This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from April 13 to December 31 in 2021. The site (115.7923° E, 40.3574° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC) (class1-9). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2021. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2021. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2021. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2021. The site (115.7880° E, 40.3491°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC) (class 1 to 9). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
1) Data content This data set includes lake evaporation data of Bamco, La'angco in the summer and autumn of 2019-2021 and Longmuco in the summer and autumn of 2020-2021. The meteorological data required for the calculation of evaporation data are obtained from the automatic meteorological station set up at the lake side, with the observation height of 1.5m. Lake location: Bamco (90.59 ° E, 31.29 ° N), La'anco (81.24 ° E, 30.72 ° N), Longmucuo (80.47 ° E, 34.60 ° N). Coordinates of automatic weather stations: Bamco AWS (90.65 ° E, 31.30 ° N), La'anco AWS (81.22 ° E, 30.73 ° N), and Longmucuo AWS (80.43 ° E, 34.59 ° N). Time resolution: 1d Spatial resolution:- Unit: mm 2) Data source and processing method Integral conveying method. The calculation formula is as follows: LH=l_ v ρ_ a c_ E U(q_s-q_a ) E=LH/( ρ l_ v ) LH and E are latent heat and evaporation respectively. The automatic weather station erected near the lake is used for meteorological data, and the observation data used include temperature, wind speed, relative humidity, etc. at 1.5m; Lake surface temperature uses ERA5 land hourly data; The momentum roughness, moisture roughness and thermal roughness are obtained by back calculation from the data obtained by the eddy correlation instrument erected by Bamco and Laoncho. 3) Data quality description The evaporation data of Bamco Lake in 2020 obtained by calculation are compared with the evaporation data from August to October obtained by the eddy correlation instrument installed on the central island of Bamco Lake. Pearson correlation coefficient r=0.57, p=2.842E-8. 4) Data application achievements and prospects Water surface evaporation is an important link in the process of water cycle and an important topic in hydrology research. As the main part of lake water loss, it is also the basic reference data for studying land surface evaporation. The evaporation calculated based on the observation data can be used as the accurate evaporation of lakes on the Qinghai Tibet Plateau, which is an important basis for studying the water balance of lakes. By obtaining the evaporation of three lakes located in different climatic regions, we can better explore the variation law of lake water surface evaporation in different climatic regions.
MA Weiyao , MA Weiqiang*, HE Jianan , XIE Zhipeng , SU Rongmingzhu , HU Wei , MA Yaoming
The global reach-level 3-hourly river flood reanalysis (GRFR) dataset includes 1) global 0.05 degree, 3-hourly/daily runoff data, 2) 3hourly/daily naturalized river discharge at 2.94 million river reaches, 3) global 3-hourly river flood events from 1980 to 2019, 4) underlying hydrography MERIT-Basins. Grounded on recent breakthroughs in global runoff hydrology, river modeling, high-resolution hydrography, and climate reanalysis, the 3-hourly river discharge record globally for 2.94 million river reaches during the 40-yr period of 1980–2019 was developed. The underlying modeling chain consists of the VIC land surface model (0.05°, 3-hourly) that is well calibrated and bias-corrected and the RAPID routing model (2.94 million river and catchment vectors), with precipitation input from MSWEP and other meteorological fields downscaled from ERA5. Flood events (above 2-yr return) and their characteristics (number, spatial distribution, and seasonality) were extracted and studied. Validations against 3-hourly flow records from 6,000+ gauges in CONUS and daily records from 14,000+ gauges globally show good modeling performance across all flow ranges, good skills in reconstructing flood events (high extremes), and the benefit of (and need for) sub-daily modeling. The GRFR database represents a pioneering effort on global reach-level flood reanalysis and may offer new opportunities for global flood studies in terms of baseline data and potential research pathways. Also, it can better help river-observing satellite missions to develop their discharge algorithms.
YANG Yuan , PAN Ming , LIN Peirong
Natural runoff simulation data products of 2.94 million river sections in the world, unit: m3/s. This data is based on the simulation of VIC hydrological process model and RAPID vector river network concentration model. The spatial resolution of the land surface hydrological process model is 0.25 °, and the river network data in the vector concentration model is extracted based on the 90-m MERIT Hydro hydrological correction terrain data product. The runoff generation part is calibrated based on the runoff characteristic values obtained by machine learning, and the grid scale runoff generation deviation correction is carried out based on the multi quantile runoff characteristic values. The data products are verified by 14000 runoff observation stations around the world, and have better verification accuracy.
LIN Peirong , PAN Ming , YANG Yuan
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
Water is one of the most direct mediums through which people perceive the effects of climate change. The flow regimes that people rely on are influenced by large-scale climate change, and identifying changes to these regimes and determining their causes requires reliable, spatiotemporally continuous runoff records. China is climate vulnerable due to its remarkable topographic gradients, monsoon climate, and rapid economic development. Climate change has increased the urgency of understanding, regulating, and forecasting China’s freshwater flows. Yet, available global and regional runoff data in China are produced from sparse, poor-quality gauged station data that have been acquired over different time scales. Our research presents a new long-term, high-quality natural runoff dataset, named the China Natural Runoff Dataset version 1.0 (CNRD v1.0) for driving hydrological and climate studies over China. It will also contribute to the global runoff database. CNRD v1.0 provides daily, monthly, and annual 0.25-degree natural runoff estimates for the period of 1 January 1961 to 31 December 2018 over China. CNRD v1.0 is generated using the Variable Infiltration Capacity macroscale hydrological model, which was used to fill in gaps or construct time series of comparable lengths. To control the model performance and thus our dataset quality, the model’s sensitive parameters are automatically calibrated using an adaptive surrogate modeling‐based optimization algorithm based on monthly natural or near-natural streamflow data from 200 hydrological gauge stations—more than in previous studies—with low fractions of missing data. Another important quality control adopted for this dataset was the use of a multiscale parameter regionalization technique to estimate model parameters for ungauged basins. Overall, the results show well-calibrated parameters for most gauged catchments, and the skill scores, the Nash–Sutcliffe model efficiency coefficient (NSE) present high values for all catchments, with an average of 0.83 and 0.80 for calibration and validation modes, respectively. The multiscale parameter regionalization technique offered the best regionalization solution (median NSE = 0.76 for the calibration period and 0.72 for the validation period. The results overall show well-calibrated and regionalized parameters for the hydrological model thus for the long-term runoff reconstruction. By the cell-to-cell comparisons between the CNRD v1.0 with the two global runoff datasets, ISIMIP and GRUN, we found that our datasets show more continuous transitions in runoff dis¬tribution compared to ISIMIP and GRUN across China, and perform well in representing the geographic distribution of China’s water resources across complex terrain and climate regions.
MIAO Chiyuan, GOU Jiaojiao
This data set is a global surface evapotranspiration product based on the PEW model. The PEW model is a water energy based surface energy balance model based on the assumption of equal proportion. Its principle is to couple the water heat balance framework based on the assumption of equal proportion on the Priestley Taylor (PT) evapotranspiration algorithm. PEW model can consider the influence of water balance constraint and energy budget process at the same time, which makes the simulation accuracy of PEW model improved compared with previous models to a certain extent. The input data of PEW include the meteorological and soil moisture changes of ERA5 land dataset. The time span of this dataset is from 1982 to 2018. The time resolution is month by month and the spatial resolution is 0.1 °. This data set can provide a basis for studying the long-term water cycle and climate change.
FU Jianyu , WANG Weiguang
This data set is the data set of water balance (precipitation, evapotranspiration, runoff, liquid soil moisture) and energy balance (short wave radiation, sensible heat, latent heat and surface soil temperature) for the source of the Yellow River and the Qilian Mountains over the past 40 years. The initial data source is ERA5 Land monthly average data, which is accumulated/averaged to the annual scale through time aggregation. The time range of the data is 1981-2020, the spatial range is 88.5 ° E – 104.5 ° E, 32 ° N – 43 ° N, and the spatial resolution is 0.1 °. The data set can be further used to study the ecological hydrological processes in the source area of the Yellow River and the Qilian Mountains, and provide scientific basis for the optimal allocation of the "mountains, rivers, forests, fields, lakes and grasses" system.
ZHENG Donghai
In the context of global change, the spatio-temporal continuous high-quality high-resolution long time series precipitation data set is of great significance for understanding the global "water carbon energy" and biogeochemical cycle mechanism. The daily total volume controlled merging and disaggregation algorithm (DTVCMDA) proposed in this study effectively considers the characteristics of continuous space-time and high spatial and temporal resolution of reanalysed precipitation data, as well as the high quality of ground analysis data, A set of AERA5 Asia (0.1 °, hourly, 1951-2015, Asia) precipitation data set with high quality and high spatial and temporal resolution for more than 70 years of long time series in Asia has been produced. The main features of the dataset are as follows: (1) AERA5 Asia is a set of data sets with high resolution, high quality, space-time continuity and long time series; (2) AERA5 Asia is significantly better than IMERG Final and ERA5 Land precipitation data, especially in terms of system deviation. In general, the deviation of AERA5 Asia, IMERG Final and ERA5 Land compared with ground observation is~5%,~11% and~20% respectively; (3) In extreme heavy rainfall (such as typhoons "Tamei" and "Tiantu"), the quality of AERA5 Asia is also significantly better than ERA5 Land and IMERG Final. AERA5 Asia will provide stable and reliable precipitation data support for relevant research in the weather, climate, hydrology and other fields in Asia, especially in China.
MA Ziqiang, MA Yaoming, MA Weiqiang*, 许金涛 XU Jintao
Terrestrial actual evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. This global ET dataset is obtained based on ETMonitor model, which combines parameterizations for different processes and land cover types, with multi-source satellite data as input. Several open accessed remote sensing variables, e.g., LAI, FVC, albedo, surface soil moisture, dynamic surface water cover and snow/ice cover, were used as input to estimate daily ET. The meteorological variables from ERA5 reanalysis dataset were also adopted. The ETMonitor model is applied at daily scale to estimate the ET components at 1-km resolution, including vegetation transpiration, soil evaporation, canopy precipitation interception loss, water surface evaporation and snow/ice sublimation on daily step, and the total actual ET is estimated as the sum of these components. Overall, the actual ET estimated by ETMonitor agreed well with ground measurements from 251 flux towers across various ecosystems and climate zones globally, with high correlation (0.75), low bias (0.08mm/d), and low root mean square error (0.93 mm/d). The estimated ET showed reasonable spatial patterns, and superior in presenting the spatial variation of ET especially in the mountain regions and in the arid irrigated cropland regions. The ET estimation is conducted at daily temporal step and 1km spatial resolution. For easier publication, the daily/1-km ET from ETMonitor (https://doi.org//10.12237/casearth.6253cddc819aec49731a4bc2) was summed to obtain monthly ET in this dataset. The data type is 16-bit signed integer, the scale factor is 0.1, and the unit is mm/month. The missing values were filled by -1.
ZHENG Chaolei , JIA Li , HU Guangcheng
Based on the Sentinel-2 and Landsat 5/7/8 multispectral instrument imageries combined with in-situ measured hydrological data, bankfull river geometry of six major exorheic river basins of the Qinghai-Tibet Plateau (the upper Yellow River, upper Jinsha River, Yalong River, Lantsang River, Nu River and Yalung Zangbo River) are presented. River surface of six mainstreams and major tributaries are included. For each river basin, two types of rivers are included: connected and disconnected rivers. Format of the dataset is .shp exported from the ArcGIS 10.5. Three products are included in the dataset: one original product (bankfull river surface dataset) and two derived products (bankfull river width dataset and bankfull river surface area dataset with a 1 km river length interval). These three products are in three folders. The first folder, “1-Bankfull River Surface”, contains river surface vectors for six river basins in the .shp file. The second folder, “2-Bankfull River Width”, contains bankfull river widths and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. The river width vectors in the .shp files are also provided in the second folder. The third folder, “3-Bankfull River Surface Area”, contains bankfull river surface areas and corresponding coordinates with a 1 km-step river length for six mainstreams and some connected tributaries in .xlsx format. Three Supplementary Files are included: Supplementary File 1, tables and figures related to the dataset; Supplementary File 2, used for river surface extraction based on GEE platform; Supplementary File 3, used for river width extraction based on Matlab. The provided planform river hydromorphology data can supplement global hydrography datasets and effectively represent the combined fluvial geomorphology and geological background in the study area.
LI Dan , XUE Yuan , QIN Chao , WU Baosheng , CHEN Bowei , WANG Ge
The water resources simulation data of Southeast Asian countries and the Lancang Mekong River Basin (1980-2019) is the result of using the meteorological data output from the WRF model as the driving data and simulation through the ways model. The data includes evapotranspiration, surface runoff, underground runoff, total runoff, groundwater, infiltration and soil moisture data of Southeast Asia land area from 1980 to 2019. The temporal resolution is daily and the spatial resolution is 3km. The data is generally good, but due to the limitations of the model, there are certain errors in the simulation results of a few variables. It is not recommended to use the research with high requirements for data accuracy. The data can reflect the situation of water resources in Southeast Asia to a certain extent, and provide data support for relevant research.
LIU Junguo
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
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