Ⅰ. Overview FAO (Food and Agriculture Organization of the United Nations) and IIASA (International Institute for Applied Systems Analysis) combined the soil information of all regions and countries in the world with the world soil map of FAO-UNESCO, formed a new soil database - Harmonized World Soil Database (HWSD). The data source in China is 1:1 million soil data provided by Nanjing Soil Research Institute of the second national land survey. The database will be of great significance to improve people's understanding of current and future soil productivity, soil carbon storage, land resources, water resources and soil degradation. Ⅱ. Data processing description The data comes from the Harmonized World Soil Database (HWSD) constructed by FAO and IIASA. The data in China comes from the 1:1 million soil data provided by Nanjing Soil Research Institute of the second national land survey. The main soil classification system is FAO-90. Ⅲ. Data content description The main fields of soil attribute table include: SU_SYM90 (soil name in FAO90 soil classification system): SU_SYM85 (FAO85 classification); T_TEXTURE (top soil texture); DRAINAGE (19.5); ROOTS: String (depth classification with obstacles to the bottom of soil); SWR: String (soil water content characteristics); ADD_PROP: Real (agricultural use related in soil unit) Specific soil type); 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 weight); T_OC: Real (organic carbon content); T_PH_H2O: Real (PH); T_CEC_CLAY: Real (cation exchange of clayey soil); T_CEC_SOIL: Real (cation exchange capacity of soil); 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 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). Ⅳ. Data usage description Through this database, people's understanding of current and future soil productivity, soil carbon storage and global soil carbon storage will be improved. It can help people to understand the limitation of land and water resources, and correctly assess the risk of soil degradation, especially soil loss. Through understanding the physical and chemical properties of soil, it can also help people to obtain the following information, such as the filtering function of soil on waste, the impact on biological growth, etc. The potential of soil production and the response of soil to climate change were correctly judged.
XUE Xian, DU Heqiang, Food and Agriculture Organization of the United Nations（FAO）
The output data of the distributed eco hydrological model in the upper reaches of Heihe River includes the spatial distribution data of 1-km grid and the discharge time series data of the outlet of the basin. (1) Spatial distribution data of 1-km grid, monthly average soil moisture, actual evapotranspiration, runoff depth and other spatial distribution data of 1-km resolution. (2) Runoff time series daily flow data of river basin outlet.
YANG Dawen
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of A’rou Superstation from January 1 to December 31, 2018. The site (100.464° E, 38.047° N) was located on a cold grassland surface in the Caodaban village, A’rou Town, Qilian County, Qinghai Province. The elevation is 3033 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45C; 1, 2, 5, 10, 15 and 25 m, towards north), wind speed profile (010C; 1, 2, 5, 10, 15 and 25 m, towards north), wind direction profile (020C; 2 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 5 m, towards south), four-component radiometer (CNR4; 5 m, towards south), two infrared temperature sensors (SI-111; 5 m, towards south, vertically downward), photosynthetically active radiation (PAR-LITE; 5 m, towards south, vertically upward), soil heat flux (HFP01SC; 3 duplicates, -0.06 m, 2 m in the south of tower), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m, 2 m in the south of tower), soil temperature profile (109; 0, -0.02, -0.04, -0.06, -0.1, -0.15, -0.2, -0.3, -0.4, -0.6, -0.8, -1.2, -1.6, -2, -2.4, -2.8 and -3.2 m, 3 duplicates in -0.04 m and -0.1 m), and soil moisture profile (CS616; -0.02, -0.04, -0.06, -0.1, -0.15, -0.2, -0.3, -0.4, -0.6, -0.8, -1.2, -1.6, -2, -2.4, -2.8 and -3.2 m, 3 duplicates in -0.04 m and -0.1 m). The observations included the following: air temperature and humidity (Ta_1 m, Ta_2 m, Ta_5 m, Ta_10 m, Ta_15 m and Ta_25 m; RH_1 m, RH_2 m, RH_5 m, RH_10 m, RH_15 m and RH_25 m) (℃ and %, respectively), wind speed (Ws_1 m, Ws_2 m, Ws_5 m, Ws_10 m, Ws_15 m and Ws_25 m) (m/s), wind direction (WD_2 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/(s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm_1, Ts_4 cm_2, Ts_4 cm_3, Ts_6 cm, Ts_10 cm_1, Ts_10 cm_2, Ts_10 cm_3, Ts_15 cm, Ts_20 cm, Ts_30 cm, Ts_40 cm, Ts_60 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm, Ts_200 cm, Ts_240 cm, Ts_280 cm and Ts_320 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm_1, Ms_4 cm_2, Ms_4 cm_3, Ms_6 cm, Ms_10 cm_1, Ms_10 cm_2, Ms_10 cm_3, Ms_15 cm, Ms_20 cm, Ms_30 cm, Ms_40 cm, Ms_60 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm, Ms_200 cm, Ms_240 cm, Ms_280 cm and Ms_320 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The average soil temperature was rejected during February 16 to March 31 and April 15 to May 20 because of broken of the sensor line; Soil heat flux were wrong occasionally during November to December. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, LI Xin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei
1. Data overview 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 '25.3 "E, 38 ° 15' 37.97" N. Soil Temperature was observed using HOBO Pendant® Temperature/Light Data Logger 64k-ua-002-64 Temperature recorder. 2. data content 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 200cm.The frequency of observation is 1 time /15 minutes.The time range of observation data is from September 7, 2012 to May 6, 2013.
SUN Ziyong, CHANG Qixin
1. Data overview The data set of the base camp integrated environmental observation system is a set of ENVIS (IMKO, Germany) which was installed at the base camp observation point by qilian station.It is stored automatically by ENVIS data mining system. 2. Data content This data set is the daily scale data from January 1, 2013 to December 31, 2013.Including air temperature 1.5m, humidity 1.5m, air temperature 2.5m, humidity 2.5m, soil moisture 0cm, precipitation, wind speed 1.5m, wind speed 2.5m, wind direction 1.5m, geothermal flux 5cm, total radiation, surface temperature, ground temperature 20cm, ground temperature 40cm, ground temperature 60cm, ground temperature 80cm, ground temperature 120cm, ground temperature 160cm, CO2, air pressure. 3. Space and time scope Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2980.2 m
CHEN Rensheng, HAN Chuntan
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
It mainly includes the field soil moisture, groundwater level, soil physical properties, temperature, flux, plant growth, soil nutrients, trunk stem flow, farmland microclimate, soil profile water content and other observation data.
SHAO Mingan
The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.
MA Yaoming
In the ecosystem, soil and vegetation are two interdependent factors. Plants affect soil and soil restricts vegetation. On the one hand, there are a lot of nutrients such as carbon, nitrogen and phosphorus in the soil. On the other hand, the availability of soil nutrients plays a key role in the growth and development of plants, directly affecting the composition and physiological activity of plant communities, and determining the structure, function and productivity level of ecosystems. Soil moisture content (or soil moisture content): In the 9 sections from Daxihaizi to taitema lake in the lower reaches of Tarim River, plant sample plots are set in the direction perpendicular to the river channel according to the arrangement of groundwater level monitoring wells. Dig one soil profile in each sample plot, collect one soil sample from 0-5 cm, 5-15 cm, 15-30 cm, 30-50 cm, 50-80 cm, 80-120 cm and 120-170cm soil layers from bottom to top in each profile layer, each soil sample is formed by multi-point sampling and mixing of corresponding soil layers, each soil layer uses aluminum boxes to collect soil samples, weighs wet weight on site, and measures soil moisture content (or soil moisture content) by drying method. Soil nutrient: the mixed soil sample is used for determining soil nutrient after removing plant root system, gravel and other impurities, air-drying indoors and sieving. Organic matter is heated by potassium dichromate, total nitrogen is treated by semi-micro-Kjeldahl method, total phosphorus is treated by sulfuric acid-perchloric acid-molybdenum antimony anti-colorimetric method, total potassium is treated by hydrofluoric acid-perchloric acid-flame photometer method, effective nitrogen is treated by alkaline hydrolysis diffusion method, effective phosphorus is treated by sodium bicarbonate leaching-molybdenum antimony anti-colorimetric method, effective potassium is treated by ammonium acetate leaching-flame photometer method, PH and conductivity are measured by acidimeter and conductivity meter respectively (water to soil ratio is 5: 1). Soil water-soluble total salt was determined by in-situ salinity meter. Drought stress is the most common form of plant adversity and is also the main factor affecting plant growth and development. Plant organs will undergo membrane lipid peroxidation under adverse circumstances, thus accumulating malondialdehyde (MDA), the final decomposition product of membrane lipid peroxide. MDA content is an important indicator reflecting the strength of membrane lipid peroxidation and the damage degree of plasma membrane, and is also an important parameter reflecting the damage of water stress to plants. At the same time, under adverse conditions, the increased metabolism of reactive oxygen species in plants will lead to the accumulation of reactive oxygen species or other peroxide radicals, thus damaging cell membranes. Superoxide dismutase (SOD) and peroxidase (POD) in plants can remove excess active oxygen in plants under drought and other adversities, maintain the metabolic balance of active oxygen, protect the structure of the membrane, and finally enhance the resistance of plants to adversities. The analysis samples take Populus euphratica, Tamarix chinensis and Phragmites communis as research objects. According to the location of groundwater monitoring wells, six sample plots are set up starting from the riverside, with an interval of 50 m between each sample plot, which are sample plots 1, 2, 3, 4, 5 and 6 in turn. Fresh leaves of plants are collected, stored at low temperature, and pretreated (dried or frozen) on the same day. PROline (Pro), cell membrane system protective enzymes superoxide dismutase (SOD) and peroxidase (POD) were tested indoors. Preparation of enzyme solution: weigh 0.5g of fresh material and add 4.5mL pH7.8 with ph 7.8. The materials were homogenized in a pre-frozen mortar, which was placed in an ice bath. Centrifuge at 10000 r/min for 15 min. The supernatant was used for determination of superoxide dismutase, peroxidase and malondialdehyde (MDA). PRO determination: put 0.03 g of material into a 20 mL large test tube, add 10mL ammonia-free distilled water, seal it, put it in a boiling water bath for 30min, cool it, filter, filtrate 5 mL+ ninhydrin 5 mL, develop color in boiling water for 60min, and extract with toluene. The extract was colorized with Shimadzu UV-265 UV spectrophotometer at 515 nm. SOD activity was measured by NBT photoreduction. The order of sample addition for enzyme reaction system is: pH 7.8 PBS 2.4mL+ riboflavin 0.2 mL+ methionine 0.2 mL+EDTA0.1 mL+ enzyme solution 0.1 mL+NBT0.2 mL. Then the test tube was reacted under 40001ux light for 20 min, and photochemical reduction was carried out. SOD activity was measured at 650 nm wavelength by UV-265 ultraviolet spectrophotometer. POD activity determination: the reaction mixture was 50 ml PBS with pH 6.0+28 μ L guaiacol+19 UL30% H2O2. 2 mL of reaction mixture +1 mL of enzyme solution, immediately start timing, reading every 1 min, reading at 470 nm. Determination of chlorophyll: ethanol acetone mixed solution method. After cutting the leaves, the mixed solution of 0.2 g and acetone: absolute ethanol = 1: 1 was weighed as the extraction solution. After extracting in the dark for 24 h, the leaves turned white and chlorophyll was dissolved in the extraction solution. The OD value of chlorophyll was measured by spectrophotometer at 652nm. Determination method of soluble sugar: phenol sulfate method is adopted. (1) The standard curve is made by taking 11 20 ml graduated test tubes, numbering them from 0 to 10 points, and adding solution and water according to Table 1 respectively. Then add 1 ml of 9% phenol solution to the test tube in sequence, shake it evenly, then add 5 ml of concentrated sulfuric acid from the front of the tube for 5 ~ 20 s, the total volume of the colorimetric solution is 8 ml, and leave it at constant temperature for 30 minutes for color development. Then, with blank as control, colorimetric determination was carried out at 485 nm wavelength. With sugar as abscissa and optical density as ordinate, a standard curve was drawn and the equation of the standard curve was obtained. (2) Extraction of soluble sugar: fresh plant leaves are taken, surface dirt is wiped clean, cut and mixed evenly, 0.1-0.3 g are weighed, 3 portions are respectively put into 3 calibration test tubes, 5-10 ml distilled water is added, plastic film is sealed, extraction is carried out in boiling water for 3O minutes, the extraction solution is filtered into a 25 ml volumetric flask, repeated flushing is carried out, and the volume is fixed to the calibration. (3) Absorb 0.5 g of sample solution into the test tube, add 1.5 ml of distilled water, and work out the content of soluble sugar in the same way as the standard curve. The amount of solution and water in each test tube Pipe number 0 1-2 3-4 5-6 7-8 9-10 1.100μg/L sugar solution 0.20 0.40 0.60 1.0 2. water/ml 2.0 1.8 1.6 1.4 1.2 1.0 3. Soluble sugar content/μ g 0 20 40 60 80 100 Determination of malondialdehyde: thiobarbituric acid method. Fresh leaves were cut to pieces, 0．5 g was weighed, 5% TCA5 ml was added, and the homogenate obtained after grinding was centrifuged at 3 000 r／rain for 10 rain. Take 2 ml supernatant, add 0.67% TBA 2 ml, mix, boil in 100 water bath for 30 rain, cool and centrifuge again. Using 0.67% TBA solution as blank, the OD values at 450, 532 and 600 nm were determined. Methods for analysis and testing of plant hormones (GA3, ABA, CK, IAA): 0.1 0.005 g plant samples were taken and ground in liquid nitrogen. 500μl methanol was extracted overnight at 4℃. Centrifuge the sample and freeze-dry the supernatant. 30μl10％% CH3CN dissolved the sample. 10μl of sample solution was analyzed by HPLC. The external standard method was used to quantify plant hormones. Standard plant hormones were purchased from sigma Company. See (Ruan Xiao, Wang Qiang, et al., 2000, Journal of Plant Physiology.26 (5), 402-406) for analysis methods.
CHEN Yaning, HAO Xingming
1. Data Description The soil temperature monitoring of the typical soil profile of Hongnigou catchment distributes in seven different depth, which are 20cm, 40cm, 60cm, 80cm, 120cm, 160cm and 200cm. The observation frequency is 1 time every 30 minutes. The time range of the monitoring data is from May 7, 2013 to August 25, 2013. 2. Sampling Location The soil temperature monitoring site of typical soil profile in Hulugou small catchment is located along the middle and lower part of Hongnigou. The geographic coordinates are 99°52′25.3′′E, 38°15′37.97′′N. 3.Testing Method Soil temperature observations were performed using a HOBO Pendant® Temperature/Light Data Logger 64K - UA-002-64 temperature recorder.
CHANG Qixin, SUN Ziyong
1. Data overview The data set of the base camp integrated environmental observation system is a set of ENVIS (IMKO, Germany) which was installed at the base camp observation point by qilian station.It is stored automatically by ENVIS data mining system. 2. Data content This data set is the scale data from January 1, 2012 to December 31, 2012.Including air temperature 1.5m, humidity 1.5m, air temperature 2.5m, humidity 2.5m, soil moisture 0cm, precipitation, wind speed 1.5m, wind speed 2.5m, wind direction 1.5m, geothermal flux 5cm, total radiation, surface temperature, ground temperature 20cm, ground temperature 40cm, ground temperature 60cm, ground temperature 80cm, ground temperature 120cm, ground temperature 160cm, CO2, air pressure. 3. Space and time scope Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2980.2 m.
CHEN Rensheng, HAN Chuntan
Soil moisture, also known as soil moisture. It's the water that stays in the pores of the soil. The main source of soil water in Picea crassifolia forest is atmospheric precipitation, which is the only source of water absorbed by Picea crassifolia to maintain its growth. This data is the soil moisture data of Picea crassifolia forest measured by the soil moisture intelligent neutron instrument.
CHANG Xuexiang
The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
ZHANG Pei, ZHENG Donghai, WEN Jun, ZENG Yijian, WANG Xin, WANG Zuoliang, MA Yaoming, SU Zhongbo
The output data of the distributed eco hydrological model (gbehm) in the upper reaches of Heihe River includes the spatial distribution data series of 1-km grid. Region: Heihe River (Yingluo gorge), Beida River (Binggou new land), temporal resolution: Monthly Scale, spatial resolution: 1km, period: 1960-2014. Data include precipitation, evapotranspiration, runoff depth, soil volume water content (0-100cm). All data are in ASCII format. Please refer to the basin.asc file in the reference directory for the spatial range of the basin. Projection parameters of model results: sphere_Arc_Info_Lambert_Azimuthal_Equal_Area
YANG Dawen
The data set includes the sample survey data of alpine grassland and alpine meadow in Maduo County in September 2016. The sample size is 50cm × 50cm. The investigation contents include coverage, species name, vegetation height, biomass (dry weight and fresh weight), longitude and latitude coordinates, slope, aspect, slope position, soil type, vegetation type, surface characteristics (litter, gravel, wind erosion, water erosion, saline alkali spot, etc.), utilization mode, utilization intensity, etc.
LI Fei, Fei Li, Zhijun Zhang, Fei Li, Zhijun Zhang
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
PAN Xiaoduo
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 dataset is the HWSD soil texture dataset in the north slope of the Tianshan River Basin. The data comes from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). Version 1.1 was released on March 26, 2009. The data resolution is 1km. The soil classification system used is mainly FAO-90. The main fields of the soil attribute table include: SU_SYM90 (soil name in FAO90 soil classification system) SU_SYM85 (FAO85 classification) T_TEXTURE (top soil texture) DRAINAGE (19.5); ROOTS: String (depth classification of obstacles to the bottom of the soil); SWR: String (soil moisture characteristics); ADD_PROP: Real (a specific soil type related to agricultural use in the soil unit); 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 cohesive layer soil); T_CEC_SOIL: Real (cation exchange capacity of soil) 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 beginning with T_ indicates the upper soil attribute (0-30cm), and the attribute field beginning with S_ indicates the lower soil attribute (30-100cm) (FAO 2009). The data can provide model input parameters for modelers of the Earth system, and the agricultural perspective can be used to study eco-agricultural zoning, food security, and climate change.
Food and Agriculture Organization of the United Nations（FAO）
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 dataset is the HWSD soil texture dataset of the Qinghai Lake Basin. The data comes from the Harmonized World Soil Database (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). Version 1.1 was released on March 26, The data resolution is 1km. The soil classification system used is mainly FAO-90. The main fields of the soil attribute table include: SU_SYM90 (soil name in FAO90 soil classification system) SU_SYM85 (FAO85 classification) T_TEXTURE (top soil texture) DRAINAGE (19.5); ROOTS: String (depth classification of obstacles to the bottom of the soil); SWR: String (soil moisture characteristics); ADD_PROP: Real (a specific soil type related to agricultural use in the soil unit); 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 cohesive layer soil); T_CEC_SOIL: Real (cation exchange capacity of soil) 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 beginning with T_ indicates the upper soil attribute (0-30cm), and the attribute field beginning with S_ indicates the lower soil attribute (30-100cm) (FAO 2009). The data can provide model input parameters for modelers of the Earth system, and the agricultural perspective can be used to study eco-agricultural zoning, food security, and climate change.
Food and Agriculture Organization of the United Nations（FAO）
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