This dataset is global respiration data, including autotrophic respiration (ra) and heterotrophic respiration (rh). It is simulated by TaiESM1 model in Phase 6 of the Coupling Model Comparison Plan (CMIP6) under historical scenarios. The data time range is 1850-2014, the time resolution is month, and the spatial resolution is about 0.9 ° x1.25 °. Analog Data Details Visible Link https://www.wdc-climate.de/ui/cmip6?input=CMIP6.CMIP.AS -RCEC.TaiESM1.historical。
Program for Climate Model Diagnosis and Intercomparison (PCMDI)
The three pole soil microbial post-treatment products in typical years collected the distribution and analysis results of soil samples from the north and south polar regions from 2005 to 2006 and the distribution and analysis results of soil samples from the Qinghai Tibet Plateau in 2015. Through sorting and summarizing, the post-processing data products of soil microorganisms in the three pole region are obtained. The data format is excel, which is convenient for users to view. Among them, the collection time of samples from the north and south polar regions was from December 13, 2005 to December 8, 2006, including 52 samples from three regions in the Arctic (Spitsbergen slijeringa, Spitsbergen vestpynten, and Alexandra fjord Highlands), and 171 samples from five regions in the Antarctic (Mitchell Peninsula, Casey station main power house, Robinson ridge, herring Island, browning Peninsula); The Qinghai Tibet Plateau was collected from July 1 to July 15, 2015, including meadow, grassland and desert ecosystems. There were 18 sampling points in total, and the number of samples at each sampling point was 3-5. The precipitation, air temperature and drought degree of the sampling point are estimated from the meteorological information for reference. The soil surface samples were collected and stored in liquid nitrogen, then transported back to the Sydney Laboratory for extraction by fastprep DNA kit. The extracted DNA samples were amplified with the 16S rRNA gene fragment using 27F (5'-gagttttgatcntggctca-3') and 519r (5'-gtnttacngcgckctg-3'). The amplified fragments were sequenced by 454 method, and the original data were analyzed by mothur software. The sequences with poor sequencing quality were first removed, and then the chimeric sequences were sequenced and removed. After that, the similarity between sequences is calculated. Sequences with a similarity of more than 97% are clustered into one OTU, and OTU representative sequences are defined. The OTU representative sequences were aligned with the Silva database, and were identified to belong to the first level when the reliability was greater than 80%.
YE Aizhong
Soil freezing depth (SFD) is necessary to evaluate the balance of water resources, surface energy exchange and biogeochemical cycle change in frozen soil area. It is an important indicator of climate change in the cryosphere and is very important to seasonal frozen soil and permafrost. This data is based on Stefan equation, using the daily temperature prediction data and E-factor data of canems2 (rcp45 and rcp85), gfdl-esm2m (rcp26, rcp45, rcp60 and rcp85), hadgem2-es (rcp26, rcp45 and rcp85), ipsl-cm5a-lr (rcp26, rcp45, rcp60 and rcp85), miroc5 (rcp26, rcp45, rcp60 and rcp85) and noresm1-m (rcp26, rcp45, rcp60 and rcp85), The data set of annual average soil freezing depth in the Qinghai Tibet Plateau with a spatial resolution of 0.25 degrees from 2007 to 2065 was obtained.
PAN Xiaoduo, LI Hu
Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
Rainfall erosivity is one of the important basic data to quantify soil erosion in the Tibet Plateau. High precision rainfall erosivity data is the key to understand the current situation of soil and water loss in theTibet Plateau and formulate soil and water conservation measures. Meanwhile, it can provide a powerful reference for the prevention and control of geological disasters in the Tibet Plateau. Based on the 1-min dense precipitation observations and the grid precipitation product, a new annual rainfall erosivity dataset in Tibet Plateau from 1950 to 2020 is constructed through the steps of correction, reconstruction and validation. This dataset is the rainfall erosivity data set with the highest accuracy and the longest time series in the Tibet Plateau.
CHEN Yueli
Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.
CHEN Yongzhe, FENG Xiaoming, FU Bojie
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
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 data set contains the data set (98 ° 29′16″E, 31 ° Based on hobo temperature, moisture and small meteorological station, the monitoring data of shallow ground temperature, moisture and field meteorological elements of 36 ′ 36 ″ n) freeze-thaw landslide and thaw mud flow are obtained through field monitoring. The observation time is between August 31, 2019 and July 14, 2020. Through on-site monitoring of a complete freeze-thaw cycle, the monitoring data of ground temperature, moisture and meteorological elements automatically obtained by on-site sensors are downloaded. Through certain quality control, the data when the sensors are not fully adapted to the soil environment and the system error caused by sensor failure are eliminated. The observation depth of ground temperature is 10cm, 20cm, 40cm, 60cm, 80cm, 100cm, 150cm and 200cm, with a total of 8 layers. The observation depth of water is 20cm, 50cm, 100cm and 200cm, with a total of 4 layers. Meteorological observation elements mainly include temperature, rainfall, wind speed, wind direction and solar radiation. The observation interval is 30 minutes (Note: the maximum range of solar radiation sensor is 1276.8 w / m2, and the actual solar radiation value is 1276.9 w / m2 when it is greater than the maximum range; The minimum starting wind speed of the wind speed sensor is 0.5m/s. When the actual wind speed is less than the starting wind speed, the display value is 0. Therefore, the data can not reflect the phenomenon of super solar constant and wind speed below 0.5m/s). Quality control includes eliminating the data when the sensor is not fully adapted to the soil environment and the system error caused by sensor failure. The corrected final data is stored in Excel file. The integrity and accuracy of the obtained field data are more than 95% after review by many people. The monitoring data can provide the necessary data support for the research of freeze-thaw landslide and thaw mud flow in Southeast Tibet.
NIU Fujun
Terrestrial actual evapotranspiration (ETa) is an important component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. However, accurately monitoring and understanding the spatial and temporal variability of ETa over the Tibetan Plateau (TP) remains very difficult. Here, the multiyear (2000-2018) monthly ETa on the TP was estimated using the MOD16-STM model supported by datasets of soil properties, meteorological conditions, and remote sensing. The estimated ETa correlates very well with measurements from 9 flux towers, with low root mean square errors (average RMSE = 13.48 mm/month) and mean bias (average MB = 2.85 mm/month), and strong correlation coefficients (R = 0.88) and the index of agreement values (IOA = 0.92). The spatially averaged ETa of the entire TP and the eastern TP (Lon > 90°E) increased significantly, at rates of 1.34 mm/year (p < 0.05) and 2.84 mm/year (p < 0.05) from 2000 to 2018, while no pronounced trend was detected on the western TP (Lon < 90°E). The spatial distribution of ETa and its components were heterogeneous, decreasing from the southeastern to northwestern TP. ETa showed a significantly increasing trend in the eastern TP, and a significant decreasing trend throughout the year in the southwestern TP, particularly in winter and spring. Soil evaporation (Es) accounted for more than 84% of ETa and the spatial distribution of temporal trends was similar to that of ETa over the TP. The amplitudes and rates of variations in ETa were greatest in spring and summer. The multi-year averaged annual terrestrial ETa (over an area of 2444.18×103 km2) was 376.91±13.13 mm/year, equivalent to a volume of 976.52±35.7 km3/year. The average annual evapotranspirated water volume over the whole TP (including all plateau lakes, with an area of 2539.49×103 km2) was about 1028.22±37.8 km3/year. This new estimated ETa dataset is useful for investigating the hydrological impacts of land cover change and will help with better management of watershed water resources across the TP.
MA Yaoming, CHEN Xuelong,
This data includes the soil microbial composition data in permafrost of different ages in Barrow area of the Arctic. It can be used to explore the response of soil microorganisms to the thawing in permafrost of different ages. This data is generated by high through-put sequencing using the earth microbiome project primers are 515f – 806r. The region amplified is the V4 hypervariable region, and the sequencing platform is Illumina hiseq PE250; This data is used in the articles published in cryosphere, Permafrost thawing exhibits a greater influence on bacterial richness and community structure than permafrost age in Arctic permafrost soils. The Cryosphere, 2020, 14, 3907–3916, https://doi.org/10.5194/tc-14-3907-2020https://doi.org/10.5194/tc-14-3907-2020 . This data can also be used for the comparative analysis of soil microorganisms across the three poles.
KONG Weidong
Agricultural irrigation consumes a large amount of available freshwater resources and is the most immediate human disturbance to the natural water cycle process, with accelerated regional water cycles accompanied by cooling effects. Therefore, estimating irrigation water use (IWU) is important for exploring the impact of human activities on the natural water cycle, quantifying water resources budget, and optimizing agricultural water management. However, the current irrigation data are mainly based on the survey statistics, which is scattered and lacks uniformity, and cannot meet the demand for estimating the spatial and temporal changes of IWU. The Global Irrigation Water Use Estimation Dataset (2011-2018) is calculated by the satellite soil moisture, precipitation, vegetation index, and meteorological data (such as incoming radiation and temperature) based on the principle of soil water balance. The framework of IWU estimation in this study coupled the remotely sensed evapotranspiration process module and the data-model fusion algorithm based on differential evolution. The IWU estimates provided from this dataset have small bias at different spatial scales (e.g., regional, state/province and national) compared to traditional discrete survey statistics, such as at Chinese provinces for 2015 (bias = −3.10 km^3), at U.S. states for 2013 (bias = −0.42 km^3), and at various FAO countries (bias = −10.84 km^3). Also, the ensemble IWU estimates show lower uncertainty compared to the results derived from individual precipitation and soil moisture satellite products. The dataset is unified using a global geographic latitude and longitude grid, with associated metadata stored in corresponding NetCDF file. The spatial resolution is about 25 km, the time resolution is monthly, and the time span is 2011-2018. This dataset will help to quantitatively assess the spatial and temporal patterns of agricultural irrigation water use during the historical period and support scientific agricultural water management.
ZHANG Kun, LI Xin, ZHENG Donghai, ZHANG Ling, ZHU Gaofeng
The data set includes soil pH 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 field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
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
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 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/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. 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: 2018-6-10 10:30. 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.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
The data is based on the Harmonized World Soil Database version 1.1 (HWSD) constructed by the Food and Agriculture Organization of the United Nations (FAO) and the Vienna International Institute for Applied Systems (IIASA). The data source of China is 1: 1 million soil data in the second national land survey provided by the Nanjing Soil Research Institute. The data can provide model input parameters for modelers, in agricultural perspective, it can be used to study eco-agricultural zoning, food security and climate change. The data format is grid and the projection is WGS84. The soil classification system used is mainly FAO-90. The main fields of the soil property table include: SU_SYM90 (the soil name in the FAO90 soil classification system); SU_SYM85 (FAO85 classification); T_TEXTURE (top soil texture); DRAINAGE (19.5); REF_DEPTH (soil reference depth); AWC_CLASS (19.5); AWC_CLASS (soil effective water content); PHASE1: Real (soil phase); PHASE2: String (soil phase); ROOTS: String (depth classification with 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 clay 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). For the meaning of specific attribute values, please refer to the documentation * .pdf and database * .mdb in the folder.
Food and Agriculture Organization of the United Nations（FAO）, International Institute for Applied Systems Analysis
This data set comprises the plateau soil moisture and soil temperature observational data based on the Tibetan Plateau, and it is used to quantify the uncertainty of model products of coarse-resolution satellites, soil moisture and soil temperature. The observation data of soil temperature and moisture on the Tibetan Plateau (Tibet-Obs) are from in situ reference networks at four regional scales, which are the Nagqu network of cold and semiarid climate, the Maqu network of cold and humid climate, and the Ali network of cold and arid climate，and Pali network. These networks provided representative coverage of different climates and surface hydrometeorological conditions on the Tibetan Plateau. - Temporal resolution: 1hour - Spatial resolution: point measurement - Measurement accuracy: soil moisture, 0.00001; soil temperature, 0.1 °C; data set size: soil moisture and temperature measurements at nominal depths of 5, 10, 20, 40 - Unit: soil moisture, cm ^ 3 cm ^ -3; soil temperature, °C
BOB Su, YANG Kun
Soil bulk density, porosity, water content, water characteristic curve, saturated hydraulic conductivity, particle analysis, infiltration rate, and sampling point location information in the upper reaches of the Heihe River Basin. 1. The data is for 2014 supplementary sampling for 2012, using the ring knife to take the original soil; 2. The soil bulk density is the dry bulk density of the soil and is measured by the drying method. The original ring-shaped soil sample collected in the field was thermostated at 105 ° C for 24 hours in an oven, and the soil dry weight was divided by the soil volume (100 cubic centimeters) , unit: g/cm 3 . 3. Soil porosity is obtained according to the relationship between soil bulk density and soil porosity; 4. Soil infiltration analysis data set, the data is the field experimental measurement data from 2013 to 2014. 5. The infiltration data is measured by “MINI DISK PORTABLE TENSION INFILTROMETER”, and the approximate saturated hydraulic conductivity under a certain negative pressure is obtained. 6. Soil particle size data was measured at the Grain Granulation Laboratory of the Key Laboratory of the Ministry of Education of Lanzhou University. The measuring instrument is a Malvern laser particle size analyzer MS2000. 7. The saturated hydraulic conductivity is measured according to the enamel hair self-made instrument of Yi Yanli (2009). The Marioot bottle was used to maintain the head during the experiment; at the same time, the Ks measured at the time was converted to the Ks value at 10 °C for analysis and calculation. 8. Soil water content data is measured using ECH2O, including 5 layers of soil water content and soil temperature. 9. The water characteristic curve is measured by the centrifuge method: the undisturbed soil of the ring cutter collected in the field is placed in a centrifuge, and each of the speeds is measured at 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200, 11600. The secondary rotor weight is obtained.
HE Chansheng
The dataset includes soil physical and chemical attributes: pH value, organic matter fraction, cation exchange capacity, root abundance, total nitrogen (N), total phosphorus (P), total potassium (K), alkali-hydrolysable N, available P, available K, exchangeable H+, Al3+, Ca2+, Mg2+, K+ , Na+, horizon thickness, soil profile depth, sand, silt and clay fractions, rock fragment, bulk density, porosity, structure, consistency and soil color. Quality control information (QC) was provided. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m) for convenience of use in the Common Land Model and the Community Land Model (CLM). 1.THSCH.nc: Saturated water content of FCH 2.PSI_S.nc: Saturated capillary potential of FCH 3.LAMBDA.nc: Pore size distribution index of FCH 4.K_SCH.nc: Saturate hydraulic conductivity of FCH 5.THR.nc: Residual moisture content of FGM 6.THSGM.nc: Saturated water content of FGM 7.ALPHA.nc: The inverse of the air-entry value of FGM 8.N.nc: The shape parameter of FGM 9.L.nc: The pore-connectivity parameter of FGM 10.K_SVG.nc: Saturated hydraulic conductivity of FGM 11.TH33.nc: Water content at -33 kPa of suction pressure, or field capacity 12.TH1500.nc: Water content at -1500 kPa of suction pressure, or permanent wilting point
DAI Yongjiu, SHANGGUAN Wei
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from August 31 to December 24, 2018. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 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/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System for soil moisture observation at the Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation area, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following variables: battery (Batt, V), temperature (T, C), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), sample time of fast neutrons (N1ET, s), and 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) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), 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 by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
ZHU Zhongli, XU Ziwei, LI Xin, CHE Tao, TAN Junlei, REN Zhiguo, ZHANG Yang
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Guazhou Station from September 23 to December 31, 2018. The site (95.673E, 41.405N) was located on a desert in the Liuyuan Guazhou, which is near Jiuquan city, Gansu Province. The elevation is 2016 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, 8, 16, 32, and 48 m, towards north), wind speed and direction profile (windsonic; 2, 4, 8, 16, 32, and 48 m, towards north), air pressure (1.5 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m, -0.6m and -0.8m in south of tower), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, Ta_8 m, Ta_16 m, Ta_32 m, and Ta_48 m; RH_2 m, RH_4 m, RH_8 m, RH_16 m, RH_32 m, and RH_48 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, Ws_8 m, Ws_16 m, Ws_32 m, and Ws_48 m) (m/s), wind direction (WD_2 m, WD_4 m, WD_8 m, WD_16 m, WD_32 m, and WD_48 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/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m^2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_80 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_80 cm) (%, volumetric water content),soil water potential (SWP_5cm, SWP_10cm, SWP_20cm, SWP_40cm, SWP_60cm, and SWP_80cm)(kpa), soil conductivity (Ec_5cm, Ec_10cm, Ec_20cm, Ec_40cm, Ec_60cm, and Ec_80cm)(μs/cm), sun time (h). 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 soil water potential in the area is so low that it has exceeded the sensor measurements. (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: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 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/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). 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 data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (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: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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
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
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Sidaoqiao Superstation from January 1 to December 31, 2018. The site (101.137° E, 42.001° N) was located on a tamarix (Tamarix chinensis Lour.) surface in the Sidaoqiao, Dalaihubu Town, Ejin Banner, Inner Mongolia Autonomous Region. The elevation is 873 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HC2S3; 5, 7, 10, 15, 20 and 28 m, towards north), wind speed profile (010C; 5, 7, 10, 15, 20 and 28 m, towards north), wind direction profile (020C; 15 m, towards north), air pressure (CS100; in waterproof box), rain gauge (TE525M; 28 m, towards south), four-component radiometer (CNR4; 10 m, towards south), two infrared temperature sensors (SI-111; 10 m, towards south, vertically downward), two photosynthetically active radiation (PQS-1; 10 m, towards south, one vertically upward and one vertically downward), soil heat flux (HFP01SC; 3 duplicates with G1 below the tamarix; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (installed on 17 July, 2013, TCAV; -0.02, -0.04 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6, -2.0 m), and soil moisture profile (install on 7 December, 2013, ML2X; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6, -2.0 m). The observations included the following: air temperature and humidity (Ta_5 m, Ta_7 m, Ta_10 m, Ta_15 m, Ta_20 m and Ta_28 m; RH_5 m, RH_7 m, RH_10 m, RH_15 m, RH_20 m and RH_28 m) (℃ and %, respectively), wind speed (Ws_5 m, Ws_7 m, Ws_10 m, Ws_15 m, Ws_20 m and Ws_28 m) (m/s), wind direction (WD_15 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/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)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm, Ts_200 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm, Ms_200 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 precipitation data was wrong during January to June because of the sensor problem; the air pressure data was wrong during July to October because of sensor line broken. 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, REN Zhiguo, TAN Junlei
This dataset includes data recorded by Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dayekou Station from January 1 to December 31, 2018. The site (100.285° E, 38.555° N) was located on a glassland in the Dayekou, which is near Zhangye city, Gansu Province. The elevation is 2694 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (8 m), air pressure (2 m), rain gauge (2 m), infrared temperature sensors (2 m, towards south, vertically downward), soil heat flux (below the vegetation, -0.05 m; towards south), soil soil temperature/moisture/electrical conductivity profile (-0.05 m) photosynthetically active radiation (2 m, towards south), four-component radiometer (2 m, towards south), sunshine duration sensor(2 m, towards south). The observations included the following: air temperature and humidity (Ta_8m; RH_3m, RH_5 m, RH_8m) (℃ and %, respectively), wind speed (Ws_8m) (m/s), wind direction (WD_8m) (°), 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/m^2), infrared temperature (℃), photosynthetically active radiation (PAR) (μmol/ (s m^2)), soil heat flux (Gs_5 cm) (W/m^2), soil temperature (Ts_5cm)(℃), soil moisture (Ms_5cm)(%, volumetric water content), photosynthetically active radiation (μmol/ (s m^2)), soil water potential (Swp_5cm)(kpa), soil conductivity (Ec_5cm)(μs/cm), sun time(h). 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 data were missing during Aug 29 to Oct 18 because the battery is unstable; Some meterological data were wrong because the malfunction of datalogger (1.3-1.6；1.8-1.11；1.14-1.20；1.23-1.30；2.9-2.22；2.28-3.23；3.28-5.12); The air humidity data were rejected due to program error. (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: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Xiyinghe Station from January 1 to December 31, 2018. The site (101.853E, 37.561N) was located on a alpine meadow in the Menyuan,Qinghai Province. The elevation is 3639 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, and 8 m, towards north), wind speed and direction profile (windsonic; 2, 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, and Ta_8 m; RH_2 m, RH_4 m, and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 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/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s/m^2)), soil heat flux (Gs_5 cm, Gs_10cm) (W/m^2), soil temperature (Ts_20 cm, Ts_40 cm) (℃), soil moisture (Ms_20 cm, Ms_40 cm) (%, volumetric water content), soil water potential (SWP_20cm , SWP_40cm)(kpa) , soil conductivity (Ec_20cm, Ec_40cm)(μs/cm), sun time (h). 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 meteorological data were missing during Aug. 29 to Oct.18 because of unstable power supply due to battery box flooding; The wind speed and direction profile data were rejected because of sensor failure; The precipitation data were rejected because of program error; The air humidity data before Mar. 2 were rejected due to program error; (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: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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
Soil is mineral particles of different sizes formed by weathering of rocks. Soil not only provides nutrients and water for crops, but also has a transforming effect on various nutrients. In addition, the soil also has a self-cleaning function, which can improve organic matter content, soil temperature and humidity, pH value, anion and cation. The soil pollution causes several environmental problems: industrial sewage, acid rain, exhaust emissions, accumulations, agricultural pollution. After the land is polluted, the contaminated tops with high concentration of heavy metals are easily entered under the action of wind and water. Other secondary ecological and environmental problems such as air pollution, surface water pollution, groundwater pollution and ecosystem degradation in the atmosphere and water.he data set comes from the World Soil Database (Harmonized World Soil Database version 1.1) (HWSD) UN Food and Agriculture (FAO) and the Vienna International Institute for Applied Systems Research Institute (IIASA) constructed, which provides data model input parameters for the modeler, At the same time, it provides a basis for research on ecological agriculture, food security and climate change.
Food and Agriculture Organization of the United Nation FAO
The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
FANG Huajun
Based on the field survey, the aboveground and underground biomass of vegetation, and soil carbon and nitrogen contents in Nagqu， in the north of Zoige, eastern of Tibet plateau and the wind vacanofrom 2015 to 2017 were collected, and the data were collated and preliminarily analyzed. Dataset consists both of the aboveground and underground biomass of vegetation and soil carbon and nitrogen contents in different elevation gradient (subalpine meadow, alpine meadow, alpine shrub meadow), different moisture gradient (wetland, degraded swamp, swamp meadow, wet meadow, dry meadow and degraded meadow) and the different desertification degree (mild desertification, moderate desertification, severe desertification, desertification). The differences and trends of vegetation biomass and soil carbon and nitrogen contents under different gradients were analyzed. This dataset provides a theoretical basis for understanding and rational utilization of grassland resources, and also provides strong support for exploring the prediction of alpine grassland productivity under the global climate change.
ZHANG Xianzhou, ZHANG Yangjian, SU Peixi, YANG Yan
Soil data is important both on a global scale and on a local scale, and due to the lack of reliable soil data, land degradation assessments, environmental impact studies, and sustainable land management interventions have received significant bottlenecks . Affected by the urgent need for soil information data around the world, especially in the context of the Climate Change Convention, the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO) and the Kyoto Protocol for Soil Carbon Measurement and FAO/International The Global Agroecological Assessment Study (GAEZ v3.0) jointly established the Harmonized World Soil Database version 1.2 (HWSD V1.2). Among them, the data source in China is the second national land in 1995. Investigate 1:1,000,000 soil data provided by Nanjing Soil. The resolution is 30 seconds (about 0.083 degrees, 1km). The soil classification system used is mainly FAO-90. The core soil system unit unique verification identifier: MU_GLOBAL-HWSD database soil mapping unit identifier, connected to the GIS layer. MU_SOURCE1 and MU_SOURCE2 source database drawing unit identifiers SEQ-soil unit sequence in the composition of the soil mapping unit; The soil classification system utilizes the FAO-7 classification system or the FAO-90 classification system (SU_SYM74 resp. SU_SYM90) or FAO-85 (SU_SYM85). The main fields of the soil property sheet include: ID (database ID) MU_GLOBAL (Soil Unit Identifier) (Global) SU_SYMBOL soil drawing unit SU_SYM74 (FAO74 classification); SU_SYM85 (FAO85 classification); SU_SYM90 (name of soil in the FAO90 soil classification system); SU_CODE soil charting unit code SU_CODE74 soil unit name SU_CODE85 soil unit name SU_CODE90 soil unit name DRAINAGE (19.5); REF_DEPTH (soil reference depth); AWC_CLASS(19.5); AWC_CLASS (effective soil water content); PHASE1: Real (soil phase); PHASE2: String (soil phase); ROOTS: String (depth classification to the bottom of the soil); SWR: String (soil moisture content); ADD_PROP: Real (specific soil type in the soil unit related to agricultural use); T_TEXTURE (top soil texture); T_GRAVEL: Real (top gravel volume percentage); (unit: %vol.) T_SAND: Real (top sand content); (unit: % wt.) T_SILT: Real (surface layer sand content); (unit: % wt.) T_CLAY: Real (top clay content); (unit: % wt.) T_USDA_TEX: Real (top layer USDA soil texture classification); (unit: name) T_REF_BULK: Real (top soil bulk density); (unit: kg/dm3.) T_OC: Real (top organic carbon content); (unit: % weight) T_PH_H2O: Real (top pH) (unit: -log(H+)) T_CEC_CLAY: Real (cation exchange capacity of the top adhesive layer soil); (unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of top soil) (unit: cmol/kg) T_BS: Real (top level basic saturation); (unit: %) T_TEB: Real (top exchangeable base); (unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top sulfate content); (unit: % weight) T_ESP: Real (top exchangeable sodium salt); (unit: %) T_ECE: Real (top conductivity). (Unit: dS/m) S_GRAVEL: Real (bottom crushed stone volume percentage); (unit: %vol.) S_SAND: Real (bottom sand content); (unit: % wt.) S_SILT: Real (bottom sludge content); (unit: % wt.) S_CLAY: Real (bottom clay content); (unit: % wt.) S_USDA_TEX: Real (bottom USDA soil texture classification); (unit: name) S_REF_BULK: Real (bottom soil bulk density); (unit: kg/dm3.) S_OC: Real (underlying organic carbon content); (unit: % weight) S_PH_H2O: Real (bottom pH) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying adhesive layer soil); (unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of the bottom soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation); (unit: %) S_TEB: Real (underlying exchangeable base); (unit: cmol/kg) S_CACO3: Real (bottom carbonate or lime content) (unit: % weight) S_CASO4: Real (bottom sulfate content); (unit: % weight) S_ESP: Real (underlying exchangeable sodium salt); (unit: %) S_ECE: Real (underlying conductivity). (Unit: dS/m) The database is divided into two layers, with the top layer (T) soil thickness (0-30 cm) and the bottom layer (S) soil thickness (30-100 cm). For other attribute values, please refer to the HWSD1.2_documentation documentation.pdf, The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description and HWSD.mdb.
Meng Xianyong, Wang Hao
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 the slope aspect (resolution: 30 m) factor affecting soil erosion on the Loess Plateau and the slope aspect data extracted from the elevation data of the Loess Plateau. Each theme map is divided into frames according to the 1:250000 scale standard map cartography method, and the frames are denoted by the 1:250000 scale standard map cartography number. The geographical coordinate is WGS1984; the accuracy can meet the requirements of regional scale hydrology and soil erosion analysis and forecasting.
LIU Baoyuan, SHI Haijing
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
This data set contains the results of the calculation of Net Primary Productivity (NPP) on the Tibetan Plateau based on ecological models and remote sensing data from 1982 to 2006. Ecosystem NPP of the Tibetan Plateau was generated based on the remote sensing Advanced Very High Resolution Radiometer (AVHRR) data and the Carnegie-Ames-Stanford Approach (CASA) model(1982-2006), the soil carbon content was generated based on the second soil census data, and the biomass carbon data were generated based on the High Resolution Biosphere Model (HRBM) model. Forest ecosystem NPP of the Tibetan Plateau (1982-2006): npp_forest82.e00，npp_forest83.e00，npp_forest84.e00，npp_forest85.e00，npp_forest86.e00， npp_forest87.e00，npp_forest88.e00，npp_forest89.e00，npp_forest90.e00，npp_forest91.e00， npp_forest92.e00，npp_forest93.e00，npp_forest94.e00，npp_forest95.e00，npp_forest96.e00， npp_forest97.e00，npp_forest98.e00，npp_forest99.e00，npp_forest00.e00，npp_forest01.e00， npp_forest02.e00，npp_forest03.e00，npp_forest04.e00，npp_forest05.e00，npp_forest06.e00 Grassland ecosystem NPP of the Tibetan Plateau(1982-2006)： npp_grass82.e00，npp_grass83.e00，npp_grass84.e00，npp_grass85.e00，npp_grass86.e00， npp_grass87.e00，npp_grass88.e00，npp_grass89.e00，npp_grass90.e00，npp_grass91.e00， npp_grass92.e00，npp_grass93.e00，npp_grass94.e00，npp_grass95.e00，npp_grass96.e00， npp_grass97.e00，npp_grass98.e00，npp_grass99.e00，npp_grass00.e00，npp_grass01.e00，npp_grass02.e00，npp_grass03.e00，npp_grass04.e00，npp_grass05.e00，npp_grass06.e00. Biomass carbon and soil carbon of the Tibetan Plateau： Biomass.e00，Socd.e00. The soil carbon content data （Socd） are generated based on data of the second soil census of China and Soil Map of China (1:1,000,000) by soil subclass interpolation. The NPP data are generated from the CASA model and AVHRR data simulation: Potter CS, Randerson JT, Field CB et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811–841. The biomass carbon data are generated via HRBM model simulation： McGuire AD, Sitch S, et al. Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Global Biogeochem. Cycles, 2001, 15 (1), 183-206. The raw data are mainly remote sensing data and field observation data with high accuracy; the verification and adjustment of the measured data in the field during the production were undertaken to maintain the error of the simulation results and the field measured data within the acceptable range as much as possible; the verification results of the NPP data and the field measured data show that the error remains within 15%. The spatial resolution is 0.05°×0.05° (longitude×latitude).
ZHOU Caiping
The data products of mixed soil moisture of the Tibetan Plateau utilize remote sensing observation, in situ measurement and model simulation techniques. In situ soil moisture (SM) observation combines the classification of the Tibetan Plateau climate zone and is used to generate in situ measurements of SM climatology at plateau scales. The resulting in situ SM climatology of the Tibetan Plateau scale is used to scale the SM data simulated by the model, which are then used to scale the SM satellite observations. The climatological-scale satellites and model-simulated SMs are then objectively mixed by applying triple configuration and least square matching. The final mixed SM can replicate SM dynamics in different climate zones, from subhumid areas to semiarid and arid regions of the Tibetan Plateau. - Time resolution: day, starting from 01/05/2008 - Spatial resolution: 0.25° × 0.25° - Data set size: 61 × 121 × 975 - Unit: cm^3 cm^-3 The data quality is open to assessment.
ZENG Yijian
The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
HU Zeyong
This data set includes meteorological data observed by the carbon flux station in the Guoluo Army Ranch in Qinghai. The temporal coverage is from 2005 to 2009, and the temporal resolution is 1 day. Meteorological and carbon flux data observation methods: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. Both carbon flux and meteorological data were automatically recorded by the instruments and manually checked. During the data observation process, the operation of the instrument and the selection of the observation objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and productivity estimation. This data contains observation items as follows: Temperature °C Precipitation mm Wind speed m/s Soil temperature at 5 cm depth °C Photosynthetically active radiation µmol/m²s Total radiation W/m²
ZHAO Xinquan
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
CMADS (The China Meteorological Assimilation Driving Datasets for The SWAT model) The soil temperature component (hereinafter referred to as cmads-st) USES The China Meteorological Administration Land Data Assimilation System [CLDAS] to force The common Land surface model3.5 [CLM3.5]) (Community Land model, numerical simulation of Land surface, circulation 10 spin - up simulation, get basic stability model initial field, and obtain high space-time resolution of soil temperature data sets, eventually hierarchical data model is utilized to extract, quality control, a nested loop, re-sampling, and a variety of technologies such as bilinear interpolation method is finally established. Cmads-st series data set space covers the whole east Asia (0 ° n-65 ° N, 60 ° e-160 ° E), the spatial resolution is respectively cmads-st V1.0 version: 1/3 °, cmads-st V1.1 version: 1/4 °, cmads-st V1.2 version: 1/8 ° and cmads-st V1.3 version:The above resolutions are daily (the basic resolution of the soil temperature component output in CLM3.5 mode is 1/16°, which ensures the highest resolution of the cmads-st data set is 1/16°). The time scale is 2009-2013.The data set published on this page is the cmads-st V1.0 data set (spatial resolution :1/3°).Temporal resolution: daily.Space coverage: east Asia (0 ° n-65 ° N, 60 ° e-160 ° E).Number of stations: 58,500.Supply factors: the average daily soil temperature of 10 layers (the depth of node hierarchy is in order: the first layer :0.00710063521m; the second layer :0.0279249996m; the third layer :0.0622585751m; the fourth layer :0.118865065m; the fifth layer :0.2121934m; the sixth layer :0.3660658m; the seventh layer :0.619758487m; the eighth layer :1.03802705m; the ninth layer :1.72763526m;Floor 10 :2.8646071m).Provide data format: TXT. The path of the cmads-st V1.0 soil temperature data set is: CMADS - ST - V1.0\2009 \ layer1 V1.0\2009 \ layer10 to CMADS - ST CMADS - ST - V1.0\2010 \ layer1 V1.0\2010 \ layer10 to CMADS - ST CMADS - ST - V1.0\2011 \ layer1 V1.0\2011 \ layer10 to CMADS - ST CMADS - ST - V1.0\2012 \ layer1 V1.0\2012 \ layer10 to CMADS - ST CMADS - ST - V1.0\2013 \ layer1 V1.0\2013 \ layer10 to CMADS - ST Cmads-st V1.0 subset file path and file name description Where, daily soil temperature (ten layers) is shown in layer1-layer10\.Are located in the following directories (take 2009 as an example): \2009\layer1\ 2009 layer1 (0.00710063521m) soil temperature directory \2009\layer2\ 2009 layer2 (0.0279249996m) soil temperature directory \2009\layer3\ 2009 layer3 (0.0622585751m) soil temperature catalogue \2009\layer4\ 2009 layer4 (0.118865065m) soil temperature catalogue \2009\layer5\ 2009 layer5 (0.2121934m) soil temperature catalogue \2009\layer6\ 2009 layer6 (0.3660658m) soil temperature catalogue \2009\layer7\ 2009 layer7 (0.619758487m) soil temperature directory \2009\layer8\ 2009 layer8 (1.03802705m) soil temperature catalogue \2009\layer9\ 2009 layer9 (1.72763526m) soil temperature catalogue \2009\layer10\ 2009 10th layer (2.8646071m) soil temperature catalogue
Meng Xianyong, Wang Hao
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer on November 21-22, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 21-22, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, which can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.5m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 19 to 20, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 19-20, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.5m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 18 to 19, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 18-19, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 3.5m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
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
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
This set of data is the simulation result of the newly developed land eco-hydrological model CLM_LTF.This model is on top of the land-surface process model CLM4.5 developed by NCAR, coupling the groundwater lateral flow module and considering the role of human irrigation. The model runs from 1981 to 2013, with a spatial resolution of 30 arc seconds (0.0083 degrees), a time step of 1,800 seconds, and a simulation range of the heihe river basin.Air force in 1981-2012 is used by the Chinese academy of sciences institute of the qinghai-tibet plateau of qinghai-tibet plateau more layers of data assimilation and simulation center development areas of China high space-time resolution ground meteorological elements drive data set, air is forced to use 2013 national meteorological information center of wind pressure high resolution made by the wet precipitation temperature radiation data set.The land cover data is a 1km land cover grid data set for the MICLCover heihe river basin, and the irrigation data is shown in "monthly 30-arcsecond resolution surface water and groundwater irrigation data set for the heihe river basin 1981-2013" of the scientific data center for cold and dry regions.The mode output is the monthly average. The document is described as follows: Groundwater depth data: heihe_zwt.nc 2cm soil moisture data: heihe_h2osoi_2cm. nc 100cm soil moisture data: heihe_h2osoi_100cm.nc Evaporation data: Heihe_evaptanspiration. Nc The data is in netcdf format.There are three dimensions, which are month, lat, and lon. Where, month is a month, and the value is 0-395, representing each month from 1981 to 2013. Lat is grid latitude information, and lon is grid longitude information. The data is stored in the data variable. The underground water depth data is in m, the soil moisture data is in m^3/m^3, and the evapotranspiration data is in mm/month
XIE Zhenghui
On June 15, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5 m intervals. Each line has 23 points (north-south direction) at 5 m intervals. Four hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 500 points on the quadrat were obtained, and each point was observed twice, i.e. in each sampling point, once in the film (marked a in the data record) and once out of the film (marked b in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked b in both data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. The vegetation team completed the measurement of biomass, Lai, vegetation water content, plant height, row ridge distance, chlorophyll, etc. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The aim of the simultaneous observation of river surface temperature is obtaining the river surface temperature of different places, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the river surface temperature data will be used for validation of the retrieved river surface temperature from thermal infrared sensor and the analysis of the scale effect of the river surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation sites and other details Ten river sections were chosen to observe surface temperature simultaneously in the midstream of Heihe River Basin on 3 July and 4 July, 2012, including Sunan Bridge, Binhe new area, Heihe Bridge, Railway Bridge, Wujiang Bridge, Gaoya Hydrologic Station, Banqiao, Pingchuan Bridge, Yi’s Village, Liu’s Bridge. Self-recording point thermometers (observed once every 6 seconds) were used in Railway Bridge and Gaoya Hydrologic Station while handheld infrared thermometers (observed once of the river section temperature for every 15 minutes) were used in other eight places. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. All instruments were calibrated on 6 July, 2012 using black body during observation. 3. Data storage All the observation data were stored in excel.
HE Xiaobo, Jia Shuzhen
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer and synchronous measurement from November 24-25, 2013 in the desert of Minle County, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 24-25, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage, 18.7ghz h polarization damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.3m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
During the period of middle stream experiment in 2012, closed chamber and gas chromatography method was used to measure soil respiration of different land surface, including farmland, orchard, wetland, sparse grassland (Huazhaizi), Gobi, desert. Instrument: Assimilation Chamber Measuring method: Assimilation chamber consists of two parts: the base and the box. Base made of PVC material, the bottom buried in the soil. The box is made of stainless steel cubes, with one open side. When measuring the box cover on the base, air in the box was sampled using injector. The extracted air was injected into the gas sampling bag, and shipped back to the laboratory analysis of the concentration of CO2 by gas chromatography in Institute of Botany, The Chinese Academy of Sciences. Using the difference of concentration of CO2 at two times to calculate soil respiration. Each measurement points are located three repeat. After five minutes sealed box cover start mining the 1st sample, and then taken once every sample interval of 10 minutes, four times in total mining. Date content: Data content includes header information and once every 10 days three times repeated observations and the average of the three times. Measuring location: Gobi (Bajitan Gobi station), Wetland (Zhangye wetland Station), Sparse grassland (Huazhaizi desert steppe Station), Desert (Shenshawo sandy desert Station), Orchard (site No.17 eddy covariance system), Maize Farmland (Daman Superstation) Measuring time: 16-6-2012, 28-6-2012, 9-7-2012, 18-7-2012, 30-7-2012, 11-8-2012, 21-8-2012, 2-9-2012, 13-9-2012, 22-9-2012 (UTC+8).
MA Mingguo, LI Xianglan
On July 7, 2012, airborne ground synchronous observation was carried out in plmr quadrats of Yingke oasis and huazhaizi desert. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern edge of Zhangye Oasis and anyangtan desert, on the west side of Zhangye Daman highway, and across the trunk canal of Longqu in the north and the south, which is divided into two parts. In the southwest, there is a 1 km × 1 km desert quadrat. Because the desert is relatively homogeneous, here 1 The soil moisture of 5 points (1 point and center point around each side, and several more points can be measured during walking along the road in the actual measurement process) is collected in KM quadrat. The four corner points are 600 m apart from each other except the diagonal direction. The southwest corner point is huazhaizi desert station, which is convenient to compare with the data of meteorological station. On the northeast side, a large sample with an area of 1.6km × 1.6km was selected to carry out synchronous observation on the underlying surface of oasis. The selection of quadrat is mainly based on the consideration of the representativeness of surface coverage, avoiding residential buildings and greenhouses as much as possible, crossing oasis farmland and some deserts in the south, accessibility, and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 11 splines (east-west distribution) were collected at the interval of 160 m in the east-west direction. Each line has 21 points (north-south direction) at the interval of 80 M. four hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 230 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. Vegetation parameter observation was carried out in some representative soil water sampling points, and the measurement of plant height and biomass (vegetation water content) was completed. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
This data set includes the 2013 observation data of 10 water net nodes in the 5.5km × 5.5km observation matrix (red box in the thumbnail) of Yingke / Daman irrigation area in the middle reaches of Heihe River. The 10 water net nodes contain 4cm and 10cm two-layer hydro probe II probes to observe the main variables such as soil moisture, soil temperature, conductivity and complex permittivity; the si-111 infrared temperature probe is set up at 4m height to observe the surface infrared radiation temperature of the underlying surface. The time and frequency of conventional observation is 10 minutes. In order to ensure the accurate synchronization of si-111 and remote sensing, one minute intensive observation is conducted at 00:00-04:30, 08:00-18:00 and 21:00-24:00 every day. This data set can provide spatiotemporal continuous observation data set for remote sensing estimation of key water and heat variables of heterogeneous surface, remote sensing authenticity test, ecological hydrology research, irrigation optimization management and other research. For details, please refer to "2013 middle reaches of Heihe River waternet data document 20141231. Docx"
KANG Jian, LI Xin, MA Mingguo
Soil respiration rate was measured at the super station of Daman irrigation district in Zhangye city using the open circuit soil carbon flux measurement system LI-8100 (LI-COR, Lincoln, NE, USA) 1) Objective: The aim of soil respiration rate measurement is to explore the diurnal variation characteristics of soil respiration rate and to provide a scientific basis for the assessment of farmland ecosystem carbon cycle and carbon balance. 2) Measurement instruments and ways Measurement instruments: the open type of cold dry soil carbon flux measurement system LI-8100 (LI-COR, Lincoln, NE, USA). Measurement means: soil respiration chamber was placed in PVC ring (10 cm of diameter, 5 cm of height), which was inserted into the soil about 1 to 2 cm 1 d before measurement. The observation is automatic with a power supply of solar panels. 3) Measurement time Soil respiration rate was continuously measured mainly in the corn growing season. The time used in this dataset is in UTC+8 Time. 4) Data processing The data was periodically collected from the data collection instrument and saved as *.81x file, then was converted to text format file using LI-8100 (M) PC Client v2.0.0 software.
Wang Jing, Huang Yongsheng, LI Yuan, LI Xin, MA Mingguo
This data was measured in middle stream of the Heihe River Basin in year 2012. Soil texture, porosity, bulk density, saturated water conductivity, soil organic matter were measured for each layer of the soil profile which is very close to the AMS sites. This data can be used in land surface model and ecological model. Soil profile position: The coordinate of the profile is listed as follow. No.1 to No.17 is corresponding to the AMS number in the Matrix. No. x y 1 100.3582 38.89322 2 100.3541 38.88697 3 100.3763 38.89057 5 100.3506 38.87577 6 100.3597 38.8712 7 100.3652 38.87677 8 100.3765 38.87255 9 100.3855 38.87241 10 100.3957 38.87569 11 100.342 38.86994 12 100.3663 38.86516 13 100.3785 38.86077 14 100.3531 38.85869 16 100.3641 38.8493 17 100.3697 38.84512 15 (superstation) 100.3721 38.85547 Gebi 100.3058 38.91801 Huazhaizi 100.3189 38.7652 Shenshawo 100.4926 38.78794 Instruments: Soil texture: Microtrac laser particle analyzer Porosity: Ring sampler law Bulk density: Ring sampler law Saturated Water Conductivity: hydrostatic head method Soil organic matter: Total organic carbon analyzer (TOC-VCPH) Measuring time: 2012-5-20 to 2012-7-10 (UTC+8). Measuring content: Soil texture, porosity, bulk density, saturated water conductivity, soil organic matter.
MA Mingguo, WANG Xufeng, WANG Haibo, YU Wenping
The first dataset of ground truth measurements synchronizing with airborne Polarimetric L-band Multibeam Radiometer (PLMR) mission was obtained in the Yingke oasis and Huazhaizi desert steppe on 28-29 June, 7, 10, 26 July, 2 August, 2012 (UTC+8). The dataset of ground truth measurements synchronizing with airborne Polarimetric L-band Multibeam Radiometer (PLMR) mission was obtained in the Linze Inland River Basin Comprehensive Research Station on 3 July, 2012. PLMR is a dual-polarization (H/V) airborne microwave radiometer with a frequency of 1.413 GHz, which can provide multi-angular observations with 6 beams at ±7º, ±21.5º and ±38.5º. The PLMR spatial resolution (beam spot size) is approximately 0.3 times the altitude, and the swath width is about twice the altitude. The measurements were conducted in the southwest part of the Zhangye Oasis, which included two sampling plots. One was located in Gobi desert with an area of 1 km × 1 km. Due to its homogeneous landscape, around 10 points were sampled to acquire the situation of soil water content. The other sampling plot was designed in farmlands with a dominant plant type of maize. Ground measurements took place along 16 transects, which were arranged parallelly with an interval of 160 m between each other in the east-west direction. In each 2.4 km long transect, soil moisture was sampled at every 80 m in the north-south direction. Steven Hydro probes were used to collect soil moisture and other measurements. For each sampling point in farmland, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). The field campaign started from 11:00 AM, but stopped at 4:00 PM on 28 June because of rain. The rest of measurements were completed from 10:30 AM to 5:30 PM on 29 June. Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within the farmland sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, vegetation water content, canopy height. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 22 to 24, 2013 in Desert Park desert, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 22-24, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 7.4M 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
During the 2012 aerial remote sensing experiment conducted midstream, Li-Cor8100 was used to measure soil respiration every five days in the EC matrix area. Instrument: LI-Cor8100 Measuring Method: Soil respiration ring was made using PVC pipe with length of 10 cm. Before measuring soil respiration, soil respiration ring was inserted into the soil, 4 cm in soil and 6 cm above soil. Soil respiration measurement should be taken after standing for at least 24 hours science ring was inserted in soil. Sample measurement time is during 9-12 in the morning. Set of three replicates per plot. Marked according to EC site name. Data content: Data content includes header information, and once every five days repeated three times observations value and the average value. Measuring location: EC sites within the matrix core experiment area (No. EC01 to EC17), each plot set three repeat samples. For the superstation (EC15) plot set nine repeat samples. Measuring time: From 6 June to 20 August, 2012, once every five days for site EC01, EC03, EC05, EC10, EC11, EC12, EC13, EC14, and EC17; from 1 July to 20 August, 2012, once every five days for site EC02, EC04, EC06, EC07, EC08, EC09 and EC16. The time used in this dataset is in UTC+8 Time. Part of the observation points during the observation just irrigation, these times are not observable.
LI Yuan, SHI Weiyu, SONG Yi
On June 26, 2012, the satellite transit ground synchronous observation was carried out in the TerraSAR-X sample near the super station in the dense observation area of Daman. TerraSAR-X satellite carries X-band synthetic aperture radar (SAR). The daily transit image is HH / VV polarized, with a nominal resolution of 3 m, an incidence angle of 22-24 ° and a transit time of 19:03 (Beijing time), which mainly covers the ecological and hydrological experimental area of the middle reaches artificial oasis. The local synchronous data set can provide the basic ground data set for the development and verification of active microwave remote sensing soil moisture retrieval algorithm. Quadrat and sampling strategy: Six natural blocks are selected in the southeast of the super station, with an area of about 100 m × 100 m. One plot in the northwest corner of the sample plot is watermelon field, others are corn. The basis of sample selection is: (1) considering different vegetation types, i.e. watermelon and corn; (2) considering the visible light pixel, the sample size of 100m square can guarantee at least 4 30 M-pixel is located in the sample; (3) the location of the sample is near the super station, with convenient transportation. The observation of the super station is in the north, and there is a water net node on both sides of the East and the west, which makes it possible to integrate these observations in the future; (4) in addition, there are some obvious points around the sample, which can ensure that the geometric correction of the SAR image is more accurate in the future. Considering the resolution of the image, 21 splines (distributed from east to West) are collected at 5m intervals. Each line has 21 points (north-south direction) at 5m intervals. Three hydroprobe data acquisition systems (HDAS, reference 2) are used to measure at the same time. The sampling interval is controlled by the scale and moving splines on the measuring line to make up for the lack of using hand-held GPS. Measurement content: About 440 points on the quadrat were obtained, and each point was observed twice, i.e. two times in each sampling point, one time inside the film (marked as a in the data record) and one time outside the film (marked as B in the data record); although the watermelon land was also covered with film, considering that it was not laid horizontally, only the soil moisture at the non covered position was measured (marked as B in the two data records). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: The data format of this data set is vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, MA Mingguo, LI Xin
On August 2, 2012, airborne ground synchronous observation was carried out in plmr quadrats of Yingke oasis and huazhaizi desert. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern edge of Zhangye Oasis and anyangtan desert, on the west side of Zhangye Daman highway, and across the trunk canal of Longqu in the north and the south, which is divided into two parts. In the southwest, there is a 1 km × 1 km desert quadrat. Because the desert is relatively homogeneous, here 1 The soil moisture of 5 points (1 point and center point around each side, and several more points can be measured during walking along the road in the actual measurement process) is collected in KM quadrat. The four corner points are 600 m apart from each other except the diagonal direction. The southwest corner point is huazhaizi desert station, which is convenient to compare with the data of meteorological station. On the northeast side, a large sample with an area of 1.6km × 1.6km was selected to carry out synchronous observation on the underlying surface of oasis. The selection of quadrat is mainly based on the consideration of the representativeness of surface coverage, avoiding residential buildings and greenhouses as much as possible, crossing oasis farmland and some deserts in the south, accessibility, and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 11 splines (east-west distribution) were collected at the interval of 160 m in the east-west direction. Each line has 21 points (north-south direction) at the interval of 80 M. four hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 230 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. No synchronous vegetation sampling was carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
On July 3, 2012, airborne ground synchronous observation was carried out in plmr sample belt near Linze station. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: According to the typical ground surface type represented by three points near Linze station and taking part of neutron tube observation into account, the three routes from northwest to southeast are designed, with an interval of 200 m, a design altitude of about 300 m and a plmr ground resolution of 100 m. According to the observation characteristics of the route and plmr, three observation transects are designed on both sides of the route, each of which is about 6 km long. From west to East are L1, L2 and L3 respectively. Among them, L1 and L2 are centered on the middle route, 80 m apart; L2 and L3 are 200 m apart. Four hydroprobe data acquisition systems (HDAS, ref. 2) were used to measure at the same time. Measurement content: About 4500 points on the sample belt were obtained, each point was observed twice, that is to say, in each sampling point, once in the film (marked as a in the data record) and once out of the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. Vegetation parameter observation was carried out in some representative soil water sampling points, and the measurement of plant height and biomass (vegetation water content) was completed. Note: the observation date coincides with the irrigation of large area of farmland in this area, which makes it difficult for the observer to move forward, the field block is difficult to enter, and the observation point position deviates from the preset point position. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
The first dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 4 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The second dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 15 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The third dataset of ground truth measurements synchronizing with TerraSAR-X was obtained in the Daman foci experimental area on 26 June, 2012. The satellite image was in StripMap mode and HH/VV polarization with an incidence angle of 22-24°, and the overpass time was approximately at 19:00 UTC+8. The measurements were conducted at a sampling plot southeast to the Daman Superstation with an area of around 100 m × 100 m, which was dominantly planted with maize. Steven Hydro probes were used to collect soil moisture and other measurements with an interval of 5 m. For each sampling point, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within this sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, LAI, vegetation water content, canopy height, row distance and leaf chlorophyll content. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by vehicle borne microwave radiometer from November 17 to 18, 2013 in Wuxing village farmland, Ganzhou District, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 17-18, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 3.6m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer on November 15-16, 2013 in the farmland of jiushe, Kangning, Zhangye City, Gansu Province. The surface temperature includes the soil temperature data observed by the temperature sensor at the soil depth of 0 cm, 1 cm, 3 cm, 5 cm and 10 cm. The time frequency of conventional observation of soil temperature is 5 minutes. Data details: 1. Time: November 15-16, 2013 2. data: Bright temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz v-polarization and H-polarization data (10.65ghz band instrument damaged) Soil temperature: use the sensor installed on dt85 to measure the soil temperature of 0cm, 1cm, 3cm, 5cm and 10cm Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 4.8m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
On July 26, 2012, the airborne ground synchronous observation was carried out in the plmr quadrat in the dense observation area of Daman. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the matrix of the dense observation area of Daman, and the detailed plan with an area of 3.0KM × 2.4km is selected to carry out synchronous observation on the underlying surface of oasis. The selection of the sample is mainly based on the representativeness of the surface coverage, accessibility and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 5 splines (east-west distribution) were collected at an interval of 450 m in the east-west direction. Each line has 31 points (north-south direction) at an interval of 100 m, and 5 hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 150 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
On July 10, 2012, the airborne flight and ground observation was synchronously carried out in the PLMR quadrat of Yingke Oasis and the Huazhaizi Desert. PLMR (Polarimetric L-band Multibeam Radiometer) is a dual-polarized (H/V) L-band microwave radiometer with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, and a resolution of 1 km (relative flight height of 3 km).The radiometer has 6 beams to observe synchronously, and the incident angles are ±7º，±21.5º，±38.5º, and the sensitivity is less than 1K. The flight observation mainly covers the artificial oasis eco-hydrological test area in the middle reaches. This ground-synchronized data set provides a basic ground dataset for developing and validating passive microwave remote sensing inversion soil moisture algorithms. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern margin of Zhangye Oasis and Anyang beach desert, the west side of Zhang (Zhangye)-Da (Daman) highway. It is divided into two parts by the main canal of the Dragon Canal from North to South. The Southwest area is a desert quadrat with the size of 1 km×1 km. The desert is relatively homogeneous, so soil moisture of 5 points in the 1 km quadrat are collected (1 point of each corner and the center point, in the actual measurement process, several extra points can be measured along the road). The four corner points are 600 meters away from each other,except the diagonal direction. And the southwest corner point is Huazhaizi Desert Station, for the convenience of comparison with weather station data. On the northeast side, a large size quadrat of 6 km×1.6 km is selected for simultaneous observation of the oasis underlying surface.In order to obtain the brightness temperature comparison with the PLMR observation, the quadrat was chose based on the following factors :surface coverage representative, avoiding the residential and greenhouses, crossing the oasis farmland and part of the Southern desert, accessibility, and observation time(road consumption). Taking the resolution of PLMR observations into consideration, in the synchronous observation, 11 sampling lines (East-West distribution) were collected with an interval of 160 meters from the East to the West. Each line from the North to the South was separated by 21 points with an interval of 80 meters. And 4 Hydraprobe Data Acquisition System (HDAS, Reference 2) were used to measure simultaneously. Measurement contents: About 230 points of the quadrat were collected, 2 observations were performed on each point, that is, 2 observations were performed on each sampling point of the film mulched corn field, 1 inside the film (marked as a in the data record), 1 outside the film (marked as b in the data record). Since the HDAS system useed the POGO portable soil sensor, the soil temperature, soil moisture (volumetric water content), loss tangent, soil electrical conductivity, soil complex dielectric real part and imaginary part were obtained by observation. No special simultaneous sampling of vegetation was carried out on the same day. Data: The data set includes two parts: soil moisture observation and vegetation observation. The former saves the data as a vector file, the spatial position is the position of each sampling point (WGS84+UTM 47N), and the measurement information of soil moisture is recorded in the attribute file.
WANG Shuguo, LI Xin
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;
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 organic carbon content in different layers are produced by using the digital soil mapping method. 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_soc_layer1.tif: 0-5cm soil organic carbon content; hh_soc_layer2.tif: 5-15cm soil organic carbon content; hh_soc_layer3.tif: 15-30cm soil organic carbon content; hh_soc_layer4.tif: 30-60cm soil organic carbon content; hh_soc_layer5.tif: 60-100cm soil organic carbon content;
ZHANG Ganlin
According to the principle of soil landscape model, the key hydrological parameters spatial distribution map data products are made by digital soil mapping method. 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 / Albers · conic · equal · area; Spatial resolution: 90m; Data format: TIFF; Data content: spatial distribution of saturated water content, field water capacity, wilting water content and saturated conductivity Prediction method: enhanced regression tree Environmental variables: main soil forming factors Dataset content: Pr_0kpsm.tif: saturated water content (unit:%) Pr_33kp SM. TIF: field capacity (unit:%) X1500kp sm.tif: wilting water content (unit:%) SHC sm.tif: saturated hydraulic conductivity (unit: KS / (mm · min-1))
ZHANG Ganlin,
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). The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Data content: spatial distribution of soil clay, silt and sand content Prediction method: enhanced regression tree Environmental variables: main soil forming factors
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 organic carbon content in different layers are produced by using the digital soil mapping method. The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. 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, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers; Spatial resolution: 100M; Data format: TIFF; Data content: spatial distribution of soil organic carbon content Prediction method: enhanced regression tree Environmental variables: main soil forming factors
ZHANG Ganlin,
The 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). The prediction method is mainly based on the soil landscape model. The basic theory of the model is the classic soil genesis theory. The model regards the soil as the product of the comprehensive effects of climate, topography, parent material, biology and time. Scope: Heihe River Basin; Projection: Albers ﹣ conic ﹣ equal ﹣ area; Spatial resolution: 90m; Data format: ArcGIS grid; Data content: spatial distribution of soil thickness Prediction method: enhanced regression tree Environmental variables: main soil forming factors
ZHANG Ganlin,
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 includes the county-level data of characteristic agriculture distribution in the Qinghai Tibet Plateau, which lays the foundation for the spatial distribution and development of characteristic agriculture in the Qinghai Tibet Plateau.
MA Rui , HU Yalu
Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).
ZHANG Ganlin,
Using digital soil mapping method to produce soil surface pH spatial distribution data products. 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).
ZHANG Ganlin,
Select the soil mechanical composition data with a depth of 0-20cm on the surface of the soil, select the optimal spatial prediction mapping method for soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The classification standard of soil particle size is American classification. The source data of this data set are from the data center of cold and drought regions, soil physical properties-soil bulk density and mechanical composition data set soil sampling profile data of Tianlaochi watershed in Qilian mountain.
YUE Tianxiang, ZHAO Na
This data set includes the observation data of 25 water net sensor network nodes in Babao River Basin in the upper reaches of Heihe River from January 2015 to December 2015. 4cm and 20cm soil moisture / temperature is the basic observation of each node; some nodes also include 10cm soil moisture / temperature, surface infrared radiation temperature, snow depth and precipitation observation. The observation frequency is 5 minutes. The data set can be used for hydrological simulation, data assimilation and remote sensing verification. For details, please refer to "2015 data document 20160501. Docx of water net of Babao River in the upper reaches of Heihe River"
KANG Jian, LI Xin, MA Mingguo
Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).
YUE Tianxiang, ZHAO Na
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 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
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
This dataset includes the observational data that were collected by two sets of Cosmic-ray Soil Moisture Observation System (COSMOS), named crs_a and crs_b, which were installed near the Daman Superstation in the flux observation matrix from 1 June through 20 September 2012. The land cover in the footprint was maize crop, and the site was located with the cropland of the Daman Irrigation District, Zhangye, Gansu Province. Crs_a was located at 100.36975° E, 38.85385° N and 1557.16 m above sea level; Crs_b was located at 100.37225° E, 38.85557° N and 1557.16 m above sea level. The bottom of the probe was 0.5 m above the ground; 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), sample time of fast neutrons (N1ET, s), and 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 removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) then 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 contained in the equipment manual. The procedure was previously described by Jiao et al. (2013) and Zreda et al. (2012). 3) Calibration After the quality control and corrections were applied, 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 must be calibrated using the in situ observed soil moisture within the footprint. This procedure was previously described by Jiao et al. (2013) and Zreda et al. (2012) 4) Computing the soil moisture Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. This procedure was previously described by Jiao et al, (2013) and Zreda et al. (2012) For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Zhu et al. (2015) (for data processing) in the Citation section.
LIU Shaomin, ZHU Zhongli, XU Ziwei, LI Xin
This data set includes the 2015 observation data of 9 water net nodes in the 5.5km × 5.5km observation matrix (red box in the thumbnail) of Yingke / Daman irrigation area in the middle reaches of Heihe River. The nine nodes contain 4cm and 10cm two-layer hydro probe II probes to observe the main variables such as soil moisture, soil temperature, conductivity and complex permittivity; the si-111 infrared temperature probe is set up at 4m height to observe the surface radiation infrared temperature of the underlying surface. The observation time frequency is 5 minutes. This data set can provide spatiotemporal continuous observation data set for remote sensing estimation of key water and heat variables of heterogeneous surface, remote sensing authenticity test, ecological hydrology research, irrigation optimization management and other research.
KANG Jian, LI Xin, MA Mingguo
1. The data set is the soil water content data set of the upper reaches of Heihe River Basin, and the data is the measured data of location points from 2013 to 2014. 2. The infiltration data is measured with ech2o. Including 5 layers of soil moisture content and soil temperature 3. Some instruments lack of data due to insufficient battery life, broken roads, stolen instruments and other reasons
HE Chansheng
The dataset is the field soil measurement and analysis data of the upstream of Heihe River Basin from 2013 to 2014, including soil particle analysis, water characteristic curve, saturated water conductivity, soil porosity, infiltration analysis, and soil bulk density I. Soil particle analysis 1. The soil particle size data were measured in the particle size laboratory of the Key Laboratory of the Ministry of Education, West Ministry of Lanzhou University.The measuring instrument is Marvin laser particle size meter MS2000. 2. Particle size data were measured by laser particle size analyzer.As a result, sample points with large particles cannot be measured, such as D23 and D25 cannot be measured without data.Plus partial sample missing. Ii. Soil moisture characteristic curve 1. Centrifuge method: The unaltered soil of the ring-cutter collected in the field was put into the centrifuge, and the rotor weight of each time was measured with the rotation speed of 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200 and 11600 respectively. 2. The ring cutter is numbered from 1 to the back according to the number. Since three groups are sampled at different places at the same time, in order to avoid repeated numbering, the first group is numbered from 1, the second group is numbered from 500, and the third group is numbered from 1000.It's consistent with the number of the sampling point.You can find the corresponding number in the two Excel. 3. The soil bulk density data in 2013 is supplementary to the sampling in 2012, so the data are not available at every point.At the same time, the soil layer of some sample points is not up to 70 cm thick, so the data of 5 layers cannot be taken. At the same time, a large part of data is missing due to transportation and recording problems.At the same time, only one layer of data is selected by random points. 4. Weight after drying: The drying weight of some samples was not measured due to problems with the oven during the experiment. 3. Saturated water conductivity of soil 1. Description of measurement method: The measurement method is based on the self-made instrument of Yiyanli (2009) for fixing water hair.The mariot bottle was used to keep the constant water head during the experiment.At the same time, the measured Ks was finally converted to the Ks value at 10℃ for analysis and calculation.Detailed measurement record table refer to saturation conductivity measurement description.K10℃ is the data of saturated water conductivity after conversion to 10℃.Unit: cm/min. 2. Data loss explanation: The data of saturated water conductivity is partly due to the lack of soil samples and the insufficient depth of the soil layer to obtain the data of the 4th or 5th layer 3. Sampling time: July 2014 4. Soil porosity 1. Use bulk density method to deduce: according to the relationship between soil bulk density and soil porosity. 2. The data in 2014 is supplementary to the sampling in 2012, so the data are not available at every point.At the same time, the soil layer of some sample points is not up to 70 cm thick, so the data of 5 layers cannot be taken. At the same time, a large part of data is missing due to transportation and recording problems.At the same time, only one layer of data is selected by random points. 5. Soil infiltration analysis 1. The infiltration data were measured by the "MINI DISK PORTABLE specific vector INFILTROMETER".The approximate saturation water conductivity under a certain negative pressure is obtained.The instrument is detailed in website: http://www.decagon.com/products/hydrology/hydraulic-conductivity/mini-disk-portable-tension-infiltrometer/ 2.D7 infiltration tests were not measured at that time because of rain. Vi. Soil bulk density 1. The bulk density of soil in 2014 refers to the undisturbed soil taken by ring cutter based on the basis of 2012. 2. The soil bulk density is dry soil bulk density, which is measured by drying method.The undisturbed ring-knife soil samples collected in the field were kept in an oven at 105℃ for 24 hours, and the dry weight of the soil was divided by the soil volume (100 cubic centimeters). 3. Unit: G /cm3
HE Chansheng
This data is the longitude and latitude information of soil water sampling points in the "observation experiment of Soil Hydrological heterogeneity in the upper reaches of Heihe River and its impact on the hydrological process in mountainous areas" (91125010) of Heihe project, which is mainly used to express the spatial distribution of soil water sampling points in this project.
HE Chansheng
The dataset includes the saturated hydraulic conductivity data of typical soil samples in Heihe River Basin from July 2012 to August 2013. The collection method of typical soil sample points in Heihe River Basin is representative sampling, which means that the typical soil types in the landscape area can be collected, and the sample points with higher representativeness can be collected as much as possible, and the saturated hydraulic conductivity of each type of soil can be measured three times for the average value.
ZHANG Ganlin,
This dataset includes soil moisture and soil temperature observations of 75 BNUNET nodes during the period from May to September 2012 (UTC+8), which is one type of WSN nodes in the Heihe eco-hydrological wireless sensor network (WSN). The BNUNET located in the observation matrix of the HiWATER artificial oasis eco-hydrology experimental area. Each BNUNET node observes the soil temperature at 4 cm, 10 cm and 20 cm depth, and soil moisture at 4 cm depth with 10 minutes interval. This dataset can be used in the estimation of surface hydrothermal variables and their validation, eco-hydrological research, irrigation management and so on. The detail description please refers to "Data introduction.docx".
Liu Jun, KOU Xiaokang, MA Mingguo
This data set includes 26 bnunet nodes in the 0.5 °× 0.5 ° observation matrix around Zhangye City in the middle reaches of Heihe River from September 2013 to March 2014. The configuration of 26 nodes is the same, including 3 layers of soil temperature probe with depth of 1cm, 5cm and 10cm and 1 layer of soil moisture probe with depth of 5cm. The observation frequency is 2 hours. This data set can provide spatiotemporal continuous observation data set for remote sensing authenticity test of surface heterogeneity and ecological hydrology research. The time is UTC + 8. Please refer to "bnunet data document. Docx" for details
ZHAO Shaojie, WANG Qi, LU Zheng, MA Mingguo, CHAI Linna
The data set contains soil observation data of typical sample points in Heihe River Basin: pH value and soil texture 1. Soil pH value: longitude, latitude and pH value of typical soil sample points. 2. Soil texture: including soil texture data of typical soil samples in Heihe River Basin from July 2012 to August 2013. The typical soil sampling method in Heihe River Basin is representative sampling, which means that the typical soil types in the landscape area can be collected, and the representative sample points should be collected as far as possible. According to the Chinese soil taxonomy, soil samples from each profile were taken based on the diagnostic layers and diagnostic characteristics.
ZHANG Ganlin,
The data set contains the location information and soil systematic type data of typical soil samples from the Heihe River Basin from July 2012 to August 2014. The typical soil sample collection method in the Heihe River Basin is representative sampling, which refers to the typical soil types that can be collected in the landscape area, and collects highly representative samples as much as possible. According to the Chinese soil systematic classification, the soil type of each section is divided based on the diagnostic layer and diagnostic characteristics. The sample points are divided into 8 soil orders: organic soil, anthropogenic soil, Aridisol, halomorphic soil, Gleysol, isohumicsoill , Cambisol, Entisol, and 39 sub-categories.
ZHANG Ganlin,
The output data of the distributed eco-hydrological model (GBEHM) of the upper reaches of the black river include the spatial distribution data series of 1-km grid. Region: upper reaches of heihe river (yingxiaoxia), time resolution: month scale, spatial resolution: 1km, time period: 2000-2012. The data include evapotranspiration, runoff depth and soil volumetric water content (0-100cm). All data is in ASCII format. See basan.asc file in the reference directory for the basin space range. The projection parameter of the model result is Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area.
YANG Dawen
The output data of the distributed eco-hydrological model (GBEHM) of the upper reaches of the black river include the spatial distribution data series of 1-km grid. Region: upper reaches of heihe river (yingxiaoxia), time resolution: month scale, spatial resolution: 1km, time period: 1980-2010. The data included precipitation, evapotranspiration, runoff depth, and soil volumetric water content (0-100cm). All data is in ASCII format. See basan.asc file in the reference directory for the basin space range. The projection parameter of the model result is Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area.
YANG Dawen
The evapotranspiration and soil evapotranspiration of lycium rubra and red sand of small shrubs in typical desert weather were observed by using infrared gas analyzer to measure water vapor flux. The measurement system consists of li-8100 closed-circuit automatic measurement of soil carbon flux (li-cor, USA) and an assimilation box designed and manufactured by Beijing ligotai technology co., LTD. Li-8100 is an instrument produced by li-cor for soil carbon flux measurement. It USES an infrared gas analyzer to measure the concentration of CO2 and H2O.The length, width and height of the assimilation box are all 50cm.The assimilation box is controlled by li-8100. After setting up the measurement parameters, the instrument can run automatically.
SU Peixi
The experimental data of Yingke Daman in Heihe River Basin is supported by the key fund project of Heihe River plan, "eco hydrological effect of agricultural water saving in Heihe River Basin and multi-scale water use efficiency evaluation". Including: soil bulk density, soil water content, soil texture, corn sample biomass, cross-section flow, etc Data Description: 1. Sampling location of Lai and aboveground biomass: Yingke irrigation district; sampling time: May 2012 to September 2012; Lai and aboveground biomass of maize were measured by canopy analyzer (lp-80), and aboveground biomass was measured by sampling drying method; sample number: 16. 2. Soil texture: Sampling location: Yingke irrigation district and Shiqiao Wudou Er Nongqu farmland in Yingke irrigation district; soil sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm; sampling time: 2012; measurement method: laboratory laser particle size analyzer; sample number: 38. 3. Soil bulk density: Sampling location: Yingke irrigation district and Daman irrigation district; sampling depth of soil bulk density is 100 cm, sampling levels are 0-50 cm and 50-100 cm respectively; sampling time: 2012; measurement method: ring knife method; number of sample points: 34. 4. Soil moisture content: this data is part of the monitoring content of hydrological elements in Yingke irrigation district. The specific sampling location is: Shiqiao Wudou Er Nongqu farmland in Yingke Irrigation District, planting corn for seed production; soil moisture sampling depth is 140 cm, sampling levels are 0-20 cm every 10 cm, 20-80 cm every 20 cm, 80-140 cm every 30 cm Methods: soil drying method and TDR measurement; sample number: 17. 5. Cross section flow: Sampling location: the farmland of Wudou Er Nong canal in Shiqiao, Yingke irrigation district; measure the flow velocity, water level and water temperature of different canal system sections during each irrigation, record the time and calculated flow, monitor once every 3 hours until the end of irrigation; sampling time: 2012.5-2012.9; measurement method: Doppler ultrasonic flow velocity meter (hoh-l-01, Measurement times: Yingke irrigation data of four times.
HUANG Guanhua, JIANG Yao
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
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