Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.

1) The optical depth, vertical mass concentration and extinction coefficient of dust, sulfate, organic carbon, black carbon and sea salt aerosols and total aerosols were measured; 2) Data source: numerical simulation, processing method: Based on CALIPSO satellite vertical observation and global aerosol model, it is generated by four-dimensional local ensemble transformation Kalman filter assimilation method; 3) The data quality is good; 4) It can also be used to study the spatiotemporal distribution of aerosols and their spatial and temporal characteristics of precipitation and their assimilation.

This dataset covers the 2017 sulfur dioxide, nitrogen oxides, PM2.5 emissions grid list of Pan-third polar regions (South Asia: Nepal, Bhutan, India, Pakistan, Bangladesh, Sri Lanka, Maldives; Central Asia: Turkistan, Kyrgyzstan, Uzbekistan, Tajikistan, Kazakhstan, Afghanistan; Josiah: Iran, Iraq, azerbaijan, Georgia, Armenia, Turkey, Syria, Jordan, Israel, Palestine, Saudi Arabia, yemen, bahrain, Qatar, Oman, united Arab emirates, Kuwait, Lebanon, Cyprus). The emission inventory is derived from the data set publicly available in IIASA network. By using ArcGIS software technology, the emission inventory is processed into a GRID data set of 50km*50km, whose quality can be guaranteed. The data can be used by modelers to further study climate and air quality in the third polar region.

High resolution pollen records from ice cores can indicate the relationship between seasonal vegetation changes and climate indicators. High resolution sporopollen analysis was carried out on the 32 m ice core sediments of Zuopu ice core in Qinghai Tibet Plateau. 117 SPOROPOLLEN ASSEMBLAGES were obtained. All the data are sporopollen percentage data, which are arranged in order of depth.

1) Data content : total column water / precipitable water; 2) Data sources and processing methods: ECMWF-interm monthly mean analysis; 3) Data quality description: time resolution: monthly, spatial resolution: 0.7°*0.7°; 4) Data application results and prospects: this data can be used for analysis of water resources in the air.

1) Data content: including the central Asian region, the regional scope: 30°N ~ 60°N, 40°E ~ 90°E; 2) Data source: process the CMIP data set and use bilinear interpolation to interpolate the data of different resolution modes to 0.5°× 0.5°，CRU observation data from 1901 to 2014;; 3) Data quality: the time length is long, the data quality is good, and the missing values are marked by 999; 3) Prospect of data application achievement set: the data has been used to evaluate the simulation capability of temperature in central Asia, and the capability of climate system model to simulate historical climate change in central Asia has been evaluated through calculation and analysis of regional mean, relative error, root-mean-square error, Taylor diagram, EOF. 4) data reliability: by comparing and analyzing the annual changes of the observed and simulated data, the data results show a significant warming trend. By carrying out correlation test on the data results, they all pass the 99% reliability test.At the same time, CMIP plan data and CRU data are also common data sets, which are often used in many studies on climate change.

The temperature humidity index (THI) was proposed by J.E. Oliver in 1973. Its physical meaning is the temperature after humidity correction. It considers the comprehensive impact of temperature and relative humidity on human comfort. It is an important index to measure regional climate comfort. On the basis of referring to the existing classification standards of physiological and climatic evaluation indexes, combined with the natural and geographical characteristics of the Qinghai Tibet Plateau and facing the needs of human settlements suitability evaluation in the Qinghai Tibet Plateau, the temperature and humidity index and its suitability zoning results of the Qinghai Tibet Plateau (more than 3000 meters) are developed (including unsuitable, critical suitable, general suitable, relatively suitable and highly suitable).

High Mountain Asia is the third largest cryosphere on earth other than the Antarctic and Arctic regions. The large amounts of glaciers and snow over the High Mountain Asia play an important role not only on global water cycle but also on water resources and ecology of the arid regions of central Asia. The snowline, as the lower boundary of the snow covered area at the end of melting season, its altitude changes can directly reflect the changes in snow and glaciers. The snowline altitude provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is an indicator for the glacier mass balance. In this dataset, the daily MODIS snow cover products from 2001 to 2019 are used as the main data source. The cloud removal of the daily MODIS snow cover products was firstly carried out based on the developed cubic spline interpolation cloud-removel method, and snow covered days (SCD) are extracted using the cloud-removed MODIS snow cover products. In addition, the MODIS SCD threshold for estimating perennial snow cover is calibrated using the observed data of glacier annual mass balance and Landsat data at the end of melting season. The altitude value of the snowline at the end of melting season is determined by combining the perennial snow cover area and the hypsometric (area-elevation) curve. Finally, the 30km gridded dataset of snowline altitude in the High Mountain Asia during 2001-2019 is generated. This dataset can provide data support for the study of cryosphere and climate change over the High Mountain Asia.

Aiming at sustainable agriculture and food production in Central Asia, the vulnerability of land resources is investigated from the view of exploitation risk of land resources. The evaluation indices of land resources for farmland include topographic factors (such as elevation and slope), land use type, soil texture, etc. The evaluation indices of sustainable agriculture include GDP per capita, grain production per capita, growth rate of agricultural economy, urbanization rate, natural growth rate of population, soil organic matter content, etc. The evaluation indices above which can indicate the properties of land resources directly are used as the evaluation indices of land resources vulnerability. Further, the weighted average of these indices is taken as the land resources vulnerability. The land resources vulnerability is one element of land resources exploitation risk, and the weights of land resources vulnerability evaluation indices are determined with multiple linear regression when the land resources exploitation risk is evaluated. The datasets include land resources vulnerabilities in 1995s (1992-1996), 2000s (1997-2001), 2005s (2002-2006), 2010s (2007-2011), 2015s (2012-2017) and 1995-2015 with a spatial resolution of 0.5°×0.5°. It is expected to provide basic information for agricultural production and land resources exploitation in five countries in Central Asia.

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

The data include K, Na, CA, Mg, F, Cl, so 4 and no 3 in the glacier runoff of zhuxigou, covering most of the inorganic dissolved components. The detection limit is less than 0.01 mg / L and the error is less than 10%; The data can be used to reflect the contribution of chemical weathering processes such as sulfide oxidation, carbonate dissolution and silicate weathering to river solutes in zhuxigou watershed, and then accurately calculate the weathering rates of carbonate and silicate rocks, so as to provide scientific basis for evaluating the impact of glaciation on chemical weathering of rocks and its carbon sink effect.

Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM2M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours (or 6 hours), daily and yearly scales.