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 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
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 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
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
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
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 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
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
The data is the phytoplankton data of 70 points in 26 lakes in Tibet in 2020. The sampling time is from August to September. The sampling method is the conventional phytoplankton sampling method. 1.5 liters of samples are collected, fixed by Lugo's solution, siphoned and concentrated after static precipitation, and the results are examined by inverted microscope. The data includes the density data of different phytoplankton of 77 species / genus in 10 categories, including diatom, green algae, cyanobacteria, dinoflagellate, naked algae, cryptoalgae, brown algae, brown algae and CHAROPHYTA. This data is original and unprocessed. The unit is piece / L. The data can be used to characterize the composition and abundance of phytoplankton in the open water areas of these lakes, and can also be used to calculate the diversity of phytoplankton communities in these lakes.
ZHANG Min
Glacier is the supply water source of rivers in the western mountainous area, and it is one of the most basic elements for people to survive and develop industry, agriculture and animal husbandry in the western region. Glaciers are not only valuable fresh water resources, but also the source of serious natural disasters in mountainous areas, such as sudden ice lake outburst flood, glacier debris flow and ice avalanche. Glacier hydrological monitoring is the basis for studying the characteristics of glacier melt water, the replenishment of glacier melt water to rivers, the relationship between glacier surface ablation and runoff, the process of ice runoff and confluence, and the calculation and prediction of floods and debris flows induced by glacier and seasonal snow melt water. Glacial hydrology refers to the water and heat conditions of glacial covered basins (i.e. glacial action areas), that is, the water and heat exchange between glaciers and their surrounding environment, the physical process of water accumulation and flow on the surface, inside and bottom of glaciers, the water balance of glaciers, the replenishment of glacial melt water to rivers, and the impact of water bodies in cold regions on climate change. At present, hydrological monitoring stations are mainly established at the outlet of the river basin to carry out field monitoring《 Glacial water resources of China (1991), hydrology of cold regions of China (2000) and glacial Hydrology (2001) summarize the early studies on glacial hydrology. China has carried out glacier hydrological monitoring on more than 20 glaciers in Tianshan, Karakorum, West Kunlun, Qilian, Tanggula, Nianqing Tanggula, gangrigab, Hengduan and Himalayas. This data set is the monthly runoff data of representative glaciers.
YANG Wei, LI Zhongqin, WANG Ninglian, QIN Xiang
Lakes collect runoff, sediment and nutrients from upstream watersheds and are an important "destination" of material migration at the watershed scale. Therefore, the attributes of lake water and sediment are affected by catchment attributes (e.g. climate, terrain and vegetation conditions) to a large degree. This dataset delineates the watershed boundaries of 1525 Lakes (with an area from 0.2 to 4503 square kilometers) on the Tibetan Plateau, and calculates 721 catchment-scale attributes on the aspects of lake body, terrain, climate, vegetation, soil/geology and anthropogenic activities. This is the first dataset of lake-catchment characteristics on the Tibetan Plateau, which can provide foundamental data for the study of lakes in the Tibetan Plateau.
LIU Junzhi
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
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
The data set mainly includes the ice observation frequency (ICO) of north temperate lakes in four periods from 1985 to 2020, as well as the location, area and elevation of the lakes. Among them, the four time periods are 1985-1998 (P1), 1999-2006 (P2), 2007-2014 (P3) and 2015-2020 (P4) respectively, in order to improve the "valid observation" times in the calculation period and improve the accuracy. The ICO of the four periods is calculated by the ratio of "icing" times and "valid observation" times counted by all Landsat images in each period. Other lake information corresponds to the HydroLAKEs data set through the "hylak_id" column in the table. In addition, the data only retains about 30000 lakes with an area of more than 1 square kilometer, which are valid for P1-P4 observation. The data set can reflect the response of Lake icing to climate change in recent decades.
WANG Xinchi
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
Glacier is the supply water source of rivers in the western mountainous area, and it is one of the most basic elements for people to survive and develop industry, agriculture and animal husbandry in the western region. Glaciers are not only valuable fresh water resources, but also the source of serious natural disasters in mountainous areas, such as sudden ice lake outburst flood, glacier debris flow and ice avalanche. Glacier hydrological monitoring is the basis for studying the characteristics of glacier melt water, the replenishment of glacier melt water to rivers, the relationship between glacier surface ablation and runoff, the process of ice runoff and confluence, and the calculation and prediction of floods and debris flows induced by glacier and seasonal snow melt water. Glacial hydrology refers to the water and heat conditions of glacial covered basins (i.e. glacial action areas), that is, the water and heat exchange between glaciers and their surrounding environment, the physical process of water accumulation and flow on the surface, inside and bottom of glaciers, the water balance of glaciers, the replenishment of glacial melt water to rivers, and the impact of water bodies in cold regions on climate change. At present, hydrological monitoring stations are mainly established at the outlet of the river basin to carry out field monitoring《 Glacial water resources of China (1991), hydrology of cold regions of China (2000) and glacial Hydrology (2001) summarize the early studies on glacial hydrology. China has carried out glacier hydrological monitoring on more than 20 glaciers in Tianshan, Karakorum, West Kunlun, Qilian, Tanggula, Nianqing Tanggula, gangrigab, Hengduan and Himalayas. This data set is the monthly runoff data of representative glaciers.
YANG Wei, LI Zhongqin, WANG Ninglian, QIN Xiang
The data set consists of four sub tables, which are remote sensing monitoring of Lake area from 2000 to 2019, total lake water storage based on underwater 3D simulation model, Lake area volume equation based on underwater 3D simulation model, and key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province. The first sub table is the time series Lake area data from 2000 to 2019 from remote sensing image data monitoring. The third sub table stores the area storage capacity equation of the lake based on the underwater three-dimensional simulation model of the lake. The second sub table is the estimation result by combining the time series Lake area data and the area storage capacity equation, Finally, the key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province from 2000 to 2019 are obtained, including simulated water depth, maximum water depth, simulated reference water level and corresponding Lake area of each lake, which are stored in the fourth sub table.
FANG Chun, LU Shanlong, JU Jianting, TANG Hailong
Lake salinity is an important parameter of lake water environment, an important embodiment of water resources, and an important part of climate change research. This data is based on the measured salinity data of lakes in the Qinghai Tibet Plateau. The salinity is characterized by the practical salinity unit (PSU), which is converted from the specific conductivity (SPC) measured by the conductivity sensor. ArcGIS software was used to convert the measured data into space vector format. SHP format, and the measured salinity spatial distribution data file was obtained. The data can be used as the basic data of lake environment, hydrology, water ecology, water resources and other related research reference.
ZHU Liping
This dataset provides the in-situ lake water parameters of 124 closed lakes with a total lake area of 24,570 km2, occupying 53% of the total lake area of the TP.These in-situ water quality parameters include water temperature, salinity, pH,chlorophyll-a concentration, blue-green algae (BGA) concentration, turbidity, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), and water clarity of Secchi Depth (SD).
ZHU Liping
The data consists of three fields: longitude, latitude and lake depth. Using sonar equipment to measure the depth of water on the lake, GPS synchronous measurement of longitude and latitude. The salinity and temperature data of lake water are used to correct the depth data measured by sonar, and the outliers are eliminated. The underwater topographic map of lake can be formed by interpolation of water depth data. Using the underwater topographic map, the water storage of lakes can be calculated and the total water quantity of lakes in the Qinghai Tibet Plateau can be evaluated. The underwater topographic map combined with remote sensing data can also be used to study the characteristics and influencing factors of lake water quantity variation in the Qinghai Tibet Plateau, which is an important part of the study of water quantity variation in the Asian water tower.
ZHU Liping
Terrestrial actual evapotranspiration (ETa) is an important component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. However, accurately monitoring and understanding the spatial and temporal variability of ETa over the Tibetan Plateau (TP) remains very difficult. Here, the multiyear (2000-2018) monthly ETa on the TP was estimated using the MOD16-STM model supported by datasets of soil properties, meteorological conditions, and remote sensing. The estimated ETa correlates very well with measurements from 9 flux towers, with low root mean square errors (average RMSE = 13.48 mm/month) and mean bias (average MB = 2.85 mm/month), and strong correlation coefficients (R = 0.88) and the index of agreement values (IOA = 0.92). The spatially averaged ETa of the entire TP and the eastern TP (Lon > 90°E) increased significantly, at rates of 1.34 mm/year (p < 0.05) and 2.84 mm/year (p < 0.05) from 2000 to 2018, while no pronounced trend was detected on the western TP (Lon < 90°E). The spatial distribution of ETa and its components were heterogeneous, decreasing from the southeastern to northwestern TP. ETa showed a significantly increasing trend in the eastern TP, and a significant decreasing trend throughout the year in the southwestern TP, particularly in winter and spring. Soil evaporation (Es) accounted for more than 84% of ETa and the spatial distribution of temporal trends was similar to that of ETa over the TP. The amplitudes and rates of variations in ETa were greatest in spring and summer. The multi-year averaged annual terrestrial ETa (over an area of 2444.18×103 km2) was 376.91±13.13 mm/year, equivalent to a volume of 976.52±35.7 km3/year. The average annual evapotranspirated water volume over the whole TP (including all plateau lakes, with an area of 2539.49×103 km2) was about 1028.22±37.8 km3/year. This new estimated ETa dataset is useful for investigating the hydrological impacts of land cover change and will help with better management of watershed water resources across the TP.
MA Yaoming, CHEN Xuelong,
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
We comprehensively estimated water volume changes for 1132 lakes larger than 1 km2. Overall, the water mass stored in the lakes increased by 169.7±15.1 Gt (3.9±0.4 Gt yr-1) between 1976 and 2019, mainly in the Inner-TP (157.6±11.6 or 3.7±0.3 Gt yr-1). A substantial increase in mass occurred between 1995 and 2019 (214.9±12.7 Gt or 9.0±0.5 Gt yr-1), following a period of decrease (-45.2±8.2 Gt or -2.4±0.4 Gt yr-1) prior to 1995. A slowdown in the rate of water mass increase occurred between 2010 and 2015 (23.1±6.5 Gt or 4.6±1.3 Gt yr-1), followed again by a high value between 2015 and 2019 (65.7±6.7 Gt or 16.4±1.7 Gt yr-1). The increased lake-water mass occurred predominately in glacier-fed lakes (127.1±14.3 Gt) in contrast to non-glacier-fed lakes (42.6±4.9 Gt), and in endorheic lakes (161.9±14.0 Gt) against exorheic lakes (7.8±5.8 Gt) over 1976−2019.
ZHANG Guoqing
This data is from the hydrological station of kafinigan River, a tributary of the upper Amu Darya River. The station is jointly built by Urumqi Institute of desert meteorology of China Meteorological Administration, Institute of water energy and ecology of Tajik National Academy of Sciences and Tajik hydrometeorological Bureau. The data can be used for scientific research such as water resources assessment and water conservancy projects in Central Asia. Data period: November 3, 2019 to December 3, 2020. Data elements: Hourly velocity (M / s), hourly water level (m) and hourly rainfall (m). Site location: 37 ° 36 ′ 01 ″ n, 68 ° 08 ′ 01 ″ e, 420m 1、 300w-qx River velocity and water level observation instrument （1） Flow rate parameters: 1 power supply voltage 12 (9 ~ 27) V (DC) The working current is 120 (110 ~ 135) MA 3 working temperature (- 40 ~ 85) ℃ 4 measurement range (0.15 ~ 20) m / S The measurement accuracy is ± 0.02m/s The resolution is less than 1 mm The detection range is less than 0.1 ~ 50 m 8 installation height 0.15 ~ 25 m 9 sampling frequency ＜ 20sps （2） Water level parameters: 1 measuring range: 0.5 ~ 20 m The measurement accuracy is ± 3 mm The resolution is less than 1 mm The repeatability was ± 1 mm 2、 SL3-1 tipping bucket rain sensor 1. Water bearing diameter Φ 200mm 2. The measured precipitation intensity is less than 4mm / min 3. Minimum precipitation of 0.1 mm 4. The maximum allowable error is ± 4% mm 3、 Flow velocity, frequency of data acquisition of the observation instrument: the sensor measures the flow velocity and water level data every 5S 4、 Calculation of hourly average velocity: the hourly average velocity and water level data are obtained from the average of all the velocity and water level data measured every 5S within one hour 5、 Description of a large number of values of 0 in water level data: the value of 0 in water level data is caused by power failure and restart of sensor due to insufficient power supply. The first data of initial start-up is 0, resulting in the hourly average value of 0. After the power supply transformation on July 26, 2020, the data returned to normal. At the end of September 2020, the power supply began to be insufficient. After the secondary power supply transformation on December 25, 2020, the data returned to normal 6、 Description of water level monitoring (such as line 7358, 2020 / 11 / 3, 16:00, maximum water level 6.7m, minimum water level 0m, how to explain? In addition, the maximum value of the highest water level is 6.7m, which appears many times in the data. It seems that 6.7m is the limit value of the monitoring data. Is this the case? ）: 6.7m is the height from the initial sensor to the bottom of the river bed. The appearance of 6.7m is the abnormal data when the sensor is just started. The sensor is restarted due to the power failure caused by the insufficient power supply of the equipment. This abnormal value appears in the initial start-up. After the power supply transformation on December 25, 2020, the data returns to normal
HUO Wen, SHANG Huaming
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
In order to investigate the variation characteristics of agricultural water resources vulnerability in Central Asia, an index system was established with 18 indicators from three components, namely exposure, sensitivity and adaptation, according to the scheme of vulnerability assessment. Based on the socio-economic, topography, land cover and soil data, agricultural water resources vulnerability were calculated using the Equal-Weights and Principal Component Analysis (PCA) method. Each original raster data 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 1992-1996, 1997-2001, 2002-2006, 2007-2011, 2012-2017and 1992-2017with a spatial resolution of 0.5°*0.5°. It is expected to provide basic data support for agricultural water supply and demand, development and utilization analysis in five central Asian countries.
LI Lanhai, YU Shui
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,
Lake ice is an important parameter of Cryosphere. Its change is closely related to climate parameters such as temperature and precipitation, and can directly reflect climate change. Therefore, lake ice is an important indicator of regional climate parameter change. However, due to the poor natural environment and sparsely populated area, it is difficult to carry out large-scale field observation, The spatial resolution of 10 m and the temporal resolution of better than 30 days were used to monitor the changes of different types of lake ice, which filled in the blank of observation. The hmrf algorithm is used to classify different types of lake ice. The distribution of different types of lake ice in some lakes with an area of more than 25km2 in the three polar regions is analyzed by time series to form the lake ice type data set. The distribution of different types of lake ice in these lakes can be obtained. The data includes the sequence number of the processed lake, the year and its serial number in the time series, and vector The data set includes the algorithm used, sentinel-1 satellite data, imaging time, polar region, lake ice type and other information. Users can determine the change of different types of lake ice in time series according to the vector file.
Tian Bangsen, QIU Yubao
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,
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.
DONG Lingxiao
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 2021, and provided a long and dense lake observations between the 1970s and 2021. We found that the number of lakes, with area larger than 1 k㎡ , has increased to ~1400 in 2021 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.
ZHANG Guoqing
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
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
The data of this study is mainly based on Google Earth Engine big data cloud processing platform. Sentinel-2 of The Three River Headwater region, Pul and Yukon River Basins in 2017 is selected as the basic data, STRM-DEM and Global Surface Water are used as auxiliary data. AWEIn，AWEIs，WI2015，MNDWI，NDWI and other index threshold extraction are selected to obtain seasonal water body and permanent water body according to annual water frequency(spatial resolution 10m). This water data product provides effective basic data for high spatial-temporal resolution water body change and permafrost hydrological analysis.
RAN Youhua
GLObal WAter BOdies database（GLOWABO）were obtained based on the GeoCoverTM Water bodies Extraction Method, Charles verpoorer et al， by Landsat 7 ETM + image in 2000 ± 3 years. The water extraction method combines the principal component analysis, threshold extraction, texture feature extraction and other methods, with a spatial resolution of 15 m and an overall accuracy of 91%. The data also includes water area, perimeter, shape index, elevation and other information. In this data set, The Three River Headwater region, Pul River Basin and Yukon River Basin, are selected to provide data support for polar hydrological research in the northern hemisphere.
Charles Verpoorter
Based on the data of Keyhole satellite in 1960s, using object-oriented supervised classification and manual visual interpretation and correction, water data products are produced. The total interpretation area is 645,000 km2, accounting for 96.28% of the study area, of which 18,844 km2 is missing in The Three River Headwater region, 4,220 km2 is missing in the Yukon River basin study area in Alaska, and 1,954 km2 is missing in the Pul River basin in West Siberia. The width of the minimum linear figure is more than 8 meters, the area of the minimum surface figure is more than 100 square meters, the trace accuracy is 2 pixels, and the first-class interpretation accuracy is more than 95%. The obtained high spatial resolution surface water data products provide effective data for the study of water changes in the 1960s and reliable basis for the study of frozen soil changes.
RAN Youhua
The multi-decadal lake number and area changes in China during 1960s–2020 are derived from historical topographic maps and >42151 Landsat satellite images, including lakes as fine as ≥1 km^2 in size for the past 60 years (1960s, 1970s, 1990, 1995, 2000, 2005, 2010, 2015, 2020). From the 1960s to 2020, the total number of lakes (≥ 1 km ^ 2) in China increased from 2127 to 2621, and the area expanded from 68537 km ^ 2 to 82302 km ^ 2.
ZHANG Guoqing
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of Yulei station on Qinghai lake from January 1 to October 12, 2018. The site (100° 29' 59.726'' E, 36° 35' 27.337'' N) was located on the Yulei Platform in Erlangjian scenic area, Qinghai Province. The elevation is 3209m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 12 and 12.5 m above the water surface, towards north), wind speed and direction profile (windsonic; 14 m above the water surface, towards north) , rain gauge (TE525M; 10m above the water surface in the eastern part of the Yulei platform ), four-component radiometer (NR01; 10 m above the water surface, towards south), one infrared temperature sensors (SI-111; 10 m above the water surface, towards south, vertically downward), photosynthetically active radiation (LI190SB; 10 m above the water surface, towards south), water temperature profile (109, -0.2, -0.5, -1.0, -2.0, and -3.0 m). The observations included the following: air temperature and humidity (Ta_12 m, Ta_12.5 m; RH_12 m, RH_12.5 m) (℃ and %, respectively), wind speed (Ws_14 m) (m/s), wind direction (WD_14 m) (°) , precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), water temperature (Tw_20cm、Tw_50cm、Tw_100cm、Tw_200cm、Tw_300cm) (℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The other data in addition to the four-component radiation data during January 1 to October 12 were missing because the malfunction of datalogger. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-1-1 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
The distribution of lakes in space and its change over time are closely related to agricultural, environmental and ecological issues, and are critical factors for human socio-economic development. In the past decades, satellite based remote sensing has been developed rapidly to provide essential data sources for monitoring temporal lakes dynamics with its advantage of rapidness, wide coverage, and lower cost. This dataset was produced from Landsat images using the automated water detection method (Feng et al, 2015). We collected 96,278 Landsat images (about 25 terabytes) that acquired since 2000 with less than 80% cloud contamination in the arid region of central Asia and Tibetan Plateau. Water is detected in each of the image and then aggregated to monthly temporal resolution by taking advantage of the high-performance processing capability and large data storage provided by Global Land Cover Facility (GLCF) at University of Maryland. The results are validated systematically and quantitatively using manually interpreted dataset, which consists of a set of locations collected by a stratified random sampling strategy to effectively represent different spatial-temporal distributions in the region. The validation suggests high accuracy of the results (overall accuracy: 99.45(±0.59); user accuracy: 85.37%±(3.74); produce accuracy: 98.17(±1.05)).
FENG Min, CHE Xianghong
The third pole 1:100,000 Water data set includes：Different grades of river lines（Tibet_River）、Polygonal drainage pattern（Tibet_Water_poly）vector space data set and its attribute name：Name（name）、Type（Type）、water leng（leng）、water area（Area）. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
The dataset includes vector map of the lakes larger than 1k㎡ on Tibetan Plateau in 1970s, 1990, 2000, 2010. The lake boundry data was extracted from remote sensing image like Landsat MSS, TM, ETM+, by means of visual interpretation. The data type is vector data, and it's attribute class includes Area (km²). The Projected Coordinate System is Albers Conical Equal Area. It is mainly used in the study of changes in lakes, hydrological and meteorological on the Tibetan Plateau.
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
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
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
River and lake resources are important components for studying the Earth ecological environment, affecting global ecosystems, heat, material exchange and balance and serving as an important basis for studying changes in the global environmental mechanism. At present, the lack of global lake vector data with large-scale, high-precision, and large-range has hindered hydrological research on rivers and lakes. Taking the data collection of global rivers and lakes of Jun Chen as the source data and combining the domestic high-resolution image GF data of 2 to 3 years before and after 2010, a data set of global rivers and lakes was generated. This data set makes up for the shortcomings of low precision in some areas and is an editable lake and river vector data set with high accuracy.
QIU Yubao
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
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
There are three types of glacial lakes: supraglacial lakes, lakes attached to the end of the glacier and lakes not attached to the end of the glacier. Based on this classification, the following properties are studied: the variation in the number and area of glacial lakes in different basins in the Third Pole region, the changes in extent in terms of size and area, distance from glaciers, the differences in area changes between lakes with and without the supply of glacial melt water runoff, the characteristics of changes in the glacial lake area with respect to elevation, etc. Data source: Landsat TM/ETM+ 1990, 2000, 2010. The data were visually interpreted, which included checking and editing by comparing the original image with Google Earth images when the area was greater than 0.003 square kilometres. The data were applied to glacial lake changes and glacial lake outburst flood assessments in the Third Pole region. Data type: Vector data. Projected Coordinate System: Albers Conical Equal Area.
ZHANG Guoqing
This is the 1976, 1991, 2000, and 2010 vector data set of glaciers and glacial lakes in the Boqu Basin in Central Himalaya based on Landsat satellite images. The data source is from Landsat remote images. 1976: LM21510411975306AAA05, LM21510401976355AAA04 1991: LT41410401991334XXX02, LT41410411991334XXX02 2000: LE71410402000279SGS00, LE71400412000304SGS00, LE71410402000327EDC00, LE71410412000327EDC00 2010: LT51400412009288KHC00, LT51410402009295KHC00, LT51410412009311KHC00, LT51410402011237KHC00. The boundaries of glaciers and glacial lakes are extracted manually from the various remote sensing images. The extraction error of the boundaries of glaciers and glacial lakes is estimated to be 0.5 pixels. Data file: Glacial_1976: Glacier vector data in 1976 Glacial_1991: Glacier vector data in 1991 Glacial_2000: Glacier vector data in 2000 Glacial_2010: Glacier vector data in 2010 Glacial_Lake_1976: Glacial lake vector data in 1976年 Glacial_Lake_1991: Glacial lake vector data in 1991 Glacial_Lake_2000: Glacial lake vector data in 2000 Glacial_Lake_2010: Glacial lake vector data in 2010 The glacial lake vector data fields include Number, name, latitude and longitude, altitude, area, orientation, type of glacial lake, length, width, and distance from the glacier.
WANG Weicai
The data set of lake dynamics on the Tibetan Plateau was mainly derived from Landsat remote sensing data. Band ratio and the threshold segmentation method were applied. The temporal coverage of the data set was from 1984 to 2016, with a temporal resolution of 5 years. It covered the whole Tibetan Plateau at a spatial resolution of 30 meters. The water body area extraction method mainly adopted the band ratio (B4/B2) or water body index to construct the classification tree. The algorithm construction considered the spatial and temporal variations of the spectral characteristics of the water body and adjusted the threshold of the decision tree by the slope and the slope aspect information of the water body. The long-term sequence satellite-borne data came from different sensors, e.g., Landsat MSS, TM, ETM+, and OLI. The minimum unit for extracting water body information was 2*2 pixels, and all water body areas less than 0.36*10^-2 Km² were removed. The water body information extracted by high-resolution remote sensing data and the verification of the water body checkpoint determined by visual interpretation indicated that the overall accuracy of the water body area information for the Tibetan Plateau was above 95%. The data were saved as a shape file, and projected by Albers projection, with a central meridian of 105 ° and a double standard latitude of 25 ° and 47 °.
SONG Kaishan, DU Jia
Microbial diversity data of lakes on the Tibetan Plateau. One hundred and thirty-eight samples were collected from July 1st to July 15th, 2015, from 28 lakes (Bamco, Baima Lake, Bange Salt Lake, Bangong Lake, Bengco, Bieruozeco, Cuoeco, Cuoe (Pingcuo North), Dawaco, Dangqiongco, Dangreyongco, Dongco, Eyacuoqiong, Gongzhuco, Guogenco, Jiarebuco, Mapangyongco, Namco, Nieerco (Salt Lake), Normaco, Pengyanco, Pengco, Qiangyong, Selinco, Wuruco, Wumaco, Zharinanmuco, and Zhaxico). The salinity gradients range from 0.07-118 ppm. The DNA extraction method: The DNA was extracted using an MO BIO PowerSoil DNA kit after the lake water was filtered onto a 0.45 membrane. The 16S rRNA gene fragment amplification primers were 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 909r (5'-GGACTACHVGGGTWTCTAAT-3'). The sequencing method was Illumina MiSeq PE250, and the raw data were analyzed by Mothur software, including quality filtering and chimera removal. The sequence classification was based on the Silva109 database, and archaea, eukaryotic and unknown source sequences have been removed. OTUs were classified by 97% similarity, and sequences that appear once in the database were then removed. Finally, each sample was resampled to 7,230 sequences/sample. GPS coordinates, evolutionary information, and environmental factors are listed in the data.
JI Mukan
The data set includes the river level observation data of No. 4 point in the dense runoff observation of the middle reaches of Heihe River from May 20, 2015 to March 11, 2016. The instrument maintenance was completed again on May 20, 2015. The observation point is located in Heihe bridge, Shangbao village, Jing'an Township, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39.065 °, e100.433056 °, 1431m above sea level, and 58m wide river channel. In 2012, hobo pressure type water level gauge was used for water level observation with acquisition frequency of 30 minutes; since 2013, sr50 ultrasonic distance meter was used with acquisition frequency of 30 minutes. On June 25, 2014, the instrument was damaged and re purchased. The record was restarted on May 20, 2015. The data includes the following parts: Water level observation, observation frequency 30 minutes, unit (cm); 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
The data set includes the observation data of river water level and velocity at No.7 point in the dense observation of runoff in the middle reaches of Heihe River from January 1, 2015 to March 11, 2016. The sensor was abnormal at the end of 2014, and the commissioning was normal on March 25 after maintenance. The observation point is located in Heihe bridge, Pingchuan Township, Linze County, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39.331667 °, e100.099722 °, altitude 1375 meters, and channel width 130 meters. In 2015, sr50 ultrasonic distance meter was used for water level observation, with acquisition frequency of 30 minutes. Data description includes: Water level observation, observation frequency 30 minutes, unit (cm); 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).
LIU Shaomin, LI Xin, XU Ziwei
Hydrological data of Heihe River: investigation data of water diversion process of Heihe River. Methods: field investigation, interview, data collection and electronization; Content overview: this data includes the documents, documents and research reports obtained from the investigation of the water diversion process of Heihe River by Tsinghua University, mainly including the interview records of Mr. Zhou Kan, the party who made the water diversion plan. Time and space: 1950-2010; Heihe River Basin
WANG Zhongjing, ZHENG Hang
The dataset includes channel flow measured at the second irrigation stage in spring (22 May, 2012), the third irrigation stage in spring (18 June, 2012) and the first irrigation stage in autumn (16 July, 2012). The time used in this dataset is in UTC+8 Time. 1.1 Objective of measurement Objective of measuring channel flow are to provide the conference data for irrigation water optimal allocation model according to obtain reality water volume measured at Dou channel and Mao channel. Data set also is used to reference data for other observations such as eddy, biophysical parameters. 1.2 Observation measures and principle Measures: flow meter named Flowatch, which is made in Switzerland, observation precision: 0.1m/s; and rule, observation of which is 1cm. Principle: Flowatch, which is mechanical-based, is used to compute the velocity of the fluid according to vanes speed. The flow of channels is computed by using observed flow velocity and channel sectional area calculated on the basis of channel engineer sectional parameters and water level. 1.3 Observation location and items Observation spots include Yingyi branch San dou (Liu She, Shang’er She, and Xia’er She of Shiqiao village), Si Dou (Qi She, Ba She, and Jiu She of Shiqiao village), and Wu Dou (Yi She of Shiqiao village) at Yingke irrigation district, and seven Mao channels branched from five star branch channel Si Dou San Nong. Observation time is described as followed: Second stage irrigation in summer: 2012-5-22: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-5-23: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-5-24 to 2012-5-25: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-5-26 to 2012-5-28: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-5-28 to 2012-5-29: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-5-29 to 2012-5-30: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-5-30 to 2012-6-2: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-6-6: Yi Mao, Er Mao, San Mao, Si Mao, and Wu Mao branched from Five star branch channel Si Dou San Nong: Five star village 2012-6-7: Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Third stage irrigation in summer: 2012-6-18 to 2012-6-19: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-6-19 to 2012-6-20: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-6-20 to 2012-6-21: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-6-22 to 2012-6-24: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-6-24 to 2012-6-26: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-6-26 to 2012-6-27: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-6-27 to 2012-6-30: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-7-1 to 2012-7-2: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village First stage irrigation in Autumn: 2012-7-16 to 2012-7-18: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-7-18 to 2012-7-19: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-7-19 to 2012-7-21: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-7-21 to 2012-7-24: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-7-24 to 2012-7-25: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-7-25 to 2012-7-27: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-7-27 to 2012-7-31: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-7-27 to 2012-7-28: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Second stage irrigation in Autumn: 2012-8-8 to 2012-8-9: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-8-9 to 2012-8-10: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-8-10 to 2012-8-12: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-8-13 to 2012-8-15: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-8-15 to 2012-8-17: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-8-17 to 2012-8-19: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-8-19 to 2012-8-22: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-8-24 to 2012-8-25: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Observed items: average flow velocity of channel (m/s), water level of channel (m), water temperature (℃), engineer sectional parameters of channel (investigation). Average flow velocity and water level of channel are measured one time per hour when channel flow is stable. However, the two items are measured two times or more times when channel flow is unstable. 1.4 Data process Observed data is saved in excel sheet, types of which include channel flow velocity, channel sectional area, water level, and water temperature. Channel flow and irrigation water volume are calculated by using observed data according to data per-process approach.
GE Yingchun, MA Chunfeng, Xu Fengying, LI Xin
The dataset includes two parts that are: 1) channel flow, crop pattern, field management, and socio-economy data measured at super-station in 2008, 2010, 2011, 2012 (UTC+8), respectively. 2) irrigation data, crop pattern, and socio-economy data investigated at Daman irrigation district and Yingke irrigation district, respectively. 1.1 Objective of investigation Objectives of investigation for two parts data are to obtain crop pattern and irrigation water volume change with time, and to supply parameter for irrigation water optimal allocation model. 1.2 Investigation spots and items Investigation spots include six water management stations that are Dangzhai, Hua’er, Daman, Xiaoman, Jiantan, and Ershilidun, respectively, at Daman irrigation district. Investigation items comprise water allocation time, branch channel inflow, Dou channel inflow, irrigation area, channel water use efficiency, water price, and water fee. Investigation time is described as followed: 2012.03.16 to 2012.04.04, Spring irrigation; 2012.04.04 to 2012.05.14, Summer irrigation; 2012.05.20 to 2012.06.24, Summer irrigation; 2012.05.16 to 2012.07.06, Summer irrigation; 2012.07.15 to 2012.08.02, Autumn irrigation; 2012.08.10 to 2012.08.26, Autumn irrigation. Investigation spots include eight water management station that are Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, and Yangou, respectively, at Yingke irrigation district. Investigation time and items is described as followed: Year Data items Spots 2008, 2010, 2011 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Xiaoman county, Shangtouzha village 2012 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, Yangou 2012 Well data: Well deep, groundwater abstraction, irrigation area Chang’an, Liangjiadun, Shangqin 2012 Socio-economy data: population, agricultural income, un-agricultural income, water use for living, average residential area, education Chang’an, Xiaoman, Liangjiadun, Shangqin 2012 Field management: fertilizer name, fertilization time, fertilization rate, pesticide name, pesticide rate, time Chang’an, Xiaoman, Liangjiadun, Shangqin 2008, 2010, 2011, 2012 Crop pattern: crop name, seed time, harvest time, crop area, irrigation quota, field water use efficiency, crop yield, crop production value Xiaoman, Chang’an, Liangjiadun, Shangqin 1.3 Data collection Data was collected by cooperating with water management department of Yingke and Daman.
GE Yingchun, Xu Fengying, LI Xin
This project use distributed HEIFLOW Ecological hydrology model (Hydrological - Ecological Integrated watershed - scale FLOW model) of heihe river middle and lower reaches of the eco Hydrological process simulation.The model USES the dynamic land use function, and adopts the land use data of the three phases of 2000, 2007 and 2011 provided by hu xiaoli et al. The space-time range and accuracy of simulation are as follows: Simulation period: 2000-2012, of which 2000 is the model warm-up period Analog step size: day by day Simulation space range: the middle and lower reaches of heihe river, model area 90589 square kilometers Spatial accuracy of the simulation: 1km×1km grid was used on both the surface and underground, and there were 90589 hydrological response units on the surface.Underground is divided into 5 layers, each layer 90589 mobile grid The data set of HEIFLOW model simulation results includes the following variables: (1) precipitation (unit: mm/month) (2) observed values of main outbound runoff in the upper reaches of heihe river (unit: m3 / s) (3) evapotranspiration (unit: mm/month) (4) soil infiltration amount (unit: mm/month) (5) surface yield flow (unit: mm/month) (6) shallow groundwater head (unit: m) (7) groundwater evaporation (unit: m3 / month) (8) supply of shallow groundwater (unit: m3 / month) (9) groundwater exposure (unit: m3 / month) (10) river-groundwater exchange (unit: m3 / month) (11) simulated river flow value of four hydrological stations of heihe main stream (gaoya, zhengyi gorge, senmaying, langxin mountain) (unit: cubic meter/second) The first two variables above are model-driven data, and the rest are model simulation quantities.The time range of all variables is 2001-2012, and the time scale is month.The spatial distributed data precision is 1km×1km, and the data format is tif. In the above variables, if the negative value is encountered, it represents the groundwater excretion (such as groundwater evaporation, groundwater exposure, groundwater recharge channel, etc.).If groundwater depth is required, the groundwater head data can be subtracted from the surface elevation data of the model. In some areas, the groundwater head may be higher than the surface, indicating the presence of groundwater exposure. In addition, the dataset provides: Middle and downstream model modeling scope (format:.shp) Surface elevation of the middle and downstream model (in the format of. Tif) All the above data are in the frame of WGS_1984_UTM_Zone_47N. Take heiflow_v1_et_2001m01.tif as an example to illustrate the naming rules of data files: HEIFLOW: model name V1: data set version 1.0 ET: variable name 2001M01: January 2000, where M represents month
ZHENG Chunmiao
1、 Data Description: data includes doc and DIC values of river water and groundwater in hulugou small watershed from July to September 2015. The sampling frequency is once every two weeks. 2、 Sampling location: (1) there are two river water sampling points. The first sampling point is located at the hydrological section at the outlet of hulugou Small Watershed at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 47.7 ″ E and 38 ° 16 ′ 11 ″ n. The second sampling point of the river is located at the outlet of hulugou area II at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n. (2) Underground water spring and well water sampling points. The spring sampling point is located at 20 m to the east of the drainage basin outlet, with the longitude and latitude of 99 ° 52 ′ 50.9 ″ E and 38 ° 16 ′ 11.44 ″ n. The well water sampling point is located near the intersection of the East and West Branch ditches, with the longitude and latitude of 99 ° 52 ′ 45.38 ″ E and 38 ° 15 ′ 21.27 ″ n. 3、 Test method: Doc and DIC values of samples were measured by oiaurora 1030w TOC instrument, detection range: 2ppb c-30000ppm C.
MA Rui , HU Yalu
一. data description The data included the precipitation, river water and groundwater in the small calabash valley from July to September 2015 2H, 18O, with a sampling frequency of 2 weeks/time. 二. Sampling location (1) the precipitation sampling point is located in the ecological hydrology station of the institute of cold and dry regions, Chinese academy of sciences, with the latitude and longitude of 99 ° 53 '06.66 "E, 38 ° 16' 18.35" N. (2) the sampling point of the river is located at the outlet flow weir of haugugou small watershed in the upper reaches of the heihe river, with the latitude and longitude of 99 ° 52 '47.7 "E and 38 ° 16' 11" N.The water sampling point number 2 position for heihe river upstream hoist ditch Ⅱ area exports, latitude and longitude 99 ° 52 '58.40 "E, 38 ° 14' 36.85" N. (3) underground water spring and well water sampling points.The sampling point of spring water is located at 20m to the east of the outlet of the basin, with the latitude and longitude of 99°52 '50.9 "E, 38°16' 11.44" N. The well water sampling point is located near the intersection of east and west branches, with the latitude and longitude of 99 ° 52 '45.38 "E, 38 ° 15' 21.27" N. 三. Test method The δ2H and δ18O values of the samples were measured by PICARRO L2130-i ultra-high precision liquid water and water vapor isotope analyzer. The results were expressed by the test accuracy value of v-smow relative to the international standard substance, and the measurement accuracy was 0.038‰ and 0.011‰, respectively.
MA Rui , XING Wenle
1、 Data Description: the data includes the samples of anions and anions of river water and groundwater in hulugou small watershed from July to September 2015 for test and analysis. The sampling frequency is once every two weeks. 2、 Sampling location: (1) there are two river water sampling points. One is located at the outlet flow weir of hulugou small watershed in the upper reaches of Heihe River, with latitude and longitude of 99 ° 52 ′ 47.7 ″ E and 38 ° 16 ′ 11 ″ n. The second sampling point of the river is located at the outlet of hulugou area II at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n. (2) Underground water spring and well water sampling points are 20 m to the east of the drainage basin outlet, with longitude and latitude of 99 ° 52 ′ 50.9 ″ E and 38 ° 16 ′ 11.44 ″ n. The well water sampling point is located near the intersection of the East and West Branch ditches, with the longitude and latitude of 99 ° 52 ′ 45.38 ″ E and 38 ° 15 ′ 21.27 ″ n. 3、 Test method: the cation of sample is tested by inductively coupled plasma atomic emission spectrometer (ICP-AES), the test accuracy is 0.05mg/l, and the anion is tested by ion chromatograph (ics1100), the test accuracy is 0.002mg/l.
MA Rui , HU Yalu
1、 Data Description: the data includes the river flow data at the outlet of No.2 catchment of hulugou small watershed from May 4, 2016 to September 3, 2016. 2、 Sampling location: the coordinates of river flow monitoring section are located at the outlet of No. 2 catchment near the red wall, with the coordinates of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n.
MA Rui , HU Yalu
The No. 8 hydrological section is located at Gaotai Heihe River Bridge (39 ° 23′22 .93 ″ N, 99 ° 49′37 .29″ E, 1347 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.8 hydrological section from 17 June, 2012, to 24 November, 2012. The width of this section is 130 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The data set includes the river level observation data of point 2 in the dense runoff observation of the middle reaches of Heihe River from January 1, 2015 to December 31, 2015. The observation point is located in Heihe bridge, 312 National Road, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n38.996667 °, e100.427222 °, altitude 1485m, river width 70m and 20m. Sr50 ultrasonic range finder is used for water level observation, with acquisition frequency of 30 minutes. The data includes the following parts: Water level observation, observation frequency 30 minutes, unit (CM); In 2015, the section of bridge no.2-312 was frequently disturbed by human beings. The dam was built within 1km of the upstream and downstream of the section. The unstable area of the hydrological section led to the disorder of the water level and flow curve. During the measurement, the stable flow and water level curve could not be obtained. The observation of water level is based on the manual observation of water level at 0:00 on January 1, 2015. In the later stage, the hydrological section of river undercut changes. The result is that the datum water level changes and negative value appears; Refer to Li et al. (2013) for hydrometeorological network or station information, and he et al. (2016) for observation data processing
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 1 hydrological section is located at 213 Heihe River Bridge (100.345° E, 38.912° N, 1546 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.1 hydrological section from 13 June, 2012, to 6 September, 2013. This section consists of two river sections, i.e., the east section,which is denoted as No. 1 and the west section, which is denoted as No. 2. The width of this section is 330 meters and consists of a gravel bed; the cross-sectional area is unstable because of human factors. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 2 hydrological section is located at 312 Heihe River Bridge (100.411° E, 38.998° N, 1485 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.2 hydrological section from 19 June, 2012, to 31 December, 2013. This section consists of two river sections, i.e., the east section, which is denoted as No. 1 and the west section, which is denoted as No. 2. The width of this section is 90 meters and consists of a gravel bed; the cross-sectional area is unstable because of human factors. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The data set includes the observation data of river water level and velocity at No.2 point in the runoff densification observation of the middle reaches of Heihe River from January 1, 2014 to December 31, 2014. The observation point is located in Heihe bridge, 312 National Road, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation points are N38 ° 59 ′ 51.71 ″, E100 ° 24 ′ 38.76 ″, with an altitude of 1485 meters, and a channel width of 70 meters and 20 meters. Sr50 ultrasonic range finder is used for water level observation, with acquisition frequency of 30 minutes. The data description includes the following parts: For water level observation, the observation frequency is 30 minutes, unit (CM); the data covers the period from January 1, 2014 to December 31, 2014; for flow observation, unit (M3); for flow monitoring according to different water levels, the water level flow curve is obtained, and the runoff change process is obtained based on the observation of water level process. The section of bridge no.2-312 is frequently disturbed by human beings, and the unstable area of hydrological section leads to the disorder of water level and flow curve. During the measurement, the stable flow and water level curve cannot be obtained. The missing data is uniformly represented by string-6999. Refer to Li et al. (2013) for hydrometeorological network or station information and he et al. (2016) for observation data processing.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The data set includes the observation data of river water level and velocity at No. 4 point in the dense observation of runoff in the middle reaches of Heihe River from January 1 to June 25, 2014. The observation point is located in Heihe bridge, Shangbao village, Jing'an Township, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39 ° 03'53.23 ", E100 ° 25'59.31", with an altitude of 1431m and a width of 58m. In 2012, hobo pressure type water level gauge was used for water level observation with acquisition frequency of 30 minutes; since 2013, sr50 ultrasonic distance meter was used with acquisition frequency of 30 minutes. The data description includes the following parts: For water level observation, the observation frequency is 30 minutes, unit (CM); the data covers the period from January 1, 2014 to June 25, 2014; for flow observation, unit (M3); for flow monitoring according to different water levels, the water level flow curve is obtained, and the runoff change process is obtained based on the observation of water level process. The missing data is uniformly represented by string-6999. Refer to Li et al. (2013) for hydrometeorological network or station information and he et al. (2016) for observation data processing.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
This dataset contains data on river water level and flow velocity at No.8 in the intensive runoff observation in the middle reaches of Heihe River runoff from January 1, 2014 to December 31, 2014. The observation point is located at Heihe Bridge, Gaotai County, Zhangye City, Gansu Province. The riverbed is sediment and the section is stable. The latitude and longitude of the observation point is N39°23'22.93", N 99°49'37.29", the altitude is 1347 meters, and the river channel width is 210 meters. The water level observation is measured by SR50 ultrasonic range finder with a frequency of 30 minutes. The data declaration includes the following two parts: Water level observation, observation frequency 30 minutes, unit (cm); data covering time period from January 1, 2014 to December 31, 2014; Flow observation, unit (m3); monitoring flow and obtaining water level flow curve according to different water levels. The process of the runoff changing is obtained by observing the water level process. The No. 8 point-Gaotaiqiao section only monitored the water level because the water body of the wetland park basically stopped flowing. The missing data is uniformly represented by the string -6999. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to He et al. (2016).
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
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
The data set includes the observation data of river water level and velocity at No.7 point in the dense observation of runoff in the middle reaches of Heihe River from January 1, 2014 to December 28, 2014. The observation point is located in Heihe bridge, Pingchuan Township, Linze County, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39 ° 20'2.03 ", E100 ° 5'49.63", with an altitude of 1375m and a channel width of 130m. In 2014, sr50 ultrasonic distance meter was used for water level observation, with acquisition frequency of 30 minutes. Data description includes the following two parts: Water level observation, observation frequency 30 minutes, unit (cm); The data covers the period from January 1, 2014 to December 28, 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
Based on the meteorological data of 105 meteorological stations in and around the Qinghai Tibet Plateau from 1980 to 2019, the National Meteorological Science Data Center of China Meteorological Administration (CMA) was established. By calculating the oxygen content, it is found that there is a significant linear correlation between oxygen content and altitude, y = - 0.0263x + 283.8, R2 = 0.9819. Therefore, the oxygen content distribution map can be calculated based on DEM data grid. Due to the limitation of the natural environment in the Qinghai Tibet Plateau, there are few related fixed-point observation institutions. This data can reflect the distribution of oxygen content in the Qinghai Tibet Plateau to a certain extent, and has certain reference significance for the research of human living environment in the Qinghai Tibet Plateau.
HE Xiaobo, ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
This dataset contains data on river water level and flow velocity at No.3 in the intensive runoff observation in the middle reaches of Heihe River runoff from July 28, 2014 to December 31, 2014. The observation point is located at Heihe Bridge, Lan-Xin Railway, Zhangye City, Gansu Province. The riverbed is gravel and the section is stable. The latitude and longitude of the observation point is N39°2'33.08", E100°25'49.42", the altitude is 1443 meters, and the river channel width is 50 meters. The water level observation is measured by SR50 ultrasonic range finder with a frequency of 60 minutes. The flow profile observation is conducted by StreamPro micro ADCP. The data declaration includes the following two parts: Water level observation, the observation frequency is 60 minutes, unit (cm); data covering time period from July 28, 2014 to December 31, 2014; Flow observation, unit (m3); monitoring flow and obtaining water level flow curve according to different water levels. The process of the runoff changing is obtained by observing the water level process. The missing data is uniformly represented by the string -6999. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to He et al. (2016).
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 7 hydrological section is located at Pingchuan Heihe River Bridge (100.097° E, 39.334° N, 1375 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.7 hydrological section from 17 June, 2012, to 31 December, 2013. The width of this section is 130 meters. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 6 hydrological section is located at Gaoya Hydrological Station (100.433° E, 39.135° N, 1420 m a.s.l.) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. This hydrological section is for intercomparison of flow measurement between ADCP and manual method. The dataset contains recorded by the No. 6 hydrological section from 10 August, 2012 to 31 December, 2013. The width of this section is 58 meters. The water level was measured using an HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 7 hydrological section is located at Pingchuan Heihe River Bridge (39 ° 20′2.03 ″ N, 100° 5′49.63″ E, 1375 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.7 hydrological section from 13 June, 2012, to 24 November, 2012. The width of this section is 130 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 3 hydrological section is located at Railway Heihe River Bridge (39°02′33.08″ N, 100° 25′49.42″ E, 1443 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.3 hydrological section from 14 June, 2012, to 24 November, 2012. The width of this section is 50 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 5 hydrological section is located at Ban Bridge (100.276° E, 39.259° N, 1398 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.5 hydrological section from 19 June, 2012, to 10 August, 2012. The width of this section is 270 meters. The water level was measured using an HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 6 hydrological section is located at Ban Heihe River Bridge (39°15′32.41″ N，100°16′33.95″ E, 1398 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.6 hydrological section from 19 June, 2012, to 10 August, 2012. The width of this section is 270 meters. The water level was measured using HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 2 hydrological section is located at 312 Heihe River Bridge (38°59′51.71″ N, 100° 24′38.76″ E, 1485 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.2 hydrological section from 19 June, 2012, to 24 November, 2012. This section consists of two river sections, i.e., the east section is marked as No. 1 and the west section is marked as No. 2. The width of this section is 90 meters. This section consists of a gravel bed; the cross-sectional area is unstable because of human factors. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 1 hydrological section is located at 213 Heihe River Bridge (38°54′43.55″ N, 100° 20′41.05″ E, 1546 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.1 hydrological section from 13 June, 2012, to 24 November, 2012. This section consists of two river sections, i.e., the east section is marked as No. 1 and the west section is marked as No. 2. The width of this section is 330 meters. This section consists of a gravel bed; the cross-sectional area is unstable because of human factors. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The data set includes the observation data of river water level and velocity at NO.5 point in the dense runoff observation of the middle reaches of Heihe River from January 1 to April 30, 2014 and from July 18 to July 26, 2014,. The observation point is located in Heihe bridge, Banqiao Township, Linze County, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39 ° 15'32.41 ", E100 ° 16'33.95", with an altitude of 1398 meters and a channel width of 270 meters. In 2014, the water level was observed by sr50 ultrasonic distance meter with acquisition frequency of 30 minutes. During the observation period, the instrument failure was returned to the factory for maintenance, and the failure was not eliminated after later installation.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 4 hydrological section is located at Wujin Heihe River Bridge (100.433° E, 39.065° N, 1431 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains recorded by the No.4 hydrological section from 10 June, 2012 to 10 August, 2012, and from 6 September, 2013 to 31 December, 2013. The width of this section is 58 meters and the cross-sectional area is unstable because of human factors. The water level was measured using an HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 3 hydrological section is located at Railway Heihe River Bridge (100.430° E, 39.043° N, 1443 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.3 hydrological section from 14 June, 2012, to 31 December, 2013. The width of this section is 50 meters. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameter: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The No. 5 hydrological section is located at Gaoya Hydrological Station (39°08′06.35″ N，100°25′58.23″ E, 1420 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. This hydrological section is for intercomparison of flow measurement between ADCP and manual method. The dataset contains observations from the No.5 hydrological section from 10 August, 2012, to 24 November, 2012. The width of this section is 58 meters. The water level was measured using HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 8 hydrological section is located at Gaotai Heihe River Bridge (N39° 23′22.93 ″, E 99° 49′37.29″, 1347 m a.s.l.) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains observations recorded by the No.8 hydrological section from 17 June, 2012, to 31 December, 2013. The width of this section is 210 meters. The water level was measured using an SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
The data is clipped from "1: 1 million wetland data of China". "1: 1 million wetland data of China" mainly reflects the national marsh wetland information in the 2000s. It is expressed in geographic coordinates using the decimal degree. The main contents include: marsh wetland types, wetland water supply types, soil types, main vegetation types, geographical area, etc. Implemented the "Standard for Information Classification and Coding of Sustainable Development Information Sharing System of China". Data source of this database: 1:20 swamp map (internal version), Tibetan Plateau 1: 500,000 swamp map (internal version), swamp survey data 1: 1 million and national 1: 4 million swamp map; processing steps are: data source selection, preprocessing, digitization and encoding of marsh wetland elements, data editing processing, establishing topological relationships, edge processing, projection conversion, linking with attribute databases such as place names and obtaining attribute data.
ZHANG Shuqing
The output data of the distributed eco-hydrological model (GBEHM) of the upper reaches of the black river include the spatial distribution data series of 1-km grid. Region: upper reaches of heihe river (yingxiaoxia), time resolution: month scale, spatial resolution: 1km, time period: 2000-2012. The data include evapotranspiration, runoff depth and soil volumetric water content (0-100cm). All data is in ASCII format. See basan.asc file in the reference directory for the basin space range. The projection parameter of the model result is Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area.
YANG Dawen
The output data of the distributed eco-hydrological model (GBEHM) of the upper reaches of the black river include the spatial distribution data series of 1-km grid. Region: upper reaches of heihe river (yingxiaoxia), time resolution: month scale, spatial resolution: 1km, time period: 1980-2010. The data included precipitation, evapotranspiration, runoff depth, and soil volumetric water content (0-100cm). All data is in ASCII format. See basan.asc file in the reference directory for the basin space range. The projection parameter of the model result is Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area.
YANG Dawen
Agricultural irrigation, which accounts for about 80% of human water consumption, is the most important part of human water resources management and closely related to human survival and development.Irrigation is also an important part of the water cycle. Large-scale irrigation can affect the water cycle and even the local climate by affecting evapotranspiration.Excessive diversion of irrigation water will lead to unsustainable utilization of water resources, and at the same time, will reduce river flow and aquifer water reserves, thus harming the ecological environment. Therefore, determining the spatial and temporal distribution and variation of irrigation is critical to studying past human water use, the impact of irrigation on ecological and hydrological processes, the environment and climate, and the development of future irrigation plans. By integrating the irrigation amount of channel diversion water and irrigation amount of groundwater intake from different data sources, and combining the evapotranspiration data of land surface model CLM4.5 simulation and remote sensing inversion, a set of spatio-temporal continuous surface water and groundwater irrigation amount data set with spatial resolution of 30 arcseconds (0.0083 degrees) on the scale of 1981-2013 in heihe river basin was made. It has been verified that this data set has a high reliability from 2000 to 2013, and a lower reliability from 1981 to 1999 than from 2000 to 2013 due to the absence of remote sensing data and the absence of soil utilization changes. The document is described as follows: Monthly surfacewater irrigation volume file name: monthly_surfacewater_irrigation gation_1981-2013.nc Monthly groundwater_irrigation gation_1981-2013.nc The data is in netcdf format.There are three dimensions, which are month, lat, and lon. Where, month is a month, and the value is 0-395, representing each month from 1981 to 2013. Lat is grid latitude information, and lon is grid longitude information.
XIE Zhenghui
This data set consists of three parts: the first part is the monthly flow data of Yingluo gorge and caotanzhuang water conservancy project from 1979 to 2014; the second part is the S213 bridge (N38 ° 54'43.55 ", E100 ° 20'41.05") on the main stream of Heihe River from 1979 to 2014, G312 bridge (N38 ° 59'51.71 ", E100 ° 24'38.76"), railway bridge (n39 ° 2'33.08 ", E100 ° 25'49.42"), Gaoya bridge (n39 ° 08'06.35 ", E100 ° 25'58.23") and Pingchuan bridge (n39 ° The third part is the daily discharge and water level data of S213 bridge, G312 bridge, railway bridge, Gaoya bridge and Pingchuan bridge in the main stream of Heihe River from 1979 to 2014. Among them, the flow data refers to the section flow of Heihe River, and the water level data refers to the water level at the runoff densification observation point in the middle reaches of hiwater. The reliability of monthly data is higher than that of daily data, and the reliability of flow is higher than that of water level.
XIE Zhenghui
The experimental data of Yingke Daman in Heihe River Basin is supported by the key fund project of Heihe River plan, "eco hydrological effect of agricultural water saving in Heihe River Basin and multi-scale water use efficiency evaluation". Including: soil bulk density, soil water content, soil texture, corn sample biomass, cross-section flow, etc Data Description: 1. Sampling location of Lai and aboveground biomass: Yingke irrigation district; sampling time: May 2012 to September 2012; Lai and aboveground biomass of maize were measured by canopy analyzer (lp-80), and aboveground biomass was measured by sampling drying method; sample number: 16. 2. Soil texture: Sampling location: Yingke irrigation district and Shiqiao Wudou Er Nongqu farmland in Yingke irrigation district; soil sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm; sampling time: 2012; measurement method: laboratory laser particle size analyzer; sample number: 38. 3. Soil bulk density: Sampling location: Yingke irrigation district and Daman irrigation district; sampling depth of soil bulk density is 100 cm, sampling levels are 0-50 cm and 50-100 cm respectively; sampling time: 2012; measurement method: ring knife method; number of sample points: 34. 4. Soil moisture content: this data is part of the monitoring content of hydrological elements in Yingke irrigation district. The specific sampling location is: Shiqiao Wudou Er Nongqu farmland in Yingke Irrigation District, planting corn for seed production; soil moisture sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm Methods: soil drying method and TDR measurement; sample number: 17. 5. Cross section flow: Sampling location: the farmland of Wudou Er Nong canal in Shiqiao, Yingke irrigation district; measure the flow velocity, water level and water temperature of different canal system sections during each irrigation, record the time and calculated flow, monitor once every 3 hours until the end of irrigation; sampling time: 2012.5-2012.9; measurement method: Doppler ultrasonic flow velocity meter (hoh-l-01, Measurement times: Yingke irrigation data of four times.
HUANG Guanhua, JIANG Yao
The data include the collection of elements and isotopes of river water and groundwater (including spring water) in hulugou small watershed of Heihe River. Sampling location: (1) There are two river water sampling points, one of which is located at the outlet weir of hulugou small watershed in the upper reaches of Heihe River, with longitude and latitude of 99 ° 52 ′ 47.7 ″ E and 38 ° 16 ′ 11 ″ n. The second sampling point is located at the outlet of hulugou area II in the upper reaches of Heihe River, with longitude and latitude of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n. (2) The sampling points of groundwater spring and well water are located at 20m to the east of the drainage basin outlet, with longitude and latitude of 99 ° 52 ′ 50.9 ″ E and 38 ° 16 ′ 11.44 ″ n. The well water sampling point is located near the intersection of East and West Branch ditches, with longitude and latitude of 99 ° 52 ′ 45.38 ″ E and 38 ° 15 ′ 21.27 ″ n. Data Description: 1. Doc and DIC values of river water and groundwater at the outlet of hulugou small watershed from July to September 2014 were analyzed. The DOC and DIC values of the samples were tested by oiaurora 1030w TOC instrument, and the detection range was 2ppb c-30000ppm C. 2. From July to September 2014, the δ D and δ 18O values of precipitation, river water and groundwater in hulugou small watershed were measured by Picaro l2130-i ultra-high precision liquid water and water vapor isotope analyzer. The results were expressed by δ values relative to the international standard material v-smow, with the measurement accuracy of 0.038 ‰ and 0.011 ‰ respectively. 3. Doc values of river water and soil water at the outlet of hulugou small watershed from May to September 2013 were determined by analytikjena multi n / C 3100 total nitrogen and total carbon tester. 4. Doc and DIC values of river water and groundwater at the outlet of hulugou small watershed from July to September 2014 were measured by oiaurora 1030w TOC instrument, and the detection range was 2ppb c-30000ppm C.
MA Rui , CHANG Qixin
Data of industrial structure change and water use evolution trend of social and economic development in Heihe River Basin
DENG XiangZheng
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