Referring to the temperature-humidity index formula proposed by J.E. Oliver in 1973, the temperature-humidity index of thethe Green Silk Road Countries(GSRCs) is calculated based on the annual average temperature and relative humidity. The climate suitability assessment of human settlements of the GSRCs is carried out on the basis of the temperature-humidity index. the climate suitability of human settlements in different areas of GSRCs can be divided into five categories: Non-suitable area,Critically suitable area, Low suitable area, Moderately suitable area and High suitable area, based on the distribution characteristics of temperature-humidity index and its correlation with population distribution, according to the regional characteristics and differences of temperature and relative humidity, and referring to the physiological climate evaluation standard of temperature-humidity index.
FENG Zhiming LIN Yumei
Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. The relative humidity isone of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of relative humidity is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.
FENG Zhiming
Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. Temperature is one of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of temperature is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.
LIN Yumei
The data set includes altitude data and temperature characteristics from 1988 to 1994 in Tibet. The data were derived from the Tibet Society and Economics Statistical Yearbook and the Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The table contains 10 fields. Field 1: Year Interpretation: Year of the data Field 2: Location Field 3: Altitude Unit: meter Field 4: Extreme maximum temperature date Field 5: Extreme maximum temperature Unit: °C Field 6: Extreme minimum temperature date Field 7: Extreme minimum temperature Unit: °C Field 8: Annual average temperature Unit: °C Field 9: Average temperature in January Unit: °C Field 10: Average temperature in July Unit: °C
Tibet Autonomous Region Provincial Bureau of Statistics
The data set covers 599 meteorological stations in five Central Asian countries, including the following elements: * daily maximum temperature, * daily minimum temperature, * observed temperature, * Precipitation (i.e. rain, melting snow), covering the following dates: 1980-1986; 1996-2005; 2010; 2014; 2015 The data comes from ghcn-d, a data set containing global land area daily observation data, which integrates climate records. The data is a direct measurement of surface temperature, without interpolation or model assumptions, and contains many long-term site records. The disadvantage is uneven space coverage. Due to changes in observation time, site location, and the type of thermometer used, the records contain many heterogeneity. For more information about this dataset, see https://www.ncdc.noaa.gov/ghcnd-data-access
ZHANG Chi
The data set includes data on precipitation, hail days and gale days in Tibet from 1989 to 1994. The data were derived from the Tibet Society and Economics Statistical Yearbook and the Tibet Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The table contains 7 fields. Field 1: Year Interpretation: Year of the data Field 2: Location Field 3: Annual precipitation Unit: mm Field 4: Precipitation during May to October Unit: mm Field 5: Precipitation during November to the next April Unit: mm Field 6: Hail day Field 7: Gale day
National Bureau of Statistics
Shule River Basin is one of the three inland river basins in Hexi corridor. In recent years, with the obvious change of climate and the aggravation of human activities, the shortage of water resources and the problem of ecological environment in Shule River Basin have become increasingly prominent. It is of great significance to study the runoff change of Shule River Basin in the future climate situation for making rational water resources planning and ecological environment protection. The Shule River basin boundary is cut from "China's 1:100000 desert sand data set". Taking the 2000 TM image as the data source, it interprets, extracts, revises, and uses remote sensing and geographic information system technology to combine with the 1:100000 scale mapping requirements to carry out thematic mapping of desert, sand and gravel gobi. Data attribute table: Area (area), perimeter (perimeter), ash_ (sequence code), class (desert code), ash_id (desert code). The desert code is as follows: mobile sand 2341010, semi mobile sand 2341020, semi fixed sand 2341030, Gobi 2342000, salt alkali land 2343000. Collect and sort out the basic, meteorological, topographical and geomorphic data of Shule River Basin, and provide data support for the management of Shule River Basin.
WANG Jianhua
