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
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
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
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 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
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
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
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
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
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
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
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
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-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 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
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,
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
The 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
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
This dataset is the water balance dataset in the Yellow River source region and Qilian Mountains in the future 50 years (runoff, precipitation, evapotranspiration, soil liquid water content). It is simulated by the Geomorphology-Based Ecohydrological Model (GBEHM). The variables in the dataset include monthly runoff, monthly precipitation, monthly evapotranspiration, the monthly average 5cm soil liquid water content and the monthly average 50cm soil liquid water content. The temporal range is 2020-2070 and the spatial resolution is 1 km. The input data of the model include meteorological forcings, vegetation, soil and land use data, and the meteorological forcings are obtained from the ensemble mean of 38 CMIP6 models under SSP2-4.5 scenario. The simulation results can reflect the spatio-temporal changes of the hydrological variables in the Yellow River source region and Qilian Mountains. The dataset can be further used for researches into the eco-hydrological processes in the Yellow River source region and Qilian Mountains, and help provide a scientific basis for the optimal allocation of " mountains, rivers, forests, farmlands, lakes and grasslands " system.
WANG Taihua, YANG Dawen
Asian, and is divided into 1100 sub-basins for distributed hydrological modelling. The integrated water system model (HEQM) is improved to simulate the freezing and thawing processes of snow cover and glacier in this region. The historical daily weather inputs (i.e., precipitation and temperature) with high spatial resolution (0.45 degree) are obtained using the image fusion of NECP and ECMWF based on compressed sensing in the domain of Fourier coefficients, and the long-term annual runoff observations from 1940 to 2000 at 22 stations were used to implement of HEQM calibration and validation. Furthermore, the future weather inputs are rebuilt using the median of daily climate outputs of five GCMs in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) and then drive the well-calibrated HEQM to project the development and utilization potentials of agricultural water resources in the future. The data sets includes three time periods of 2000s (2001-2005), 2010s (2006-2010) and 2015s (2011-2015) for the historical period, and two periods of 2040s (2041-2070) and 2070s (2071-2099) for the future period in the RCP4.5 and RCP8.5 with a spatial resolution of 0.5°*0.5°. It is expected to provide basic data support for distributed water cycle simulation, water supply and demand, development and utilization analysis in the Central Asian.
ZHANG Yongyong, LIU Yu , YANG Peng
This dataset contains 10 years (2010-2019) global daily surface soil moisture . The resolution is 36 km , the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017，2021). This study transfers the merits of SMAP to FY-3B/MWRI through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with FY-3B/MWRI brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content，which is comparable with that of SMAP. (evaluation accuracy of 14 dense ground network globally.)
YAO Panpan, LU Hui, ZHAO Tianjie, WU Shengli , SHI Jiancheng
This dataset is an 8-year (2011-2018) global spatiotemporally consistent surface soil moisture dataset with a 25km spatial grid resolution and daily temporal step in unit of cm3/cm3. This dataset is developed by applying a linear weight fusion algorithm based on the Triple Collocation Analysis (TCA) to merge the five soil moisture data products, i.e., SMOS, ASCAT, FY3B, CCI and SMAP in two steps. The first step is to fuse the SMOS, ASCAT and FY3B soil moisture products from 2011 to 2018. The second step is to refuse the merged soil moisture product in the first step, CCI and SMAP products from 2015 to 2018, and to obtain the finally merged soil moisture product from 2011 to 2018. In addition, the measured soil moisture data from seven ground observation networks around the world are used to evaluate and analyze the merged soil moisture product. The fused soil moisture product has the global spatial coverage ratio of more than 80%. With rhe minimum RMSE (root mean square error) of 0.036 cm3/cm3.
JIA Li , XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, HU Guangcheng
This dataset is the water balance dataset in the Yellow River source region and Qilian Mountains in the past 40 years (runoff, precipitation, evapotranspiration, soil liquid water content). It is simulated by the Geomorphology-Based Ecohydrological Model (GBEHM). The variables in the dataset include monthly runoff, monthly precipitation, monthly evapotranspiration, the monthly average 5cm soil liquid water content and the monthly average 50cm soil liquid water content. The temporal range is 1980-2019 and the spatial resolution is 1 km. The input data of the model include meteorological forcings, vegetation, soil and land use data. The simulation results can reflect the spatio-temporal changes of the hydrological variables in the Yellow River source region and Qilian Mountains. The dataset can be further used for researches into the eco-hydrological processes in the Yellow River source region and Qilian Mountains, and help provide a scientific basis for the optimal allocation of " mountains, rivers, forests, farmlands, lakes and grasslands " system.
WANG Taihua, YANG Dawen
This data set is the monthly runoff data of nijnii hydrological station, the main stream of the upper reaches of Amu Darya River in Central Asia from 1967 to 2017. The station is located on the main stream of the border between Tajikistan and Afghanistan. The data is from Tajikistan hydrometeorological Bureau. The data are processed according to the country's hydrological observation specifications and quality control process. The data period is 1967-2017. The hydrological station is located at 37.193121 ° n, 68.590218 ° e, 328m above sea level, and the unit of runoff is m3 / s. The data can be used for scientific research and water conservancy engineering services such as water resources assessment in Central Asia mountainous areas.
SHANG Huaming
1. Glacial lake data sets (1960s−2020) This data set contains glacial lake data for the 1960s, 2016, 2017, 2018, 2019, and 2020, mapped from Korona KH-4, Sentinel-2, and Sentinel-1 imagery. 2. Potential Outburst Flood Hazard level of Bhutanese glacial lakes This data contains the Potential Outburst Flood Hazard level of Bhutanese glacial lakes with an area greater than 0.05 km2 (n=278). The value for each hazard assessment criteria is also provided in the data attributes.
RINZIN Sonam, ZHANG Guoqing
The data include four types: water levels of 244 lakes extracted in CryoSat-2 L1B Baseline D (2010-2020); water levels of 356 lakes extracted in ICESat-2 ATL13 (2018-2020); water levels of 125 lakes extracted in Sentinel-3A SRAL L2 (2016- 2020); water levels in 120 lakes extracted from Sentinel-3B SRAL L2 (2018-2020). Data include date, decimal date, water level, standard deviation, and geographic location of each lake. Please see the paper for detailed data processing procedures.
XU Fenglin, ZHANG Guoqing
This data set is the version 2 of "High temporal and spatial resolution precipitation data of Upper Brahmaputra River Basin (1981-2016) ", with additional data from 2017 to 2019. 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 1981-2019, 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
This dataset contains the flux measurements from the scintillometer at Arou Superstation in the Heihe integrated observatory network. The north tower was set up with the receiver, and the south tower was equipped with transmitter. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 13.0 m, and the path length was 2390 m. The raw data acquired at 1 min intervals for the near infrared scintillometer and 200 Hz for the optical µwave scintillometer were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat and latent heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) raw data processing and calculating the intensity variance. (2) Calculating the structural parameters of air refractive index. (3) Calculating the meteorological structural parameters. (4) Calculating the sensible and latent heat flux。 The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index for near infrared scintillometer (Cn2, m-2/3), intensity variance for LAS, MWS and OMS (Var_LAS, Var_MWS, Var_OMS), the sensible heat flux (H, W/m^2), and the latent heat flux (LE, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, SHI Shengjin, XU Ziwei
The data set contains the plant liquid flow meter data (101.1346 ° e, 41.9900 ° n) of the mixed forest station in the surface process comprehensive observation network of Heihe River Basin from October 20, 2019 to December 7, 2020. The study area is located in the Populus euphratica forest in Ejina Banner, Alashan League, inner Mongolia Autonomous Region, at an altitude of 874m. According to different heights and DBH of Populus euphratica forest, sample trees are selected to install the developed plant liquid flow instrument. Each sample tree is installed with two groups, with a height of 1.3m. The original observation data of the plant liquidometer is the temperature difference between the probes, and the time is 10 minutes. The published data is the temperature difference data delta every 10 minutes_ T (℃), liquid flow rate V (cm / h) and daily transpiration t (mm / D). Firstly, the liquid flow rate and liquid flow volume are calculated according to the temperature difference between the probes, and then the transpiration t is calculated according to the Populus euphratica forest area and tree spacing at the observation point. At the same time, the post-processing of the calculated rate and flux values: (1) eliminate the data obviously beyond the physical meaning or beyond the instrument range; (2) Missing data are marked with - 6999; (3) Suspicious data caused by probe failure and other reasons shall be identified in red font, and the data confirmed to be problematic shall be eliminated. Please refer to Liu et al. (2018) for site information and Qiao et al. (2015) for observation data processing.
LIU Shaomin, SHI Shengjin, XU Ziwei
This dataset includes data recorded by the Heihe integrated observatory network obtained from a mesoscale soil moisture measurement system of soil moisture of Daman Superstation from January 1 to December 31, 2020. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, C), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
QIAO Yunfeng, LIU Shaomin, XU Ziwei
This dataset contains the soil moisture data from August 31, 2020 to December 22, 2020 (except some data interruption events in short durations due to rainfall and field irrigation). This instrument is installed at the Yucheng Comprehensive Experimental Station, CAS (Yucheng, Shandong，116°22' E，36°40'N，23m above sea level) in the field of winter wheat and summer maize during the monitoring period. The instrument was place directly on the smooth ground so that it’s close contact with soil surface. The data sampling interval is once an hour which can be remotely set through the app installed in a mobile phone or manually monitor any time in situ. The instrument can collect the frequency values by three groups of electrodes with different specifications passing through the soil profile. The data processing procedure is as follow: the different frequency values collected by this instrument will be uploaded to the cloud platform through the Internet of Things；the system removes abnormal values from database；averages and normalizes the remaining data, and then calculates and outputs soil volumetric moisture at different soil layers by a built-in calibration function.
QIAO Yunfeng, LIU Shaomin
1) Data content: daily water level change data of Nam Co in 2019. The coordinates of observation points are 90.96 ° E, 30.77 ° N, 4730m above sea level, and the underlying surface is alpine grassland. (2) Data source and processing method: measure by manually reading the water level gauge. The original observation data shall be processed and quality controlled by a specially assigned person according to the observation records. (3) Data quality description: because the data is obtained by manual reading of water gauge, it is greatly affected by the harsh environment, and the data is missing and discontinuous in some periods. (4) Data application prospect: the data can be applied to scientific research fields such as Lake hydrology and hydrological process in high and cold areas.
WANG Junbo
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station in the Hai River Basin from January 1 to December 31 in 2020. There were two types of LASs at Huailai Station: BLS450 and zzlas, produced by Germany and China, respectively. The north tower was set up with the zzlas receiver and the BLS450 transmitter, and the south tower was equipped with the zzlas transmitter and the BLS450 receiver. The site (north: 115.8023E，40.3596N; south: 115.7825E，40.3522N) was located in Donghuayuan Town, Huailai County, Hebei Province. The underlying surfaces between the two towers were corn. The elevation is 480 m. The effective height of the LASs was 14 m, and the path length was 1870m. The data were sampled 1 minute. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion. (2) The data were rejected when the demodulation signal was small. (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). Several instructions were included with the released data. (1) The data were primarily obtained from BLS450 measurements, and missing flux measurements from the BLS450 instrument were substituted with measurements from the zzlas instrument. The missing data were denoted by -6999. (2) The dataset contained the following variables: Date/Time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for observation experiment or sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset includes the observation data from 01 Jan. 2020 through 31 Dec. 2020, collected by lysimeters, which are located at 115.788E, 40.349N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost and overrange data were replaced by -6999; 2) deleting the abnormal data due to maintenances; 3) keeping the consistent date and time format (e.g. 2020-01-01 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xlsx.
LIU Shaomin, ZHU Zhongli, XU Ziwei
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2020. 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_5 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, and Ws_40 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: 2020-6-10 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, 2020. 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: 2020-6-10 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 14 to December 31 in 2020. 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 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), which represent high-, medium-, and low-quality data, respectively. 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 (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. (2015) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
The data set contains the variations of water level, area, and volume for ten lakes in Jiangsu Province (Taihu Lake, Hongze Lake, Gaoyou Lake, Luoma Lake, Shijiu Lake, Gehu Lake, Yangcheng Lake, Baima Lake, Shaobo Lake and Dianshan Lake) from 2003 to 2019, which provides important parameters for the study of lake hydrological ecosystem balance in Jiangsu Province in recent years. The water level data of the ten lakes were obtained from altimetry satellites Envisat/RA-2, Cryosat-2, ICESat, and ICESat-2. The water area data were obtained from Landsat TM/OLI images bsed on Modified Normalized Difference Water Index. For the four lakes with complete water level data (Hongze Lake, Gaoyou Lake, Gehu Lake and Taihu Lake), the water volume changes from 2003 to 2019 were estimated according to the water level and area results. Compared with the in-situ water level data, the water level extracted from altimetry data showed significantly consistent (α = 0.05) for all the ten lakes, with the average absolute error of 0.168 m. The data set provides the variations of water level, area, and volume for the ten lakes in Jiangsu Province from 2003 to 2019, which can provide data support for the management and dispatching of water resources in Jiangsu Province.
KE Changqing, CHANG Xiangyu, CAI Yu, XIA Wentao
This dataset contains the fluxes and meteorological data of Weishan (Gaoying) flux site of Tsinghua University from May 17, 2005 to September 26, 2006. The site (116.0542° E, 36.6487° N, 30 m above sea level) was built on March 18, 2005 and is located in Xiaozhuang Town, Chiping District, Liaocheng City, Shandong Province. It belongs to Weishan Irrigation District along the lower Yellow River. The local climate is characterized as temperate monsoons, with an average annual temperature of 13.8 ℃, an average annual precipitation of 553mm, most of which occurs between June and October, and an average annual potential evaporation of 1950mm. The soil type is silt loam. For the soil of the top 5 cm, the average saturated soil water content, field capacity and wilting point in volumetric values are 0.43, 0.33 and 0.10 m3m-3, respectively. The height of the flux tower is 10m, and the area within about 1 km radius around the flux tower is largely homogeneous winter wheat-summer maize rotation cropland. The winter wheat is generally sown in mid-October and harvested in early June of the following year, while the summer maize is usually planted directly into the stubbles of wheat at the same location immediately after the harvest of wheat and is harvested in late September to early October. See the file named “Supplementary data_WeishanGaoying20052006.xlsx” for specific sowing, harvesting and irrigation dates. The surface flux data is measured by the eddy covariance system, which is composed of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA) and an open-path infrared gas analyzer (IRGA) (LI-7500, LI-COR, Inc., Lincoln, NE, USA) with an installation height of 3.7m. The 30-minute net ecosystem carbon exchange (NEE), latent heat flux (LE) and sensible heat flux (H) data were obtained after the raw 10Hz data were processed by Eddypro software. The preprocessing steps included despiking, double coordinate rotation, 30-min block averaging, time lag compensation, spectral corrections, the Webb-Pearman-Leuning (WPL) density correction, a quality check using the “0-1-2 system”. Then the 30-min data were screened as follows: (1) remove bad quality fluxes with quality flag 2; (2) limit H and LE to - 200 ~ 500 W m-2 and - 200 ~ 800 W m-2, respectively; (3) the data during the precipitation events were excluded. Then, REddyproc software is used to filter the data under low turbulence mixing conditions (i.e. filter the flux data according to the friction wind speed u*), fill the gaps in the time series, and then the NEE was divided into ecosystem respiration (Reco) and gross primary production (GPP) by the nighttime partitioning method. The published dataset includes: year, month, day, time, atmospheric pressure (P), infrared surface temperature (Tsurf), wind speed (Ws), wind direction (Wd), air temperature (Tair) and relative humidity (rH) at 2m, downward short wave radiation (Rsd), upward short wave radiation (Rsu), downward long wave radiation (Rld), upward long wave radiation (Rlu), Net radiation (Rn), incident photosynthetically active radiation (PAR_dn), reflected photosynthetically active radiation (PAR_up), precipitation (precip), groundwater level (GW), 5cm/10cm/20cm/40cm/80cm/160cm soil water content (soil_VW_ 5cm / 10cm / 20cm / 40cm / 80cm / 160cm) and soil temperature (soil_T_5cm / 10cm / 20cm / 40cm / 80cm / 160cm), soil heat flux at 5cm depth (soil_ G) , raw data of net ecosystem carbon exchange (NEE_raw), raw data of latent heat flux (LE_raw), raw data of sensible heat flux (H_raw), net ecosystem carbon exchange after gap filling (NEE_ f) , latent heat flux after gap filling (LE_f), sensible heat flux after gap filling (H_f), ecosystem respiration imputation (Reco_f), gross primary productivity (GPP_f). The data are stored in .xlsx format at 30-minute intervals. Null values in the dataset are represented by NA. Please refer to Lei and Yang (2010a, 2010b) for detailed information of this site and the observation instruments.
LEI Huimin
Accurate evapotranspiration (ET) estimation is important for understanding hydrological cycle and water resources management in the cropland. Based on eight flux sites within the North China Plain (NCP) and the surrounding area, which were integrated together for the first time, we applied support vector regression method to develop ET dataset for the cropland in NCP from 1982 to 2015 with 1/12° spatial resolution and eight-day temporal interval.
LEI Huimin
Accurate evapotranspiration (ET) estimation is important for understanding hydrological cycle and water resources management in the cropland. Based on eight flux sites within the North China Plain (NCP) and the surrounding area, which were integrated together for the first time, we applied support vector regression method to develop ET dataset for the cropland in NCP from 2001 to 2015 with 1km spatial resolution and eight-day temporal interval.
LEI Huimin
The study of chemical weathering is of great significance to understand how the plateau uplift regulates the mechanism of climate change and the circulation of elements and materials in the sphere. The data set is the seasonal major element concentration and stable isotope data of the river water at the hydrological station of the Yellow River Basin originating from the Qinghai Tibet Plateau. There are two hydrological stations in total: 1. Longmen hydrological station in the middle reaches of the Yellow River is the high-resolution (weekly) sample data collected in 2013, and the element concentrations include K, CA, Na, Mg, SO4, HCO3, Cl, etc. The cation data of collected water samples are tested on ICP-AES of Institute of earth environment, Chinese Academy of Sciences, and the anion data are tested on ion chromatograph (ics1200) of Nanjing Institute of geography and lakes, Chinese Academy of Sciences. The uncertainty is within 5%, and HCO3 is tested by titration. The high-resolution (weekly) Li isotope data of river water was tested in MC-ICP-MS of Institute of earth environment, Chinese Academy of Sciences in 2017, and the test accuracy 2sd is better than 5 ‰; 2. Tangnaihai hydrological station on the Yellow River is the river water (month by month) data set collected from July 2012 to June 2014. The major element concentrations include K, CA, Na, Mg, SO4, HCO3, Cl, etc., and the stable isotope data include s, O and H. The data set can be used to study the modern weathering process under the background of the uplift of the Qinghai Tibet Plateau, and provides the first-hand reliable data for the study of physical erosion and chemical weathering in the basin.
JIN Zhangdong, ZHAO Zhiqi
The soil surface roughness data set measured simultaneously during the Soil Moisture Experiment in the Luan River (SMELR) in 2018, which covers (1) 30 quadrats in the north-south flight region of 70 km ×12 km typical experimental area, and (2) 8 quadrats in the northeast-southwest flight region of 165 km×5 km complex experimental area. The data were measured on September 17, September 18 and September 20, 2018 respectively. The soil surface roughness along the row (East-West) direction and cross the row (North-South) direction of typical features in each sample area were measured. The surface roughness of the dataset is described using three parameters; root mean square height (RMSH) and correlation length (CL). The root mean square height describes the random surface characteristics, while the correlation length and correlation function describe the periodicity of the surface. The surface roughness was calculated through the steps of soil surface height digitization, slope correction, periodic correction, and roughness calculation.
GUO Peng
The data includes ten typical hydropower stations in Datong River Basin of Qinghai-Tibet Plateau in July 2020, including Duolong Hydropower Station, Gousikou Hydropower Station, Jinxing Hydropower Station, Kasuoxia Hydropower Station, Liancheng Hydropower Station, Nazixia Hydropower Station, Stone Gorge Hydropower Station, Tianwanggou Hydropower Station, Tiemai Hydropower Station and Xueyitan Hydropower Station. Data are helpful to study the distribution and use of hydropower stations in Datong River Basin. The data were taken by the expedition team through aerial photography using DJI UAV RTK and Royal Series, and spliced by DJI mapping software. The aerial image data has high definition, which can obviously observe the water level difference between upstream and downstream of the hydropower station and the topographic distribution around the hydropower station. The data can be applied to the research field of hydropower stations in Qinghai-Tibet Plateau, providing relevant analysis data.
FU Bin
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
This data set includes the statistical data of water resources in Tibet and Qinghai. The data comes from Tibet water resources bulletin and Qinghai water resources bulletin. The statistical scale is the municipal unit scale, including Xining City, Haidong City, Haibei Prefecture, Hainan prefecture, Huangnan Prefecture, Guoluo Prefecture, Yushu prefecture and Haixi Prefecture in Qinghai Province, Lhasa, Changdu, Shannan, Shigatse, Naqu and other municipal units in Tibet Ali, Linzhi and other municipal units; Variables include annual precipitation, surface water resources, groundwater resources, repeated calculation, total water resources, per capita water resources, water production modulus, surface water supply, groundwater supply, total water supply, agricultural water consumption, industrial water consumption, domestic water consumption, ecological environment water consumption and total water consumption. The data set can be used in the fields of water resources management and ecological environment protection in the Qinghai Tibet Plateau.
LIU Zhaofei, YAO Zhijun
This data set contains the surface temperature, soil temperature, and soil moisture data measured simultaneously during the Soil Moisture Experiment in the Luan River (SMELR) in 2018, which is used as "true value" to validate the remote sensing retrieval. The dataset includes soil moisture (volumetric water content, %) of the surface layer (0-5cm), soil moisture of the deeper layers (5, 10, 20, 40 cm), temperature (℃) of shaded soil, illuminated soil, 5-cm soil, shaded and illuminated vegetation. The ground synchronous sampling quadrats were distributed in the upstream of Luan River (Shandian River Watershed and Xiaoluan River Watershed), and the sampling time was September in 2018. ML3 soil moisture sensor, TR-52i temperature sensor, soil ring sampler were used for measurement. The sampling scheme of Large Quadrat--Small Quadrat--Sampling Location was adopted to obtain data.
ZHAO Tianjie, YAO Panpan, CUI Qian, JIANG Lingmei, CHAI Linna, ZHENG Chaolei, LU Hui, MA Jianwei, LV Haishen, WU Jianjun, ZHAO Wei, YANG Na, LI Yuxia, PAN Jinmei, LIU Mingyu, WEI Zushuai, ZHANG Ziqian, WANG Jian, YANG Jianwei, LIU Xiaojing, LIU Jin, YIN Yanmin, LI Yishan, NI Shaoqiang, ZHU Peng, HONG Zhiming, WANG Xiaoyi, LIU Chen, YANG Jianhua, TIAN Feng, WANG Wei, HE Juelin, CHEN Yongqiang, XU Shaobo, CHENG Yuan, GAO Siyuan, HAO Zhen, YI Zhenyan, WANG Haoyu, HU Xin, PENG Yifeng, DU Xiaozheng, HU Fengmin, SUN Yayong, GENG Deyuan, YANG Gang, ZHONG Hao, WU Song, ZHENG Jie, YANG Beibei, ZHAO Jiacheng, ZHOU Qian
Based on China's daily meteorological elements data set and National Geographic basic data, the extreme precipitation, extreme temperature, drought intensity, drought frequency and other indicators in Hengduan Mountain area were calculated by using rclimdex, nspei and bilinear interpolation methods. The data set includes basic data set of disaster pregnant environment, basic data set of extreme precipitation index, basic data set of extreme temperature index, basic data set of drought intensity and frequency. The data set can provide a basic index system for regional extreme high temperature, precipitation and drought risk assessment.
SUN Peng
This dataset contains daily land surface evapotranspiration products of 2020 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, MERRA meteorological data, and China Meteorological Forcing Dataset.
YAO Yunjun, LIU Shaomin, SHANG Ke
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