: 1823188 Maximum X Value: 2000644 Minimum Y Value: 1306643 Maximum Y Value: 1433476 For a detailed data description, please refer to the data file and report.

Glacial lake inventory of the Pumqu Basin in the Himalayan Region of China (2004)

This glacial lake inventory receives joint support from the International Centre for Integrated Mountain Development (ICIMOD) and the United Nations Environment Programme/Regional Resources Centre for Asia and the Pacific (UNEP/RRC-AP), Cold and Arid Region Environmental and Engineering Research Institute (CAREERI). 9. This glacial lake cataloging uses Landsat (TM and ETM), Aster and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in the Himalayas in 2004. 10. Glacial lake catalogue coverage: the Himalayan region, Pumqu (Arun), Rongxer (Tama Koshi), Poiqu (Bhote-Sun Koshi), Jilongcangbu (Trishuli), Zangbuqin (Budhigandaki), Majiacangbu (Humla Karnali) and others. 11. Glacial Lake cataloging includes glacial lake cataloging, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 12. Data projection information: Projection: Transverse_Mercator False_Easting: 500000.000000 False_Northing: 0.000000 Central_Meridian: 87.000000 Scale_Factor: 0.999600 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) Geographic Coordinate System: GCS_WGS_1984 Angular Unit: Degree (0.017453292519943299) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_WGS_1984 Spheroid: WGS_1984 Semimajor Axis: 6378137.000000000000000000 Semiminor Axis: 6356752.314245179300000000 Inverse Flattening: 298.257223563000030000 For a detailed data description, please refer to the data file and report.

The data of project on the impact of climate and glacier evolution on resources and sustainable development in Lijiang Yulong Snow Mountain Region

Impact of Climate and Glacier Evolution in Southwest Monsoon Region on Resources and Sustainable Development in Lijiang-Yulong Snow Mountain Region Project is a major research program of "Environmental and Ecological Science in Western China" sponsored by the National Natural Science Foundation. The person in charge is a researcher from He Yuanqing, Institute of Environment and Engineering in Cold and Arid Regions, Chinese Academy of Sciences. The project runs from January 2004 to December 2006. This project collects data: the data of Yulong Snow Mountain Glacier and Environment Observation and Research Station are compiled in word document, and the data content includes: 1. Material Balance of Baishui Glacier No.1 from September to December 2008 (Profile, Lever, Accumulation and Dissipation) 2.Changes of Baishui Glacier No.1 in Yulong Snow Mountain from 1997 to 2008 (date, end elevation, end advancing and retreating distance, south advancing and retreating distance) 3. Monthly Average Flow Statistics of Mujia Station from 1979 to 2003 (Annual Average Flow, Annual Maximum Flow, Annual Maximum Time, Annual Minimum Flow, and Annual Minimum Time) 4. Meteorological data of the test station of Yulong Snow Mountain Glacier Observation Room From 2000 to 2008, the daily average temperature (℃), daily precipitation (mm), daily average relative humidity, daily average sunshine hours, daily air pressure value and daily average wind speed of the base camp weather station. From 2006 to 2008, Ganhaizi Meteorological Station daily average temperature (℃), daily precipitation (mm), daily average relative humidity, daily average sunshine hours, daily air pressure value and daily average wind speed In 2008, the day-to-day average temperature table (℃), day-to-day precipitation (mm), day-to-day average relative humidity, day-to-day average sunshine hours, day-to-day air pressure value and day-to-day average wind speed in the Baishui No.1 glacier accumulation area of Yulong Snow Mountain. In 2008, the day-to-day average temperature table (℃), day-to-day precipitation (mm), day-to-day average relative humidity, day-to-day average sunshine hours, day-to-day air pressure, and day-to-day average wind speed at the end of glacier Baishui No.1 in Yulong Snow Mountain were recorded. Dew point temperature of Ganhaizi from 2006 to 2008 Daily average CO2 content (ppm) at Ganhaizi Meteorological Station from 2006 to 2007 Air Water Vapor Pressure (kPa) at Glacier Terminal Meteorological Station Air Water Vapor Pressure (kPa) of Meteorological Station in Glacier Accumulation Area 5. glacier ice Temperature Data of Baishui No.1, Yulong Snow Mountain Measured resistance values of ice temperature holes at measuring points 1, 2 and 3

China long-sequence surface freeze-thaw dataset——decision tree algorithm (1987-2009)

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}