This is the LAINet dataset measured in the corn field at the Xiaoman irrigation district (from 25 June, to 24 August, 2012). The time used in this dataset is in UTC+8 Time. Instrument: LAINet- A wireless sensor network for leaf area index measurement, Beijing Normal University Measurement Mode: LAINet observation system is formed by 3 kinds of sensor nodes, they are respectively (1) node below the canopy, sensors up-looking are used for measure the transmitted radiation through the canopy, which are deployed horizontally; (2) node above canopy: sensors up-looking are used for measure the total sun incident radiation, which are deployed horizontally; (3) sink or router node, which is designed for receiving and transmitting data measured by the above node and below node. Data Processing: the original data obtained from sensors is received by sink nodes, and forms the original dataset in days after pre-processed. The observation for transmittance of the canopy is acquired by calculating the ratio of the radiation through the canopy and the total incident radiation above the canopy at different sun elevation angles during a day. The retrieval of LAI is based on the multi-angle transmittance data. LAINet dataset is composed of original LAI data, LAI data after calculating the mean value in 5 days interval and the longitude and latitude of the measurement nodes. All the data are stored in the format of Excel. As for the data after calculating the mean value in 5 days, we take the number of aggregation nodes as the name of the sheet. Data saved in a sheet is from an sink node which receives the measurement data from the child nodes. The original data records the LAI of every node in the observation day. In the sheet of two kinds of data above, the meaning of the column is as follows: DOY, node one, node two, …, and node N.
0 2019-09-13
1) The data set is composed of global atmospheric reanalysis data jointly produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). These grid data are generated by reanalysing the global meteorological data from 1948 to present by applying observation data, forecasting models and assimilation systems. The data variables include surface, near-surface (.995 sigma layer) and multiple meteorological variables in different barospheres, such as precipitation, temperature, relative humidity, sea level pressure, geopotential height, wind field, heat flux, etc. 2) The coverage time is from 1948 to 2018, and the data from 1948 to 1957 are non-Gaussian grid data. The data cover the whole world. The spatial resolution is a 2.5° latitude by 2.5° longitude grid. The vertical resolution is a 17-layer standard pressure barosphere, with layer boundaries at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa, and 28 sigma levels. Some variables are calculated for 8 layers (omega) or 12 layers (humidity), with temporal resolutions of 6 hours, daily, monthly or a long-term monthly average (from 1981 to 2010). The daily data are obtained by averaging the daily values of 0Z, 6Z, 12Z and 18Z. 3) Missing values are assigned a value of -9.99691e+36f. The data are stored in the .nc format with the file name var.time.stat.nc, and each file includes data on latitude, longitude, time, and atmospheric variables. For detailed data specifications, please visit http://www.esrl.noaa.gov/pad/data.
0 2020-09-14
In east Asia, institute of atmospheric physics, Chinese Academy of Sciences key laboratory of regional climate and environment development of regional integration environment with independent copyright system model RIEMS 2.0, on the basis of the regional climate model RIEMS 2.0 in the United States center for atmospheric research and the development of the university of binzhou mesoscale model (MM5) is a static dynamic framework, coupled with some physical processes needed for the study climate solutions.These processes include the biosphere - atmosphere transmission solutions, using FC80 closed Grell cumulus parameterization scheme, MRF planetary boundary condition and modify the CCM3 radiation, such as the heihe river basin observation and remote sensing data of important parameters in the model for second rate, USES the heihe river basin vegetation data list data of land use in 2000 and 30 SEC DEM data in heihe river basin, build up suitable for the study of heihe river basin ecological - hydrological processes of the regional climate model. Drive field: ERA-INTERIM reanalysis data Spatial scope: the grid center of the simulation area is located at (40.30n, 99.50e), the horizontal resolution is 3 km, and the number of simulated grid points in the model is 161 (meridional) X 201 (zonal). Projection: LAMBERT conformal projection, two standard latitudes of 30N and 60N. Time range: from January 1, 2011 to December 31, 2016, with an interval of 6 hours Description of file contents: monthly storage by grads without format.Except the maximum and minimum temperature as the daily scale, the other variables are all 6-hour data. MATLAB can be used to read, visible tmax_erain_xiong_heihe.m file description. Data description of heihe river basin: 1) Anemometer west wind (m/s) college usurf for short 2) Anemometer south wind(m/s), vsurf for short College 3) Anemometer temperature (deg) K tsurf College 4) maximal temperature (deg) K tmax 5) minimal temperature (deg K) abbreviated as tmin 6) college Anemom specific humidity (g/kg) college qsurf for short 7) value (mm/hr) is simply value p College 8) Accumulated evaporation (mm/hr) evap 9) sensible heat (watts/m**2/hr) for short College 10) Accumulated net infrared radiation (watts/m * * 2 / hr) netrad for short College definition file name: -erain-xiong. Month and year
0 2020-03-11
Reservoir refers to the artificial water area formed in valley, river or low-lying area by dam, dike, sluice, weir and other projects. It is the main measure used for runoff regulation to change the distribution process of natural water resources and plays an important role in social and economic development. Many reservoirs have been built in Heihe River Basin, which has an important impact on the utilization of water resources in this area. In order to facilitate the mapping needs of users, we use topographic map and remote sensing image to prepare the reservoir distribution map of the Heihe River Basin. The location and shape of the reservoir are mainly obtained by manual interpretation based on Google map image, which basically shows the current situation of the reservoir distribution in the Heihe River Basin around 2010.
0 2020-10-12
The data set provided the cloudless Fractional Snow Cover area (FSC) time-series product basing on the MODIS data and covered the Heihe River Basin from January 2010 to December 2013. They also provide the high spatial (500 m) and temporal (1 day) resolution. Firstly, the end-member were automatically extracted by the fast autonomous spectral end-member determination (N-FINDR) maximizing volume iteration algorithm. Combining N-FINDR with the orthogonal subspace projection (OSP) approach, we propose an improved end-member extraction algorithm using a maximizing, volume-based iterative method. All the 6 end-members were extracted including snow, soil, water, bare land, vegetation, and cloud, respectively. Then, the 10-day spectral library time series based on prior knowledge of Heihe basin are built for 2009. The primary data were produced using the fully constrained least squares (FCLS) linear spectral mixture analysis method by the spectral library. Finally，the cubic spline interpolation algorithm were used to the eliminate the cloud pixels completely and obtain the data set. The data are validated by the fractional snow cover derived from Landsat imagery and the results indicate that the improved algorithm can obtain the end-member information accurately, and the retrieved fractional snow cover has better accuracy than the MODIS fractional snow-cover product (MOD10A1). So the data set can provide more accurate input for the hydrology and climate model.
0 2019-09-12
This data set includes the daily values of temperature, air pressure, relative humidity, wind speed, precipitation, total radiation, etc. observed at Namuco station from January 1, 2017 to December 31, 2018.
0 2019-11-21
This data set is based on China's second inventory data, Landsat series optical image data with a spatial resolution of 30 meters and cloud coverage of less than 10% and SRTM and other data using ArcGIS, ENVI, Google Earth and other processing software and extracting the glacial lake boundary within 10 km of the glacier boundary by artificial visual interpretation. In addition, the data set adds attributes such as glacial lake type, the mountain range, the province, and the basin to the data as well as quality checking and accuracy verification for the interpreted data. The spatial resolution is 30 meters. It consists of two parts: the glacial lake distribution area vector file and the Inventory Data set of glacial lakes in west China in 2015. It can provide reference data for glacial lake-glacier coupling, water resource utilization and management in west China and can also be used as basic data for regional climate change and cryospheric studies.
0 2019-09-15
The dataset of soil moisture observations was obtained by the cutting ring (50cm^3) and ML2X Soil Moisture Tachometer in the Linze station foci experimental area. Surface soil (0-5cm) was measured 2-3 times in 40 subplots of the west-east desert strip on May 24, 25, 28, Jun. 27 and Jul. 11, 2008, 2-4 times in 9 subplots of north-south strip on May 24, 25, 28, Jun. 27 and Jul. 11, 17 times from P1 to P6 strips on Jul. 4 and 8, nine times along LY06 strip on Jun. 6, 15, 29 and Jul. 11, LY07 strip on May 30, Jun. 6, 10, 15, 29 and Jul. 11 and LY08 strip on May 30, Jun. 6 and 10, and once by the cutting ring and three times by ML2X Soil Moisture Tachometer in Wulidun farmland quadrates on May 24, 25, 28, Jun. 29 and Jul. 11. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
0 2019-09-15
This dataset contains the flux measurements from the Zhangye wetland station eddy covariance system (EC) in the flux observation matrix from 25 June to 26 September, 2012. The site (100.44640° E, 38.97514° N) was located in a wetland surface, which is near Zhangye city, Gansu Province. The elevation is 1460.00 m. The EC was installed at a height of 5.2 m; 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 (Gill&Li7500A) was 0.25 m. Raw data acquired at 10 Hz were processed using the Eddypro post-processing software (Li-Cor Company, http://www.licor.com/env/products/ eddy_covariance/software.html), including spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, angle of attack 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. Moreover, 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), which was proposed by Foken and Wichura [1996]: 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), representing 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 before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% 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; the missing data were replaced with -6999. Moreover, suspicious data were marked in red. 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/m^3), CO2 mass density (CO2, mg/m^3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m^2), latent heat flux (LE, W/m^2), carbon dioxide flux (Fc, mg/ (m^2s)), 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 *.xlsx format. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
0 2019-09-15
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative humidity index is one of the indicators to characterize soil drought and can directly reflect the status of crops' available water.
0 2020-03-10
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