Data Overview: Zhangye's channels are divided into five levels: dry, branch, bucket, agricultural and Mao channels, of which the agricultural channels are generally unlined. Mao channels are field projects, so the three levels of dry, branch and bucket channels and a small part of agricultural channels are mainly collected. The irrigation canal system data includes 2 main canals (involving multiple irrigation districts), 157 main canals (within a single irrigation district), 782 branch canals and 5315 dou canals, with a total length of 8, 745.0km. Data acquisition process: remote sensing interpretation and GPS field measurement are adopted for data acquisition of irrigation canal system. Direct GPS acquisition channel is the most effective method, but the workload of GPS acquisition channel is too large, and we only verify the measurement in some irrigation areas. The main method is to first collect the manual maps of irrigation districts drawn by each water pipe. Most of these maps have no location, only some irrigation districts such as Daman and Shangsan have been located based on topographic maps, and some irrigation districts in Gaotai County have used GPS to locate some channels. Referring to the schematic diagram of the irrigation district, channel spatial positioning is carried out based on Quikbird, ASTER, TM remote sensing images and 1: 50000 topographic maps. For the main canal and branch canal, due to the obvious linear features on remote sensing images and the general signs on topographic maps, it can be located more accurately. For Douqu, areas with high-resolution images can be located more accurately, while other areas can only be roughly located according to fuzzy linear features of images and prompt information of irrigation district staff, with low positioning accuracy. Each water management office simultaneously provides channel attribute data, which is one-to-one corresponding to spatial data. After the first draft of the channel distribution map is completed, it is submitted twice to the personnel familiar with the channel distribution of each water pipe for correction. The first time is mainly to eliminate duplication and leak, and the second time is mainly to correct the position and perfect the attribute data. Description of data content: The fields in the attribute table include code, district and county name, irrigation area name, channel whole process, channel name, channel type, location, total length, lined, design flow, design farmland, design forest and grass, real irrigation farmland, real irrigation forest and grass, water right area, and remarks. Code example: G06G02Z15D01, where the first letter represents the county name, the 2nd and 3rd numbers represent the county (district) number, the 4th to 6th characters represent the trunk canal code, the 7th to 9th characters represent the branch canal code, and the 10th to 12th characters represent the dou canal code.
0 2020-06-08
The dataset of airborne imaging spectrometer (OMIS-II) mission was obtained in the Zhangye-Yingke-Huazhaizi flight zone on Jun. 16, 2008. Data after radiometric correction and calibration and geometric approximate correction were released. The flying time of each route was as follows: {| ! id ! flight ! file ! starttime ! lat ! long ! alt ! image linage ! endtime ! lat ! long ! alt |- | 1 || 3-1 || 2008-06-16_14-26-53_DATA.BSQ || 14:44:01 || 38.992 || 100.446 || 3250.7 || 6698 || 14:51:28 || 38.744 || 100.286 || 3237.7 |- | 2 || 3-2 || 2008-06-16_14-52-37_DATA.BSQ || 14:55:47 || 38.731 || 100.284 || 3214.9 || 7202 || 15:03:47 || 38.981 || 100.445 || 3237.6 |- | 3 || 3-3 || 2008-06-16_15-04-57_DATA.BSQ || 15:09:29 || 38.989 || 100.457 || 3230.2 || 6740 || 15:16:58 || 38.739 || 100.297 || 3236.0 |- | 4 || 3-4 || 2008-06-16_15-18-07_DATA.BSQ || 15:21:19 || 38.728 || 100.296 || 3200.6 || 7256 || 15:29:23 || 38.979 || 100.457 || 3170.8 |- | 5 || 3-5 || 2008-06-16_15-30-32_DATA.BSQ || 15:35:06 || 38.983 || 100.466 || 3221.9 || 6627 || 15:42:28 || 38.736 || 100.307 || 3227.9 |- | 6 || 3-6 || 2008-06-16_15-43-37_DATA.BSQ || 15:47:39 || 38.726 || 100.308 || 3249.2 || 7013 || 15:55:27 || 38.975 || 100.467 || 3219.1 |- | 7 || 3-7 || 2008-06-16_15-56-36_DATA.BSQ || 16:00:46 || 38.981 || 100.476 || -1.0 || 6639 || 16:08:09 || 38.732 || 100.317 || 3276.8 |- | 8 || 3-8 || 2008-06-16_16-09-18_DATA.BSQ || 16:13:15 || 38.723 || 100.317 || 3212.7 || 7106 || 16:21:09 || 38.973 || 100.479 || 3216.1 |- | 9 || 3-9 || 2008-06-16_16-22-18_DATA.BSQ || 16:26:28 || 38.981 || 100.490 || 3218.6 || 6850 || 16:34:05 || 38.725 || 100.325 || 3235.9 |- | 10 || 3-10 || 2008-06-16_16-35-14_DATA.BSQ || 16:39:23 || 38.716 || 100.326 || 3261.3 || 7056 || 16:47:14 || 38.967 || 100.488 || 3208.4 |- | 11 || 3-11 || 2008-06-16_16-48-23_DATA.BSQ || 16:52:44 || 38.976 || 100.501 || 3204.8 || 6902 || 17:00:24 || 38.725 || 100.338 || 3230.1 |- | 12 || 3-12 || 2008-06-16_17-01-33_DATA.BSQ || 17:05:19 || 38.710 || 100.336 || 3253.8 || 7033 || 17:13:08 || 38.965 || 100.500 || 3225.6 |- | 13 || 3-13 || 2008-06-16_17-14-17_DATA.BSQ || 17:19:01 || 38.973 || 100.511 || 3224.8 || 6831 || 17:26:36 || 38.722 || 100.349 || 3230.1 |- | 14 || 3-14 || 2008-06-16_17-27-46_DATA.BSQ || 17:32:06 || 38.706 || 100.346 || 3233.7 || 3235 || 17:35:44 || 38.830 || 100.426 || 3235.1 |- | 15 || 3-15 || 2008-06-16_17-36-54_DATA.BSQ || 17:35:51 || 38.8334 || 100.428 || 3235.8 || 3625 || 17:39:52 || 38.963 || 100.511 || 3250.6 |}
0 2019-09-12
This glacier inventory is supported by the International Centre for Integrated Mountain Development (ICIMOD) and the United Nation Environment Programme/Regional Resources Centre, Asia and The Pacific (UNEP/RRC-AP)。 1、The glacier inventory uses the remote sensing data of Landsat，reflecting the current status of glaciers in Nepal in 2000. 2、The spatial coverage of the glacier inventory: Nepal 3、Contents of the glacier inventory: glacier location, glacier code, glacier name, glacier area, glacier length, glacier thickness, glacier stocks, glacier type, glacier orientation, etc. 4、Data Projection： Grid Zone IIA Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 23°09'28.17"N Standard parallel 2: 28°49'8.18"N Minimum X Value: 1920240 Maximum X Value: 2651760 Minimum Y Value: 914398 Maximum Y Value: 1188720 Grid Zone IIB Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Nepal) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00'00"E Central parallel: 26°00'00"N Scale factor: 0.998786 Standard parallel 1: 21°30'00"N Standard parallel 2: 30°00'00"N Minimum X Value: 1823188 Maximum X Value: 2000644 Minimum Y Value: 1306643 Maximum Y Value: 1433476 For a detailed data description, please refer to the data file and report.
0 2020-06-09
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Sidaoqiao Superstation from January 1 to December 31, 2018. The site (101.137° E, 42.001° N) was located on a tamarix (Tamarix chinensis Lour.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 873 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HC2S3; 5, 7, 10, 15, 20 and 28 m, towards north), wind speed profile (010C; 5, 7, 10, 15, 20 and 28 m, towards north), wind direction profile (020C; 15 m, towards north), air pressure (CS100; in waterproof box), rain gauge (TE525M; 28 m, towards south), four-component radiometer (CNR4; 10 m, towards south), two infrared temperature sensors (SI-111; 10 m, towards south, vertically downward), two photosynthetically active radiation (PQS-1; 10 m, towards south, one vertically upward and one vertically downward), soil heat flux (HFP01SC; 3 duplicates with G1 below the tamarix; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (installed on 17 July, 2013, TCAV; -0.02, -0.04 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6, -2.0 m), and soil moisture profile (install on 7 December, 2013, ML2X; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6, -2.0 m). The observations included the following: air temperature and humidity (Ta_5 m, Ta_7 m, Ta_10 m, Ta_15 m, Ta_20 m and Ta_28 m; RH_5 m, RH_7 m, RH_10 m, RH_15 m, RH_20 m and RH_28 m) (℃ and %, respectively), wind speed (Ws_5 m, Ws_7 m, Ws_10 m, Ws_15 m, Ws_20 m and Ws_28 m) (m/s), wind direction (WD_15 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 of upward and downward (PAR_up and PAR_down) (μmol/ (s m^-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), 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, Ts_160 cm, Ts_200 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm, Ms_200 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 precipitation data was wrong during January to June because of the sensor problem; the air pressure data was wrong during July to October because of sensor line broken. 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: 2018-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
0 2020-07-25
The data set includes the river level observation data of point 2 in the dense runoff observation of the middle reaches of Heihe River from January 1, 2015 to December 31, 2015. The observation point is located in Heihe bridge, 312 National Road, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n38.996667 °, e100.427222 °, altitude 1485m, river width 70m and 20m. Sr50 ultrasonic range finder is used for water level observation, with acquisition frequency of 30 minutes. The data includes the following parts: Water level observation, observation frequency 30 minutes, unit (CM); In 2015, the section of bridge no.2-312 was frequently disturbed by human beings. The dam was built within 1km of the upstream and downstream of the section. The unstable area of the hydrological section led to the disorder of the water level and flow curve. During the measurement, the stable flow and water level curve could not be obtained. The observation of water level is based on the manual observation of water level at 0:00 on January 1, 2015. In the later stage, the hydrological section of river undercut changes. The result is that the datum water level changes and negative value appears; Refer to Li et al. (2013) for hydrometeorological network or station information, and he et al. (2016) for observation data processing
0 2020-03-14
The dataset of surface roughness measurements was obtained in A1, A2, A3, L1, L2, L3, L4, L5 and L6 of the A'rou foci experimental area. The quadrates were changed into 3×3 subsites during the foci experimental period, with each one spanning a 30×30 m2 plot. With the roughness plate 110cm long and the measuring points distance 1cm, the samples were collected along the strip from south to north and from east to west, respectively. As for the sampling lines, the samples were collected every 100 m along them from south to north. Photos were named in the form of A3-1EW, indicating No. 1 point in A3 measured from east to west. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 radius is also included for further checking. Those provide reliable ground data for improving and verifying the remote sensing algorithms. Nine files were included, ARou_SampleArea1, ARou_SampleArea2, ARou_SampleArea3, ARou_SampleLine1, ARou_SampleLine2, ARou_SampleLine3, ARou_SampleLine4, ARou_SampleLine5 and ARou_SampleLine6.
0 2019-05-23
Based on the study of the terrace formation age in the upper reaches of heihe river, photoluminescence samples were collected from the sediments of grade 6 river terrace near the upper reaches of qilian river.The quartz particles (38-63 microns) in the sample were isolated in the laboratory, the equivalent dose and dose rate in the quartz particles were measured, and the photoluminescence age of the sample was finally obtained.The obtained ages range from 5ka to 82ka, corresponding to the years of cutting down the terraces of all levels.
0 2020-07-28
The station data information of 21 regular meteorological observation stations in Heihe River Basin and surrounding areas and 13 national benchmark stations around Heihe River provided by Heihe plan data management center are used to make statistics and collation of daily wind speed and calculate the monthly wind speed data of 1961-2010 for many years. The spatial stability analysis is carried out to calculate the variation coefficient. If the variation coefficient is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly wind speed distribution trend is obtained; if the variation coefficient is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station wind speed value and the geographical terrain factors (longitude and latitude, elevation, slope, aspect, etc.) The trend of monthly wind speed distribution is obtained, and the residual after removing the trend is fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average wind speed distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average wind speed for many years from 1961 to 2010. Spatial resolution: 500M.
0 2020-03-28
This data set was acquired by L & K band airborne microwave radiometer on July 4, 2008, in the Biandukou-Linze flight zone. The frequency of L-band is 1.4GHz, and the backsight is 35 degrees to obtain dual polarization (H and V) information; the frequency of K-band is 18.7ghz, and there is no polarization information. The plane took off from Zhangye airport at 9:48 (Beijing time, the same below) and landed at 14:14. 10: At 16-11:40, the flight altitude was 3100-3500m and the flight speed was about 230-250km / hr. 12: 16-12:18 low flying Linze reservoir line 1-6, relative altitude 100m, flight speed 190km / hr. 12: At 26-13:42, he worked in Linze photography area, with a flight altitude of about 2000m and a flight speed of about 250km / hr. 13: 49-13:51 fly low again to Linze reservoir line 1-6. The original data is divided into two parts: microwave radiometer data and GPS data. The L and K bands of microwave radiometer are non imaging observations. The digital values obtained from the instantaneous observation are recorded in the text file, and the longitude and latitude as well as the aircraft attitude parameters are recorded in the GPS data. When using microwave radiometer to observe data, it is necessary to convert the digital value recorded into the bright temperature value according to the calibration coefficient (the calibration coefficient file is filed with the original observation data). At the same time, through the clock records of microwave radiometer and GPS, we can connect the microwave observation with GPS record and match the geographic coordinate information for the microwave observation. Due to the coarse observation resolution of microwave radiometer, the effects of aircraft yaw, roll and pitch are generally ignored in data processing. According to the target and flight relative altitude (H), after calibration and coordinate matching, the observation information can also be gridded. The resolution (x) of L band and K band is consistent with that of observation footprint. The reference resolution is: L band, x = 0.3H; K band, x = 0.24h. After the above steps, we can get the products that users can use directly.
0 2020-03-09
This dataset includes data recorded by the Hydrometeorological observation network obtained from an observation system of Meteorological elements gradient of Sidaoqiao Superstation between 11 July, 2013, and 31 December, 2013. The site (101.137° E, 42.001° N) was located on a tamarix (Tamarix chinensis Lour.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 873 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HC2S3; 5, 7, 10, 15, 20 and 28 m, towards north), wind speed profile (010C; 5, 7, 10, 15, 20 and 28 m, towards north), wind direction profile (020C; 15 m, towards north), air pressure (CS100; in waterproof box), rain gauge (TE525M; 28 m, towards south), four-component radiometer (CNR4; 10 m, towards south), two infrared temperature sensors (SI-111; 10 m, towards south, vertically downward), two photosynthetically active radiation (PQS-1; 10 m, towards south, one vertically upward and one vertically downward), soil heat flux (HFP01SC; 3 duplicates with G1 below the tamarix; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (installed on 17 July, 2013, TCAV; -0.02, -0.04 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2 and -1.6 m), and soil moisture profile (install on 7 December, 2013, ML2X; -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_5 m, Ta_7 m, Ta_10 m, Ta_15 m, Ta_20 m and Ta_28 m; RH_5 m, RH_7 m, RH_10 m, RH_15 m, RH_20 m and RH_28 m) (℃ and %, respectively), wind speed (Ws_5 m, Ws_7 m, Ws_10 m, Ws_15 m, Ws_20 m and Ws_28 m) (m/s), wind direction (WD_15 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 of upward and downward (PAR_up and PAR_down) (μmol/ (s m^-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), 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) (℃), and 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 wind speed (10 m height) data were missing before 12 November, 2013 because of the sensor problem. 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: 2013-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), Liu et al. (2011) (for data processing) in the Citation section.
0 2019-09-15
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