Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita),the corresponding industrial structure scenarios in each period were set, and each industry’s output value was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP, and, therefore, it was adjusted according to the need of the future industrial structure scenarios of the research area.

The Karuola Glacier of Tibet is located at the junction of Langkazi County, the Shannan Area of the Tibetan Autonomous Region and Jiangzi County of the Shigatse Region. Latitude: 28°54'23.30′′～28°56'50.95′′N, Longitude 90°11′42.21′′～90°09′26.23′′E. It is a continental glacier with an average elevation of 5042 meters. It is the north-south spreading part of the Ningjingangsang peak. Based on the integration of the first glacier inventory data of China from the Cold and Arid Regions Environmental and Engineering Research Institute，Chinese Academy of Sciences, the 1:100,000 inventory data of the Yarlu Zangbu River Basin Glacier from the Sharing Platform for the Earth Systematic Science Data, and Google Earth remote sensing image and field survey data, the dataset was obtained with the help of ArcGIS, ENVI and other software by the following steps: first, the research and development of the data was achieved by band combination, research area clipping, manual visual interpretation and other techniques, and the accuracy of the obtained data was then verified. This dataset includes a total of 25 statistics of vector and area data of Tibet’s Karuola Glacier. It recorded the changes at the borders of Karuola Glacier in the past 45 years and could be used as reference data for the study of glacier and climate changes on the Tibetan Plateau.

By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were forecast. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering of the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.

Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.

Based on the Global 1,000,000 Basic Geographic Data (2010) of the Resource and Environment Science Data Center of the Chinese Academy of Sciences, the railway and highway networks of Arctic countries (USA, Canada, Russia, Norway (including Greenland and the Faroe Islands), Denmark, Sweden, Finland, and Iceland) are extracted via ArcGIS. The data are stored separately by nation. The data format is the .shp format of ArcGIS, and the projection mode is GCS_WGS_1984. The railway network data are from http://www.resdc.cn/data.aspx?DATAID=208, and the highway network data are from http://www.resdc.cn/data.aspx?DATAID=207

Arctic administrative boundary data sets include Arctic_National, Arctic_Provincial, and Arctic_Prefecture vector spatial data sets of arcti-bound countries and Its corresponding name, TYPE related attribute data :(LOCAL_NAME), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (CONTINENT) The data comes from the 1:1,000,000 ADC_WorldMap global data set, which is a comprehensive, up-to-date and seamless geographic digital data. The world map coordinate system is latitude and longitude, WGS84 datum surface, and the arctic data set is the special projection parameter for the arctic (North_Pole_Stereographic).

This data set is output from WRF model. The data include ‘LU_INDEX’ (land use category), ‘ZNU’(eta values on half (mass) levels), ‘ZNW’(eta values on full (w) levels)，’ZS’(depths of centers of soil layers), ‘DZS’ (thicknesses of soil layers), ‘VAR_SSO’ (variance of subgrid-scale orography), ‘U’(x-wind component), ‘V’(y-wind component),’W’(z-wind component),’T’(perturbation potential temperature (theta-t0)), ‘Q2’ ('QV at 2 M), ‘T2’ (TEMP at 2 M), ‘TH2’ ('POT TEMP at 2 M), ‘PSFC’ (SFC pressure), ‘U10’ (U at 10 M), ‘V10’ (V at 10 M), ‘QVAPOR’ (Water vapor mixing ratio), ‘QLOUD’ (Cloud water mixing ratio),’QRAIN’ (Rain water mixing ratio), ‘QICE’ (Ice mixing ratio), ‘QSNOW’ (Snow mixing ratio), ‘SHDMAX’ (annual max veg fraction), ‘SHDMIN’ (annual min veg fraction), ‘SNOALB’ (annual max snow albedo in fraction), ‘TSLB’ (soil temperature), ‘SMOIS’ (soil moisture), ‘GRDFLX’ (ground heat flux), ‘LAI’ (Leaf area index),’ HGT’ (Terrain Height), ‘TSK’ (surface skin temperature), ‘SWDOWN’ (downward short wave flux at ground surface), ‘GLW’ (downward long wave flux at ground surface), ‘HFX’ (upward heat flux at the surface), ‘QFX’ (upward moisture flux at the surface), ‘LH’ (latent heat flux at the surface), ‘SNOWC’ (flag indicating snow coverage (1 for snow cover)), and so on. The data is in netCDF format with a spatial resolution of 10 km.

The dataset contains all individual glacial storage (unit: km3) over the Qinghai-Tibetan Plateau in 1970s and 2000s. It is sourced from the resultant data of the paper entitled "Consolidating the Randolph Glacier Inventory and the Glacier Inventory of China over the Qinghai-Tibetan Plateau and Investigating Glacier Changes Since the mid-20th Century". The first draft of this paper has been completed and is planned to be submitted to Earth System Science Data journal. The baseline glacier inventories in 1970s and 2000s are the Randolph Glacier Inventory 4.0 dataset, and the Glacier Inventory of China, respectively. Based on the individual glacial boundaries extracted from the above-mentioned two datasets, the grid-based bedrock elevation dataset (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m), and the glacier surface elevation obtained by a slope-dependent method, the individual glacier volumes in 1970s and 2000s are then calculated. In addition, the calculated results of individual glacier volumes in this study have been compared and verified with the existent results of several glacier volumes, relevant remote sensing datasets, and the global glacier thickness dataset based on the average of multiple glacier model outputs (https://www.research-collection.ethz.ch/handle/20.500.11850/315707, doi:10.3929/ethz-b-000315707), and the errors in the calculations have also been quantified. The established dataset in this study is expected to provide the data basis for the future regional water resources estimation and glacier ablation-involved researches. Moreover, the acquisition of the data also provides a new idea for the future glacier storage estimation.

There is a lack of a set of high-resolution precipitation gridded data with long time series in the main basin of the Arctic. This dataset provides daily precipitation in the main basin of the Arctic. The range of data set is from 45 ° N to 76.15 °N. The metadata used includes: the meteorological station data during 1980-2015 obtained from GSOD and the reanalysis data of ERA-interim during 1980-2018. This dataset was obtained by bias correction of ERA-interim data with the improved quantile mapping method, and the background data used for bias correction is the interpolation gridded precipitation, in interpolation process, we not only take into account the effect of elevation, but fully consider the influence of wind on gauge measurements, the gauge used in interpolation all undergo bias adjustment. This dataset performs well both in region scale and cell grid scale, with high accuracy, which providing a set of reliable precipitation gridded data for the hydrological research of the Arctic.

This glacial lake inventory is jointly supported by the International Centre for Integrated Mountain Development (ICIMOD) and United Nationenvironment Programme / Regional Resourc Centre, Asia and The Pacific (UNEP / RRC-AP). 1. The glacial lake inventory refers to remote sensing data such as Landsat 4/5 (MSS, TM1982 / 1985/1984/1999), Landsat 7 (ETM +), IRS-1C, LISS-III (1995IRS-1C), (1997 IRS-1D), etc. It reflects the current status of glacial lakes in the region in 2000. 2. Glacial lake inventory coverage: India-Uttaranchal. 3. The content of the glacial lakeinventory includes: glacial lake inventory, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 4. Projection parameters: Projection: Universal Transverse Mercator (UTM) Ellipsoid: WGS84 Datum: WGS84 Ellipsoid Parameters: a = 6378137.000                                   1 / f = 298.257223563 Northem Hemisphere: Yes MinimumX: 221473.969 MinimumY: 3300590.500 MaximumX: 513943.969 MaximumY: 3488960.500 Zone: 44 For detailed data description, please refer to data files and reports.

The data set contains all single glacial reserves (in KM3) in the Tibetan Plateau of 1970s and 2000s. This data set comes from the result data of the paper entitled "consolidating the Randolph glacier inventory and the glacier inventory of China over the Qinghai titanium plate and investigating glacier changes since the mid-20th century". The first draft of this paper has been completed and is planned to be submitted to earth system science data. The 1970s basic glacier catalog data in the dataset is extracted from Randolph glacier Inventory data set, 2000s basic glacial catalogue is from China's second glacial catalogue data set. Based on the glacial boundary extracted from the two data sets and combined with the grid based bedrock elevation data set (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m) and the glacial table obtained by a slope dependent method Based on the surface elevation data set, the single glacier reserves in the two catalogues are calculated. In addition, the calculation results of single glacier reserves obtained in this study have been compared and verified with the calculation results of partial glacier reserves, relevant remote sensing data sets, and the global glacier thickness data set based on the average of multiple glacier model sets in multiple directions, and the errors in the calculation results have also been quantified. The establishment of the data set is expected to provide the data basis for the future regional water resources estimation and glacier ablation research, and the acquisition of the data also provides a new idea for the future glacier reserves research.

"Coupling and Evolution of Hydrological-Ecological-Economic Processes in Heihe River Basin Governance under the Framework of Water Rights" (91125018) Project Data Convergence-The documents of the west of Taolai River water conservancy team project plan 1. Data summary: The documents of the west of Taolai River water conservancy team project plan 2. Data content: Taolai River water conservancy team project plan, including the project plan of reservoir irrigation and drainage in the west of the river region