The dataset of ground truth measurements for snow synchronizing with the airborne PHI mission was obtained in the Binggou watershed foci experimental area on Mar. 24, 2008. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A. (2) Snow parameters as the snow surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, and snow density by the aluminum case in BG-A1, BG-A2, BG-B, BG-D, BG-E and BG-F5 (three sampling units each) from 11:11-12:35 (BJT) with the airplane overpass. 64 points were selected by four groups. (3) Snow albedo by the total radiometer in BG-A. (4) The snow spectrum by ASD (Xinjiang Meteorological Administration) in BG-A11 Two files including raw data and preprocessed data were archived.
0 2019-09-14
On 25 July 2012, a Leica ALS70 airborne laser scanner boarded on the Y-12 aircraft was used to obtain the point cloud data. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second, third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5500 m with the point cloud density 1 points per square meter. Aerial LiDAR-DEM was obtained through parameter calibration, automatic classification of point cloud density and manual editing.
0 2019-09-15
QuickBird satellite was launched by Digital Globe corporation on October 18, 2001. It has 4 multi-spectral bands and 1 panchromatic band, with a spatial resolution of 0.61m for panchromatic bands and a spatial resolution of 2.5m for multi-spectral bands and a width of 16.5 * 16.5 km. There are two QuickBird remote sensing images in heihe river basin.The acquisition time and coverage were: 2004-03-23, covering zhangye area;2004-08-08, covering danokou and drainage ditch drainage basin. The product level is level L2 and has been geometrically corrected by the system.
0 2020-03-15
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
0 2020-06-10
The micro-meteorological field is located in the grassland of Pailugou watershed of Qilian Mountain with an altitude of 2700m. The data were recorded from January 2011 to July 2012, and the time interval was half an hour, including 1.5m humidity, 3m temperature, 2.8m air pressure, 1.3m rainfall, 2.2m wind speed, 3.1m total radiation, the units are %, °C, Pa, m, m/s, W•M-2.
0 2020-03-31
The dataset is the HWSD soil texture dataset in the north slope of the Tianshan River Basin. The data comes from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). Version 1.1 was released on March 26, 2009. The data resolution is 1km. The soil classification system used is mainly FAO-90. The main fields of the soil attribute table include: SU_SYM90 (soil name in FAO90 soil classification system) SU_SYM85 (FAO85 classification) T_TEXTURE (top soil texture) DRAINAGE (19.5); ROOTS: String (depth classification of obstacles to the bottom of the soil); SWR: String (soil moisture characteristics); ADD_PROP: Real (a specific soil type related to agricultural use in the soil unit); T_GRAVEL: Real (gravel volume percentage); T_SAND: Real (sand content); T_SILT: Real (silt content); T_CLAY: Real (clay content); T_USDA_TEX: Real (USDA soil texture classification); T_REF_BULK: Real (soil bulk density); T_OC: Real (organic carbon content); T_PH_H2O: Real (pH) T_CEC_CLAY: Real (cation exchange capacity of cohesive layer soil); T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation); T_TEB: Real (exchangeable base); T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content); T_ESP: Real (exchangeable sodium salt); T_ECE: Real (conductivity). The attribute field beginning with T_ indicates the upper soil attribute (0-30cm), and the attribute field beginning with S_ indicates the lower soil attribute (30-100cm) (FAO 2009). The data can provide model input parameters for modelers of the Earth system, and the agricultural perspective can be used to study eco-agricultural zoning, food security, and climate change.
0 2020-06-01
Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.
0 2020-04-28
CMADS (The China Meteorological Assimilation Driving Datasets for The SWAT model) The soil temperature component (hereinafter referred to as cmads-st) USES The China Meteorological Administration Land Data Assimilation System [CLDAS] to force The common Land surface model3.5 [CLM3.5]) (Community Land model, numerical simulation of Land surface, circulation 10 spin - up simulation, get basic stability model initial field, and obtain high space-time resolution of soil temperature data sets, eventually hierarchical data model is utilized to extract, quality control, a nested loop, re-sampling, and a variety of technologies such as bilinear interpolation method is finally established. Cmads-st series data set space covers the whole east Asia (0 ° n-65 ° N, 60 ° e-160 ° E), the spatial resolution is respectively cmads-st V1.0 version: 1/3 °, cmads-st V1.1 version: 1/4 °, cmads-st V1.2 version: 1/8 ° and cmads-st V1.3 version:The above resolutions are daily (the basic resolution of the soil temperature component output in CLM3.5 mode is 1/16°, which ensures the highest resolution of the cmads-st data set is 1/16°). The time scale is 2009-2013.The data set published on this page is the cmads-st V1.0 data set (spatial resolution :1/3°).Temporal resolution: daily.Space coverage: east Asia (0 ° n-65 ° N, 60 ° e-160 ° E).Number of stations: 58,500.Supply factors: the average daily soil temperature of 10 layers (the depth of node hierarchy is in order: the first layer :0.00710063521m; the second layer :0.0279249996m; the third layer :0.0622585751m; the fourth layer :0.118865065m; the fifth layer :0.2121934m; the sixth layer :0.3660658m; the seventh layer :0.619758487m; the eighth layer :1.03802705m; the ninth layer :1.72763526m;Floor 10 :2.8646071m).Provide data format: TXT. The path of the cmads-st V1.0 soil temperature data set is: CMADS - ST - V1.0\2009 \ layer1 V1.0\2009 \ layer10 to CMADS - ST CMADS - ST - V1.0\2010 \ layer1 V1.0\2010 \ layer10 to CMADS - ST CMADS - ST - V1.0\2011 \ layer1 V1.0\2011 \ layer10 to CMADS - ST CMADS - ST - V1.0\2012 \ layer1 V1.0\2012 \ layer10 to CMADS - ST CMADS - ST - V1.0\2013 \ layer1 V1.0\2013 \ layer10 to CMADS - ST Cmads-st V1.0 subset file path and file name description Where, daily soil temperature (ten layers) is shown in layer1-layer10\.Are located in the following directories (take 2009 as an example): \2009\layer1\ 2009 layer1 (0.00710063521m) soil temperature directory \2009\layer2\ 2009 layer2 (0.0279249996m) soil temperature directory \2009\layer3\ 2009 layer3 (0.0622585751m) soil temperature catalogue \2009\layer4\ 2009 layer4 (0.118865065m) soil temperature catalogue \2009\layer5\ 2009 layer5 (0.2121934m) soil temperature catalogue \2009\layer6\ 2009 layer6 (0.3660658m) soil temperature catalogue \2009\layer7\ 2009 layer7 (0.619758487m) soil temperature directory \2009\layer8\ 2009 layer8 (1.03802705m) soil temperature catalogue \2009\layer9\ 2009 layer9 (1.72763526m) soil temperature catalogue \2009\layer10\ 2009 10th layer (2.8646071m) soil temperature catalogue
0 2020-07-30
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, which was to establish the scenario by setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were predicted. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
0 2021-01-08
The experimental data of Yingke Daman in Heihe River Basin is supported by the key fund project of Heihe River plan, "eco hydrological effect of agricultural water saving in Heihe River Basin and multi-scale water use efficiency evaluation". Including: soil bulk density, soil water content, soil texture, corn sample biomass, cross-section flow, etc Data Description: 1. Sampling location of Lai and aboveground biomass: Yingke irrigation district; sampling time: May 2012 to September 2012; Lai and aboveground biomass of maize were measured by canopy analyzer (lp-80), and aboveground biomass was measured by sampling drying method; sample number: 16. 2. Soil texture: Sampling location: Yingke irrigation district and Shiqiao Wudou Er Nongqu farmland in Yingke irrigation district; soil sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm; sampling time: 2012; measurement method: laboratory laser particle size analyzer; sample number: 38. 3. Soil bulk density: Sampling location: Yingke irrigation district and Daman irrigation district; sampling depth of soil bulk density is 100 cm, sampling levels are 0-50 cm and 50-100 cm respectively; sampling time: 2012; measurement method: ring knife method; number of sample points: 34. 4. Soil moisture content: this data is part of the monitoring content of hydrological elements in Yingke irrigation district. The specific sampling location is: Shiqiao Wudou Er Nongqu farmland in Yingke Irrigation District, planting corn for seed production; soil moisture sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm Methods: soil drying method and TDR measurement; sample number: 17. 5. Cross section flow: Sampling location: the farmland of Wudou Er Nong canal in Shiqiao, Yingke irrigation district; measure the flow velocity, water level and water temperature of different canal system sections during each irrigation, record the time and calculated flow, monitor once every 3 hours until the end of irrigation; sampling time: 2012.5-2012.9; measurement method: Doppler ultrasonic flow velocity meter (hoh-l-01, Measurement times: Yingke irrigation data of four times.
0 2020-10-13
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