1. data description Soil temperature monitoring in typical soil profile of hongnigou is divided into seven layers, with depth distribution of 20cm, 40cm, 60cm, 80cm, 120cm, 160cm and 230cm.The frequency of observation is 1 time /60 minutes.The time range of observation data is from August 25, 2013 to May 1, 2014. 2. Sampling location The soil temperature monitoring point of the typical soil profile in the small basin of cucurbitou was set in the middle and lower part of the red mud ditch, and its geographical coordinates were 99 ° 52 '25.98 "E, 38 ° 15' 36.11" N. 3. Test method Soil Temperature was observed using HOBO Pendant® Temperature/Light Data Logger 64k-ua-002-64 Temperature recorder.
SUN Ziyong, CHANG Qixin
According to the global soil map. Net standard, the 0-1m soil depth is divided into 5 layers: 0-5cm, 5-15cm, 15-30cm, 30-60cm and 60-100cm. According to the principle of soil landscape model, the spatial distribution data products of soil sand content in different layers are made by using the digital soil mapping method. The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Dataset content: hh_sand_layer1.tif: 0-5cm soil sand content; hh_sand_layer2.tif: 5-15cm soil sand content; hh_sand_layer3.tif: 15-30cm soil sand content; hh_sand_layer4.tif: 30-60cm soil sand content; hh_sand_layer5.tif: 60-100cm soil sand content;
ZHANG Ganlin
According to the global soil map. Net standard, the 0-1m soil depth is divided into 5 layers: 0-5cm, 5-15cm, 15-30cm, 30-60cm and 60-100cm. According to the principle of soil landscape model, the spatial distribution data products of soil silt content in different layers are made by using the digital soil mapping method. The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Dataset content: hh_silt_layer1.tif: 0-5cm soil silt content; hh_silt_layer2.tif: 5-15cm soil silt content; hh_silt_layer3.tif: 15-30cm soil silt content; hh_silt_layer4.tif: 30-60cm soil silt content; hh_silt_layer5.tif：60-100cm soil silt content;
ZHANG Ganlin
According to the global soil map. Net standard, the 0-1m soil depth is divided into 5 layers: 0-5cm, 5-15cm, 15-30cm, 30-60cm and 60-100cm. According to the principle of soil landscape model, the spatial distribution data products of soil clay content in different layers are made by using the digital soil mapping method. The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Dataset content: hh_clay_layer1.tif: 0-5cm soil clay content; hh_clay_layer2.tif: 5-15cm soil clay content; hh_clay_layer3.tif: 15-30cm soil clay content; hh_clay_layer4.tif: 30-60cm soil clay content; hh_clay_layer5.tif: 60-100cm soil clay content;
ZHANG Ganlin
A multi-layer soil particle-size distribution dataset (sand, silt and clay content), based on USDA (United States Department of Agriculture) standard for regional land and climate modelling in China. was developed The 1:1,000,000 scale soil map of China and 8595 soil profiles from the Second National Soil Survey served as the starting point for this work. We reclassified the inconsistent soil profiles into the proper soil type of the map as much as possible because the soil classification names of the map units and profiles were not quite the same. The sand, silt and clay maps were derived using the polygon linkage method, which linked soil profiles and map polygons considering the distance between them, the sample sizes of the profiles, and soil classification information. For comparison, a soil type linkage was also generated by linking the map units and soil profiles with the same soil type. The quality of the derived soil fractions was reliable. Overall, the map polygon linkage offered better results than the soil type linkage or the Harmonized World Soil Database. The dataset, with a 1-km resolution, can be applied to land and climate modelling at a regional scale. Data characteristics： projection：projection Coverage: China Resolution: 0.00833 (about 1 km) Data format: FLT, TIFF Value range: 0%-100% Document describing： Floating point raster files include: Sand1. FLT, clay1. FLT -- surface (0-30cm) sand, clay content. Sand2. FLT, clay2. FLT -- content of sand and clay in the bottom layer (30-100cm). PSD. HDR -- header file: Ncols - the number of columns Nrows- rows Xllcorner - latitude in the lower left corner Yllcorner - longitude of the lower left corner Cellsize - cellsize NODATA_value - a null value byteorder - LSBFIRST, Least Significant Bit First TIFF raster files include: Sand1. Tif, clay1. Tif - surface (0-30cm) sand, clay content. Sand2. Tif, clay2. Tif - bottom layer (30-100cm) sand, clay content.
SHANGGUAN Wei, DAI Yongjiu
The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
ZHANG Ganlin
The data set includes soil bulk density data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
The data set includes soil organic carbon concentrations data of representative soil samples collected from July 2012 to August 2013 in the Heihe River Basin. The first soil survey was conducted in 2012. After the representativeness evaluation of collected samples, we conducted an additional sampling in 2013. These samples are representative enough to represent the soil variation in the Heihe River Basin, of which the soil variation in each landscape could be accounted for. The sampling depths in field refer to the sampling specification of Chinese Soil Taxonomy, in which soil samples were taken from genetic soil horizons.
ZHANG Ganlin
The data set of bacterial diversity in Tibetan soil provides the microbial distribution characteristics of the soil surface (0-2 cm) of the Tibetan Plateau. The samples were collected from July 1st to July 15th, 2015, from three types of ecosystems: meadows, grasslands and desert. The soil samples were stored in ice packs and transported to the Ecological Laboratory of the Institute of Tibetan Plateau Research in Beijing. The DNA from the soil was extracted using an MO BIO Power Soil DNA kit. The soil surface samples were stored in liquid nitrogen after collection, shipped to the Sydney laboratory, and then extracted using a Fast Prep DNA kit. The extracted DNA samples adopted 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 909r (5'-GGACTACHVGGGTWTCTAAT-3') to amplify the 16S rRNA gene fragments. The amplified fragments were sequenced by the Illumina Miseq PE250 method, and the raw data were analyzed using Mothur software. The sequences with poor sequencing quality were first removed; the sequences were sorted, and the chimeric sequences were removed. The similarities between the sequences were then calculated, the sequences with similarities above 97% were clustered into one OTU, and the OTU representative sequence was defined. The OTU representative sequence was compared with the Silva database and identified as level one when the reliability exceeded 80%. The microbial diversities in these data on the Tibetan Plateau were systematically compared, which made them significant to the study of the microbial distribution on the Tibetan Plateau.
JI Mukan
This dataset includes the observational data from 20 September, 2012, through 31 December, 2013, collected by the Cosmic-ray Soil Moisture Observation System (COSMOS), called crs, which waslocated at 100.372° E, 38.856° N and 1557 m above sea level,near the Daman Superstation in the Daman Irrigation District, Zhangye City, Gansu Province. The land cover in the footprint was a maize crop. The bottom of the probe was 0.5 m above the ground, and the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, ℃), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), the sample time of fast neutrons (N1ET, s), and the sample time of thermal neutrons (N2ET, s). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Quality control Data were deleted and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity exceeded 80% inside the probe box, (c) the samping durationwere less than 59 minutes or greater than 61 minutes and (d) the neutron count differed from the previous value by more than 20%. 2) Air pressure correction An air pressure correction was applied to the quality-controlled raw data according to the equation containedin the equipment manual. 3) Calibration After the quality control and corrections were applied, the soil moisture was calculated using the equation in Desilets et al. (2010), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture recordedby SoilNET within the footprint. 4) Soil moisture computation Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation specified in the equipment manual. For more information, please refer to Liu et al. (2018) (for hydrometeorological observation network or sites information), Zhu et al. (2015) (for data processing) in the Citation section.
LIU Shaomin, ZHU Zhongli, LI Xin, XU Ziwei
The data set contains observation data of cosmic-ray instrument (crs) from January 1, 2017 to December 31, 2017. The site is located in the farmland of Daman Irrigation District, Zhangye, Gansu Province, and the underlying surface is cornfield. The latitude and longitude of the observation site is 100.3722E, 38.8555N, the altitude is 1556 meters. The bottom of the instrument probe is 0.5 meter from the ground, and the sampling frequency is 1 hour. The original observation items of the cosmic-ray instrument include: voltage Batt (V), temperature T (°C), relative humidity RH (%), air pressure P (hPa), fast neutron number N1C (number / hour), thermal neutron number N2C (number / hour), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s). The data was released after being processed and calculated. The data includes: Date Time, P (pressure hPa), N1C (fast neutrons one/hour), N1C_cor (pressure-corrected fast neutrons one/hour) and VWC ( soil water content %), it was processed mainly by the following steps: 1) Data Screening There are four criteria for data screening: (1) Eliminating data with a voltage less than or equal to 11.8 volts ; (2) Eliminating data with a relative humidity greater than or equal to 80%; (3) Eliminating data with a sampling time interval not within 60 ± 1 minute; (4) Eliminating data with fast neutrons that vary by more than 200 in one hour. In addition, missing data is supplemented with -6999. 2) Air Pressure Correction The original data is corrected by air pressure according to the fast neutron pressure correction formula mentioned in the instrument manual, and the corrected fast neutron number N1C_cor is obtained. 3) Instrument Calibration In the process of calculating soil moisture, it is necessary to calibrate the N0 in the calculation formula. N0 is the number of fast neutrons under the situation with low antecedent soil moisture . Usually, soil samples in the source area are used to obtain measured soil moisture (or obtained by relatively dense soil moisture wireless sensors) θm (Zreda et al. 2012) and the fast neutron correction data N in corresponding time periods, then NO can be obtained by reversing the formula. Here, the instrument is calibrated according to the Soilnet soil moisture data in the source region of the instrument, and the relationship between the soil volumetric water content θv and the fast neutron is established. The data of June 26-27, and July 16-17, respectively, which have obvious differences in dry and wet conditions, were selected. The data from June 26 to 27 showed low soil moisture content, so the average of the three values of 4 cm, 10 cm and 20 cm was used as the calibration data, and the variation range was 22% to 30%; meanwhile , the data from July 16 to 17 showed high soil moisture content, so the average of the two values of 4cm and 10 cm was used as the calibration data, and the variation range was 28% - 39%, and the final average N0 was 3597. 4) Soil Moisture Calculation According to the formula, 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.
LIU Shaomin, ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo
The data set contains cosmic ray instrument (CRS) observations from January 1, 2016 to December 31, 2016.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. Original observations of cosmic ray instruments include: 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. Here, according to Soilnet soil water data in the source area of the instrument, the instrument was calibrated to establish the relationship between soil volumetric water content v and fast neutrons.Selection of dry and wet conditions are the obvious difference of June 26, 2012-27 and July 16-17, 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 16-17 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%, final N0 an average of 3597. 4) soil moisture calculation According to the formula, the hourly soil water content data were calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
LIU Shaomin, ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo
The data set contains cosmic ray instrument (CRS) observations from January 1, 2015 to December 31, 2015.The station is located in dachman super station, dachman irrigation district, zhangye city, gansu province.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. Original observations of cosmic ray instruments include: 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 SW (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. Here, according to Soilnet soil water data in the source area of the instrument, the instrument was calibrated to establish the relationship between soil volumetric water content v and fast neutrons.Selected dry wet condition are the obvious difference of June 26-27 and July 16-17, 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 the three values of average as calibration data, the change range of 22% to 30%, and July 16-17 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%, final N0 an average of 3597. 4) soil moisture calculation According to the formula, the hourly soil water content data were calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
LIU Shaomin, ZHU Zhongli, LI Xin, XU Ziwei
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.
LIU Shaomin, ZHU Zhongli, LI Xin, XU Ziwei
I. Overview This data set contains the terrain data, soil data, meteorological data, land use data, NDVI data, etc. required for the operation of the IWEMS model. All maps and relevant point coordinates (weather stations) use the isometric projection UTM / WGS94 coordinate system. Ⅱ. Data processing description All maps and related point coordinates (weather stations) use the isometric projection UTM / WGS84 coordinate system. Ⅲ. Data content description The data content mainly includes: The basic terrain data includes the Cuneiform Desert (DEM) and the river network. The river network is used as the boundary for wind and sand transmission. The size of the DEM grid is 250 * 250 m. The river network was extracted using the ASTER-GDEM terrain data with the river burning method. Soil data, including soil physics, chemistry, and spatial distribution of soil types. It is derived from 1: 1 million soil database of China and converted to ESRI-grid format with a grid size of 250 * 250 m. Meteorological data, including daily data from Baotou, Dongsheng and Linhe meteorological stations around the Kubuqi Desert, from 2002 to 2010. Includes precipitation, wind speed and wind direction data. Land use data, 2000 land use data, scale is 1: 100,000. Convert it to ESRI-grid format with a grid size of 250 * 250 m. Ⅳ. Data usage description Evaluate wind and sand hazards along the Yellow River, estimate the amount of wind and sand entering the upper reaches of the Yellow River, and provide data support for establishing an early warning system for wind and sand hazards in the region.
XUE Xian, DU Heqiang
The dataset is the HWSD Soil texture data set of the qaidam basin. The data is from the Harmonized World Soil Database (HWSD) constructed by the United Nations food and agriculture organization (FAO) and Vienna institute for international applied systems (IIASA), which was released in version 1.1 on March 26, 2009.The data resolution is 1km.The main soil classification system adopted is fao-90.The main fields in the soil property list include SU_SYM90 (soil name in the FAO90 soil classification system) SU_SYM85(FAO85 classification) T_TEXTURE(top layer soil texture) (19.5);ROOTS: String(deep classification of obstacles to the bottom of the soil);SWR: String (soil moisture content characteristics);ADD_PROP: Real (specific type of soil in a soil unit related to an agricultural use);T_GRAVEL: Real (percent by volume);T_SAND: Real;T_SILT: Real (silt content);T_CLAY: Real;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 T_CEC_CLAY: Real;T_CEC_SOIL: Real (cation exchange capacity of soil) T_BS: Real (basic saturation);T_TEB: Real (commutative base);T_CACO3: Real (carbonate or lime content) T_CASO4: Real (sulfate content);T_ESP: Real (exchangeable sodium);T_ECE: Real.The attribute field beginning with T_ represents the upper soil attribute (0-30cm), and the attribute field beginning with S_ represents the lower soil attribute (30-100cm) (FAO 2009).This data can provide model input parameters for earth system modelers, and agricultural perspectives can be used to study eco-agricultural zoning, food security and climate change.
Food and Agriculture Organization of the United Nations（FAO）
The vegetation regulation mechanism project of soil water cycle in arid desert areas belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by li xinrong, a researcher of the institute of environment and engineering in dry and cold areas, Chinese academy of sciences, with the running time of 2003.1-2005.12. Remittance data of the project: 1. Dataset of observation field of shapotou railway vegetation sand fixation protection system (excel) Plant and soil information in the vegetation-sand fixation zone established in 1956, 1964, 1981 and 1987.Since the establishment of the observation field, long-term soil moisture and vegetation surveys have been conducted. This database records the soil moisture data after the neutron tube installation in August 2002, the vegetation data from 2003 to 2005 (vegetation structure, herb structure, shrub structure, etc.), and the soil physical and chemical properties data (particle size, total N,P2O5,K2O, hydrolyzed N) of the irregular surveys. 2. Physiological data set of desert plant stress (excel) From 2003 to 2005, the physiological and biochemical characteristics of typical plant communities and their dominant species in steppe desert under natural and simulated environmental conditions were analyzed.(including photosynthetic transpiration, fluorescence, biochemistry and other indicators) 3. Soil infiltration and evapotranspiration data set (excel) Precipitation infiltration process, soil water dynamics and evapotranspiration of fixed sand dunes monitored by desert artificial vegetation using TDR and Lysimeters from 2002 to 2005. 4. Data set of comprehensive survey on soil and vegetation in the southeastern margin of tengger desert (excel) In 2003-2004, silver (sichuan), yan (latour) highway, silver (sichuan) (state) highway through the tengger desert area, set up along the road of eight samples, 449 samples of soil conductivity, Ph, organic matter, total nitrogen (content) and vegetation (plants, coverage, average height, biomass, strains, coverage, high average, biomass).
LI Xinrong
The project on the impact of agricultural development in northwest Lvzhou on watershed scale water cycle and eco-environmental effects belongs to the major research program of "Environmental and Ecological Science in Western China" sponsored by the National Natural Science Foundation. The person in charge is Professor Kang Shaozhong of Northwest China Agriculture and Forestry University. The project runs from January 2003 to December 2005. Data collected from this project: soil experimental data of Shiyang River Basin, including: 1. Saturated hydraulic conductivity (excel table): includes four fields: number, sampling point, measured value and saturated hydraulic conductivity. 2. Conductivity (excel table): including number, sampling point, measured value, temperature, temperature correction value and conductivity. 3. Original indoor infiltration data (excel table): including number, time, cumulative value and reading. 4. Field Infiltration Data (excel Form): Including Number, Time, Cumulative Value and Reading. 5. Sampling point of horizontal infiltration data (excel form): including time, measuring cylinder (ml), wetting peak (ml), wet weight, dry weight, box weight and distance. 6. soil particle analysis (excel form): including numbers, > 0.25 mm, < 0.05 mm, < 0.01 mm, < 0.005 mm, < 0.001 mm. 7. Soil moisture characteristic curve (excel table): including soil weight and drying weight when the pressure of pressure membrane instrument is 0,0.05,0.1,0.3,0.5,0.8,1.5,3,5,14.4. 8. Organic matter (excel form): including number, sampling point, amount of soil taken (G), titration amount (ml) 9. Sampling Point Coordinates (excel Form)
KANG Shaozhong
The data comes from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and International Institute for Applied System Analysis in Vienna (IIASA), which released version 1.1 on March 26, 2009. The data resolution is 1 km. The data source in China is 1: 1 million soil data. The soil classification system used is mainly FAO-90. The main fields of the soil property sheet include: SU_SYM90 (name of soil in FAO90 soil classification system) SU_SYM85 (FAO85 classification) T_TEXTURE (top soil texture) DRAINAGE (19.5); ROOTS: String (depth classification to the bottom of the soil with obstacles); SWR: String (characteristics of soil water content); ADD_PROP: Real (specific soil type in the soil unit related to agricultural use); 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 the sticky layer soil); T_CEC_SOIL: Real (soil cation exchange capacity) 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 at the beginning of T_ indicates the upper soil attribute (0-30 cm), and the attribute field at the beginning of S_ indicates the lower layer soil attribute (30-100 cm) (FAO 2009). This data provides model input parameters for Earth system modelers, and in agricultural perspective, it can be used to study eco-agricultural divisions, food security, and climate change.
Food and Agriculture Organization of the United Nations（FAO）
The research project on the function and mechanism of sand-fixing afforestation of waste lignin from straw pulp and paper making belongs to the national natural science foundation of China "environment and ecological science in western China" major research program, led by wang hanjie, a researcher of the institute of aviation meteorology and chemical protection, air force equipment research institute. The project ran from January 2004 to December 2006 Remittance data of the project: 1. 2005-08-10 - sand lake - jinsha wan test site image (JPG) 2.2006 field picture of fixed sand test (JPG) 3. Meteorological data of ningxia jinshawan meteorological station (TXT text) Observation data including dry bulb temperature, wet bulb temperature, 0, 5, 10, 15, 20cm ground temperature, evaporation and air temperature were observed at 8:00,14:00 and 20:00 on August 13, 2005 4. Growth data of jinshawan community in ningxia (TXT text) The data of crown diameter and height of four samples are included. 5. Soil water data of jinshawan, ningxia (excel) Soil moisture data of 16 samples at depths of 20CM and 12CM in clear water control area and lignin spraying area by 2 hours in the daytime on August 19, 2005. 6. Soil water data of shahu lake in ningxia (excel) On August 10,11, 2005, soil moisture data of various depths of 10CM,12CM and 20CM were obtained 7. Plant growth data of sand fixation community in shahu, ningxia (excel) Plant growth statistics of 5 sample plots: species name,x,y, base, crown, height, number of plants.
WANG Hanjie
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
