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
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 set includes the social, economic, resource and other relevant index data of Gansu, Qinghai, Sichuan, Tibet, Xinjiang and Yunnan in the Qinghai Tibet Plateau from 2000 to 2015. The data are derived from Gansu statistical yearbook, Qinghai statistical yearbook, Sichuan statistical yearbook, Xizang statistical yearbook, Xinjiang statistical yearbook, Yunnan statistical Yearbook China county (city) socio economic statistical yearbook And China economic network, guotai'an, etc. The statistical scale is county-level unit scale, including 26 county-level units such as Yumen City, Aksai Kazak Autonomous Region and Subei Mongolian Autonomous County in Gansu Province, 41 county-level units such as Delingha City, Ulan county and Tianjun County in Qinghai Province, 46 counties such as Shiqu County, Ruoergai County and ABA County in Sichuan Province, and 78 counties such as Ritu County, Gaize county and bango County in Tibet, 14 counties including Wuqia County, aktao county and Shache County in Xinjiang Province, and 9 counties including Deqin County, Zhongdian county and Fugong County in Yunnan Province; Variables include County GDP, added value of primary industry, added value of secondary industry, added value of tertiary industry, total industrial output value of Industrial Enterprises above Designated Size, total retail sales of social consumer goods, balance of residents' savings deposits, grain output, total sown area of crops, number of students in ordinary middle schools and land area. The data set can be used to evaluate the social, economic and resource status of the Qinghai Tibet Plateau.
A long-term (1980-2017) land evaporation (E) product with a spatial resolution of 0.25 degree. This is a merged product from three model-based E products using the Reliability Ensemble Averaging (REA) method which minimizes errors. These include the fifth-generation ECMWF Re-Analysis (ERA5), the second Modern-Era Retrospective analysis for Research and Applications (MERRA2), and the Global Land Data Assimilation System (GLDAS). To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain daily and monthly mean data (e.g., REA_1980_day.nc and REA_1980_mon.nc). The dataset is stored in NetCDF format, containing the variable E, representing land evaporation, produced in millimeters (mm) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.875E to 179.875E, the latitude from -59.875N to 89.875N. Complete time coverage is from January 1, 1980, to December 31, 2017.
LU Jiao, WANG Guojie, CHEN Tiexi, LI Shijie, HAGAN Daniel, KATTEL Giri, PENG Jian, JIANG Tong, SU Buda
Data content: albedo data of Aral Sea basin from 2015 to 2018. Data source and processing method: from NASA medium resolution imaging spectrometer, extract the "brdf_albedo_parameters_nn. Num_parameters_01", "brdf_albedo_parameters_nn. Num_parameters_02" and "brdf_albedo_parameters_nn. Num_parameters_03" bands in mcd43a1 product, refer to the official MODIS algorithm, Calculate the daytime and nighttime albedo multiplied by a scale factor of 0.001. Data quality: the spatial resolution is 500m × 500m, the time resolution is 8 days, and the value of each pixel is the average value of surface albedo in 8 days. Data application results: as an important parameter, surface evapotranspiration can be retrieved.
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.
This dataset is the water balance dataset in the Yellow River source region and Qilian Mountains in the future 50 years (runoff, precipitation, evapotranspiration, soil liquid water content). It is simulated by the Geomorphology-Based Ecohydrological Model (GBEHM). The variables in the dataset include monthly runoff, monthly precipitation, monthly evapotranspiration, the monthly average 5cm soil liquid water content and the monthly average 50cm soil liquid water content. The temporal range is 2020-2070 and the spatial resolution is 1 km. The input data of the model include meteorological forcings, vegetation, soil and land use data, and the meteorological forcings are obtained from the ensemble mean of 38 CMIP6 models under SSP2-4.5 scenario. The simulation results can reflect the spatio-temporal changes of the hydrological variables in the Yellow River source region and Qilian Mountains. The dataset can be further used for researches into the eco-hydrological processes in the Yellow River source region and Qilian Mountains, and help provide a scientific basis for the optimal allocation of " mountains, rivers, forests, farmlands, lakes and grasslands " system.
WANG Taihua, YANG Dawen
Near-surface air temperature variability and the reliability of temperature extrapolation within glacierized regions are important issues for hydrological and glaciological studies that remain elusive because of the scarcity of high-elevation observations. Based on air temperature data in 2019 collected from 12 automatic weather stations, 43 temperature loggers and 6 national meteorological stations in six different catchments, this study presents air temperature variability in different glacierized/nonglacierized regions and assesses the robustness of different temperature extrapolations to reduce errors in melt estimation. The results show high spatial variability in temperature lapse rates (LRs) in different climatic contexts, with the steepest LRs located on the cold-dry northwestern Tibetan Plateau and the lowest LRs located on the warm-humid monsoonal-influenced southeastern Tibetan Plateau. Near-surface air temperatures in high-elevation glacierized regions of the western and central Tibetan Plateau are less influenced by katabatic winds and thus can be linearly extrapolated from off-glacier records. In contrast, the local katabatic winds prevailing on the temperate glaciers of the southeastern Tibetan Plateau exert pronounced cooling effects on the ambient air temperature, and thus, on-glacier air temperatures are significantly lower than that in elevation-equivalent nonglacierized regions. Consequently, linear temperature extrapolation from low-elevation nonglacierized stations may lead to as much as 40% overestimation of positive degree days, particularly with respect to large glaciers with a long flowline distances and significant cooling effects. These findings provide noteworthy evidence that the different LRs and relevant cooling effects on high-elevation glaciers under distinct climatic regimes should be carefully accounted for when estimating glacier melting on the Tibetan Plateau.
This dataset is the water balance dataset in the Yellow River source region and Qilian Mountains in the past 40 years (runoff, precipitation, evapotranspiration, soil liquid water content). It is simulated by the Geomorphology-Based Ecohydrological Model (GBEHM). The variables in the dataset include monthly runoff, monthly precipitation, monthly evapotranspiration, the monthly average 5cm soil liquid water content and the monthly average 50cm soil liquid water content. The temporal range is 1980-2019 and the spatial resolution is 1 km. The input data of the model include meteorological forcings, vegetation, soil and land use data. The simulation results can reflect the spatio-temporal changes of the hydrological variables in the Yellow River source region and Qilian Mountains. The dataset can be further used for researches into the eco-hydrological processes in the Yellow River source region and Qilian Mountains, and help provide a scientific basis for the optimal allocation of " mountains, rivers, forests, farmlands, lakes and grasslands " system.
WANG Taihua, YANG Dawen
A standardized field survey was carried out from August to September 2020 in the source areas of rivers and lakes in Tibet Autonomous Region. A total of 25 samples and 75 quadrats were investigated. The data set includes sample number, plot number, latitude and longitude, altitude, aboveground biomass, species number and coverage of the plot, and the data format is Excel. The area volume of collected and investigated samples was 100cm*100cm, and each sample site had 3 quadrats named Plot1, Plot2 and Plot3. All data were collected and measured in the field, and the quality of data was ensured in the field survey according to the vegetation survey specifications. This dataset provides theoretical basis for rational utilization of grassland resources and data support for comprehensive evaluation of environmental effects of typical land use change.
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 is the simulated data of glacier distribution in the alpine region of Asia since the last glacial maximum, It includes the annual resolution glacier area change sequence of typical regions (High mountain Asia, Tianshan Mountains, Himalayas and Pamir Plateau) and typical periods (LGM (20000 ~ 19000ka), HS1 (17000 ~ 16000ka), BA (~ 14900 ~ 14350ka), yd (12900 ~ 12000ka), eh (9500 ~ 8500ka), MH (6500 ~ 5500ka), LH (3500 ~ 2500ka) and modern (1951 ~ 1990)) 1 km resolution glacier distribution in High Mountain Asia. This data are created by taking the trace full forcing simulation based on ccsm3 climate model as the external forcing field to drive the 1 km resolution PISM ice sheet model. This data can be used to study the changes of glacier distribution in the alpine region of Asia since the last glacial maximum and its impact on environmental and climatic factors such as lake water level, runoff and landform.
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.
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
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 Holocene single greenhouse gas concentration change simulation results (11.5-0 ka) data set is based on the Earth system model CESM model (horizontal resolution: about 2° for the atmosphere and land surface module; about 1° for the ocean and sea ice module), carry out the Holocene transient simulation test considering the change of greenhouse gas concentration. The spatial resolution is 2°; the spatial range: North: 90°N, South: 90°S, West: -180°, East: 180°; the regional range is global; the time range is Holocene. The simulation results can be used to study Holocene changes of westerly-monsoon in Eurasia under the influence of individual greenhouse gas concentration changes.
TIAN Zhiping, ZHANG Ran
This data includes the seismic data of the Qinghai Tibet Plateau, the Asian water tower region and the Himalayas region from 1971 to 2021, The main attributes include earthquake occurrence time (UTC), longitude, latitude, earthquake depth, magnitude, magnitude type and occurrence area. It is divided into shp files and tabular data, which can be more convenient for relevant personnel to use. This data can help relevant personnel understand the earthquake distribution on the Qinghai Tibet Plateau and interpret the relationship between earthquake occurrence location and relevant structural zones. This data is derived from https://earthquake.usgs.gov/data/pager/ , download by selecting the initial target area and time, export by using ArcGIS tools, filter and make according to the edited files of the scientific research area of the Qinghai Tibet Plateau.
The monitoring section is located in the high plain of chumar River (dk1043 + 500-dk1067 + 022). The frozen soil under the subgrade at the section is mainly multi frozen soil, ice saturated frozen soil and thick underground ice, belonging to the low-temperature basically stable multi-year frozen soil subregion (zone III). A total of 5 monitoring sections are arranged in this section, including 2 plain soil subgrade sections, 1 block stone subgrade, 1 block stone slope protection subgrade and 1 U-shaped block stone subgrade section respectively. 4-5 test holes are arranged in each section, with a test depth of 15 ~ 20m, and the deepest hole in the section is 40m. The main element of monitoring is permafrost ground temperature, and the monitoring period is from 2003 to 2021. This data is based on Permafrost Engineering The temperature measuring probe made by the State Key Laboratory was obtained through field monitoring. Every year, the monitoring data of each monitoring section is collected on site through cr3000 data acquisition instrument. Through certain quality control, including eliminating the data when the sensor does not fully adapt to the soil environment and the systematic error caused by sensor failure. The corrected final data is stored in Excel file. The field data obtained has been reviewed by many people, and the data integrity and accuracy have reached more than 95%. The data can provide a reference for the long-term stability evaluation of block stone subgrade.
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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.