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), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
The water level observation data set of lakes on the Tibetan Plateau contains the daily variations of water levels for three lakes: Zhari Namco, Bamco and Dawaco. The lake water level was obtained by a HOBO water level gauge (U20-001-01) installed on the lakeshore, then corrected using the barometer installed on the shore or pressure data of nearby weather stations, and then the real water level changes were obtained. The accuracy was less than 0.5 cm. The items of this data set are as follows: Daily variation data of water level in Zhari Namco from 2009 to 2014; Daily variation data of water level in Bamco from 2013 to 2014; Daily variation data of water level in Dawaco from 2013 to 2014. Water level, unit: m.
LEI Yanbin
This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include 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 in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
This data includes bacterial 16S ribosomal RNA gene sequence data from 25 lakes in the middle of the Qinghai Tibet Plateau. The sample was collected from July to August 2015, and the surface water was sampled three times with a 2.5 liter sampler. The samples were immediately taken back to the Ecological Laboratory of the Beijing Qinghai Tibet Plateau Research Institute, and the salinity gradient of the salt lake was 0.14~118.07 g/L. This data is the result of amplification sequencing. Concentrate the lake water to 0.22 at 0.6 atm filtration pressure μ The 16S rRNA gene fragment amplification primers were 515F (5 '- GTGCCAAGCCGCGGTAA-3') and 909r (5 '- GGACTACHVGGGTWTCTAAT-3'). The Illumina MiSeq PE250 sequencer was used for end-to-end sequencing. The original data was analyzed by Mothur software. The sequence was compared with the Silva128 database and divided into operation classification units (OTUs) with 97% homology. This data can be used to analyze the microbial diversity of lakes in the Qinghai Tibet Plateau.
KONG Weidong
The data set includes the observation data of river water level and velocity at No. 6 point in the dense observation of runoff in the middle reaches of Heihe River from January 1, 2014 to December 31, 2014. The observation point is located in Gaoya National Hydrological Station, zhaojiatunzhuang, Ganzhou District, Zhangye City, Gansu Province. The riverbed is sandy gravel with stable section. The longitude and latitude of the observation point are n39 ° 08'06.35 ", E100 ° 25'58.23", 1420 m above sea level, and 50 m wide river channel. Hobo pressure water level gauge is used for water level observation, with acquisition frequency of 60 minutes. Data description includes the following two parts: Water level observation, 60 minutes in unit (cm) in 2014; Data covers the period of January 1, 2014 solstice December 31, 2014; Flow observation, unit (m3); According to the monitoring flow of different water levels, the flow curve of water levels was obtained, and the change process of runoff was obtained by observing the process of water levels.The missing data are uniformly represented by the string -6999. For information of hydrometeorological network or station, please refer to Li et al.(2013), and for observation data processing, please refer to He et al.(2016).
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
This dataset contains the annual variation of runoff from the major hydrological stations in the Yarlung Zangbo River (annual average runoff volume, annual extremum ratio, coefficient of variation, etc.). It can be used to study the hydrological characteristics of the Yarlung Zangbo River. The original data are the national hydrological station data, and the quality requirements are the same as the national standards. Spatial Coverage: 4 hydrological stations in the main streams of the Yarlung Zangbo River basin, which are Lazi, Nugesha, Yangcun and Nuxia. This data sheet has five fields. Field 1: Station Name Field 2: Annual average runoff volume Field 3: Annual Extreme Ratio Field 4: Coefficient of variation Field 5: Data Series Length
YAO Zhijun
Known as the "Asian water tower", the Qinghai Tibet Plateau is the source of many rivers in Southeast Asia. As an important and easily accessible water resource, the runoff provided by it supports the production and life of billions of people around it and the diversity of the ecosystem. The glacier runoff data set in the five river source areas of the Qinghai Tibet Plateau covers the period from 2005 to 2010, with a time resolution of every five years. It covers the source areas of the five major rivers in the Qinghai Tibet Plateau (the source of the Yellow River, the source of the Yangtze River, the source of the Lancang River, the source of the Nujiang River, and the source of the Yarlung Zangbo River). The spatial resolution is 1km. Based on multi-source remote sensing, simulation, statistics, and measured data, GIS methods and ecological economics methods are used, The value of water resources service in the cryosphere in the source area of the river and river is quantified, and all its data are subject to quality control.
WANG Shijin
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).
WANG Lei
This product provides the data set of key variables of the water cycle of Arctic rivers (North America：Mackenzie, Eurasia：Lena) from 1998 to 2017, including 7 variables: precipitation, evapotranspiration, surface runoff, underground runoff, glacier runoff, snow water equivalent and three-layer soil humidity, which are numerically simulated by the land surface model vic-cas developed by the project team. The spatial resolution of the data set is 50km and the temporal resolution is month. This data set can be used to analyze the change of water balance in the Arctic River Basin under climate change, and can also be used to compare and verify remote sensing data products and the simulations of other models.
ZHAO Qiudong, WANG Ninglian, WU Yuwei
The data set includes the observed and simulated runoff into the sea and the composition of each runoff component (total runoff, glacier runoff, snowmelt runoff, rainfall runoff) of two large rivers in the Arctic (North America: Mackenzie, Eurasia: Lena), with a time resolution of months. The data is a vic-cas model driven by the meteorological driving field data produced by the project team. The observed runoff and remote sensing snow data are used for correction. The Nash efficiency coefficient of runoff simulation is more than 0.85, and the model can also better simulate the spatial distribution and intra/inter annual changes of snow cover. The data can be used to analyze the runoff compositions and causes of long-term runoff change, and deepen the understanding of the runoff changes of Arctic rivers.
ZHAO Qiudong, WU Yuwei
The basic data of hydrometeorology, land use and DEM were collected through the National Meteorological Information Center, the hydrological Yearbook, the China Statistical Yearbook and the Institute of geographical science and resources of the Chinese Academy of Sciences. The distributed time-varying gain hydrological model (DTVGM) with independent intellectual property rights is adopted for modeling, and the Qinghai Tibet Plateau is divided into 10937 sub basins with a threshold of 100 square kilometers. The daily flow data of 14 flow stations in Heihe River, Yarlung Zangbo River, Yangtze River source, Yellow River source, Yalong River, Minjiang River and Lancang River Basin were selected to draft and verify the model. The daily scale Naxi efficiency coefficient is above 0.7 and the correlation coefficient is above 0.8. The model simulates the water cycle process from 1998 to 2017, and gives the spatial and temporal distribution of 0.01 degree daily scale runoff in the whole Qinghai Tibet Plateau.
YE Aizhong
This product provides the data set of key variables of the water cycle of major Arctic rivers (North America: Mackenzie, Eurasia: Lena from 1971 to 2017, including 7 variables: precipitation, evapotranspiration, surface runoff, underground runoff, glacier runoff, snow water equivalent and three-layer soil humidity, which are numerically simulated by the land surface model vic-cas developed by the project team. The spatial resolution of the data set is 0.1degree and the temporal resolution is month. This data set can be used to analyze the change of water balance in the Arctic River Basin under long-term climate change, and can also be used to compare and verify remote sensing data products and the simulation results of other models.
ZHAO Qiudong, WANG Ninglian, WU Yuwei
The Qinghai Tibet Plateau is known as the "Asian water tower", and its runoff, as an important and easily accessible water resource, supports the production and life of billions of people around, and supports the diversity of ecosystems. Accurately estimating the runoff of the Qinghai Tibet Plateau and revealing the variation law of runoff are conducive to water resources management and disaster risk avoidance in the plateau and its surrounding areas. The glacier runoff segmentation data set covers the five river source areas of the Qinghai Tibet Plateau from 1971 to 2015, with a time resolution of year by year, covering the five river source areas of the Qinghai Tibet Plateau (the source of the Yellow River, the source of the Yangtze River, the source of the Lancang River, the source of the Nujiang River, and the source of the Yarlung Zangbo River), and the spatial resolution is the watershed. Based on multi-source remote sensing and measured data, it is simulated using the distributed hydrological model vic-cas coupled with the glacier module, The simulation results are verified with the measured data of the station, and all the data are subject to quality control.
WANG Shijin
The long-term sequence data set of lake areas on the Tibetan Plateau contains area data of 364 lakes with areas greater than 10 square kilometers from 1970s to 2013. Based on Landsat images, Landsat data in October are mainly used, and one data is taken every three years to reduce seasonal variation and make the available data reach the maximum. The data set is extracted by the NDWI Water Index, and each lake undergoes manual visual inspection and edition. The data set can be used to study lake change, lake water balance and climate change on the Tibetan Plateau. Data type: Vector data. Projection: WGS84.
ZHANG Guoqing
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
PAN Xiaoduo
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,
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
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.
CHEN Yongzhe, FENG Xiaoming, FU Bojie
The data set is the seasonal hydrological observation data of the Yellow River from the hydrological station of the Qinghai Tibet Plateau. There are two hydrological stations: 1. Longmen hydrological station in the middle reaches of the Yellow River, which is the weekly hydrological data in 2013, including water temperature (T), runoff (QW), physical erosion rate (per) and pH. 2. Tangnaihai hydrological station of the Yellow River is monthly data from July 2012 to June 2014, including runoff (QW), sediment (salt), pH and EC. The data set was commissioned to be observed by the staff of the hydrological station of the Yellow River Water Conservancy Commission to provide basic hydrological data for the study of hydrology, hydrochemistry and hydrosphere cycle under the background of Qinghai Tibet Plateau uplift.
JIN Zhangdong, ZHAO Zhiqi
Agricultural Water Resources Supply, Demand and Development Data Set in the Five Central Asia Countries from 1980 to 2015 are derived from the Global Land Surface Data Assimilation System, including precipitation, evapotranspiration and runoff data output based on Noah, Mosaic and VIC models, respectively. The data set has high temporal and spatial resolution and good longitude. It is widely used in global and regional scale research. The results of precipitation, evapotranspiration and runoff simulation of Noah, Mosaic and VIC models are consistent in spatial distribution. It can be used to analyze the spatial and temporal variation of water resources in Central Asia, to analyze the supply and demand relationship of agricultural water resources and to evaluate the potential of water resources development.
ZHANG Yongyong
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
