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Temperature and precipitation data at meteorological stations in five Central Asian countries (1980-2015)

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


Net primary productivity data set of the Tibetan Plateau (1980-2018)

The data set is based on the NPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the net primary productivity of the ecosystem. Data was derived from Le Quéré et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.


Integrated multi-hazard population risk in the peri-Himalayan and Asian water tower regions (2021)

This data uses a landslide hazard risk assessment model consisting of four modules: landslide hazard causative factors, landslide susceptibility model, exposed population and population casualty rate. The module of hazard-causing factors includes DEM, slope, rainfall, temperature, snow cover, GDP, and vegetation cover factors. The landslide hazard susceptibility model is a statistical analysis using a logistic regression model to obtain landslide susceptibility probability values. The population exposure module uses the landslide susceptibility values overlaid with population data. The population casualty rate module is based on the ratio of historical landslide casualties to the population exposed to landslides during the same period. Finally, by substituting the 2020 population data, the exposed population under different levels of landslide hazard susceptibility is calculated and multiplied with the historical period landslide hazard population casualty rate to assessIntegrated multi-hazard population risk in the peri-Himalayan and Asian water tower regions


A daily, 0.05° Snow depth dataset for Tibetan Plateau (2000-2018)

Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.


Land cover data for Southeast Asia (2015)

This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.


Basic geographic data of Qinghai Tibet Plateau (2015)

The data set is the basic data of the Qinghai Tibet Plateau in 2015. The original data comes from the National Basic Geographic Information Center, and the data of the Qinghai Tibet plateau region is formed by splicing and clipping the segmented data. The data content includes 1:1 million provincial administrative divisions, 1:1 million roads and 1:250000 water system. The data attributes of administrative divisions include name, code and Pinyin; Road data attributes include: GB, RN, name, rteg and type (basic geographic information classification code, road code, road name, road grade and road type); Water system data attributes include: GB, hydc, name, period (basic geographic information classification code, water system name code, name, season).


Socio-economic data set of Qinghai-Tibet Plateau (1982-2018)

The data set includes county-level demographic data of 252 areas in Qinghai-Tibet Plateau in 1982, 1990, 2000, 2010 and 2018, and GDP data in 1988, 1995, 2000, 2010 and 2015. The demographic data includes registered population, resident population, urban population, rural population, male population, female population and non-agricultural population. GDP data includes total GDP output value and GDP output value of primary, secondary and tertiary industries. The data are helpful to study the impact of human activities on the ecological climate of Qinghai-Tibet Plateau, and to explore the urbanization development, urban and rural population mobility, resident population change, local birth rate and agricultural population change in Qinghai-Tibet Plateau. The data were obtained by contacting the local statistics bureau, relevant statistical yearbooks and annual statistical bulletins of various places during the second scientific investigation of Qinghai-Tibet Plateau.


Socio-demographic data of five Central Asian Countries (1991-2017)

The data set records the total socio-demographic data of five central Asian countries from 1991 to 2017.Population indicators including annual population, estimated life expectancy, total fertility rate (1000 people), and total mortality (1000 people), infant mortality, maternal mortality, the total marriage rates, the overall divorce rate, migration of all flow balance, the number of medical institutions, hospital beds (m), the number of preschool institutions (a), kindergarten school student number (m) number, number of middle school, high school students (m), the number of the university, the number of students, institutions of higher learning, the number of students of institutions of higher learning.The data are from the statistical yearbooks of five central Asian countries.


Grassland actual net primary production, potential net primary production and potential aboveground biomass on the Tibetan Plateau from 2000 to 2017

Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.


In-situ water quality parameters of the lakes on the Tibetan Plateau (2009-2020)

This dataset provides the in-situ lake water parameters of 124 closed lakes with a total lake area of 24,570 km2, occupying 53% of the total lake area of the TP.These in-situ water quality parameters include water temperature, salinity, pH,chlorophyll-a concentration, blue-green algae (BGA) concentration, turbidity, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), and water clarity of Secchi Depth (SD).