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
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 actual evaporation simulation is basically consistent with the station observation published by the Meteorological Bureau. The model simulates the water cycle process from 1998 to 2017. After verification, the spatial and temporal distribution of the actual evaporation (including soil evaporation and plant transpiration) on the 0.01 degree daily scale in the whole Tibetan Plateau is given.
YE Aizhong
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 of bacterial post-treatment products and conventional water quality parameters of some lakes in the third pole in 2015 collected the bacterial analysis results and conventional water quality parameters of some lakes in the Qinghai Tibet Plateau during 2015. Through sorting, summarizing and summarizing, the bacterial post-treatment products of some lakes in the third pole in 2015 are obtained. The data format is excel, which is convenient for users to view. The samples were collected by Mr. Ji mukan from July 1 to July 15, 2015, including 28 Lakes (bamuco, baimanamuco, bangoso (Salt Lake), Bangong Cuo, bengcuo, bieruozhao, cuo'e (Shenza), cuo'e (Naqu), dawaco, dangqiong Cuo, dangjayong Cuo, Dongcuo, eyaco, gongzhucuo, guogencuo, jiarehbu Cuo, mabongyong Cuo, Namuco, Nier CuO (Salt Lake), Norma Cuo, Peng yancuo (Salt Lake), Peng Cuo, gun Yong Cuo, Se lincuo, Wu rucuo, Wu Ma Cuo, Zha RI Nan Mu Cuo, Zha Xi CuO), a total of 138 samples. The extraction method of bacterial DNA in lake water is as follows: the lake water is filtered onto a 0.45 membrane, and then DNA is extracted by Mo bio powerOil DNA kit. The 16S rRNA gene fragment amplification primers were 515f (5'-gtgccagcmgcgcggtaa-3') and 909r (5'-ggactachvggtwtctaat-3'). The sequencing method was Illumina miseq PE250. The original data were analyzed by mothur software, including quality filtering and chimera removal. The sequence classification was based on the silva109 database. The archaeal, eukaryotic and unknown source sequences had been removed. OTU classifies with 97% similarity and then removes sequences that appear only once in the database. Conventional water quality detection parameters include dissolved oxygen, conductivity, total dissolved solids, salinity, redox potential, nonvolatile organic carbon, total nitrogen, etc. The dissolved oxygen is determined by electrode polarography; Conductivity meter is used for conductivity; Salinity is measured by a salinity meter; TDS tester is used for total dissolved solids; ORP online analyzer was used for redox potential; TOC analyzer is used for non-volatile organic carbon; The water quality parameters of total nitrogen were obtained by Spectrophotometry for reference.
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
This product provides the monthly runoff, evapotranspiration and soil water of major Arctic river basins in 2018-2065 based on the land surface model Vic. The spatial accuracy is 10km. Major Arctic river basins include Lena, Yenisey, ob, Kolyma, Yukon and Mackenzie basins. According to the rcp2.6 (low emission intensity) and rcp8.5 (high emission intensity) scenario results provided by the ipsl-cm5a-lr model in cmip5 in the fifth assessment report of IPCC, the future climate scenario driving data applicable to the Arctic region of 0.1 ° is obtained through statistical downscaling. Using the calibrated land surface hydrological model Vic on a global scale, based on the future climate scenario driven data of 0.1 °, the monthly time series of runoff, soil water and evapotranspiration of the Arctic River Basin in the middle of this century under future climate change are estimated.
TANG Yin , TANG Qiuhong , WANG Ninglian, WU Yuwei
A long-term (1980-2017) land evaporation (E) product with a spatial resolution of 0.25 degree. This is a merged product from three model-based E products using the Reliability Ensemble Averaging (REA) method which minimizes errors. These include the fifth-generation ECMWF Re-Analysis (ERA5), the second Modern-Era Retrospective analysis for Research and Applications (MERRA2), and the Global Land Data Assimilation System (GLDAS). To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain daily and monthly mean data (e.g., REA_1980_day.nc and REA_1980_mon.nc). The dataset is stored in NetCDF format, containing the variable E, representing land evaporation, produced in millimeters (mm) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.875E to 179.875E, the latitude from -59.875N to 89.875N. Complete time coverage is from January 1, 1980, to December 31, 2017.
LU Jiao, WANG Guojie, CHEN Tiexi, LI Shijie, HAGAN Daniel, KATTEL Giri, PENG Jian, JIANG Tong, SU Buda
The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
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
As an important part of global semi-arid grassland, adequately understanding the spatio-temporal variability of evapotranspiration (ET) over the temperate semi-arid grassland of China (TSGC) could advance our understanding of climate, hydrological and ecological processes over global semi-arid areas. Based on the largest number of in-situ ET measurements (13 flux towers) within the TSGC, we applied the support vector regression method to develop a high-quality ET dataset at 1 km spatial resolution and 8-day timescale for the TSGC from 1982 to 2015. The model performed well in validation against flux tower‐measured data and comparison with water-balance derived ET.
LEI Huimin
The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
ZHANG Pei, ZHENG Donghai, WEN Jun, ZENG Yijian, WANG Xin, WANG Zuoliang, MA Yaoming, SU Zhongbo
Meteorological forcing dataset for Arctic River Basins includes five elements: daily maximum, minimum and average temperature, daily precipitation and daily average wind speed. The data is in NetCDF format with a horizontal spatial resolution of 0.083°, covering Yenisy, Lena, ob, Yukon and Mackenzie catchments. The data can be used to dirve hydrolodical model (VIC model) for hydrological process simulation of the Arctic River Basins. The further quality control were made for daily observation data from Global Historical Climatology Network Daily database(GHCN-D), Global Summary of the Day (GSPD),The U.S. Historical Climatology Network (USHCN),Adjusted and homogenized Canadian climate data (AHCCD) and USSR / Russia climate data set (USSR / Russia). The thin plate spline interpolating method, which similar to the method used in PNWNAmet datasets (Werner et al., 2019), was employed to interpolate daily station data to 5min spatial resolution daily gridded forcing data using WorldClim and ClimateNA monthly climate normal data as a predictor.
ZHAO Qiudong, WU Yuwei
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
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,
The dataset includs borehole core lithology, altitude survey, soil thickness and slop measurement, hydrogeological survey, and hydrogeophysical survey in the Maqu catchment of the Yellow River source region in the Tibetan Plateau. The borehole lithology data is from the 2017 drilled borehole ITC_ Maqu_ 1; altitude survey was carried out using RTK in 2019; Soil thickness and slope data were collected by auger and inclinometer in 2018 and 2019; hydrogeological survey includes groundwater table depth measurements in 2018 and 2019, and aquifer test data obtained in 2019; hydrogeological survey includes Magnetic Resonance Sounding (MRS) , Electrical Resistivity Tomography (ERT) , Transient Electromagnetic (TEM) , and magnetic susceptibility measurements. MRS and ERT surveys were conducted in 2018. TEM and magnetic susceptibility measurements were carried out in 2019.
LI Mengna, ZENG Yijian, Maciek W. LUBCZYNSKI, BOB Su, QIAN Hui
The data set includes annual mass balance of Naimona’nyi glacier (northern branch) from 2008 to 2018, daily meteorological data at two automatic meteorological stations (AWSs) near the glacier from 2011 to 2018 and monthly air temperature and relative humidity on the glacier from 2018 to 2019. In the end of September or early October for each year , the stake heights and snow-pit features (snow layer density and stratigraphy) are manually measured to derive the annual point mass balance. Then the glacier-wide mass balance was then calculated （Please to see the reference). Two automatic weather stations (AWSs, Campbell company) were installed near the Naimona’nyi Glacier. AWS1, at 5543 m a. s.l., recorded meteorological variables from October 2011 at half hourly resolution, including air temperature (℃), relative humidity (%), and downward shortwave radiation (W m-2) . AWS2 was installed at 5950 m a.s.l. in October 2010 at hourly resolution and recorded wind speed (m/s), air pressure (hPa), precipitation (mm). Data quality: the quality of the original data is better, less missing. Firstly, the abnormal data in the original records are removed, and then the daily values of these parameters are calculated. Two probes (Hobo MX2301) which record air temperature and relative humidity was installed on the glacier at half hour resolution since October 2018. The observed meteorological data was calculated as monthly values. The data is stored in Excel file. It can be used by researchers for studying the changes in climate, hydrology, glaciers, etc.
ZHAO Huabiao
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
The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
YAO Junqiang, CHEN Jing, LI Jiangang
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
This dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. ELBARA-III horizontal and vertical brightness temperature are computed from measured radiometer voltages and calibrated internal noise temperatures. The data is reliable, and its quality is evaluated by 1) Perform ‘histogram test’ on the voltage samples (raw-data) of the detector output at sampling frequency of 800 Hz. Statistics of the histogram test showed no non-Gaussian Radio Frequency Interference (RFI) were found when ELBAR-III was operated. 2) Check the voltages at the antenna ports measured during sky measurements. Results showed close values. 3) Check the instrument internal temperature, active cold source temperature and ambient temperature. 3) Analysis the angular behaviour of the processed brightness temperatures. -Temporal resolution: 30 minutes -Spatial resolution: incident angle of observation ranges from 40° to 70° in step of 5°. The area of footprint ranges between 3.31 m^2 and 43.64 m^2 -Accuracy of Measurement: Brightness temperature, 1 K; Soil moisture, 0.001 m^3 m^-3; Soil temperature, 0.1 °C -Unit: Brightness temperature, K; Soil moisture, m^3 m^-3; Soil temperature, °C/K
BOB Su, WEN Jun
This data set includes the monthly average actual evapotranspiration of the Tibet Plateau from 2001 to 2018. The data set is based on the satellite remote sensing data (MODIS) and reanalysis meteorological data (CMFD), and is calculated by the surface energy balance system model (SEBS). In the process of calculating the turbulent flux, the sub-grid scale topography drag parameterization scheme is introduced to improve the simulation of sensible and latent heat fluxes. In addition, the evapotranspiration of the model is verified by the observation data of six turbulence flux stations on the Tibetan Plateau, which shows high accuracy. The data set can be used to study the characteristics of land-atmosphere interaction and the water cycle in the Tibetan Plateau.
HAN Cunbo, MA Yaoming, WANG Binbin, ZHONG Lei, MA Weiqiang*, CHEN Xuelong, SU Zhongbo
Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
MA Ziqiang
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
This data set describes the temporal and spatial distribution of precipitation in the Upper Brahmaputra River Basin. We integrate (CMA, GLDAS, ITP-Forcing, MERRA2, TRMM) five sets of reanalysis precipitation products and satellite precipitation products, and combine the observation precipitation of 9 national meteorological stations from China Meteorological Administration (CMA) and 166 rain gauges of the Ministry of Water Resources (MWR) in the basin. The time range is 1981-2016, the time resolution is 3 hours, the spatial resolution is 5 km, and the unit is mm/h. The data will provide better data support for the study of Upper Brahmaputra River Basin, and can be used to study the response of hydrological process to climate change. Please refer to the instruction document uploaded with the data for specific usage information.
WANG Yuanwei, WANG Lei, LI Xiuping, ZHOU Jing
The UHSLC offers tide gauge data with two levels of quality-control (QC). Fast Delivery (FD) data are released within 1-2 months of data collection and receive only basic QC focused on large level shifts and obvious outliers. The GLOSS/CLIVAR (formerly known as the WOCE) "fast" sea level data is distributed as hourly, daily, and monthly values. This project is supported by the NOAA Climate and Global Change program, and is one of the activities of the University of Hawaii Sea Level Center. Each file is given a name "h###.dat" where "h" denotes hourly sea level data and "###" denotes the station number. A file exists for every station with hourly data. The UHSLC datasets are GLOSS data streams (read more here). There are many tide gauge records in the UHSLC database, but the backbone is the GLOSS Core Network (GCN) – a global set of ~300 tide gauge stations that serve as the foundation of the global in situ sea level network. The network is designed to provide evenly distributed sampling of global coastal sea level variation at a variety of time-scales.
DONG Wen, University of hawaii sealevel center （UHSLC）
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
PML_V2 terrestrial evapotranspiration and total primary productivity dataset, including gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall , Ei) and water body, ice and snow evaporation (ET_water), a total of 5 elements. The data format is tiff, the space-time resolution is 8 days, 0.05°, and the time span is 2002.07-2019.08. Based on the Penman-Monteith-Leuning (PML) model, PML_V2 is coupled to the GPP process based on stomatal conductance theory. GPP and ET mutually restrict and restrict each other, which makes PML_V2 in ET simulation accuracy, which is greatly improved compared with the previous model. The parameters of PML_V2 are divided into different vegetation types and are determined on 95 vorticity-related flux stations around the world. The parameters were then migrated globally according to the MODIS MCD12Q2.006 IGBP classification. PML_V2 uses GLDAS 2.1 meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 terrestrial evapotranspiration and total primary productivity data sets.
ZHANG Yongqiang
There is a lack of a set of high-resolution precipitation gridded data with long time series in the main basin of the Arctic. This dataset provides daily precipitation in the main basin of the Arctic. The range of data set is from 45 ° N to 76.15 °N. The metadata used includes: the meteorological station data during 1980-2015 obtained from GSOD and the reanalysis data of ERA-interim during 1980-2018. This dataset was obtained by bias correction of ERA-interim data with the improved quantile mapping method, and the background data used for bias correction is the interpolation gridded precipitation, in interpolation process, we not only take into account the effect of elevation, but fully consider the influence of wind on gauge measurements, the gauge used in interpolation all undergo bias adjustment. This dataset performs well both in region scale and cell grid scale, with high accuracy, which providing a set of reliable precipitation gridded data for the hydrological research of the Arctic.
LEI Huajin, LI Hongyi
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).
WANG Lei
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
WANG Lei
Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).
LIU Shaomin, LI Xiang , XU Ziwei
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
Runoff is formed by atmospheric precipitation and flows into rivers, lakes or oceans through different paths in the basin. It is also used to refer to the amount of water passing through a certain section of the river in a certain period of time, i.e. runoff. Runoff data plays an important role in the study of hydrology and water resources, which affects the social and economic development of Adam land. This data is the flow of five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan), which comes from the hydrometeorological bureaus of Central Asian countries. The time scale is the average annual data of 2015. This data provides basic data for the project, which is convenient to analyze the situation of eco hydrological water resources in Central Asia, and provides data support for project data analysis.
LIU Tie
The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.
MA Yaoming
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
Using ETWatch model with the system complete the heihe river basin scale 1 km resolution 2014 surface evaporation data with middle oasis 30 meters resolution on scale data set, the surface evaporation raster image data of the data sets, it is the time resolution of scale from month to month, the spatial resolution of 1 km scale (covering the whole basin) and 30 meters scale (middle oasis area), the unit is mm.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: 1) 1 km resolution remote sensing data set Monthly cumulative ET value file name: heihe-1km_2014m01_eta.tif Heihe refers to heihe river basin, 1km means the resolution is 1km, 2014 means the year of 2014, m01 means the month of January, eta means the actual evapotranspiration data, and tif means the data is tif format. Name of quarterly cumulative ET value file: heihe-1km_2014s01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, s01 represents the period from January to march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-1km_2014y_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format. 2) remote sensing data set with a resolution of 30 meters Monthly cumulative ET value file name: heihe-midoasa-30m_2014m01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, m01 represents January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-midoasa-30m_2014s01_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents 2014, s01 represents january-march, and the first quarter, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Annual cumulative value file name: heihe-midoasa-30m_2014y_eta.tif Heihe represents the heihe river basin, midoasis represents the mid-range oasis area, 30m represents the resolution of 30 meters, 2014 represents the year of 2014, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
WU Bingfang
The output data of the distributed eco hydrological model (gbehm) in the upper reaches of Heihe River includes the spatial distribution data series of 1-km grid. Region: Heihe River (Yingluo gorge), Beida River (Binggou new land), temporal resolution: Monthly Scale, spatial resolution: 1km, period: 1960-2014. Data include precipitation, evapotranspiration, runoff depth, soil volume water content (0-100cm). All data are in ASCII format. Please refer to the basin.asc file in the reference directory for the spatial range of the basin. Projection parameters of model results: sphere_Arc_Info_Lambert_Azimuthal_Equal_Area
YANG Dawen
The output data of the distributed eco hydrological model (gbehm) in the upper reaches of Heihe River includes the spatial distribution data series of 1-km grid. Region: upper reaches of Heihe River (Yingluo gorge), temporal resolution: Monthly Scale, spatial resolution: 1km, period: 2015-2070 (future scenario). The data include precipitation, evapotranspiration, runoff depth and average temperature. All data are in ASCII format. Please refer to the basin.asc file in the reference directory for the spatial range of the basin. Projection parameters of model results: sphere_Arc_Info_Lambert_Azimuthal_Equal_Area
YANG Dawen
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
The site No. 1 EC towers were used for the intercomparison field in the Yingke irrigation district (1552.75 m, 38°59′51.71″ N, 100°24′38.76″ E). The land surface is homogeneous and dominated by vegetables in the middle reaches of the Heihe River Basin. The precipitation comparison dataset was collected between 12 June, 2012, and 22 November, 2012. The dataset includes data for five different rain gauge types, i.e., pit gauge, Chinese standard manual precipitation gauge, siphon rain gauge, tipping bucket gauge, and weighting gauge. The mountain heights for these gauges were 0.0, 0.7, 1.2, 1.5, and 1.5 m, respectively. The data were recorded every 1 hour, 1 day, 10 minutes, 10 minutes, and 10 minutes, respectively. The main objective of the data collection was to perform an intercomparison of in situ rainfall measurements. 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 monthly precipitation data set of China's alpine mountains includes the qilian mountains (1960-2013), tianshan mountains (1954-2013) and Yangtze river source (1957-2014). The distributed hydrological model needs high-precision spatial distribution information of precipitation as input.Because of the scarcity of stations, the precipitation interpolation at stations cannot reflect the spatial distribution of precipitation in the alpine mountainous areas.Generation method of this dataset: (1) collect precipitation data of national meteorological stations and hydrological stations in various regions, and add precipitation observation data of field stations of Chinese academy of sciences above an altitude of 4000m; (2) use the temperature data of each station to correct the collected precipitation data of different precipitation types; (3) establish the relationship between precipitation data and altitude, longitude and latitude, and fit monthly to generate monthly precipitation data set of 1km scale. The interpolation year of this data is 1954-2014. The data projection method is Albers projection. The spatial interpolation precision is 1-km, and the time precision is monthly data.The results show that the interpolation precipitation is reliable. The data is stored in ASCII files. The file names of the monthly precipitation data files of tianshan mountain and Yangtze river source are in the form yyyymm.txt. YYYY is the year and MM is the month.The monthly precipitation data of qilian mountain is named as: month_10001.txt, this file is the precipitation data of January 1960, successively month_10002.txt is the precipitation of February 1960, and month_10013.txt is the precipitation data of January 1961,......Month_10648.txt represents the precipitation data for December 2013.Each ASCII file represents the grid precipitation data of the day in mm.
CHEN Rensheng, LIU Junfeng
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
The output data of the distributed eco hydrological model in the upper reaches of Heihe River includes the spatial distribution data of 1-km grid and the discharge time series data of the outlet of the basin. (1) Spatial distribution data of 1-km grid, monthly average soil moisture, actual evapotranspiration, runoff depth and other spatial distribution data of 1-km resolution. (2) Runoff time series daily flow data of river basin outlet.
YANG Dawen
Data investigation method: investigation and collection of Heihe River Basin Authority. The data include: the water distribution plan of the main stream of Heihe River (including Liyuan River) prepared by the Yellow River Water Conservancy Commission of the Ministry of water resources in 1996; the brief report on the water conservancy planning of the main stream of Heihe River prepared by Lanzhou survey and Design Institute of the Ministry of water resources in 1992; the short term management plan of Heihe River Basin approved by the State Council in 2001; the compilation of historical documents of water regulation of Heihe River by the administration of Heihe River Basin in 2008 》In 2014, the research on the reasonable allocation scheme of water resources in Jiuquan Basin of the Taolai River Basin was compiled by the Taolai River Basin Authority.
ZHENG Hang, WANG Zhongjing
From 1947 to 1948, the Hexi Water Conservancy Project Corps of the Ministry of Water Resources of the Republic of China compiled the Heihe Mainstream Water Conservancy Project (15 items). This is the first comprehensive engineering plan compiled by the whole basin based on modern hydraulic engineering principles. This batch of planning mainly focus on irrigation projects, taking into account inter-basin water transfer and flood control projects. Most of these projects achieved varying degrees of realization after 1949, but the plan to introduce the Datong River into the Heihe River has never been implemented. The collection of hydrological and socioeconomic data in these documents was mostly completed during the Anti-Japanese War, and was completed by the Gansu Irrigation Works, Plantation and Pasturage Company. It is the earliest and systematic data of the basin. It has irreplaceable value for analyzing and understanding the water conservancy development and socio-economic situation of the Heihe River mainstream during the Republic of China. The main contents of this data include Zhangye, Shandan, Minle, Linze, Gaotai reservoir projects, groundwater interception and irrigation projects, surface runoff irrigation projects, irrigation canal system consolidation projects and other plans.
WANG Zhongjing
1. Data overview: This data set is the scale artificial evaporation dish and precipitation data of qilian station from January 1, 2011 to December 31, 2011.The artificial evaporator is a 20cm standard evaporator, and the precipitation is a 20cm standard rain gauge. 2. Data content: (1) the evaporation capacity is measured at 20:00 every day with 20 special measuring cups;It is before a day commonly 20 when measure clear water 20 millimeter with special measure cup (original quantity) pour into implement inside, 24 hours hind namely in the same day 20 hour, again measure the water inside implement (allowance), its reduce quantity is evaporation quantity.Namely: evaporation = original quantity - residual quantity.If there is precipitation between 20:00 of the previous day and 20:00 of the same day, the calculation formula is: evaporation = original quantity + precipitation - residual quantity. (2) precipitation is generally observed in two stages, namely once at 8 o 'clock and once at 20 o 'clock each day. In the rainy season, observation periods are increased, and additional measurements are needed when the rainfall is large.The daily rainfall is divided into 8 a.m. of each day, and the precipitation from 8 a.m. to 8 a.m. of the next day is the precipitation of the current day.If it is rain, measure it with 20 special measuring cups. When it snows, only use the outer tube as snow bearing equipment, and then weigh it with an electronic balance (shenyang longteng es30k-12 type electronic balance, the minimum sensible amount is 0.2g). 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, SONG Yaoxuan, LIU Junfeng, YANG Yong, QING Wenwu, LIU Zhangwen, HAN Chuntan
1. Data overview: This data set is the scale meteorological gradient data of qilian station from January 1, 2012 to December 31, 2012 (installed at the end of September 2011).VG1000 gradient observation system carries out long-term monitoring of wind speed, wind direction, air temperature, humidity, radiation and other conventional meteorological elements, and carries out data storage and processing analysis in combination with the data collector with high precision and high scanning frequency. 2. Data content: The main observation factors include four layers of air temperature, humidity and two-dimensional ultrasonic wind, rain and snow volume meter, eight layers of ground temperature, soil moisture content, etc. 3. Space and time range: Geographical coordinates: longitude: longitude: 99° 52’e;Latitude: 38°15 'N;Height: 3232.3 m
CHEN Rensheng, HAN Chuntan
1. Data overview: This data set is the scale artificial evaporation dish and precipitation data of qilian station from January 1, 2013 to December 31, 2013. The artificial evaporator is a 20cm standard evaporator, and the precipitation is a 20cm standard rain gauge. 2. Data content: (1) the evaporation capacity is measured at 20:00 every day with 20 special measuring cups;It is before a day commonly 20 when measure clear water 20 millimeter with special measure cup (original quantity) pour into implement inside, 24 hours hind namely in the same day 20 hour, again measure the water inside implement (allowance), its reduce quantity is evaporation quantity.Namely: evaporation = original quantity - residual quantity.If there is precipitation between 20:00 of the previous day and 20:00 of the same day, the calculation formula is: evaporation = original quantity + precipitation - residual quantity. (2) precipitation is generally observed in two stages, namely once at 8 o 'clock and once at 20 o 'clock each day. In the rainy season, observation periods are increased, and additional measurements are needed when the rainfall is large.The daily rainfall is divided into 8 a.m. of each day, and the precipitation from 8 a.m. to 8 a.m. of the next day is the precipitation of the current day.If it is rain, measure it with 20 special measuring cups. When it snows, only use the outer tube as snow bearing equipment, and then weigh it with an electronic balance (shenyang longteng es30k-12 type electronic balance, the minimum sensible amount is 0.2g). 3. Space and time range: Geographical coordinates: longitude: 99° 53’e; Latitude: 38°16 'N; Height: 2981.0 m
CHEN Rensheng, HAN Chuntan, SONG Yaoxuan, LIU Junfeng, YANG Yong, LIU Zhangwen
This project is based on the gsflow model of USGS to simulate the surface groundwater coupling in Zhangye basin in the middle reaches of Heihe River. The space-time range and accuracy of the simulation are as follows: Simulation period: 1990-2012; Simulation step: day by day; The spatial scope of simulation: Zhangye basin; The spatial accuracy of simulation: the underground part is 1km × 1km grid (5 layers, the total number of grids in each layer is 150 × 172 = 25800, among which the active grid 9106); the surface part is based on the hydrological response unit (HRU) (588 in total, each HRU covers an area of several square kilometers to dozens of square kilometers). The data include: surface infiltration, actual evapotranspiration, average soil moisture content, surface groundwater exchange, shallow groundwater level, simulated daily flow of Zhengyi gorge, simulated monthly flow of Zhengyi gorge, groundwater extraction and river diversion
ZHENG Yi
1、 Data Description: from June 2012 to June 2013, the rainfall, river water and soil water in the basin were sampled and analyzed. 2、 Sampling location: rainfall sampling point is located in Qilian station of Chinese Academy of Sciences, with longitude and latitude of 99 ° 52 ′ 39.4 ″ e, 38 ° 15 ′ 47 ″ n; river water sampling point is located at the outlet of hulugou watershed, with longitude and latitude of 99 ° 52 ′ 47.7 ″ e, 38 ° 16 ′ 11 ″ n, with sampling frequency of once a week; soil water sampling point is located in the middle and lower part of hongnigou catchment, with sampling depth of 180cm underground and longitude and latitude of 99 ° 52 ′ 25.98 ″ E, 38 ° 15 ′ 36.11 ″ n, only one sample is taken. 3、 Test method: thermofisher TM flash 2000 and mat 253 gas stable isotope ratio mass spectrometer were used to measure the samples in 2012; l2130-i ultra-high precision liquid water and water vapor isotope analyzer was used to measure the samples in 2013.
SUN Ziyong, CHANG Qixin
1. Data overview: This data set is the scale artificial evaporation dish and precipitation data of qilian station from January 1, 2012 to December 31, 2012. The artificial evaporator is a 20cm standard evaporator, and the precipitation is a 20cm standard rain gauge. 2. Data content: (1) the evaporation capacity is measured at 20:00 every day with 20 special measuring cups;It is before a day commonly 20 when measure clear water 20 millimeter with special measure cup (original quantity) pour into implement inside, 24 hours hind namely in the same day 20 hour, again measure the water inside implement (allowance), its reduce quantity is evaporation quantity.Namely: evaporation = original quantity - residual quantity.If there is precipitation between 20:00 of the previous day and 20:00 of the same day, the calculation formula is: evaporation = original quantity + precipitation - residual quantity. (2) precipitation is generally observed in two stages, namely once at 8 o 'clock and once at 20 o 'clock each day. In the rainy season, observation periods are increased, and additional measurements are needed when the rainfall is large.The daily rainfall is divided into 8 a.m. of each day, and the precipitation from 8 a.m. to 8 a.m. of the next day is the precipitation of the current day.If it is rain, measure it with 20 special measuring cups. When it snows, only use the outer tube as snow bearing equipment, and then weigh it with an electronic balance (shenyang longteng es30k-12 type electronic balance, the minimum sensible amount is 0.2g). 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, SONG Yaoxuan, LIU Junfeng, YANG Yong, LIU Zhangwen, HAN Chuntan
Based on the "western data center", the daily discharge from three field observation stations (zamashk, Yingluoxia, Qilian) since 1990-1995 is sorted out.
ZHANG Zhiqiang
The data set contains all single glacial reserves (in KM3) in the Tibetan Plateau of 1970s and 2000s. This data set comes from the result data of the paper entitled "consolidating the Randolph glacier inventory and the glacier inventory of China over the Qinghai titanium plate and investigating glacier changes since the mid-20th century". The first draft of this paper has been completed and is planned to be submitted to earth system science data. The 1970s basic glacier catalog data in the dataset is extracted from Randolph glacier Inventory data set, 2000s basic glacial catalogue is from China's second glacial catalogue data set. Based on the glacial boundary extracted from the two data sets and combined with the grid based bedrock elevation data set (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m) and the glacial table obtained by a slope dependent method Based on the surface elevation data set, the single glacier reserves in the two catalogues are calculated. In addition, the calculation results of single glacier reserves obtained in this study have been compared and verified with the calculation results of partial glacier reserves, relevant remote sensing data sets, and the global glacier thickness data set based on the average of multiple glacier model sets in multiple directions, and the errors in the calculation results have also been quantified. The establishment of the data set is expected to provide the data basis for the future regional water resources estimation and glacier ablation research, and the acquisition of the data also provides a new idea for the future glacier reserves research.
WANG Zhongjing
ET (ET) monitoring is crucial to agricultural water resource management, regional water resource utilization planning and socio-economic sustainable development.The limitations of traditional ET monitoring methods mainly lie in that they cannot observe a large area at the same time and can only be limited to observation points. Therefore, the cost of personnel and equipment is relatively high, and they can neither provide surface ET data, nor provide ET data of different land use types and crop types. Quantitative monitoring of ET can be achieved by using remote sensing. The characteristics of remote sensing information are that it can not only reflect the macroscopic structure characteristics of the earth surface, but also reflect the microscopic local differences. Version 2.0 (second edition) of the surface evapotranspiration data set of the heihe river basin from 2000 to 2013 is based on multi-source remote sensing data and the latest ETWatch model is adopted to estimate the raster image data. Its temporal resolution is monthly scale and the spatial resolution is 1km scale. The data covers the whole basin in millimeters.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: Monthly cumulative ET value file name: heihe-1km_2013m01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2013 represents the year of 2013, m01 represents the month of January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-1km_2013s01_eta.tif Heihe refers to heihe river basin, 1km refers to the resolution of 1km, 2013 refers to 2013, s01 refers to january-march, is the first quarter, eta refers to the actual evapotranspiration data, and tif refers to the data in tif format. Annual cumulative value file name: heihe-1km_2013y_eta.tif Among them, heihe represents heihe river basin, 1km represents the resolution of 1km, 2013 represents the year of 2013, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
WU Bingfang
1. Data overview: This data set is the scale meteorological gradient data of qilian station from January 1, 2013 to December 31, 2013 (installed at the end of September 2011).VG1000 gradient observation system carries out long-term monitoring of wind speed, wind direction, air temperature, humidity, radiation and other conventional meteorological elements, and carries out data storage and processing analysis in combination with the data collector with high precision and high scanning frequency. 2. Data content: The main observation factors include four layers of air temperature, humidity and two-dimensional ultrasonic wind, rain and snow volume meter, eight layers of ground temperature, soil moisture content, etc. 3. Space and time range: Geographical coordinates: longitude: longitude: 99° 52’e;Latitude: 38°15 'N;Height: 3232.3 m
CHEN Rensheng, HAN Chuntan
Near-surface atmospheric driving data prepared by ETMonitor and WRF models based on remote sensing surface evapotranspiration model were used to estimate the average surface evapotranspiration of the heihe river basin with a resolution of 250m in 8 days from may to September 2012.The coordinate system is the projection of equal latitude and longitude, and the spatial range is 96.5e -- 102.5e, 37.5n -- 43N.8 days data using synthetic way of storage, the data format for GEOTIFF, naming: 2012 ddd_evapotranspiration. Tif, including a DDD, ordinal number, for example 2012121 _evapotranspiration. Tif said 2012 day ordinal number is 121-128 days, the average surface evaporation unit is mm/d.The data type is single-precision floating point with an invalid value of -9.
JIA Li
Near-surface atmospheric driving data prepared by ETMonitor and WRF models based on remote sensing surface evapotranspiration model were used to estimate the daily surface evapotranspiration of the heihe river basin at 1km from 2009 to 2011.The coordinate system is the longitude and latitude projection, and the spatial range is 96.5e -- 102.5e, 37.5n -- 43N.Using daily data storage, data format for GEOTIFF, naming: yyyyddd_EvapoTranspiration. tif, including yyyy for years, DDD for ordinal.The data type is single-precision floating point in mm/d and the invalid value is -9.
JIA Li
"Hydrological ecological economic process coupling and evolution of Heihe River Basin Management under the framework of water rights" (91125018) project data collection 2 - Dunhuang comprehensive plan for rational utilization of water resources and ecological protection (2011-2020) Planning documents mainly include: 1. Current situation and existing problems of regional water resources utilization; 2. Guiding ideology, basic principles and planning objectives; 3. Analysis of economic, social and ecological water demand; 4. Plan for water resources allocation; 5. Construction of water right system; 6. Main engineering measures; 7. Environmental impact arrangement.
"Hydrologic - ecological - economic process coupling and evolution of heihe river basin governance under the framework of Water rights" (91125018) project data exchange to 5-water-plan-california 1. Data overview: California's water resources plan for 2005 for catchment comparison 2. Data content: the public plan
WANG Zhongjing
This data includes experimental data of grassland interception control and observation data of maximum water holding capacity of grassland. The maximum water holding capacity experiment was carried out in 2011. The main vegetation types selected are Carex, Polygonum viviparum, Plantago asiatica and Potentilla chinensis. The maximum water holding capacity experiment was carried out on each type of samples and the samples were photographed. The specific data obtained are shown in the document. The grassland canopy interception was carried out in the growing season of 2012, and was completed by artificial rainfall control experiment. At the end of the growing season, the main types of grassland in the basin were sampled according to grazing and grazing ban. During artificial rainfall, rainfall and penetrating rainfall are recorded every 1min. Finally, the grassland canopy interception is calculated by the difference between rainfall and penetrating rainfall.
ZHAO Chuanyan, MA Wenying
Canopy interception field is located in 2700m forest in Pailugou watershed of Qilian mountain, with 60 precipitation interception barrels arranged at equal intervals on the ground. The specifications of the interception barrel are: the radius of the bottom surface is 10cm and the height is 35cm. The observation period was from June to July 2012 and from July to September 2013, and 17 precipitation events (including each precipitation) were recorded. The unit is mm.
HE Zhibin
Three artificial rainfall events were performed on the shady grassland at the altitude of 2700m in the Pailugou watershed of the Qilian Mountains. The times were July 15, 2011, July 16, and July 22, 2011, respectively. Runoff rate, data is recorded every half an hour. Two rainfall simulations were also performed on the sun-slope grassland at the same altitude. As a comparative experiment, the time was July 24 and 25, 2011.
HE Zhibin
Nine and six evaporation barrels were arranged in the 2700m Qinghai spruce forest and the shady grassland outside the forest in the Pailugou watershed of the Qilian Mountains. Specifications are 20cm in diameter and 80cm in height. The measurement date is from June 2012 to September 2012. Daily measurement is performed and the daily precipitation is recorded. The unit is mm.
HE Zhibin
The dataset of runoff plot observations was obtained in the Binggou watershed foci experimental area from Jun. 19 to Oct. 17, 2008. The runoff plot (38°03′, 100°13′, 3472m, with a slope of 20.16°) was 10m long, 5m wide and 80cm deep, with soil depth about 50cm and sandy clay and gravels beneath (50-80cm). The main vegetation type is scrub (about 20cm high) and grass (about 3cm high). Observation items included the surface flow, interflow (80cm down the land surface), and precipitation at a fixed point at the right of the runoff plot. One subfolder and two data files (directions on data observations and raw data) were archived.
LI Hongyi, LI Zhe, BAI Yunjie, XIN Bingjie
The dataset of the drop spectrometer observations was obtained at an interval of 30 seconds in the cold region hydrology experimental area from Mar. 14 to Apr. 14, 2008. The site was chosen in A'rou (N39.06°, E100.44°, 3002m), Qilian county, Qinghai province. The data mainly included the raindrop grain size and the terminal velocity. Besides, dual polarized radar (X-band) parameters such as ZDR and KDR could be further developed based on those data. The observation was carried out within an area of 5400mm^2; the liquid grain diameter was from 0.2-5mm, and the solid grain diameter was from 0.2-25mm.
CHU Rongzhong, ZHAO Guo, HU Zeyong, ZHANG Tong, JIA Wei
The dataset of intensive rain gauges observations was obtained in the arid region hydrology experiment area, in cooperation with dual polarized doppler radar observations. There was no single dataset for the upper stream observations for the poor quality; the middle stream dataset was collected by 29 RG3-M self-recording rain gauges: the northernmost (100.36°E, 39.16°N), the southernmost (100.34°E, 38.61°N), the easternmost (100.62°E, 38.87°N), and the westernmost (100.26°E, 38.82°N). Rain gauges R02-R09 measured from May 18 to Oct. 9, 2008, and R10-R30 from May 26 to Oct. 9, 2008. The technique criterions of these rain gauges were : (1) caliber: 165mm×254mm (2) the temperature range: 0°C —+70°C (3) resolution: 0.2mm (4) the measuring range: 0—320cm (5) the measuring accuracy: 1%
CHU Rongzhong, ZHAO Guo
The dataset of intensive runoff observations was obtained by the cup type current meter (made in Chongqing Hydrological Instrument Factory) in the Binggou watershed foci experimental area from Jan. 17, 2008 to Dec. 31, 2009. Data directions included: (1) the regular observation before Mar. 14, 2008, once per day; the intensive observation from Mar. 15, 2008 to Apr. 1, 2008, 7-8 times per day and even hourly for some intensive observations (2) three times (9, 14 and 19 BJT) per day from May 3, 2008 to Sep. 17, 2008; from Sep. 17, 2008 on, two times (9 and 18 BJT) per day; the water runoff by evenly spaced method, 20cm, 40cm and 80cm based on different situations The data were named after WATER_Runoff_BG_yyyymmdd-yyyymmdd.csv (WATER_Runoff_BG for Ginggou, yyyymmdd-yyyymmdd for the observation time). The missing data were marked "None".
BAI Yunjie, LI Hongyi, LI Zhe, XIN Bingjie
From June 10, 2011 to September 2, 2011, the observation instrument of 3100m grassland weather station in Tianlaochi watershed of Qilian mountain was a 20cm evaporating pan, a round metal basin with a diameter of 20 cm and a height of 10 cm, and the mouth of the basin was blade-shaped. In order to prevent birds and animals from drinking water, a trumpet-shaped wire mesh ring was set on the upper part of the mouth of the vessel. During measurement, the instrument shall be placed on the shelf with the mouth 70cm from the ground, and quantitative clear water shall be put in every day. After 24 hours, the remaining water quantity shall be measured by the dosage cup, and the reduced water quantity shall be the evaporation capacity. Data are daily evaporation from June 10, 2011 to September 2, 2011.
ZHAO Chuanyan, MA Wenying
This data comes from the Tianlaochi watershed sample plot. The vegetation types of the sample plot are grassland, shrub, Sabina przewalskii and Picea crassifolia. The self-made Lysimeter is mainly used to observe the soil evapotranspiration characteristics in Picea crassifolia forestry. To provide basic data for the development of watershed evapotranspiration model. At about 19:00 every day, an electronic scale with an accuracy of 1g is used to weigh the inner barrel. In case of rain, observe whether there is leakage in the leakage barrel. If there is leakage, measure the leakage amount in the leakage barrel as well. The observation period in 2011 is from May 30 to September 10. The observation period in 2012 is from June 11 to September 10. Observation instrument: 1) standard 20cm diameter rain tube rain gauge. 2) self-made lysimeter (diameter 30.5cm, barrel height 28.5). 3) Electronic balance (accuracy: 0.1g) used to observe the weight change of self-made lysimeter.
ZHAO Chuanyan, MA Wenying
From May 25, 2012 to September 8, 2012, observation was made at 3100m grassland weather station in Tianlaochi watershed of Qilian mountain. The instrument was a 20cm evaporating dish, a round metal basin with a diameter of 20 cm and a height of 10 cm. The mouth of the basin was blade-shaped. In order to prevent birds and animals from drinking water, a trumpet-shaped wire mesh ring was sleeved on the upper part of the mouth. During measurement, the instrument shall be placed on the shelf with the mouth 70cm from the ground, and quantitative clear water shall be put in every day. After 24 hours, the remaining water quantity shall be measured by the dosage cup, and the reduced water quantity shall be the evaporation capacity. Data are daily evaporation from May 25, 2012 to September 8, 2012.
ZHAO Chuanyan, MA Wenying
This data includes three parts of data, namely shrub water holding experiment, shrub interception experiment and shrub transpiration experiment data. Shrub water holding experiment: select the two shrub types of Caragana jubata and Potentilla fruticosa, respectively pick the branches and leaves of the two vegetation types, weigh their fresh weight, carry out water holding experiment, measure the saturated weight of branches and leaves, dry weight of branches and leaves, dry weight of branches and leaves after completion, and finally obtain the data of branches, leaves and total water holding capacity. Shrub interception experiment: two shrubs, Caragana jubata and Potentilla fruticosa, were also selected and investigated. 30 rain-bearing cups were respectively arranged under the two shrubs. after each rainfall, penetration rainfall was measured and observed from June 1, 2012 to September 10, 2012. Shrub Transpiration Experiment: Potentilla fruticosa on July 14, Caragana jubata on August 5, Salix gilashanica on August 15, 2012. The measurement is made every hour according to the daily weather conditions.
ZHAO Chuanyan, MA Wenying
This data includes the basic terrain data, soil data, meteorological data, land use / land cover data, etc. needed for SWAT model operation. All maps and relevant point coordinates (meteorological station, hydrological station) adopt the coordinate system of Gauss Kruger projection which is consistent with the basic topographic map of our country. Data content includes: a) The basic topographic data include DEM and river network. The size of DEM grid is 50 * 50m, and the drainage network is manually digitized from 1:100000 topographic map. b) Soil data: including soil physics, soil chemistry and spatial distribution of soil types. The scale of digital soil map is 1:1 million, which is converted into grid format of ESRI, with grid size of 50 * 50m. Each soil profile can be divided into up to 10 layers. The sampling index of soil texture required by the model adopts the American Standard. The parameters are from the second National Soil Census data and related literature. c) Meteorological data: (1) Temperature: the data of daily maximum temperature, daily minimum temperature, wind speed and relative humidity are from the daily observation data of Qilian, Shandan, tole, yeniugou and Zhangye meteorological stations in and around the basin, with the period from 1999 to 2001. (2) Precipitation: the rainfall data comes from five hydrological stations in and around the basin, i.e. OBO (1990-1996), Sunan (1990-2000), Qilian (1990-2000), Yingluoxia (1990-2000), zamashk (1990-2000), Shandan (1999-2001), tole (1999-2001), yeniugou (1999-2001), Zhangye (1999-2001) and Qilian County (1999-2001) Observation data. (3) Wind speed and relative humidity: wind speed and relative humidity come from the daily observation data of 5 meteorological stations in Shandan, tole, yeniugou, Zhangye and Qilian county. The period is from 1999 to 2001. (4) Solar radiation: solar radiation has no corresponding observation data and is generated by model simulation. d) Land use / land cover: 1995 land use data, scale 1:100000. Convert it to grid format of ESRI, with grid size of 50 * 50m. e) Meteorological data simulation tool (weather generator) database: the weather data simulation tool of SWAT model can simulate and calculate the daily meteorological input data required by the model operation according to the monthly statistical data for many years without the actual daily observation data, and can also carry out the interpolation of incomplete observation data. The meteorological data are from the surrounding meteorological stations.
NAN Zhuotong
1. Data overview: this data is the blue and green water data of Heihe River Basin simulated by SWAT model; 2. Data content: data mainly includes blue-green water and green water coefficient of the whole basin and each sub Basin; 3. Spatial and temporal scope: the data time is from 1975 to 2004, and the spatial scope includes 34 sub basins and the whole Heihe River Basin; 4. Data file: the relevant data is placed in the Swat folder, including the sub_basin folder (sub basin distribution map), "blue and green water of the whole Heihe River Basin" folder and "blue and green water of each hydrological response unit of the Heihe River Basin" folder.
LIU Junguo
The data are from 2011 to 2012. A 30m×30m Picea crassifolia canopy interception sample plot was set up in the Picea crassifolia sample plot at an altitude of 2800m m. A siphon raingauge model DSJ2 (Tianjin Meteorological Instrument Factory) was set up on the open land of the river about 50m from the sample plot to observe the rainfall outside the forest and its characteristics. Penetrating rain in the forest adopts a combination of manual observation and automatic observation. Automatic observation is mainly realized through a penetrating rain collection system arranged in the interception sample plot, which consists of a water collecting tank and an automatic recorder. Two 400cm×20cm water collecting tanks are connected with DSJ2 siphon rain gauge, and the change characteristics of penetrating rain under the forest are continuously recorded by an automatic recorder. Due to the spatial variability of the canopy structure of Picea crassifolia forest in the sample plot, a standard rainfall tube for manual observation is also arranged in the sample plot to observe the penetrating rain in the forest. Ninety rainfall tubes with a diameter of 20cm are arranged in the sample plot at intervals of 3m. After each precipitation event ends and the penetrating rain in the forest stops, the amount of water in the rain barrel will be emptied and the penetrating rain in the barrel will be measured with the rain cup.
ZHAO Chuanyan, MA Wenying
Data overview: from September 23 to September 30, 2005 and from November 5 to November 9, 2005, the remote sensing Office of hanhanyuan Institute of Chinese Academy of Sciences measured 21 hydrological sections between Yingluoxia hydrological station and zhengzhengxia hydrological station in the middle reaches of Heihe River. Data acquisition process: using two sets of zhonghaida hd8080 GPS receivers and one set of DS3 level of Southern surveying and mapping company, combining GPS and leveling. Section survey mainly includes two steps. Firstly, two differential GPS are used to select high-precision control points on both sides of the river bank or on one side of the selected section, and two GPS receivers are used to observe for 30 minutes simultaneously. Then, on the basis of these control points, the level is used for continuous measurement of the section. According to the river width, a certain number of sounding plumb lines are arranged on the section to measure the water depth and the starting point distance of each sounding plumb line. The measuring points are relatively dense in the main channel part, and the beach is relatively sparse. The distance between the two points of the main channel part is 2m. This data can provide the key basic data for the hydrological simulation of surface groundwater in the middle reaches of Heihe River.
MA Mingguo
The project of ecological security evaluation and landscape planning in the inner flow area of hexi corridor belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation, led by researcher xiao duning of the institute of cold and dry environment and engineering, Chinese academy of sciences. The project runs from Jan. 2002 to Dec. 2004. The data of the project is the ecological data of the inner flow area of hexi corridor, including heihe basin, shiyang river basin, shule river basin and river runoff. Investigation and analysis data of ejin banner in heihe river area 1. Soil moisture TDR data The data is stored in Excel format and includes both tubular and well 2002 soil moisture survey data. Tube TDR data Tubular soil moisture survey data with 1.8m underground intervals of 0.2 m on June 1, June 11, June 21, July 1, July 11, July 21, July 31, August 11 and August 21, 2002, including erdaqiao, gobi, forest farm, qidaqiao and tseng forest. Well TDR data Data of well soil moisture survey on June 21, July 1, July 11, July 21, July 31, August 11 and August 21, 2002, which included willows, gobi, populus euphratica and weeds, with intervals of more than 5 meters and 0.2 meters underground. Groundwater GPS data In Excel format, the TDR observation points were measured by GPS, including basic information such as longitude, latitude and elevation, plus information such as water level, logging type and remarks. 2. Soil nutrient salinity data To Excel format, 42 samples containing "total oxygen N %", "total phosphorus P %", "% organic matter", "hydrolysis N N mg/kg", "organic P P mg/kg", "available K K mg/kg", "% calcium carbonate", "PH", "the % of salt" and "total potassium % K" nutrient investigation and analysis of data, such as 42 samples containing "conductance value (%) computing the salt", CO3, HCO3, CI, SO4, Ca, mg, Na + K salt investigation and analysis of data, etc. 3. Soil mechanical composition In Excel format, 42 sample points contained soil particle composition information analysis tables of depth (cm), percentage of particle content at each level (sieve analysis method) (>2mm, 2-1mm, 1-0.5mm, 0.5-0.25mm and 0.25-0.1mm) and percentage of particle content at each level (straw method) (<0.1mm, 0.1-0.05mm, 0.05-0.02mm, 0.02-0.002mm and <0.002mm). 4. Meteorological data of erqi station Is the Excel sheet, including rainfall data from 1957 to 1998, evaporation data from 1957 to 1998, temperature data from 1957 to 1991, wind speed data from 1972 to 1992, maximum temperature data from 1972 to 1992, minimum temperature data from 1972 to 1992, sunshine data from 1972 to 1992 and relative humidity data from 1972 to 1992. Scan copy of jiuquan area The scanning copy of the general map of land use status in jiuquan 1:300,000, the scanning copy of the evaluation map of the distribution of cultivated land reserve resources in jiuquan 1:300,000 and the scanning copy of the district map of jiuquan 1:300,000 Zhang ye water protection information It contains the statistics of water and soil conservation in the regions of ganzhou district, gaotai district, linze county, minle county, shandan county, sunan county and zhangye city in zhangye region (stored in Excel format) and the planning report of each region (stored in Word format). Shiyang river basin Jinchang water resources survey data It includes the scan of 1:50000 water resource distribution map of jinchang city in 1997, the average decline degree of groundwater level in qinghe and jinchuan irrigation areas in jinchang city from 81 to 2000, the statistical table of annual groundwater supply in 1986, 1995 and 2001, and the survey and evaluation report of cultivated land reserve resources in jinchang city. Survey data of water resources in minqin Includes detailed minqin county area typical Wells status per acre crops irrigation water use questionnaire, irrigation, industrial and agricultural water use questionnaire, seeded area of villages and towns questionnaire, the survey data of groundwater hardness index, minqin county of surface runoff and the runoff change situation report, irrigation water quota formulation of evaluation report, minqin county water resources development and utilization of report and opinion polls irrigation works report, etc. Zoning map of soil improvement and utilization in wuwei area For the scanning part of water and soil conservation planning map of wuwei city, the scanning part of the location map of wuwei irrigation area, the scanning part of the scanning part of the administrative map of wuwei city, the scanning part of the water source and water conservancy project construction map of wuwei city, the scanning part of the planning map of wuwei sanbei phase ii shelterbelt project and the scanning part of the administrative map of liangzhou district. Yongchang county water protection information It is the scanning copy of the soil and water conservation supervision, prevention and control plan of 1994 in yongchang county at 1:20000. Shule river basin Distribution map of water resources development and utilization in yumen city It consists of four jpeg images, a 1:250,000 general scanning map of yumen's water resources development and utilization in 2002, and three high-resolution sub-maps. River runoff This data set is stored in Excel format, mainly including the total flow of three basins from 1949 to 2002, the annual runoff of each tributary of the basin, the annual runoff of detailed investigation areas such as jiuquan and the upstream inflow of yuanyang pond reservoir. Total basin Is the annual runoff data of heihe river basin, shiyang river basin and shule river basin from 1949 to 2002. Annual runoff of black river Is the annual runoff data of heihe river, liyuan river, taolai river, hongshui river, qingshui river, fengle river and hongsha river from 1949 to 2002. Annual runoff of shiyang river Is the annual runoff data of xidahe river, dongdahe river, xiying river, jinta river, zama river, huangyang river, gulang river, dajing river and other tributaries from 1949 to 2002. Annual runoff of shule river Is the annual runoff data of dang river, shule river and harten river from 1950 to 2002. Annual river runoff in jiuquan area For the annual flow data of changma gorge of shule river, dangcheng bay of danghe river, junmiao of shule river, baiyang river, icegou of toulai river, yuanyang pond of toulai river, xindi of hongshui river, fengle river, hongsha river of maying river and suang river of yulin river in jiuquan region from 1950 to 2002. Statistics of upstream inflow of yuanyang pond reservoir The data are the upstream inflow data of yuanyang pond reservoir from 1959 to 2001.
Xiao Duning
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