The stable oxygen isotope ratio (δ 18O) in precipitation is a comprehensive tracer of global atmospheric processes. Since the 1990s, efforts have been made to study the isotopic composition of precipitation at more than 20 stations located on the TP of the Tibetan Plateau, which are located at the air mass intersection between westerlies and monsoons. In this paper, we establish a database of monthly precipitation δ 18O over the Tibetan Plateau and use different models to evaluate the climate control of precipitation δ 18O over TP. The spatiotemporal pattern of precipitation δ 18O and its relationship with temperature and precipitation reveal three different domains, which are respectively related to westerly wind (North TP), Indian monsoon (South TP) and their transition.
GAO Jing
These datasets fill the data gap between GRACE and GRACE-FO, they contain CSR RL06 Mascon and JPL RL06 Mascon. They take China as the study area, and the dataset includes "Decimal_time”, "lat”, "lon”, "time”, "time_bounds”, "TWSA_REC" and "Uncertainty" 7 parameters in total. Among them, "Decimal_time” corresponds to decimal time. There are 191 months from April 2002 to December 2019 (163 months for GRACE data, 17 months for GRACE-FO data, and 11 months for the gap between GRACE and GRACE-FO. We have not filled the missing data of individual months between GRACE or GRACE-FO data). "lat" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the cumulative day of the data from January 1, 2002. And "time_bounds" corresponding to the cumulative day at the start date and end date of each month. “TWSA_REC" represents the monthly terrestrial water storage anomalies from April 2002 to December 2019 in China; "Uncertainty" is the uncertainty between the data and CSR RL06 Mascon products. We use GRACE satellite data from CSR GRACE/GRACE-FO RL06 Mascon solutions (version 02), China Gauge-based Daily Precipitation Analysis (CGDPA, version 1.0) data, and CN05.1 temperature dataset. The precipitation reconstruction model was established, and the seasonal and trend terms of CSR RL06 Mascon products were considered to obtain the dataset of terrestrial water storage anomalies in China. The data quality is good as a whole, and the uncertainty of most regions in China is within 5cm. This dataset complements the nearly one-year data gap between GRACE and GRACE-FO satellites, and provides a full time series for long-term land water storage change analysis in China. As the CSR RL06 Mascon product, the average value between 2004.0000 and 2009.999 is deducted from this dataset. Therefore, the 164-174 months (i.e., July 2017 to May 2018) of this dataset can be directly extracted as the estimation of terrestrial water storage anomalies during the gap period. The reconstruction method for the gap of JPL RL06 Mascon is consistent with that of CSR RL06 Mascon.
ZHONG Yulong, FENG Wei, ZHONG Min, MING Zutao
The main idea of water resource estimation is to build a machine learning model using runoff coefficients and runoff influencing factors (including climate, topography, land use, soil, etc.), and then calculate the runoff coefficients based on the runoff depth and precipitation data estimated by the model. First, based on global public data, we build various machine learning models for runoff depth and topography, climate, soil, and land use, evaluate the simulation accuracy and validity of different models, and select the optimal model for runoff depth estimation. Finally, the optimal model is used to estimate and generate the runoff depth distribution in the Belt and Road region, and the runoff coefficient distribution is calculated based on the precipitation distribution data in 2015.
The restrictive classification/zonation of water resource carrying capacity of the countries along the "Belt and Road" is one of the important achievements in water resource carrying capacity evaluation. Restrictive classification/zonation method of water resource carrying capacity: Based on multi-source data such as remote sensing, statistics and research, combined with water resource availability and per capita comprehensive water use evaluation study, to quantitatively evaluate the water resource carrying capacity of countries and regions along the Green Silk Road from the perspective of water and soil balance and human water balance according to the relationship between resource supply and demand balance, to study the threshold system of water resource carrying capacity evaluation, to evaluate the water resource carrying capacity of countries and regions along the Green Silk Road from the perspective of water and soil balance and human water balance. The restrictive classification/zonation of water resource carrying capacity provides a basis for water security early warning, water resource carrying capacity enhancement and control strategies.
This data set includes the monthly average actual evapotranspiration of the Tibet Plateau from 2001 to 2018. The data set is based on the satellite remote sensing data (MODIS) and reanalysis meteorological data (CMFD), and is calculated by the surface energy balance system model (SEBS). In the process of calculating the turbulent flux, the sub-grid scale topography drag parameterization scheme is introduced to improve the simulation of sensible and latent heat fluxes. In addition, the evapotranspiration of the model is verified by the observation data of six turbulence flux stations on the Tibetan Plateau, which shows high accuracy. The data set can be used to study the characteristics of land-atmosphere interaction and the water cycle in the Tibetan Plateau.
HAN Cunbo, MA Yaoming, WANG Binbin, ZHONG Lei, MA Weiqiang*, CHEN Xuelong, SU Zhongbo
"One belt, one road" delineation of the key Asian regional watershed boundaries is based on the following principles: Principle 1: along the Silk Road Principle 2: located in arid and semi-arid areas Principle 3: high water risk Principle 4: watershed integrity 1. Division basis of arid area Food and Agriculture Organization of the United Nations. FAO GEONETWORK. Global map of aridity - 10 arc minutes (GeoLayer). (Latest update: 04 Jun 2015) Accessed (6 Mar 2018). URI: http://data.fao.org/ref/221072ae-2090-48a1-be6f-5a88f061431a.html?version=1.0 2. Water resources risk data: Gassert, F., M. Landis, M. Luck, P. Reig, and T. Shiao. 2014. Aqueduct Global Maps 2.1. Working Paper. Washington, DC: World Resources Institute. 3. Poverty index data: Elvidge C D, Sutton P C, Ghosh T, et al. A global poverty map derived from satellite data. Computers & Geosciences, 2009, 35(8): 1652-1660. https://www.ngdc.noaa.gov/eog/dmsp/download_ poverty.html 4. Basic basin boundary data: (1) Watershed boundaries were derived from HydroSHEDS drainage basins data (Lehner and Grill 2013) based on a grid resolution of 15 arc-seconds (approximately 500 m at the equator), which can be free download via https://hydrosheds.cr.usgs.gov/hydro.php (2) AQUASTAT Hydrological basins: This dataset is developed as part of a GIS-based information system on water resources. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. The map is also available in the SOLAW Report 15: “Sustainable options for addressing land and water problems – A problem tree and case studies”. Data can be free download via http://www.fao.org/nr/water/aquamaps/ (3) HydroBASINS: https://www.hydrosheds.org/downloads 5. The GloRiC provides a database of river types and sub-classifications for all river reaches globally. https://www.hydrosheds.org/page/gloric 6. HydroATLAS offers a global compendium of hydro-environmental sub-basin and river reach characteristics at 15 arc-second resolution. https://www.hydrosheds.org/page/hydroatlas It covers an area of 1469400 square kilometers, including the following areas: Nujiang River Basin, Dead Sea basin, Sistan River Basin, Yellow River Basin, Jordan Syria eastern basin, Indus River Basin, Iran inland flow area, urmiya Lake Basin, Shiyang River Basin, hallelud mulgarb River Basin, Lianghe River Basin, Shule River Basin, Heihe River Basin, issekkor Lake Basin, Tata River Basin Limu River Basin, Turpan Hami basin, Ebinur Lake Basin, Junggar basin, Amu Darya River Basin, Manas River Basin, ulungu River Basin, Emin River Basin, Chu River Talas River Basin, Xil River Basin, Ili River Basin, Caspian Sea basin, Lancang River Basin, Yangtze River Basin, Qinghai lake water system, Eastern Qaidam Basin, western Qaidam Basin and Qiangtang plateau District, Yarlung Zangbo River Basin
RAN Youhua, WANG Lei, ZENG Tian, GE Chunmei, LI Hu
Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
MA Ziqiang
This data set is a multi-scale data set of water balance elements in the source area of the Yellow River and the Heihe River Basin of Qilian Mountains, which can provide basis and reference for water resources management and water balance analysis. The data are based on the simulation results of SWAT model, and then analyzed and counted by ArcGIS and Excel software. The water balance elements include precipitation, snowfall, evapotranspiration, groundwater, soil water and runoff. The time scale includes annual scale and monthly scale, while the spatial scale includes watershed scale, river reach scale, sub basin scale, altitude zone scale and vegetation type scale. The data has been applied to water resources evaluation, benefit evaluation and operation guidance of artificial precipitation enhancement.
ZOU Songbing
1. The data content is the monthly groundwater level data measured between the tail of chengdina River, Kuqa Weigan River and Kashgar river of Tarim River, which is required to be the water level data of 30 wells, but the number of wells in this data reaches 44; 2. The data is translated into CSV through hobo interpretation, and the single bit time-lapse value is found through MATLAB, and then extracted and calculated through Excel screening, that is, through the interpretation of original data, through the communication Out of date and daily data, calculated monthly data; 3. Data is measured data, 2 decimal places are reserved, unit is meter, data is accurate; 4. Data can be applied to scientific research and develop groundwater level data for local health.
CHEN Yaning, HAO Xingming
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.
WAN Chengwei
This dataset contains daily land surface evapotranspiration products of 2019 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products and China Meteorological Forcing Dataset (He Jie, Yang Kun. China Meteorological Forcing Dataset. Cold and Arid Regions Science Data Center at Lanzhou, 2011. doi:10.3972/westdc.002.2014.db).
YAO Yunjun, LIU Shaomin, SHANG Ke
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,
The data set of hydrogeological elements in the typical frozen soil area of Qilian Mountain mainly includes groundwater type, water richness (single water inflow or single spring flow), main rivers and tributaries, spring water (falling springs, spring groups, large springs, Mineral spring distribution), borehole (pressure water borehole, submerged borehole, gravity flow borehole distribution), fault zone (compressive fracture, tensile fracture), angle unconformity boundary, parallel unconformity boundary, west branch of upper Heihe River The boundary of the watershed, the seasonal frozen soil area and the permafrost distinguish the boundary, the distribution of modern glaciers and swamps. This data set of hydrogeological elements can provide background information for the hydrological ecological process and hydrogeological environment in cold regions. This data comes from the vectorization of four 1: 200,000 hydrogeological maps (Qilian, Yenigou, Qilian, and Sunan) and reintegrates the groundwater types. With higher resolution, the data can provide background information for the research on the evolution of water and soil resources and environmental changes in the source area of the Pan-Third Pole River.
SUN Ziyong
This data set is a database for the application of SWAT Model in the upper reaches of Heihe River and the source area of the Yellow River, mainly including soil and vegetation, and DEM. There are many parameters involved in soil and vegetation, including conventional soil physical and chemical parameters, vegetation parameters and biomass parameters. The determination method of parameter value includes sampling measurement, literature and other related databases, as well as calculation through related software. As the soil and vegetation database of SWAT model involves comprehensive parameters, most of them can also be used as reference for other ecological and hydrological models driving data besides SWAT model.
ZOU Songbing
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.
WANG Junbo
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
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.
GE Yong, LI Qiangzi, DONG Wen
This data set describes the temporal and spatial distribution of precipitation in the Upper Brahmaputra River Basin. We integrate (CMA, GLDAS, ITP-Forcing, MERRA2, TRMM) five sets of reanalysis precipitation products and satellite precipitation products, and combine the observation precipitation of 9 national meteorological stations from China Meteorological Administration (CMA) and 166 rain gauges of the Ministry of Water Resources (MWR) in the basin. The time range is 1981-2016, the time resolution is 3 hours, the spatial resolution is 5 km, and the unit is mm/h. The data will provide better data support for the study of Upper Brahmaputra River Basin, and can be used to study the response of hydrological process to climate change. Please refer to the instruction document uploaded with the data for specific usage information.
WANG Yuanwei, WANG Lei, LI Xiuping, ZHOU Jing
The UHSLC offers tide gauge data with two levels of quality-control (QC). Fast Delivery (FD) data are released within 1-2 months of data collection and receive only basic QC focused on large level shifts and obvious outliers. The GLOSS/CLIVAR (formerly known as the WOCE) "fast" sea level data is distributed as hourly, daily, and monthly values. This project is supported by the NOAA Climate and Global Change program, and is one of the activities of the University of Hawaii Sea Level Center. Each file is given a name "h###.dat" where "h" denotes hourly sea level data and "###" denotes the station number. A file exists for every station with hourly data. The UHSLC datasets are GLOSS data streams (read more here). There are many tide gauge records in the UHSLC database, but the backbone is the GLOSS Core Network (GCN) – a global set of ~300 tide gauge stations that serve as the foundation of the global in situ sea level network. The network is designed to provide evenly distributed sampling of global coastal sea level variation at a variety of time-scales.
DONG Wen, University of hawaii sealevel center （UHSLC）
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.
WAN Chengwei
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