Based on Landsat data (kh-9 data in 1976 as auxiliary data), glacial lake data of nearly 40 years (1970s-2018) in the western Nyainqentanglha range were obtained by manual digitization and visual interpretation. The variation characteristics of glacial lake over 0.0036 square kilometers in terms of type, size, elevation and watershed were analyzed in detail. The results show that, between 1976 and 2018, the number of glacial lakes increased by 56% from 192 to 299 and their total area increased by 35% from 6.75 ± 0.13 square kilometers to 9.12 ± 0.13 square kilometers ; the type of glacial lake is changing obviously; the smaller glacial lake is changing faster; the expansion of glacial lake is developing to higher altitude.
LUO Wei ZHANG Guoqing
This data provides the annual lake area of 582 lakes with an area greater than 1 km2 in the enorheic basin of the Qinghai-Tibet Plateau from 1986 to 2019. First, based on JRC and SRTM DEM data, 582 lakes are identified in the area that are larger than 1 km2. All Landsat 5/7/8 remote sensing images covering a lake are used to make annual composite images. NDWI index and Ostu algorithm were used to dynamically segment lakes, and the size of each lake from 1986 to 2019 is then calculated. This study is based on the Landsat satellite remote sensing images, and using Google Earth Engine allowed us to process all Landsat images available to create the most complete annual lake area data set of more than 1 km2 in the Qinghai-Tibet Plateau area; A set of lake area automatic extraction algorithms were developed to calculate of the area of a lake for many years; This data is of great significance for the analysis of lake area dynamics and water balance in the Qinghai-Tibet Plateau region, as well as the study of the climate change of the Qinghai-Tibet Plateau lake.
ZHU Liping PENG Ping
Based on the long-term observation data of the field stations in the alpine network and the overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; through the intensive observation and sample plot and sample point verification in key areas, the inversion of meteorological elements, lake water and water quality, aboveground vegetation biomass, glacier and frozen soil change and other data products are completed; based on the Internet of things, the data products are retrieved Network technology, research and establish meteorological, hydrological, ecological data management platform of multi station networking, to achieve real-time data acquisition and remote control and sharing. The hydrological data set of the surface process and environment observation network in China's alpine regions in 2019 mainly collects the measured hydrological (runoff, water level, water temperature, etc.) data at six stations, including Southeast Tibet station, Zhufeng station, Yulong Snow Mountain station, Namco station, Ali station and Tianshan station. Southeast Tibet station: flow data, including 4 times of using M9 to measure flow in 2019, including average velocity, flow and maximum water depth; relative water level data is measured by hobo pressure water level meter, including daily average relative water level and water temperature data in 2019. Namco station: discharge data, including the data measured by domestic ls-1206b hand-held current meter for 4 times in 2019, including river width and flow data. The water level data is measured by hobo pressure water level meter, including the water pressure, water temperature and electricity of the original 1 hour in 2019. The relative water level can be calculated by water pressure; Everest station: rongbuhe river discharge, including river width and discharge data measured by domestic ls-1206b hand-held current meter 13 times from June to September 2019; Ali station: flow data: including 22 times of irregular measurement data by river anchor M9 in 2019, and relative water level data measured by hobo pressure water level meter, including hourly water level and water temperature data of the whole year in 2019; Tianshan station: water level data: including daily average water level of 3 points in 2019 Yulong Xueshan station: including mujiaqiao flow data from January to October in 2019
ZHU Liping PENG Ping
The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2019. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2019 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2018, and can be used for quantitative estimation of water resource.
LI Jia LI Jianjiang LI Xin LIU Shaomin
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
ZHU Liping PENG Ping
1) data content (including elements and significance): transparency data of 152 lakes greater than 50 km2 in the Qinghai Tibet Plateau in 2000-2019 (Saybolt disk value) 2) Data source and processing method: the data inversion is based on the high-precision transparency inversion model and modis-modocga product data. The remote sensing data is converted into the remote sensing reflectivity R ﹐ RS inversion transparency value, and the annual mean value is calculated. The average value of 3 × 3 pixels in the geometric center of the lake represents the lake. For the case where the geometric center is located outside the lake, the open water area of the lake is taken for calculation. 3) Data quality description: annual average value of lakes. 4) Results and prospects of data application: climate change may change Lake transparency, while the change of Lake transparency will play a feedback role in regional climate change. In this study, the inversion of Lake transparency in the Qinghai Tibet Plateau provides basic data for the energy exchange of the lake air interface.
ZHU Liping PENG Ping
This data includes the daily average water temperature data at different depths of Nam Co Lake in Tibet which is obtained through field monitoring. The data is continuously recorded by deploying the water quality multi-parameter sonde and temperature thermistors in the water with the resolution of 10 minutes and 2 hours, respectively, and the daily average water temperature is calculated based on the original observed data. The instruments and methods used are very mature and data processing is strictly controlled to ensure the authenticity and reliability of the data; the data has been used in the basic research of physical limnology such as the study of water thermal stratification, the study of lake-air heat balance, etc., and to validate the lake water temperature data derived from remote sensing and different lake models studies. The data can be used in physical limnology, hydrology, lake-air interaction, remote sensing data assimilation verification and lake model research.
In April 2014 and may 2016, 21 Lakes (7 non thermal lakes and 14 thermal lakes) were collected in the source area of the Yellow River (along the Yellow River) respectively. The abundance of hydrogen and oxygen allogens was measured by Delta V advantage dual inlet / hdevice system in inno tech Alberta laboratory in Victoria, Canada. The isotope abundance was expressed in the form of δ (‰) (relative to the average seawater abundance in Vienna) ）Test error: δ 18O: 0.1 ‰, δ D: 1 ‰. The data also includes Lake area and lake basin area extracted from Landsat 2017 image data in Google Earth engine.
Runoff is formed by atmospheric precipitation and flows into rivers, lakes or oceans through different paths in the basin. It is also used to refer to the amount of water passing through a certain section of the river in a certain period of time, i.e. runoff. Runoff data plays an important role in the study of hydrology and water resources, which affects the development of social economy in central Adam. This data is the flow of five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan), which comes from the hydrometeorological bureaus of Central Asian countries. The time scale is the average annual data of 2015. This data provides basic data for the project, which is convenient to analyze the situation of eco hydrological water resources in Central Asia, and provides data support for project data analysis.
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)
Shang Huaming Tajikistan hydrometeorological office
Runoff is formed by atmospheric precipitation and flows into rivers, lakes or oceans through different paths in the basin. It is also used to refer to the amount of water passing through a certain section of the river in a certain period of time, i.e. runoff. Runoff data plays an important role in the study of hydrology and water resources, which affects the social and economic development of Adam land. This data is the flow of five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan), which comes from the hydrometeorological bureaus of Central Asian countries. The time scale is the average annual data of 2015. This data provides basic data for the project, which is convenient to analyze the situation of eco hydrological water resources in Central Asia, and provides data support for project data analysis.
The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu ZHANG Guoqing
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.
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2018, and provided a long and dense lake observations between the 1970s and 2018. We found that the number of lakes, with area larger than 1 km2, has increased to ~1400 in 2018 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
This data provides 38 key parameters in the land surface model CLM4.0, involving hydrology, soil and vegetation. The detailed information about key parameters can be found in the attached documents. This data has about 1 degree resolution (f09 grid) globally, and also provides high resolution product (0.1 degree) in the Heihe region. Three objectives: evapotranspiration (ET), volumetric soil moisture (VSM) and freeze/thaw (FT) have been used to calibrate the 38 parameters. Each objective was used individually to calibrate parameters for each grid. The ET, VSM and FT datasets were provided by other groups in the save project. According to our assessment, the error of ET can be reduced 23%, VSM 52% and FT 34%. But since this is single objective optimization, the three improvements cannot be obtained simultaneously. The distribution of optimal parameters can be used for improving the structure of land surface model. The optimal parameters can be used directly by just replacing the corresponding number in the CLM source code.
The basic data set of water resources research of Southeast Asian countries and Lancang Mekong basin (1901-2010) collected and sorted out the main hydrometeorological data of Southeast Asian countries and Lancang Mekong basin, including precipitation, average temperature, maximum temperature, minimum temperature, water vapor pressure, etc. the data came from CRU TS v. 4.03 (clinical research unit time series version 4.03), which is widely used in the whole world The format is NC, the time resolution is month by month, and the time length is from January 1901 to December 2018. Hydrological data includes surface runoff and underground runoff simulated by the hydrological model. The data comes from GLDAS (Global Land Data Assimilation System). The data format is NC, the time resolution is month by month, and the time length is from January 1979 to February 2019.
Climatic Research Unit CRU Global Land Data Assimilation System GLDAS
The Tibetan Plateau, featuring the most extensive lake distribution in China, has seen rapid expansion of most its lakes. These lakes are important nodes for regional water and energy cycles, and highly sensitive to climate change. It is therefore imperative to unravel lake water storage changes under climate variation and change to improve the understanding of mechanisms of the interactions between regional hydrology and climate and their changes. This developed data set provides water level, hypsometric curves, and lake storage changes for 52 large lakes across the TP from 2000 to 2017, comprising traditional altimetry water levels and a unique source of information termed as the optical water levels derived from tremendous amounts of Landsat archives using Google Earth Engine. Field experiments agree with the theoritical analysis that the uncertainty of optical water level is 0.1 - 0.2 m, comparable with that of altimetry water level. The uncertainty of altimetry water level is represented by the standard deviation of water levels obtained from effective footprints of the same cycle, which is included in the dataset. This dataset is applicable in water resource and security management, lake basin hydrological analysis, water balance analysis and the like. For instance, it has great potential in monitoring lake overflow flood.
LI Xingdong LONG Di HUANG Qi HAN Pengfei ZHAO Fanyu WADA Yoshihide
HOBO water temperature loggers (U22-001, Onset Corp., USA) were used to monitor changes in water temperature with an accuracy of ±0.2 oC. Two water temperature profiles were installed in Paiku Co’s southern (0-42 m in depth) and northern (0-72 m in depth) basins (Fig. 1). In the southern basin, water temperature was monitored at the depths of 0.4 m, 5m, 10 m, 15 m, 20 m, 30 m and 40 m. In the northern basin, water temperature was monitored at the depths of 0.4 m, 10 m, 20 m, 40 m, 50 m, 60 m and 70 m. To investigate local hydro-meteorology at Paiku Co, air temperature and specific humidity over the lake were monitored since June 2015 by using HOBO air temperature and humidity loggers (U12-012, Onset Corp., USA). The logger was installed in an outcrop ~2 m above the lake surface at the north part of the lake (Fig. 2). Lake evaporation was calculated using the energy budget (Bowen-ratio) method。
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).