The built-up area can reflect the scale, form and actual use of urban construction land in a certain period of time, which provides a basis for analyzing and studying the current situation of land use, rationally utilizing the land in the built-up area and planning the land for urban construction and development. Based on the satellite images covering 34 key nodes from 1999 to 2003 and 2013 to 2014, the supervised and unsupervised data classification process is adopted, and the data driven and knowledge driven are reasonably combined to produce the regional built-up area distribution data of key nodes in 2000 and 2014. The preliminary test shows that the information quality of the built-up area is better than other global information data extracted by automatic processing of earth observation data. In addition, the Balanced Accuracy is 0.83 and the Omission Error is 0.22. The data is raster data in TIFF format, including five unique values of 0, 1, 2, 3 and 4, in which 0 is NoData, 1 is water surface, 2 is land without built-up area, 3 is built-up area in 2014, and 4 is built-up area in 2000.
ZHOU Pu LING Feng
Soil mapping and attribution dataset of all nodes area in pan-third pole is an important information to provide extremely valuable reference for the weather process, drought and hydrological monitoring, which reflects the soil resources, soil fertility, soil environment, soil biology and other soil databases. The base of this data is from the world soil database (HWSD), which belongs to the FAO soil portal V1.2) authoritative public data are the main data, and some supplementary data such as ISRIC soil data published by ISRIC and other project data collected by the world soil data center (ISRIC). Finally the data of soil information in the expected 31 pan third pole key node regionsand 3 projects is obtained. As the research basis of soil, this data set provides the basic information of soil regionalization for the project. This data has the characteristics of grid visualization and attribute table visualization, which can be viewed through many ways. The grid visualization data provides general data on soil types and distribution. And the attribute information is included in the excel, including detailed soil parameters (including organic carbon, pH, water storage, soil depth, cation exchange capacity and clay content of soil, total exchangeable nutrients, lime and gypsum content, sodium exchange rate, salinity, structural grade and particle size, etc.).
SHANG Cheng LING Feng
The urbanization rate data of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the urbanization rate statistical data at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between urbanization rate and covariables (e.g.,night lighting NPP-VIIRS, road network density). The spatial regression analysis method is used to model relationship between the urbanization rate data and covariables, and then the county-level urbanization rate data were downscaled and predicted. Based on statistical data and spatial analysis, it is finally integrated into urbanization rate data. The data can provide important basic data for the development of social and economic research on key area and regions along the Belt and Road.
GE Yong LING Feng
In order to study the relationship between the spread of vines and human activities, we re sequenced the varieties from Qinghai Tibet Plateau and its surrounding areas, as well as Pakistan, India, Nepal, Germany, Japan and other places. At the same time, the gene families were clustered, and the unique and common genes and gene families were counted. In addition, the expansion and contraction analysis of gene families and the phylogeny were also carried out Tree construction, genome-wide replication event analysis. The purpose of this study was to analyze the molecular basis of the adaptation of traditional Cranberry varieties to the plateau under the dual pressure of human activities and regional climate environment. Therefore, this study is helpful to reveal the adaptive mechanism of C. racemosa adapting to the plateau ecological environment and the influence of artificial domestication and human selection on its genetic differentiation in the process of evolution.
DUAN Yuanwen
This data set contains the statistical information of natural disasters in Qinghai Tibet Plateau in the past 50 years (1950-2002), including drought, snow disaster, frost disaster, hail, flood, wind disaster, lightning disaster, cold wave and strong cooling, low temperature and freezing damage, gale sandstorm, insect disaster, rodent damage and other meteorological disasters. Qinghai and Tibet are the main parts of the Qinghai Tibet Plateau. The Qinghai Tibet Plateau is one of the Centers for the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the international scientific and technological circles to study climate and ecological environment changes. Its complex terrain conditions, high altitude and severe climate conditions determine that the ecological environment is very fragile, It has become the most frequent area of natural disasters in China. The data were extracted from "China Meteorological Disaster Canon · Qinghai volume" and "China Meteorological Disaster Canon · Tibet Volume", which were manually input, summarized and proofread.
Statistical Bureau Statistical Bureau
1) Data content (including elements and significance): the data includes daily values of temperature (℃), precipitation (mm), relative humidity (%) and wind speed (M / s) 2) Data source and processing method; air temperature, relative humidity and wind speed are daily mean values, precipitation is daily cumulative value; data collection location is 29 ° 39 ′ 25.2 ″ n; 94 ° 42 ′ 25.62 ″ E; 4390m; underlying surface is natural grassland; collector model Campbell Co CR1000, collection time: 10 minutes. Digital automatic data acquisition. The temperature and relative humidity instrument probe is hmp155a; the wind speed sensor is 05103; the precipitation is te525mm; 3) Data quality description; the original data of temperature, relative humidity and wind speed are the average value of 10 minutes, and the precipitation is the cumulative value of 10 minutes; the daily average temperature, relative humidity, precipitation and wind speed are obtained by arithmetic average or summation. Due to the limitation of sensors, there may be some errors in winter precipitation. 4) In addition, it is convenient for scientists to update the atmospheric data in the future. This data is updated from time to time every year.
LUO Lun
Effective evaluation of future climate change, especially prediction of future precipitation, is an important basis for formulating adaptation strategies. This data is based on the RegCM4.6 model, which is compatible with multi-model and different carbon emission scenarios: CanEMS2 (RCP 45 and RCP85), GFDL-ESM3M (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), HadGEM2-ES (RCP2.6, RCP4.5 And RCP8.5), IPSL-CM5A-LR (RCP2.6, RCP4.5, RCP6.0 and RCP8.5), MIROC5 (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). The future climate data (2007-2099) has 21 sets, with a spatial resolution at 0.25 degrees and the temporal resolution at 3 hours, daily and yearly scales.
PAN Xiaoduo ZHANG Lei
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Tibetan plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
ZHANG Yao
The data set includes the start time (year, month), location (longitude and latitude), duration (month), drought intensity and vulnerability data of vegetation response to drought in Central Asia from 1982 to 2015, with a spatial resolution of 1 / 12 °. The drought events were identified by the standardized precipitation evapotranspiration index at the time scale of 12 months (spei12) < - 1.0. The specific algorithm of drought characteristics and vegetation vulnerability is detailed in the citation. The dataset has been applied in the study of vegetation vulnerability to drought in Central Asia, and has application prospects in the research fields of spatial-temporal characteristics of drought events, drought-vegetation interaction mechanism, drought risk assessment and so on.
DENG Haoyu
The accuracy of tropical cyclone (tropical storm) track forecasting improved by nearly 50% for lead times of 24–72 h since 1990s. Over the same period forecasting of tropical cyclone intensity showed only limited improvement. Given the limited prediction skill of models of tropical cyclone intensity based on environmental properties, there have been a wealth of studies of the role of internal dynamical processes of tropical cyclones, which are largely linked to precipitation properties and convective processes. The release of latent heat by convection in the inner core of a tropical cyclone is considered crucial to tropical cyclone intensification. 16-year satellite-based precipitation, and clouds top infrared brightness temperature were used to explore the relationship between precipitation, convective cloud, and tropical cyclone intensity change. The 6-hourly TC centers were linearly interpolated to give the hourly and half hourly tropical cyclone center positions, to match the temporal resolution of the precipitation and clouds top infrared brightness temperature. More precipitation is found as storms intensify, while tropical cyclone 24 h future intensity change is closely connected with very deep convective clouds with IR BT < 208 K. Intensifying tropical cyclones follow the occurrence of colder clouds with IR BT < 208 K with greater areal extents. As an indicator of very deep convective clouds, IR BT < 208 K is suggested to be a good predictor of tropical cyclone intensity change（Ruan&Wu，2018，GRL）. The properties of the satellite-based precipitation, and clouds top infrared brightness temperature are therefore suggested to be important measurements to study tropical cyclone intensity, intensity change and their underlying mechanisms. The high resolution of the satellite-based precipitation (3h), and cloud top infrared brightness temperature (half hour) datasets also makes them possible to be used to study tropical cyclone variability associated with diurnal cycle.
WU Qiaoyan
The global Cryosat-2 GDR dataset is generated by the European Space Agency (ESA); it has a temporal coverage from 2010 to 2016 and covers the globe. On April 8, 2010, the ESA launched the Cryosat-2 high-tilt polar orbit satellite. The satellite is equipped with an SAR Interferometer Radar Altimeter (SIRAL), which is mainly used to monitor polar ice thickness and sea ice thickness changes, and, furthermore, to study the effects of melting polar ice on global sea level rise and that of global climate change on Antarctic ice thickness. The altimeter operates in the Ku-band and at a frequency of 13.575 GHz, it includes three measurement modes. One is a low-resolution altimeter measurement mode (LRM) that points to the subsatellite point to obtain all surface observations for land, sea, and ice sheets; its processing is similar to ENVISAT/RA-2, with an orbital resolution of 5 to 7 km. The second is the Synthetic Aperture Radar (SAR) measurement mode, which is mainly used to improve the accuracy and resolution of sea ice observations; it can make the resolution along the orbit reach approximately 250 m. The third is the Interferometric Synthetic Aperture Radar (InSAR), which is mainly used to improve the accuracy of areas with complex terrain such as the edges of ice sheets or ice shelves. The CryoSat -2/SIRAL data products mainly include 0-level data, 1b-level data, 2-level data and high-level data. The Cryosat-2/SIRAL products consist of two files: an XML head file (.HDR) and a data product file (.DBL). The HDR file is an auxiliary ASCII file for fast identification and retrieval of the data files. 1b-level products are stored separately according to the measurement modes, and the data recording formats of different modes are also different. Each waveform in LRM mode and SAR mode has 128 sampling points, while that in SARIn mode has 512 sampling points. 2-level GDR products are available for most scientific applications, including measurement time, geographic location, altitude, and more. In addition, the altitude information in GDR products has been obtained through instrumental calibration, transmission delay corrections, geometric corrections, and geophysical corrections (such as atmospheric corrections and tidal corrections). The GDR products are single global full-track data, that is, the measurement results of the three modes. After different processing, they are combined in chronological order; thereby, the data recording formats are unified. The data in the three modes use different waveform retracking algorithms to obtain altitude values. In the latest updated Baseline C data, the LRM mode data use three algorithms: Refined CFI, UCL and Refined OCOG.
SHEN Guozhuang FU Wenxue
The RCM employed is the International Center for Theoretical Physics (ICTP) Regional Climate Model version 4 (RegCM4, Giorgi et al., 2012). The domain used is the Coordinated Regional Climate Downscaling Experiment (CORDEX) Phase II East Asia domain, covering whole of China and its surrounding East Asia areas. The model is run at 25 km gird spacing, with its standard configuration of 18 vertical sigma layers with a model top at 10 hPa. Configuration of the model follows Gao et al. (2016, 2017), with land cover data over China was updated as reported by Han et al. (2015) to better represent the realistic vegetation. The initial and lateral boundary conditions needed to drive RegCM4 are derived from the CMIP5 models of HadGEM2-ES (RCP4.5 pathways), and the data set include temperature and precipitation.
GAO Xuejie
1) Data content: planting structure refers to the problem of planting proportion of crops in a region or country. Generally, grain crops are the main crop, supplemented by other economic crops. This data describes the spatial distribution of planting structure of irrigation area with 10m resolution. 2) Data sources and processing methods: sentinel data, random forest method. 3) Data quality description: kappa coefficient 80%. 4) Results and prospects of data application: basic data of various hydrological and ecological simulation analysis, fine calculation of agricultural evapotranspiration, agricultural water demand, infiltration and irrigation demand, and agricultural structure reaching the field level. In order to promote the healthy development of agricultural planting, it is particularly important to adjust and optimize various factors, and determine the role of each factor in the agricultural planting structure. 5) The planting structure is calculated on the GEE platform by using the random forest algorithm and the collected sample point data. In order to distinguish conveniently, in the calculation process, we use an Arabic number to represent each similar crop type. The calculated. TIF results are linked to the extracted cultivated land by the way of partition statistics. In this process, we use the words to represent the crop type The segment remains, i.e. the max field, and the crop category corresponding to each Arabic numeral is shown in the instruction document.
LIU Tie
1) Data content: planting structure refers to the problem of planting proportion of crops in a region or country. Generally, grain crops are the main crop, supplemented by other economic crops. This data describes the spatial distribution of planting structure of irrigation area with 10m resolution. 2) Data sources and processing methods: sentinel data, random forest method. 3) Data quality description: kappa coefficient 80%. 4) Results and prospects of data application: basic data of various hydrological and ecological simulation analysis, fine calculation of agricultural evapotranspiration, agricultural water demand, infiltration and irrigation demand, and agricultural structure reaching the field level. In order to promote the healthy development of agricultural planting, it is particularly important to adjust and optimize various factors, and determine the role of each factor in the agricultural planting structure. 5) The planting structure is calculated on the GEE platform by using the random forest algorithm and the collected sample point data. In order to distinguish conveniently, in the calculation process, we use an Arabic number to represent each similar crop type. The calculated. TIF results are linked to the extracted cultivated land by the way of partition statistics. In this process, we use the words to represent the crop type The segment remains, i.e. the max field, and the crop category corresponding to each Arabic numeral is shown in the instruction document.
LIU Tie
Daily and Monthly evapotranspiration (5km x 5km spatial resolution) for global land area was derived from satellite data and a surface energy balance method (EB). The global 5 km daily and monthly ET dataset is produced with the revised SEBS algorithm in Chen et al. 2019 JGR and Chen et al. 2013 (JAMC). MODIS LST, NDVI, Global forest height, GlobAlbedo, GLASS LAI have been used in this ET calculation. The ET dataset will be updated to near-present with the availability of input dataset. The global 5 km sensible heat flux, net radiation, latent heat flux will be open with the email contact with Dr. Xuelong Chen. Daily ET File name: 20001201-ET-V1.mat, 2000-year, 12-month,01-day, ET-Evapotranspiration, V1-version 1;unit: mm/day (unit8 need transfer to single or double and should be divided by 10);data type: unit8 was used to save the disk space, 255 is used for ocean and water body pixels. Monthly ET File name: ETm200012-ET-V1.mat, 2000-year, 12-month, ET-Evapotranspiration, V1-version 1;unit: mm/month (int16 need transfer to single or double and should be divided by 10);data type: int16 was used to save the disk space, 0 is used for ocean and water body pixels. The daily ET dataset is produced with a similar method and satellite data as in Chen, X., et al., 2014: Development of a 10 year (2001–2010) 0.1° dataset of land-surface energy balance for mainland China, Atmos. Chem. Phys., 14, 13097–13117, doi:10.5194/acp-14-13097-2014. The calculation of roughness length and kB_1 for global land were updated by the method in Chen, X., et al, 2019, A Column Canopy‐Air Turbulent Diffusion Method for Different Canopy Structures, Journal of Geophysical Research: Atmospheres, 2019.01.15, 124. Most of the satellite input data were from MODIS. Meteorological data was from ERA-Interim. Global canopy height information was derived from GLAS and MODIS NDVI. The daily ET has a mean bias (MB) of 0.04 mm/day, RMSE is 1.56 (±0.25) mm/day.
CHEN Xuelong
The dataset of urban land and urbanization index on the Tibetan Plateau mainly includes the spatial distribution data of all urban land on the Tibetan Plateau (2019) and urbanization index of different scales (2018). The dataset of urban land was obtained by the visual interpretation of Google Earth images (2019), and the residential place and residential area data of "1:250000 national basic geographic database - 2015 edition". The dataset of urbanization index was based on the composite night light index (CNLI) at the regional, provincial, watershed, prefecture, and county scales calculated from the night light data of Luojia-1. Our dataset will support the study of optimizing the ecological security barrier system in the key urbanization areas of the Tibetan Plateau
He Chunyang Liu Zhifeng Wang Yihang
1. Data source: MODIS/Terra Vegetation Indices 16-day L3 Global 250m SIN Grid V006 products (2000-2017) Download address https://search.earthdata.nasa.gov/ 2. Data name: (1) resize is automatically generated in the batch cropping process, which means that it has been extracted by mask and the data range after processing is xinjiang provice; (2) seven digits represent the time of data acquisition, the first four digits are years, and the last three digits are days of the year.For example, "2000049" means that the year of data acquisition is 2000 and the specific time is the 49th day of that year. (3) 250m represents the ground resolution, i.e. 250 meters; (4) 16_days represents the time resolution, that is, 16 days; (5) NDVI represents data type, namely normalized vegetation index; 3. Data time range: 2000049-2017353, data interval of 16 days; 4..Tif file and.hdr file . Tif file is the original NDVI data with the same name. HDR file is the mask data that supports normal use of. 5. To analyze the ecological effects of cryosphere
WU Xuejiao
This dataset is derived from the global atmospheric reanalysis dataset, ERA-Interim, based on the 4-dimensional variational analysis (4D-Var) released by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA-Interim represents a major undertaking by ECMWF (European Centre for Medium-Range Weather Forecasts) to produce a reanalysis with an improved atmospheric model and assimilation system which replaces those used in ERA-40, particularly for the data-rich 1990s and 2000s, and to be continued as an ECMWF Climate Data Assimilation System (ECDAS) until superseded by a new reanalysis. Through systematic increases in computing power, 4-dimensional variational assimilation (4D-Var) became feasible and part of ECMWF operations since 1997. Enhanced computing power also allowed horizontal resolution to be increased from T159 to T255, and the latest Integrated Forecasting System(IFS CY31r1 and CY31r2) to be used, taking advantage of improved model physics. ERA-interim retains the same 60 model levels used for ERA-40 with the highest level being 0.1 hPa. Besides, data assimilation of ERA-Interim also benefits from quality control that draws on experience from ERA-40 and JRA-25, variational bias correction of satellite radiance data, and more extensive use of radiances with an improved fast radiative transfer model. In addition, ERA-Interim uses the new ERS (European Remote Sensing Satellite) altimeter wave heights, EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) reprocessed winds and clear-sky radiances, GOME (Global Ozone Monitoring Experiment) ozone data from the Rutherford Appleton Laboratory, and CHAMP (CHAllenging Minisatellite Payload), GRACE (Gravity Recovery and Climate Experiment), and COSMIC (Constellation Observing System for Meteorology, Ionosphere and Climate) GPS radio occultation measurements processed and archived by UCAR (University Corporation for Atmospheric Research).
DENG Chuangwu
Precipitation and temperature are essential input variables for hydrological models. There are few meteorological stations in the big Naryn Basin of the Syr Darya, which cannot meet the needs of hydrological simulation. Precipitation data in the Syr Darya were collected through online resources and field research. The precipitation gradient in the study area is obtained. Based on the precipitation gradient, the precipitation and temperature grid products (PGMFD) (http://hydrology.princeton.edu/data.pgf.php)were then corrected to get this set of data sets. The year covered by this data is 1951-2016, the spatial precision is 10km, and the time resolution is daily. The more detail information about the correction method can be found in (Generation of High Mountain Precipitation and Temperature Data for a Quantitative Assessment of Flow Regime in the Upper Yarkant Basin in the Karakoram, Kan et al., 2018)
SU Fengge
HOBO water temperature loggers (U22-001, Onset Corp., USA) were used to monitor changes in water temperature with an accuracy of ±0.2 oC. Two water temperature profiles were installed in Paiku Co’s southern (0-42 m in depth) and northern (0-72 m in depth) basins (Fig. 1). In the southern basin, water temperature was monitored at the depths of 0.4 m, 5m, 10 m, 15 m, 20 m, 30 m and 40 m. In the northern basin, water temperature was monitored at the depths of 0.4 m, 10 m, 20 m, 40 m, 50 m, 60 m and 70 m. To investigate local hydro-meteorology at Paiku Co, air temperature and specific humidity over the lake were monitored since June 2015 by using HOBO air temperature and humidity loggers (U12-012, Onset Corp., USA). The logger was installed in an outcrop ~2 m above the lake surface at the north part of the lake (Fig. 2). Lake evaporation was calculated using the energy budget (Bowen-ratio) method。
LEI Yanbin
