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
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 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 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
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
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
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
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
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
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
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 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
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
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
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
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
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,
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
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
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