The data set includes data on lakes with areas greater than 200 square kilometers in Tibet from 1988 to 2016. 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 has 5 fields. Field 1: Year Field 2: Lake Name Field 3: Lake elevation Unit: meter Field 4: Lake area Unit: square kilometer Field 5: Lake Type
This is the groundwater level observation data set of Selincuo Lake. It can be used in Climatology, Environmental Change, Hydrologic Process in cold regions and other disciplinary areas. The data is observed from June 20, 2017 to August 18, 2017. It is measured by automatic water gauge and a piece of data is recorded every 60 minutes. The data includes the water pressure and water temperature of the groundwater level observation point on the west bank of Selincuo Lake. The original data is precise, with the pressure accurate to 0.001kP and the water temperature 0.001℃. The original data forms a continuous time series after quality control. And the daily mean index data is obtained through calculation. The data is stored as an excel file.
The dataset is clipped from the Chinese lake map by the vector boundary of the Qinghai-Tibet Plateau. The lake database is obtained by on-the-spot investigation, remote-sensing interpretation, which consist the area over 10-square-kilometer lakes. The lake code is based on lake classification. The Chinese Lake Code currently uses 8 digits. The first and second digits indicate the province where the lake is located; The third, fourth and fifth digits represent the sequence of the lake in the province; The sixth digit is on behalf of the lake surface area; The seventh number means the amount of water in the lake, that is, the volume of the lake.
This data is the daily runoff data of akjar hydrological station in Tajikistan in 2018. The data is from the hydrological and Meteorological Bureau of Tajikistan. The data are processed according to the hydrological observation specifications and quality control process of the country. The data can be used for scientific research and water conservancy engineering services such as water resources assessment in Central Asia mountainous areas. (name of hydrological station: akjar; river: Sir Darya; location: 40.666667 ° n / 70.733333 ° E; altitude: 367M; data period: January 1, 2018 to December 31, 2018; data element: daily runoff; unit: m3 / s)
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.
In this study, major ions in water samples from the Lake Balkhash catchment were analyzed using an integration of mathematical statistics, Piper three-line map, Gibbs model and principal component analysis (PCA). Water types and main mechanisms controlling the hyrdochemistry presented a visible spatial heterogeneity. The chemical composition of lake waters was dominant with SO4-Na and Cl-Na, whereas river waters were classified as HCO3-Ca. The chemical composition downward the Ili River waters evolved from bicarbonate to sulfate and chlorination type. Gibbs model suggested that the main mechanisms control the lake water chemistry were evaporation-crystallization processes and major ions in river water were affected by the processes of rock-weathering and evaporation. The controlling factors in water chemistry changed from the upstream to downstream of the Ili River, which may be contributed to the lager impacts of precipitation and discharge of snow melting water on the upper waters, whereas more influence of evaporation on the lower waters. Furthermore, PCA analysis showed that human activities also play an important role in the chemical composition of lake water, middle and lower reaches of Ili River and other rivers.
The data set is the multi parameter data of water samples collected from the Lake Aral Sea basin in 2019, which is used to obtain the basic physical and chemical index data of the lake and prepare for the subsequent modern observation and research of the lake. The data observation time is July 26, 2019. The measuring instrument is YSI EXO2 water quality multi parameter measuring instrument. Before each measurement, the instrument is calibrated according to the altitude of the lake and the local air pressure. The measurement interval is set as 1s, and the delivery speed is slow, so as to ensure the high continuity of data acquisition. The original data obtained includes the measurement data exposed in the air above the water surface, which is eliminated in the later processing. The data is stored in Excel file.
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.