This dataset contains 20 years (2002-2021) global spatio-temporal consistent surface soil moisture . The resolution is 36 km at daily scale, the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content. (evaluation accuracy of 14 dense ground network globally.)
YAO Panpan, LU Hui
This data-set contains the field measurements of meteorological parameters，trace gases, PM2. 5/PM10, particle number size distribution (12-530 nm), aerosol chemical composition (sulfate and nitrate in PM2.5) at Lulang and Xihai (29.8oN, 94.7oE, 3300 m a.s.l. and 36.9oN, 100.9oE, 3080 m a.s.l., respectively) in southeastern and northeastern part of Tibetan Plateau. The time period of this data-set is from April to May of 2021 and June of 2021. The data-set comes from two measurement campaigns in 2021. The mobile observation platform of Nanjing University, including various online measurement instruments, was used to conduct the field measurements. The data in this data-set is finalized data with the data correction according to the instruments calibration and data quality control based on the data closure research results between multiple instruments. The atmospheric components data, such as trace gases, PM2.5/PM10, particle number size distribution, aerosol chemical composition, are the observation data under actual atmospheric pressure conditions without pressure corrections. The data-set can be directly used to analyze the atmospheric physics and chemistry related scientific issues in the southeastern and northeastern part of the Tibetan Plateau. This data-set supplements the lack of field observation data related to the atmospheric environment in the northeastern part of the Tibetan Plateau.
NIE Wei, CHI Xuguang
The data were collected from the sample plot of Haibei Alpine Meadow Ecosystem Research Station (101°19′E，37°36′N，3250m above sea level), which is located in the east section of Lenglongling, the North Branch of Qilian Mountain in the northeast corner of Qinghai Tibet Plateau. Alpine meadow is the main vegetation type in this area. The data recorded the light, air temperature and humidity, wind temperature and wind speed above the alpine plant canopy. The radiation intensity above the alpine plant canopy was recorded by LI-190R photosynthetic effective radiation sensor (LI-COR, Lincoln NE, USA) and LR8515 data collector (Hioki E. E. Co., Nagano, Japan), and the recording interval was once per second. S580-EX temperature and humidity recorder (Shenzhen Huatu) and universal anemometer are used (Beijing Tianjianhuayi) record the daily dynamics of air temperature and humidity, wind temperature and wind speed every three seconds. The recording time is from 10:00 on July 13 to 21:00 on August 17, Beijing time. Due to the need to use USB storage time and replace the battery every day, 3-5min of data is missing every day, and the missing time period is not fixed. At present, the data has not been published. Through research on the data The data can further explore the microenvironment of alpine plant leaves and its possible impact on leaf physiological response.
TANG Yanhong, ZHENG Tianyu
Data content: surface temperature data of Aral Sea Basin in 2019. Data source and processing method: from NASA medium resolution imaging spectrometer, the first band of mod11a2 product is extracted as the surface temperature data, multiplied by the scale factor of 0.02. Data quality: the spatial resolution is 1000m × 1000m, the time resolution is 8 days, and the value of each pixel is the average value of surface temperature in 8 days. Data application results: under the background of climate change, it can be used to analyze the correlation between meteorological elements and vegetation characteristics, or combined with other meteorological data to analyze the regional distribution of a vegetation type.
Investigate the middle and upper reaches of the Yarlung Zangbo River and the tributaries of the Nianchu River, duoxiong Zangbo and Laiwu Zangbo, involving Nanmulin, gyangzi, Yadong, Jilong, Saga, Zhongba and other counties in Xigaze City, Tibet. New human activity relic sites were found in the blank areas of human activities in the past, such as cuochulong lake, Duoqing lake, Zhongzhu Valley, luolonggou and so on. Important stone evidence of human activities was collected in more than 30 sites, including obsidian, Jasper rock, crystal and so on. In the follow-up, the process and source of these stone tools will be further analyzed through typology, which is of great significance to reveal the temporal and spatial changes of prehistoric human activity history in the middle and upper reaches of the Yarlung Zangbo River and the exchange of culture and technology with the surrounding areas.
YANG Xiaoyan , GAO Yu
This data set is based on the evaluation of existing land cover data and the evidence theory，including a 1:100,000 land use map for the year 20 2000、a 1:1,000,000 vegetation map、a 1:1,000,000 swamp-wetland map, a glacier map and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001) were merged，Finally, the decision is made based on the principle of maximum trust, and a new 1KM land cover data of China in 2000 with IGBP classification system is produced. The new land cover data not only maintain the overall accuracy of China's land use data, but also supplement the information of vegetation types and vegetation seasons in China's vegetation map, update China's wetland map, add the latest information of China's glacier map, and make the classification system more general.
RAN Youhua, LI Xin
Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia-Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016-2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy and high resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher but the product accuracy is also higher than that of those products. In particular, hourly and daily root mean square errors of hourly and daily of the new SWDR are 104.9 and 31.5 Wm-2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 Wm-2, respectively), ERA5 (at 176.6 and 39.5 Wm-2, respectively) and GLASS (daily of 36.5 Wm-2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 Wm-2, respectively, which are comparable to that of CERES and ERA5, and even better over high altitude regions.
LETU Husi, WANG Tianxing
The Central Asia Reanalysis (CAR) dataset is generated based on the Weather Research and Forecast (WRF) model version 4.1.2 and WRF Data Assimilation (WRFDA) Version 4.1.2. Variables include temperature,, pressure, wind speed, precipitation and radiation. The reanalysis is established through cyclic assimilation, which performs data assimilation every 6 hours by 3DVAR. The assimilated data include conventional atmospheric observation and satellite radiation data. The main source of conventional data is Global Teleconnection System (GTS), including surface station, automatic station, radiosonde and aircraft report, and the observation elements include temperature, air pressure, wind speed and humidity. Satellite observations include retrievals and radiation data, The retrievals are mainly atmospheric motion vectors from polar orbiting meteorological satellites (NOAA-18, NOAA-19, MetOP-A and MetOP-B) and resampled to a horizontal resolution of 54km; the radiation data includes microwave radiation from MSU, AMSU and MHS and HIRS infrared radiation data. The simulation applies nesting with a horizontal resolution of 27km and 9km respectively, a total of 38 layers in the vertical direction and a top of the model layer of 10hPa. The lateral boundary conditions of the model are provided by ERA-Interim every 6 hours. The physical schemes used in the model are Thompson microphysics scheme, CAM radiation scheme, MYJ boundary layer scheme, Grell convection scheme and Noah land surface model. The data covers five countries in Central Asia, including Kazakhstan, Tajikistan, Kyrgyzstan, Turkmenistan and Uzbekistan, as well as lakes in Central Asia, such as Caspian Sea, Aral Sea, Balkash lake and Isaac lake, which can be used for the study of climate, ecology and hydrology in the region. Compared with gauge-based precipitation in Central Asia, the simulation by CAR shows similar performance with MSWEP ( a merged product) and outperforms ERA5 and ERA-Interim.
This data set is a digital elevation model of the Tibetan Plateau and can be used to assist in analysis and research of basic geographic information for the Tibetan Plateau. The raw data were the Shuttle Radar Topography Mission (SRTM) data, which were provided by Global Land Cover Network (GLCN), and the raw data were framing data , using the WGS84 coordinate system, including latitude and longitude, with a spatial resolution of 3″. After the mosaic processing, the Nodata (null data) generated in the mosaic process were interpolated and filled. After filling, the projection conversion process was performed to generate data as Albers equal area conical projection. After the conversion projection, the spatial resolution of the data was 90 m. Finally, the boundary of the Tibetan Plateau was used for cutting to obtain DEM data. This data table has two fields. Field 1: value Data type: long integer Interpretation: altitude elevation Unit: m Field 2: count Data type: long integer Interpretation: The number of map spots corresponding to the altitude elevation Data accuracy: spatial resolution: 90 m
Global Land Cover Network
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02℃) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements. The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems.
The data is from JAXA earth observation research center（ http://www.eorc.jaxa.jp/ALOS/en/aw3d30/ ）The product is alos World 3D - 30m (aw3d30). Select and download the map by importing the SHP boundary of Sichuan Tibet traffic corridor, and merge it into one by using relevant software. The format is raster data, the spatial resolution is 30m, and the data size is 1.3GB. The DEM data can generate topographic factor data such as slope, aspect and river network by using relevant software. They are the basic data for topographic analysis of Sichuan Tibet traffic corridor, help to understand the geomorphic form of the basin, and are also the key factors for disaster zoning research and risk assessment. The acquisition of high-precision DEM is of great significance for disaster risk management and decision-making level and reducing the loss of major geological disasters.
The data comes from radar topographic mapping SRTM product, and the download address is http://gdex.cr.usgs.gov/gdex/ 。 Import the SHP boundary of Sichuan Tibet traffic corridor on the download website to select the map within the study area and merge it into one by using ArcGIS software. The accuracy of the data is 90m, and the format is grid data. The data size is 136mb. The area is Sichuan Tibet traffic corridor. This data is applicable to the extraction of 1:100000 relevant topographic data, such as slope, aspect, river network and other topographic factor data. As the basic data for the topographic analysis of Sichuan Tibet traffic corridor, it is helpful to understand the topographic and geomorphic characteristics of the whole region and is of great significance to the disaster risk management and decision-making level of a large region.
Roadside noise barriers (RNBs) are important urban infrastructures to develop a liveable city. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study proposes a geospatial artificial intelligence framework to create a vectorized RNB dataset in China using street view imagery. To begin, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the geo-reference to download 5.6 million Baidu Street View (BSV) images. Furthermore, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. Subsequently, the RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2,227 km in 215 cities. At last, the quality of the RNB dataset is evaluated from two perspectives: first, the detection accuracy; second, the completeness and positional accuracy. Based on a set of randomly selected samples containing 10,000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61%, recall of 87.14%, precision of 76.44%, and F1-score of 81.44%. Moreover, a total length of 254 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root-mean-squared error for mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08 % ± 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies.
This data set includes precipitation data from a total of nine ground-based precipitation observation stations located in the Yadong River Valley in the middle of the Himalayas. The observation data was collected by the Hobo tumbler rain gauge developed by Onset company and exported through supporting data reading software. Accumulated counts, the rain gauge tipped once, indicating that 0.2 mm of precipitation was recorded, and the default value of -999 was used when no precipitation event occurred. We screened the collected data and eliminated abnormal values to ensure its quality. This data set has made some progress in the analysis of precipitation characteristics, satellite data verification and model simulation evaluation in this area and two academic papers have been published, which provides strong support for the analysis of precipitation characteristics in the high-altitude valleys of the Himalayas lacking ground observation data.
Near surface atmospheric forcing data were produced by using Wether Research and Forecasting (WRF) model over the Heihe River Basin at hourly 0.05 * 0.05 DEG resolution, including the following variables: 2m temperature, surface pressure, water vapor mixing ratio, downward shortwave & upward longwave radiation, 10m wind field and the accumulated precipitation. The forcing data were validated by observational data collected by 15 daily Chinese Meteorological Bureau conventional automatic weather station (CMA), a few of Heihe River eco-hydrological process comprehensive remote sensing observation (WATER and HiWATER) site hourly observations were verified in different time scales, draws the following conclusion: 2m surface temperature, surface pressure and 2m relative humidity are more reliable, especially 2m surface temperature and surface pressure, the average errors are very small and the correlation coefficients are above 0.96; correlation between downward shortwave radiation and WATER site observation data is more than 0.9; The precipitation agreed well with observational data by being verified based on rain and snow precipitation two phases at yearly, monthly, daily time scales . the correlation coefficient between rainfall and the observation data at monthly and yearly time scales were up to 0.94 and 0.84; the correlation between snowfall and observation data at monthly scale reached 0.78, the spatial distribution of snowfall agreed well with the snow fractional coverage rate of MODIS remote sensing product. Verification of liquid and solid precipitation shows that WRF model can be used for downscaling analysis in complex and arid terrain of Heihe River Basin, and the simulated data can meet the requirements of watershed scale hydrological modeling and water resources balance. The data for 2000-2012 was provided in 2013. The data for 2013-2015 was updated in 2016. The data for 2016-2018 was updated in 2019. The data for 2019-2021 was updated in 2021.
The observation data set of field meteorological stations in Central Asia and Western Asia (2019-2020) includes the monthly meteorological data of 12 field meteorological stations in Kazakhstan (5 stations), Kyrgyzstan (1 station), Tajikistan (3 stations), Uzbekistan (1 station) and Iran (2 stations), involving 21 observation indicators: Monthly average temperature (TA), monthly average pressure (PA) Monthly average relative humidity (RH), monthly total rainfall (PR), monthly average wind speed (WS), monthly average wind direction (WD), 0cm monthly average soil temperature (TS1), 5cm monthly average soil temperature (TS2), 10cm monthly average soil temperature (Ts3), 15cm monthly average soil temperature (ts4), 20cm monthly average soil temperature (ts5), 40cm monthly average soil temperature (TS6) 60cm monthly average soil temperature (ts7), 100cm monthly average soil temperature (ts8), monthly total solar radiation (SR), monthly total reflected radiation (GR), monthly total ultraviolet radiation (UVR), monthly total net radiation (NR), monthly total photosynthetic effective radiation (PAR), monthly total soil heat flux (HF) and monthly total sunshine duration (SD). The 12 field stations cover farmland, forest, grassland, desert, desert, wetland, plateau, mountain and other different ecosystem types. The data length starts from October 2019 to December 2020. The original meteorological data collected by the ground meteorological observation station is obtained after format conversion after screening and review, and the data quality is good. Central Asia has diverse climate types, fragile ecological environment and frequent meteorological disasters. The establishment of this data set provides data support for long-term research in the fields of ecological environment monitoring, disaster prevention and reduction, climate change and ecological environment in Central Asia. At present, it has been applied in the research of ecological environment monitoring in Central Asia.
LI Yaoming LI Yaoming
The list and distribution database of Alpine Periglacial plants mainly includes the collection information and identification information of Alpine Periglacial plants. The collection information document includes species name, genus name, family name, habitat, altitude, longitude and latitude, collector and collection time; The identification information document includes species name, genus name, family name, appraiser and identification time. The collected information in the database comes from the field first-hand data; The identification information comes from the identification results of famous botany experts all over the world. The quality of data in the database is high. It can not only be used for the study of flora and Regionalization in the region, but also lay a solid foundation for the study of plant diversity, ecosystem and global climate change response in the region.
This data combines the direct economic loss risk assessment results of earthquake and geological disasters. According to the obtained loss assessment results, the study area is divided into nine categories according to the risk level, which are seismic geological low-risk area, geological medium seismic low-risk area, seismic medium geological low-risk area, seismic geological medium risk area, geological high epicenter risk area and seismic high quality low-risk area, Geological high seismic low risk area, seismic high quality low risk area and seismic geological high risk area. The data results of this multi disaster direct economic loss risk assessment provide a basis for the spatial distribution of direct economic losses in the Asian water tower area and the surrounding areas of the Himalayas in the future.
With the support of the first topic "sharing and integration of three pole big data" (xda19070100) of the special space-time three pole environment project of the earth big data science project, Che Tao research group of Northwest Institute of ecological environment and resources, Chinese Academy of Sciences uses machine learning methods combined with multi-source snow depth product data The daily snow depth data set of long-time Series in the northern hemisphere is prepared. Firstly, the applicability of artificial neural network, support vector machine and random forest method in snow depth fusion is compared. It is found that random forest method has strong advantages in snow depth data fusion. Secondly, using the random forest method, combined with remote sensing snow depth products such as AMSR-E, amsr2, NHsd and globsnow and reanalysis data such as era interim and merra2, the grid snow depth products and environmental factor variables are used as the input independent variables of the model, and the data of China Meteorological Station (945), Russia meteorological station (620) and Russia snow survey data (514) The snow depth data of 43340 ground observation stations such as the daily data of the global historical meteorological network (41261) are used as the reference truth to train and verify the model, and the daily grid snow depth data set of the snow hydrological year from 1980 to 2019 (September 1 of the previous year to May 31 of the current year) is prepared on the cloud platform provided by the special "earth big data science project". Using the global snow model comparison program and independent ground observation data for verification, the quality of the fusion data set has been improved as a whole. According to the comparison between the ground observation data and the snow depth products before fusion, the determination coefficient (R2) of the fusion data is increased from 0.23 (globsnow snow depth products) to 0.91, and the corresponding root mean square error (RMSE) and mean absolute error (MAE) are also reduced to 7.69 cm and 2.7 cm. The following is the header file content of each file. Adding it to the front of each file can display the data in ArcMap. Ncols 1440 / / 1440 columns in the data matrix Nrows 360 / / the data matrix has 360 rows in total Xllcenter - 180 / / the corner coordinates of the grid at the lower left corner of the matrix in the X direction Yllcenter 0 / / the corner coordinates of the grid at the lower left corner of the y-direction axis of the matrix Cellsize 0.25 / / size of each grid NODATA_ Value - 9999 / / default value
CHE Tao, HU Yanxing, DAI Liyun, XIAO Lin