Current Browsing: 2014


HiWATER: Dataset of hydrometeorological observation network (automatic weather station of E’bao station, 2014)

This data set contains meteorological element observation data from January 1, 2014 to December 31, 2014 from the burg station upstream of heihe hydrometeorological observation network.The station is located in caochang, qilian county, qinghai province.The latitude and longitude of the observation point is 100.9151e, 37.9492n and 3294m above sea level.The air temperature and relative humidity sensors are located at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tilting bucket rain gauge is installed at 10m;The wind speed and direction sensor is set at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing due south, and the probe facing vertically downward;The soil temperature probe is buried at 0cm on the surface and 4cm underground, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm, 2m to the south of the meteorological tower.The soil water probe is buried at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The soil heat flow plates (3 pieces) are buried in the ground 6cm underground, 2m to the south of the meteorological tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: Celsius), soil moisture (Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ts_160cm) (unit: volumetric water content, percentage). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;The temperature of 4cm soil was between May 31, 2014 and June 17, 2014. Due to sensor problems, data was missing.(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: September 10, 2014, 10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al.(2018), and for observation data processing, please refer to Liu et al.(2011).

2020-04-10

HiWATER: Dataset of hydrometeorological observation network (automatic weather station of Dashalong station, 2014)

This data set contains meteorological element observation data from January 1, 2014 to December 31, 2014 from the grand salon station upstream of heihe hydrometeorological observation network.The station is located in shalantan, west of qilian county, qinghai province.The latitude and longitude of the observation point is 98.9406e, 38.8399n and 3739m above sea level.The air temperature and relative humidity sensors are located at 5m, facing due north.The barometer is installed in the anti-skid box on the ground;The tilting bucket rain gauge is installed at 10m;The wind speed and direction sensor is set at 10m, facing due north;The four-component radiometer is installed at 6m, facing due south;Two infrared thermometers are installed at 6m, facing due south, and the probe facing vertically downward;The soil temperature probe is buried at 0cm on the surface and 4cm underground, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm, 2m to the south of the meteorological tower.The soil water probe is buried at 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground, 2m to the south of the meteorological tower.The soil hot plates (3 pieces) are buried in the ground 6cm underground and 2m to the south of the weather tower. Observation projects are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit:Temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: Celsius), soil moisture (Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ts_160cm) (unit: volumetric water content, percentage). Processing and quality control of observed data :(1) ensure 144 pieces of data every day (every 10min), and mark by -6999 in case of data missing;Due to insufficient power supply, data was missing between January 1, 2014 and January 19, 2014.(2) excluding the time with duplicate records;(3) data that obviously exceeds the physical significance or the range of the instrument is deleted;(4) the part marked with red letter in the data is the data in question;(5) date and time have the same format, and date and time are in the same column.For example, the time is: September 10, 2014, 10:30;(6) the naming rule is: AWS+ site name. For information of hydrometeorological network or station, please refer to Liu et al.(2018), and for observation data processing, please refer to Liu et al.(2011).

2020-04-10

HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Dashalong station, 2014)

This data set contains the observation data of vorticity correlation-meter at da-sharon station, upstream of heihe hydrometeorological observation network, from January 1, 2014 to December 31, 2014.The station is located in qilian county, qinghai province.The longitude and latitude of the observation point are 98.9406e, 38.8399N and 3739 m above sea level.The rack height of the vortex correlativity meter is 4.5m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift, etc., shall be marked in red font.After October 20, 10Hz data was missing due to the data storage problem of the memory card, which was replaced by 30min flux data output by the collector. The published observational data include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), stability Z/L (dimensionless), sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE,Quality indicator for co2 flux QA_Fc.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Liu et al.(2018), and for observation data processing, please refer to Liu et al.(2011).

2020-04-10

HiWATER: Dataset of hydrometeorological observation network (cosmic-ray soil moisture of Daman Superstation, 2014)

This data set contains cosmic ray instrument (CRS) observations from January 1, 2014 to December 31, 2014.The station is located in gansu province zhangye city da man irrigated area farmland, under the surface is corn field.The longitude and latitude of the observation point are 100.3722e, 38.8555n, and 1556m above sea level. The bottom of the instrument probe is 0.5m from the ground, and the sampling frequency is 1 hour. The original observations of the cosmic ray instrument (CRS1000B) included: voltage Batt (V), temperature T (c), relative humidity RH (%), pressure P (hPa), fast neutron number N1C (hr), thermal neutron number N2C (hr), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s).The data published are processed and calculated. The data headers include Date Time, P (pressure hPa), N1C (fast neutron number/hour), N1C_cor (fast neutron number/hour with revised pressure) and VWC (soil volume moisture content %). The main processing steps include: 1) data filtering There are four criteria for data screening :(1) data with voltage less than and equal to 11.8 volts are excluded;(2) remove the data of air relative humidity greater than and equal to 80%;(3) data whose sampling interval is not within 60±1 minute are excluded;(4) the number of fast neutrons removed changed by more than 200 in one hour compared with that before and after.In addition, the missing data was supplemented by -6999. 2) air pressure correction According to the fast neutron pressure correction formula mentioned in the instrument instruction manual, the original data were revised to obtain the revised fast neutron number N1C_cor. 3) instrument calibration In the process of calculating soil moisture, N0 in the calculation formula should be calibrated.N0 is the number of fast neutrons under the condition of soil drying. The measured soil moisture (or through relatively dense soil moisture wireless sensor) m (Zreda et al. (1) Where m is mass water content, N is the number of fast neutrons after revision, N0 is the number of fast neutrons under dry conditions, a1=0.079, a2=0.64, a3=0.37 and a4=0.91 are constant terms. Here, the instrument was calibrated according to Soilnet soil water data in the source area of the instrument, and the relationship between soil volumetric water content (v) and fast neutrons was established according to the actual situation. In formula (1), m was replaced by v.Selected dry wet condition are the obvious difference of June 26-27 June and July 16 - July 17 four days of data, including June 26-27 rate data showed that soil moisture is small, so the selection of 4 cm, 10 and 20 cm as the rate of the three values of average data, its range is 22% 30%, and July 16 - July 17 rate data showed that soil moisture is bigger, so select 4 cm and 10 cm as two value average rate data, the range of 28% - 39%,Finally, the average values of crs_a and crs_b, N0, were 3252 and 3597, respectively. 4) soil moisture calculation According to formula (1), the hourly soil water content data is calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.

2020-04-10

HiWATER:Dataset of Hydrometeorological observation network (Thermal Dissipation sap flow velocity Probe-2014)

The data set contains the observation data of thermal diffusion fluid flow meters at the downstream mixed forest station and eupoplar forest station of the hydrometeorological observation network from January 1 to December 31, 2014. La shan au in the study area is located in the Inner Mongolia autonomous region of mesozoic-cenozoic in iminqak, according to the different height and diameter at breast height of iminqak, choose sampling tree installation TDP (Thermal Dissipation SAP flow velocity Probe, Thermal diffusion flow meter), domestic TDP pin type Thermal diffusion stem flow meter, the model for TDP30.The sample sites are TDP1 point and TDP2 point respectively, which are located near the mixed forest station and populus populus station.The height of the sample tree is TDP2 and TDP1 from high to low, and the diameter of the chest is TDP1 and TDP2 from large to small, so as to measure the trunk fluid flow on behalf of the whole area.The installation height of the probe is 1.3 meters and the installation orientation is due east and west of the sample tree. The original observation data of TDP is the temperature difference between probes, which is collected once for 10s and the average output period is 10 minutes.The published data are calculated and processed trunk flow data, including flow rate (cm/h), flux (cm3/h) and daily transpiration (mm/d) per 10 minutes.Firstly, the liquid flow rate and liquid flux were calculated according to the temperature difference between the probes, and then the transpiration Q per unit area of the forest zone was calculated according to the area of Euphrates poplar forest and the distance between trees at the observation points.At the same time, post-processing was carried out on the calculated rate and flux value :(1) data that obviously exceeded the physical significance or the instrument range were removed;(2) the missing data is marked with -6999;Among them, the data of TDP2 was missing due to power supply problems from 1.1-2.8 days, and the data of the third group of probes was missing from 2.8-3.13 days due to the problems of the third group of probes.(3) suspicious data caused by probe fault or other reasons shall be identified in red, and the data confirmed to have problems shall be removed. Please refer to Li et al.(2013) for hydrometeorological network or site information, and Qiao et al.(2015) for observation data processing.

2020-04-06

HiWATER: Dataset of hydrometeorological observation network (No.4 runoff observation system of Wujing bridge on the Heihe River, 2014)

The data set includes the observation data of river water level and velocity at No. 4 point in the dense observation of runoff in the middle reaches of Heihe River from January 1 to June 25, 2014. The observation point is located in Heihe bridge, Shangbao village, Jing'an Township, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39 ° 03'53.23 ", E100 ° 25'59.31", with an altitude of 1431m and a width of 58m. In 2012, hobo pressure type water level gauge was used for water level observation with acquisition frequency of 30 minutes; since 2013, sr50 ultrasonic distance meter was used with acquisition frequency of 30 minutes. The data description includes the following parts: For water level observation, the observation frequency is 30 minutes, unit (CM); the data covers the period from January 1, 2014 to June 25, 2014; for flow observation, unit (M3); for flow monitoring according to different water levels, the water level flow curve is obtained, and the runoff change process is obtained based on the observation of water level process. The missing data is uniformly represented by string-6999. Refer to Li et al. (2013) for hydrometeorological network or station information and he et al. (2016) for observation data processing.

2020-03-14

HiWATER: Dataset of hydrometeorological observation network (No.2 runoff observation system of 312 bridge on the Heihe River, 2014)

The data set includes the observation data of river water level and velocity at No.2 point in the runoff densification observation of the middle reaches of Heihe River from January 1, 2014 to December 31, 2014. 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 points are N38 ° 59 ′ 51.71 ″, E100 ° 24 ′ 38.76 ″, with an altitude of 1485 meters, and a channel width of 70 meters and 20 meters. Sr50 ultrasonic range finder is used for water level observation, with acquisition frequency of 30 minutes. The data description includes the following parts: For water level observation, the observation frequency is 30 minutes, unit (CM); the data covers the period from January 1, 2014 to December 31, 2014; for flow observation, unit (M3); for flow monitoring according to different water levels, the water level flow curve is obtained, and the runoff change process is obtained based on the observation of water level process. The section of bridge no.2-312 is frequently disturbed by human beings, and the unstable area of hydrological section leads to the disorder of water level and flow curve. During the measurement, the stable flow and water level curve cannot be obtained. The missing data is uniformly represented by string-6999. Refer to Li et al. (2013) for hydrometeorological network or station information and he et al. (2016) for observation data processing.

2020-03-14

HiWATER: BNUNET soil moisture and LST observation dataset in the middle reaches of the Heihe River Basin from Sep., 2013 to Mar., 2014

This data set includes 26 bnunet nodes in the 0.5 °× 0.5 ° observation matrix around Zhangye City in the middle reaches of Heihe River from September 2013 to March 2014. The configuration of 26 nodes is the same, including 3 layers of soil temperature probe with depth of 1cm, 5cm and 10cm and 1 layer of soil moisture probe with depth of 5cm. The observation frequency is 2 hours. This data set can provide spatiotemporal continuous observation data set for remote sensing authenticity test of surface heterogeneity and ecological hydrology research. The time is UTC + 8. Please refer to "bnunet data document. Docx" for details

2020-03-14

HiWATER: Dataset of fractional vegetation cover and biomass observed in the middle reaches of the Heihe River Basin (2014)

This data includes the coverage data set of vegetation in one growth cycle in five stations of Daman super station, wetland, desert, desert and Gobi, and the biomass data set of maize and wetland reed in one growth cycle in Daman super station. The observation time starts from May 10, 2014 and ends on September 11, 2014. 1 coverage observation 1.1 observation time 1.1.1 super station: the observation period is from May 10 to September 11, 2014. Before July 20, the observation is once every five days. After July 20, the observation is once every 10 days. A total of 17 observations are made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 1.1.2 other four stations: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; Other four stations: May 10, 2014, May 20, 2014, May 30, 2014, June 10, 2014, June 20, 2014, June 30, July 10, 2014, July 20, August 5, 2014, August 17, 2014, September 11, 2014 1.2 observation method 1.2.1 measuring instruments and principles: The digital camera is placed on the instrument platform at the front end of the simple support pole to keep the shooting vertical and downward and remotely control the camera measurement data. The observation frame can be used to change the shooting height of the camera and realize targeted measurement for different types of vegetation. 1.2.2 design of sample Super station: take 3 plots in total, the sample size of each plot is 10 × 10 meters, take photos along two diagonal lines in turn each time, take 9-10 photos in total; Wetland station: take 2 sample plots, each plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 3 other stations: select 1 sample plot, each sample plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 1.2.3 shooting method For the super station corn and wetland station reed, the observation frame is directly used to ensure that the camera on the observation frame is far higher than the vegetation crown height. Samples are taken along the diagonal in the square quadrat, and then the arithmetic average is made. In the case of a small field angle (< 30 °), the field of view includes more than 2 ridges with a full cycle, and the side length of the photo is parallel to the ridge; in the other three sites, due to the relatively low vegetation, the camera is directly used to take pictures vertically downward (without using the bracket). 1.2.4 coverage calculation The coverage calculation is completed by Beijing Normal University, and an automatic classification method is adopted. For details, see article 1 of "recommended references". By transforming RGB color space to lab space which is easier to distinguish green vegetation, the histogram of green component A is clustered to separate green vegetation and non green background, and the vegetation coverage of a single photo is obtained. The advantage of this method lies in its simple algorithm, easy to implement and high degree of automation and precision. In the future, more rapid, automatic and accurate classification methods are needed to maximize the advantages of digital camera methods. 2 biomass observation 2.1 observation time 2.1.1 corn: the observation period is from May 10 to September 11, 2014, once every 5 days before July 20, and once every 10 days after July 20. A total of 17 observations have been made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 2.1.2 Reed: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; 2014-5-10、2014-5-20、2014-5-30、2014-6-10、2014-6-20、2014-6-30、2014-7-10、2014-7-20、2014-8-5、2014-8-17、2014-9-11 2.2 observation method Corn: select three sample plots, and select three corn plants that represent the average level of each sample plot for each observation, respectively weigh the fresh weight (aboveground biomass + underground biomass) and the corresponding dry weight (85 ℃ constant temperature drying), and calculate the biomass of unit area corn according to the plant spacing and row spacing; Reed: set two 0.5m × 0.5m quadrats, cut them in the same place, and weigh the fresh weight (stem and leaf) and dry weight (constant temperature drying at 85 ℃) of reed respectively. 2.3 observation instruments Balance (accuracy 0.01g), oven. 3 data storage All the observation data were recorded in the excel table first, and then stored in the excel table. At the same time, the data of corn planting structure was sorted out, including the plant spacing, row spacing, planting time, irrigation time, except for the parent time, harvesting time and other relevant information.

2020-03-14

HiWATER: Dataset of vegetation phenology in the Heihe River Basin

The vegetation phenology data set of Heihe River basin provides remote sensing phenology products from 2012 to 2015. The spatial resolution is 1km and the projection type is sinusoidal. MODIS Lai product mod15a2 is used as the phenological remote sensing monitoring data source, and MODIS land cover classification product mcd12q1 is used as the auxiliary data set for extraction. The product algorithm first uses the time series data reconstruction method (bise method) to control the data quality of the input time series; then uses the main algorithm (logistic function fitting method) and the backup algorithm (piecewise linear fitting method) to extract the vegetation phenological parameters, realizes the complementary calculation method, guarantees the accuracy and improves the inversion rate. The algorithm can extract up to three growth cycles in a year, each growth cycle contains six data sets, including the start point of vegetation growth, the start point of growth peak, the end point of growth peak, the end point of growth, the fastest growth and the fastest decline. At the same time, it records the growth cycle type, growth season length, quality identification, etc., a total of 25 data sets. The phenology product reduces the missing rate of inversion, improves the stability of the product, and the data set is relatively reliable with rich information.

2020-03-13