Current Browsing: 2014


HiWATER: Dataset of Soil respiration observed by Li-8100 in the lower of Heihe River Basin from Jul to Aug , 2014

Soil respiration observation was carried out for the typical vegetation ground in the lower reaches of the Heihe River Basin during the aviation flight experiment in 2014. The observation started on 23 July, 2014 and finished on 2 August, 2014. 1. Observation time Days from 23 July to 2 August, 2014 (25 July, 2014 excepted) 2. Samples and observation methods Large areas with relatively homogeneous vegetation (greater than 100 m * 100 m) were chosen as the observation samples. And combined the flux tower sites distribution of the lower reaches, five field samples closed to the sites were selected The observation sites sampled including Populus and Tamarix mixed forest, Populus, Tamarix group, bare ground and melon quadrats. 3-5 plots were observed for each samples. The PVC soil rings were installed one day before observation and kept about 5 cm out of the ground (the inner diameter of the PVC is 19.5 cm, the outer diameter is 20.0 cm, and the height is 12.0 cm). Minimal the effects to the surface of vegetation and withered matter when install the rings. In order to avoid fluctuations of the soil respiration value by the PVC rings, soil respiration rate was obtained when it returned to its original state (about 24h after the rings install). The observation time for each day was from 8:00 to 12:00 when soil respiration is relatively stable and can represent the whole day in this time. The Li-8100 Open Path soil carbon flux automatic analyzer was used (Model 8100-103) once for each plot. Cycles of observation for all plots of the five samples were completed for every morning. The soil respiration values of the samples were obtain by averaging the values of plots of the samples. 3. Observation instrument Li 8100 4. Data storage The observation recorded data were stored in excel and the original Soil respiration data were stored in 81x files.

2019-09-14

HiWATER: Dataset of leaf area index by LAI2200 in the lower reaches of the Heihe River Basin

LAI observation was carried out for the typical underlying surface in the lower reaches of Heihe River Basin during the aviation flight experiment in 2014. The observation started on 24 July, 2014 and finished on 1 August, 2014. 1. Observation time On days of 24 July, 27 July, 30 July, 31 July and 1 August, 2014 2. Samples and observation methods Large areas with homogeneous vegetation (greater than 100 m * 100 m) were chosen as the observation samples. And forty field samples were selected according to the characteristics of vegetation distribution in the downstream. The land-use types including the cantaloupe, the Tamarix chinensis, the reeds, the weeds, the Karelinia caspica, the Sophora alopecuroides and so on. LAI data were calculated according to the transmittance derived from an A value (above-canopy readings) and four B values (below readings). More than two LAI values were obtained for each sample. At the same time, the heights of the vegetation in each sample were measured. 3. Observation instrument LAI 2200 4. Data storage The observation recorded data were stored in excel and the original LAI data were stored in txt files.

2019-09-13

HiWATER: Dataset of hydrometeorological observation network (No.3 runoff observation system of Railway bridge on the Heihe River, 2014)

This dataset contains data on river water level and flow velocity at No.3 in the intensive runoff observation in the middle reaches of Heihe River runoff from July 28, 2014 to December 31, 2014. The observation point is located at Heihe Bridge, Lan-Xin Railway, Zhangye City, Gansu Province. The riverbed is gravel and the section is stable. The latitude and longitude of the observation point is N39°2'33.08", E100°25'49.42", the altitude is 1443 meters, and the river channel width is 50 meters. The water level observation is measured by SR50 ultrasonic range finder with a frequency of 60 minutes. The flow profile observation is conducted by StreamPro micro ADCP. The data declaration includes the following two parts: Water level observation, the observation frequency is 60 minutes, unit (cm); data covering time period from July 28, 2014 to December 31, 2014; Flow observation, unit (m3); monitoring flow and obtaining water level flow curve according to different water levels. The process of the runoff changing is obtained by observing the water level process. The missing data is uniformly represented by the string -6999. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to He et al. (2016).

2019-09-13

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

This dataset contains eddy correlation instrument observation data from the Huyanglin station downstream of the Heihe Hydrological and Meteorological Observation Network from January 1, 2014 to December 31, 2014. The site is located in Sidaoqiao, Ejin Banner, Inner Mongolia, and the underlying surface is Populus euphratica. The latitude and longitude of the observation point is 101.1236E, 41.9928N, and the altitude is 876m. The vortex correlator has a height of 22 m and a sampling frequency of 10 Hz. The ultrasonic orientation is in the north direction, and the distance between the ultrasonic wind speed temperature meter (CSAT3) and the CO2/H2O analyzer (Li7500) is 17 cm. The original observation data of the eddy correlation meter is 10 Hz, and the released data is 30 minutes of data processed by Eddypro software. The main steps of the processing include: outlier removal, time-lag correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. At the same time, the quality evaluation of each flux value is conducted, it mainly contains atmosphere state stability test(Δst) and integrated turbulence characteristic test(ITC). The 30-min flux value output by Eddypro software was also screened: (1) data from the instrument error was eliminated; (2) data 1 h before and after precipitation was removed; (3) data from the deletion rate greater than 10% within every 30 min of the 10 Hz raw data. (4) eliminating observation data of weak turbulence at night (u* less than 0.1 m/s). The average time period of observation data is 30 minutes, 48 data per day, and the missing data is labeled -6999. Abnormal data caused by instrument drift and other reasons are marked in red. From February 21 to March 13, the data is missing due to problems in memory card and wireless transmission module. Published observations include: date/time Date/Time, wind direction Wdir(°), horizontal wind speed Wnd(m/s), lateral wind speed standard deviation Std_Uy(m/s), ultrasonic virtual temperature Tv(°C), 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)), sensible heat flux quality identification QA_Hs, latent heat flux quality identification QA_LE, carbon dioxide flux quality identification QA_Fc. The quality identification of sensible heat, latent heat, and carbon dioxide flux is divided into three levels (quality mark 0: (Δst <30, ITC<30); 1: (Δst <100, ITC<100); the rest is 2). The meaning of the data time, such as 0:30 represents an average of 0:00-0:30; the data is stored in *.xls format. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

2019-09-12

HiWATER: Observation dataset of fractional vegetation cover by digital camera in the downstream of the Heihe River Basin (2014)

The fractional vegetation cover observation was carried out for the typical underlying surface in the lower reaches of the Heihe River Basin during the aviation flight experiment in 2014. The observation started on 24 July, 2014 and finished on 1 August, 2014. 1. Observation time On days of 24 July, 27 July, 30 July, 31 July and 1 August, 2014 2. Samples method Large areas with homogeneous vegetation (greater than 100 m * 100 m) were chosen as the observation samples. And forty field samples were selected according to the characteristics of vegetation distribution in the low reaches. The land-use types including the cantaloupe, the Tamarix chinensis, the reeds, the weeds, the Karelinia caspica, the Sophora alopecuroides and so on. 3. Observation methods 3.1 Instruments and measurement method Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 3.2 Photographic method The photographic method used depends on the species of vegetation and planting pattern. A long stick with the camera mounted on one end is used for the Tamarix chinensisi and reeds. For the Tamarix chinensisi and reeds, rows of more than two cycles should be included in the field of view (<30), and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. For other vegetation , the photos of FVC were obtained by directly photographing for the lower heights of the vegetation. 3.3 Method for calculating the FVC The detail method of the FVC calculation can be found in the reference below. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation (see the reference). 4 Data storage The observation recorded data were stored in excel and the original FVC data were stored in photos.

2019-09-12

HiWATER: Dataset of hydrometeorological observation network (No.8 runoff observation system of Gaotai bridge on the Heihe River, 2014)

This dataset contains data on river water level and flow velocity at No.8 in the intensive runoff observation in the middle reaches of Heihe River runoff from January 1, 2014 to December 31, 2014. The observation point is located at Heihe Bridge, Gaotai County, Zhangye City, Gansu Province. The riverbed is sediment and the section is stable. The latitude and longitude of the observation point is N39°23'22.93", N 99°49'37.29", the altitude is 1347 meters, and the river channel width is 210 meters. The water level observation is measured by SR50 ultrasonic range finder with a frequency of 30 minutes. The data  declaration includes the following two parts: Water level observation, observation frequency 30 minutes, unit (cm); data covering time period from January 1, 2014 to December 31, 2014; Flow observation, unit (m3); monitoring flow and obtaining water level flow curve according to different water levels. The process of the runoff changing is obtained by observing the water level process. The No. 8 point-Gaotaiqiao section only monitored the water level because the water body of the wetland park basically stopped flowing. The missing data is uniformly represented by the string -6999. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to He et al. (2016).

2019-09-11

HiWATER: Dataset of hydro-meteorological observation network (automatic weather station of Huazhaizi Desert Steppe Station, 2014)

The data set contains the observation data of meteorological elements from the Huazhaizi Desert Steppe Station,,which is located along the middle reaches of the Heihe Hydro-meteorological Observation Network, and the data set covers data from January 1, 2014 to December 31, 2014. The station is located in Huazhaizi of Zhangye, Gansu Province. The underlying surface is piedmont desert. The latitude and longitude of the observation point is100.3186E, 38.7652N, and the altitude is 1731m. The observation instruments in Huazhaizi are installed respectively by Beijing Normal University and Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The observation instruments of Beijing Normal University are: two infrared thermometers installed 24 meters above the ground, facing south, with the probe vertical downward; soil temperature probes buried respectively at 0cm on the ground surface, 2cm、4cm、20cm、60cm and 100cmunder the ground; soil moisture sensors buried 4cm、20cm and 100cm under the ground; soil heat flow boards (3 pieces) buried 6cm under the ground. The observation instruments of Cold and Arid Regions Environmental and Engineering Research Institute are: wind speed sensor erected 10.48m、0.98m and 2.99m above the ground(3 layers),facing North; wind direction sensor erected 4 meters above the ground; air temperature and relative humidity sensors erected 1m and 2.99m above the ground(2 layers),facing North East; four-component radiometer installed 2.5 meters above the ground, facing South; barometric pressure sensor placed in the water-proof box; tipping bucket rain gauge installed 0.7 meter above the ground; soil temperature probes buried 4cm、10cm、18cm、26cm、34cm、42cm and 50cmunder the ground; soil moisture sensors buried 2cm、10cm、18cm、26cm、34cm、42cm、50cm and 58cm under the ground, 3 sensors buried at 2cm. The specific observation elements are as follows: (1) Observation elements of Beijing Normal University : surface radiation temperature (IRT_1, IRT_2) (unit: Celsius), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watt / square meter), soil moisture (Ms_4cm, Ms_20cm, Ms_100cm) (unit: percentage) and soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_20cm, Ts_60cm, Ts_100cm) (unit: Celsius). (2) Observation elements of Cold and Arid Regions Environmental and Engineering Research Institute: wind speed (WS_0.48m, WS_0.98m, WS_2.99m) (unit: m/s), wind direction (WD_4m) (unit: degree), four-component radiation (DR, UR , DLR_Cor, ULR_Cor) (unit: watt / square meter), air temperature and humidity (Ta_1m, Ta_2.99m, RH_1m, RH_2.99m) (unit: Celsius, percentage), air pressure (Press) (unit: hectopascal), precipitation (unit: mm), soil temperature (Ts_4cm, Ts_10cm, Ts_18cm, Ts_26cm, Ts_34cm, Ts_42cm, Ts_50cm) (unit: Celsius), soil moisture (Ms_2cm_1, Ms_2cm_2, Ms_2cm_3, Ms_10cm, Ms_18cm, Ms_26cm, Ms_34cm, Ms_42cm, Ms_50cm, Ms_58cm) (unit: volumetric water content, percentage). The observation elements of Beijing Normal University are 10-minute average data, and the observation elements of Cold and Arid Regions Environmental and Engineering Research Institute are 30-minute average data. Processing and quality control of observation data: (1) Ensure 144 data of Beijing Normal University per day (every 10 minutes), and 48 data of Cold and Arid Regions Environmental and Engineering Research Institute per day (every 30 minutes). If there is missing data, it is marked as -6999. Data between 12.11-12.31,2014 is missing due to storage problems. (2) Eliminate moments with duplicate records; (3) Remove data that is significantly beyond physical meaning or beyond the measuring range of the instrument; (4) Data marked by red is debatable; (5) The formats of the date and time are uniform, and the date and time are in the same column. For example, the time is: 2014-6-10 10:30; (6) The naming rule is: AWS + site name. For hydro-meteorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

2019-09-11

HiWATER:Dataset of Hydro-meteorological Observation Network (An Automatic Weather Station of Sidaoqiao Barren-land Station, 2014)

The data set contains the observation data of meteorological elements from the Barren-land Station,which is located along the lower reaches of the Heihe Hydro-meteorological Observation Network, and the data set covers data from January 1, 2014 to December 31, 2014. The station is located in Sidaoqiao,Dalaihubu Town, Ejina Banner, Inner Mongolia. The underlying surface is barren land. The latitude and longitude of the observation point is 101.1326E, 41.9993N, and the altitude is 878m. The four-component radiometer is installed 6 meters above the ground, facing South; two infrared thermometers are installed 6 meters above the ground, facing South, and the probe orientation is vertical downward; the soil temperature probes are buried respectively at 0cm on the ground surface, 2cm and 4cm under the ground, they are located 2 meters from the meteorological tower in the South; the soil moisture sensors (installed on March 15,2014) are buried 2cm and 4cm under the ground, 2 meters from the meteorological tower in the South; the soil heat flow boards (3 pieces) are buried 6cm under the ground, 2 meters from the meteorological tower in the South. Observed items include: four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watt / square meter), surface radiation temperature (IRT_1, IRT_2) (unit: Celsius) , soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watt / square meter), soil moisture (Ms_2cm , Ms_4cm) (unit: volumetric water content, percentage), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm) (unit: Celsius). Processing and quality control of observation data: (1) Ensure 144 data per day (every 10 minutes), if there is missing data, it is marked as -6999. The surface radiation temperature IRT2 data during October 12,2014 to November 8,2014 is missing because of sensor problem; Some 2cm soil moisture data during March21 to March 29 and October 12 to November 8 is missing due to probe problem. (2) Eliminate moments with duplicate records; (3) Remove data that is significantly beyond physical meaning or beyond the measuring range of the instrument; (4) Data marked by red is debatable; (5) The formats of the date and time are uniform, and the date and time are in the same column. For example, the time is: 2014-9-10 10:30; (6) The naming rule is: AWS + site name. For hydro-meteorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

2019-07-12