The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
The data include the datasets of temporal changes in water level, water storage and area of the Aral sea (1911−2017), the inter-decadal change of ecosystem structure (NDVI—Normalized Difference Vegetation Index) of the Aral sea (1977−2017), and dust intensity (EDI—Enhanced Dust Index) in the Aral sea (2000−2018). Using data fusion technology in the construction of a lake basin terrain, terrain based on remote sensing monitoring and field investigation, on the basis of the analysis of the Aral sea terrain data, generalized analyses the water - area - the changes of water content, the formation of water - water - area of temporal variation data set, can clearly reflect the Aral sea water change process and the present situation, provide basic data for the Aral sea environmental change research. The NDVI was used to reflect the vegetation ecology in the receding area. Landsat satellite data, with a spatial resolution of 30 m, was used for NDVI analysis in 1977, 1987, 1997, 2007, and 2017. Based on ENVI and GIS software, remote sensing image fusion, index calculation, and water extraction were used to determine the lake surface and lakeshore line of the Aral sea. The lakeside line in the south of the Aral sea is taken as the starting point, and it extends for 3 km to the receding area. The variation characteristics of vegetation NDVI in the lakeside zone within 0-3 km are obtained to reflect the structural changes of the lakeside ecosystem. EDI was extracted from MODIS image data. This index is introduced into the dust optical density to enhance the dust information to form the enhanced dust index. Based on remote sensing monitoring, the use of EDI, established the Aral sea area-EDI index curve, the curve as the construction of the Aral sea dry lake bed dust release and meteorological factors, quantitative relationship laid the foundation of soil physical and chemical properties, in order to determine the control of sand/salt dust in the reasonable area of the lake.
The data set of supply of agricultural water resources in Central Asian adopts the water balance method to calculate the precipitation and runoff depth on grid scale in five central Asian countries, respectively, and estimate the agricultural water resources supply in five central Asian countries. The data source is mainly the precipitation and runoff data products of NOAH model in GLDAS. Each original raster data of 0.25 ° is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes three time periods of 2000s (2001-2005), 2010s (2006-2010) and 2015s (2011-2015) with a spatial resolution of 0.5°*0.5°; The data of demand of agricultural water resources in Central Asia include irrigation water requirement of cotton and winter wheat in 2006, 2010 and 2016 over Central Asia. This was calculated by the equation of irrigation water requirement presented by FAO. It is expected to provide basic data support for distributed water cycle simulation, water supply and demand, development and utilization analysis in five central Asian countries.
1) It is also called evapotranspiration, which is the sum of leaf emission (transpiration) of plants on the ground and soil evaporation between plants. That is, the water demand of crops in irrigation project. This data set is the monthly data of evapotranspiration in Central Asia; 2) MODIS data, which is calculated by energy balance method; 3) station disk evaporation verification; 4) evapotranspiration is the total water vapor flux transported to the atmosphere by vegetation and the ground as a whole, which mainly includes vegetation transpiration, soil water evaporation and the evaporation of intercepted water or dew. As an important part of energy balance and water cycle, evapotranspiration is not only a shadow The growth, development and yield of ring plants also affect the general circulation of the atmosphere and play a role in regulating the climate
HBV hydrological model is one of the representatives of semi empirical hydrological model, which is widely used in watershed runoff simulation. Based on HBV model, the daily resolution runoff of six sub basins in the upper Indus River is simulated: 1) the daily resolution runoff data of 1980-2013 is calculated through the latest driving data; 2) HBV semi empirical hydrological model is more suitable for the simulation of alpine cold region ; 3) it is convenient to compare with the measured runoff data, so as to evaluate the applicability of the model and the reliability of the simulation results, make reasonable hydrological forecast in the downstream and prevent hydrological disasters. It plays an important role in the study of hydrological laws and practical production problems.
Data from EM-DAT. EM-DAT is a global database on natural and technological disasters, containing essential core data on the occurrence and effects of more than 21,000 disasters in the world, from 1900 to present. EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université catholique de Louvain located in Brussels, Belgium.The main objective of the database is to serve the purposes of humanitarian action at national and international levels. The initiative aims to rationalise decision making for disaster preparedness, as well as provide an objective base for vulnerability assessment and priority setting.The database is made up of information from various sources, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies. Priority is given to data from UN agencies, governments, and the International Federation of Red Cross and Red Crescent Societies. This prioritization is not only a reflection of the quality or value of the data, it also reflects the fact that most reporting sources do not cover all disasters or have political limitations that could affect the figures. The entries are constantly reviewed for inconsistencies, redundancy, and incompleteness. CRED consolidates and updates data on a daily basis. A further check is made at monthly intervals, and revisions are made at the end of each calendar year.
1) area data of 317 lakes larger than 10 km2 in 1976, 1990, 2000, 2005 and 2013 were obtained based on multi temporal Landsat images; 2) Combining SRTM DEM and Landsat images, the data of lake water volume change in 1976-1990, 1990-2000, 2000-2005 and 2005-2013 were obtained; 3) The accuracy of Lake area is controlled in one pixel, and the accuracy of water volume change is about 5%; 4) This data has been applied to the study of recent changes in lake water volume in the Qinghai Tibet Plateau, and the results have been published in remote sensing of environment. In other future studies, this data can also be used as basic data, as well as in the analysis of changes in ecological environment, climate change, Lake water quality, etc
It includes monthly data of precipitation, evaporation, water reserve change and soil water change of Tarim River. Precipitation data comes from ECMWF. Evaporation data is calculated by energy model based on Penman formula, water reserve data is retrieved by grace gravity satellite data, GLDAS data is obtained by land surface process model simulation of Noah in the United States, and NDVI data is from MODIS data products. The resolution of precipitation and evaporation is 0.5 ° * 0.5 °, and the resolution of water storage and soil water change data is 1 ° * 1 °. The data provide reference for water resource management and decision-making. Vegetation data can provide basic data for ecological change assessment.
The water resources utilization data of the Xier river basin is of statistical type, mainly including the data of the basin and sub basin stations, with the time resolution of every five years, the time range of 1980-2015, the data format of *. Xlsx, and the regional scope mainly involving the Xier River Basin; These data are mainly obtained by searching, consulting and translating a large number of data of the Central Asia SYR River Basin, mainly including the data published by Kazakhstan, Uzbekistan and Kyrgyzstan in Central Asia (downloaded from the website) and the purchase of statistical yearbooks and related professional books directly from Central Asia; The researchers went to the river basin and verified the relevant data.
1)Data content (including elements and meanings): surface meteorological observation data product of TP in 1979-2016 2)Data source and processing method: In .tif format, can be opened and analysed in arcgis. 3)Data quality description: daily resolution 4)Data application results and prospects: Based on the long-term observation data of the 17 stations of HORN, establish a series of data series of meteorological, hydrological and ecological elements in the Pan-Earth region; Strengthen observation and sample and sample verification, and complete the inversion of meteorological elements, lake water quantity and water quality, aboveground vegetation biomass, glacier and frozen soil changes; based on Internet of Things technology, develop multi-station networked meteorological, hydrological, The ecological data management platform realizes real-time acquisition and remote control and sharing of networked data.