Current Browsing: the natural oasis eco-hydrology experimental area in the lower reaches


HiWATER: Dataset of hydro-meteorological observation network (eddy covariance system of desert station, 2015)

The data set contains the observation data of the eddy covariance system of the desert station, which is located along the lower reaches of the Heihe Hydro-meteorological observation network, and the data set covers data from April 28, 2015 to December 31, 2015. The station is located in Ejina Banner, Inner Mongolia, and the underlying surface is desert. The latitude and longitude of the observation station is 100.9872E, 42.1135N, and the altitude 1054m. The height of the eddy covariance system is 4.7 meters, the sampling frequency is 10Hz, the ultrasonic orientation is positive north, and the distance between the ultrasonic wind speed and temperature monitor (CSAT3) and the CO2/H2O analyzer (Li7500) is 15cm. The original observation data of the eddy covariance system is 10 Hz, and the released data is a 30-minute data processed by Eddypro software. The main steps of the processing include: outlier eliminating, delay time correction, coordinates rotation (secondary coordinates rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. Meanwhile, the quality evaluation of each flux value was performed,mainly includes atmospheric stability (Δst) test and turbulence similarity (ITC) test. The 30-min flux value output of Eddypro software was also screened: (1) Data from the instrument error was eliminated; (2) Data obtained with one hour before and after precipitation was removed; (3) Data with a deletion rate greater than 10% of the 10 Hz raw data every 30 minutes was eliminated; (4) Observation data of weak turbulence at night (u* less than 0.1 m/s) was excluded. The average period of observation data is 30 minutes, 48 data per day, and the missing data is marked as -6999. The suspicious data caused by instrument drift and other reasons was marked by red fonts. Published observation data include: Date/Time, wind direction(°), horizontal wind speed(m/s), lateral wind speed standard deviation(m/s), ultrasonic virtual temperature (°C), water vapor density (g/m3), carbon dioxide concentration(mg/m3), friction velocity (m/s), length (m), sensible heat flux(W/m2), latent heat flux (W/m2), carbon dioxide flux (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 data of 0:00-0:30; the data is stored in *.xls format. For hydro-meteorological network or station information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

2019-09-15

HiWATER: Dataset of photosynthesis observed by LI-6400 in the lower of Heihe River Basin on Jul, 2012

The dataset of photosynthesis was observed by LI-6400XT Portable Photosynthesis System in the natural oasis eco-hydrology experimental area of the Heihe River Basin. Observation items included the main vegetation type in the lower reaches of Heihe river: Populus forest, which located in the Populus forest station and the mixed forest station of Ejinaqi. Observation periods lasted from 2014-07-24 to 2014-07-31. This dataset included the raw observation data of the Populus forest observed by LI-6400 during the observation periods. 1) Objectives of observation The photosynthetic datasets can be used in the study of plant physiological ecology characteristic and the simulation and validation for the eco-hydrological models. 2) Instrument and theory of the observation Measuring instrument: LI-6400XT Portable Photosynthesis System. Measuring theory: Using the infrared gas analyzer to measure the change of CO2 concentration, and then measuring the differences of CO2 concentration between the sample chamber and the referenced chamber so as to acquire the net productivity of the leaf. 3) Time and site of observation Observation site in the Populus forest station. Observation time: 2014-07-24 Observation site in the mixed forest station. Observation time: From 2014-07-25 to 2014-07-31. 4) Data processing The raw data of LI-6400 were archived in text format and can be opened by text editor or excel, the preprocessed data were in Excel format. Every time period of observation was archived in a single document, named as “date + type”.

2019-09-15

HiWATER: Dataset of hydrometeorological observation network (an automatic weather station of Sidaoqiao cropland station, 2013)

This dataset includes data recorded by the Hydrometeorological observation network obtained from the automatic weather station (AWS) at the observation system of Meteorological elements gradient of Sidaoqiao cropland station between 9 July, 2013, and 31 December, 2013. The site (101.134° E, 42.005° N) was located on a cropland (melon) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 875 m. The installation heights and orientations of different sensors and measured quantities were as follows: four-component radiometer (CM21; 6 m, south), two infrared temperature sensors (SI-111; 6 m, south, vertically downward), two photosynthetically active radiation (PQS-1; 6 m, south, one vertically upward and one vertically downward), soil heat flux (HFP01; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), and soil temperature profile (AV-10T; 0, -0.02 and -0.04 m). The observations included the following: 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)), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), the soil temperature (Ts_0 cm, Ts_2 cm and Ts_4 cm) (℃). 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 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.

2019-09-15

HiWATER: Simultaneous observation dataset of land surface temperature in the lower of Heihe River Basin on Aug. 01, 2014

The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature for different kinds of underlying surface, including the lager areas of homogeneous vegetation with high coverage, water, and concrete floor, while the thermal imager go into the experimental areas of the low reaches. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal imager and the analysis of the scale effect of the land surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation time On 1 August, 2014 2. Observation samples Three field samples were chosen in the fly zone, which were large areas of homogeneous vegetation (with high coverage), water, and concrete floor. 3. Observation method Surface temperature values were observed continuously for each sample using handheld infrared thermometers during the imager went into the flying area. 4. Instrument parameters and calibration The field of view of the handheld infrared thermometer is one degree and the emissivity was assumed to be 0.95. All instruments were calibrated on 31 July, 2014 using a black body. 5. Data storage All the observation data were stored in an excel.

2019-09-15

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 hydrometeorological observation network (large aperture scintillometer of Sidaoqiao Superstation, 2013)

This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Sidaoqiao Superstation (two sites) in the hydrometeorological observation network of Heihe River Basin. There were two types of LASs at site 1: German BLS900 and Netherlands Kipp&zonen. The north tower was set up with the BLS900/Kipp&zonen receiver, and the south tower was equipped with the BLS900/Kipp&zonen transmitter. The observation period of BLS900_1 and Kipp&zonen were from 11 July to 13 November, 2013, and 11 July to 12 September, 2013, respectively. There was one type of LAS at site 2: German BLS900. The north tower was set up with the BLS900 receiver, and the south tower was equipped with the BLS900 transmitter. BLS900_2 has been in use since 16 September, 2013. The Sidaoqiao Superstation (site1, north: 101.147° E, 42.005° N, south: 101.131° E, 41.987° N; site 2, north: 101.137° E, 42.008° N, south: 101.121° E, 41.990° N) was located in Ejinaqi, Inner Mongolia. The underlying surfaces between the two towers were tamarisk, populus, bare land and farmland. The elevation is 873 m. The effective height of the LASs was 25.5 m, and the path length of site 1 and site 2 were 2390 m and 2380 m, respectively. The data were sampled at 5 Hz and 1 Hz intervals for BLS900 and zzlas, respectively, and then averaged over 1 min. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900_1: Cn2>7.25E-14, Kipp&zonen: Cn2>7.84E-14, BLS900_2: Cn2>7.33E-14). (2) The data were rejected when the demodulation signal was small (BLS900: Average X Intensity<1000; Kipp&zonen: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl, 1992 and Andreas, 1988 were selected for BLS900 and Kipp&zonen, respectively. Several instructions were included with the released data. (1) The data of site 1 were primarily obtained from BLS900_1 measurements, and missing flux measurements from the BLS900_1 instrument were substituted with measurements from the Kipp&zonen instrument. The missing data were denoted by -6999. The data of site 2 were obtained from BLS900_2 measurements, missing data were denoted by -6999. Due to the problems of BLS900_1 transmitter, the data after 13 November, 2013, were not collected. (2) The dataset contained the following variables: data/time (yyyy-m-d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xls format. 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.

2019-09-14

HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of Populus forest station, 2013)

This dataset contains the flux measurements from the Populus forest station eddy covariance system (EC) in the lower reaches of the Heihe hydrometeorological observation network from 12 July to 31 December, 2013. The site (101.124° E, 41.993° N) was located in the Populus surface, Ejin Banner in Inner Mongolia. The elevation is 876 m. The EC was installed at a height of 22 m, and 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 (CSAT3&Li7500) was 0.17 m. The raw data acquired at 10 Hz were processed using the Edire post-processing software (University of Edinburgh, http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/), including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature 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. 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), as 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), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened using a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected 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 0.2 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Due to the CF card storage problem, data during 17 September to 9 December were replaced with the 30 min output flux data in the data logger. 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 *.xls format. 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.

2019-09-14

HiWATER: Dataset of hydrometeorological observation network (eddy covariance system of mixed forest station, 2013)

This dataset contains the flux measurements from the mixed forest station eddy covariance system (EC) in the lower reaches of the Heihe hydrometeorological observation network from 12 July to 31 December, 2013. The site (101.134° E, 41.990° N) was located in the Populus and Tamarix surface, Ejin Banner in Inner Mongolia. The elevation is 874 m. The EC was installed at a height of 22 m, and 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 (CSAT3&Li7500) was 0.17 m. The raw data acquired at 10 Hz were processed using the Edire post-processing software (University of Edinburgh, http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/), including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature 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. 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), as 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), which represent high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened using a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected 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 0.2 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Due to the malfunction of sonic anemometer, data during 16 August to 17 September were missing. 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 *.xls format. 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.

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 (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