The long time series data set of extreme precipitation index in Central Asia arid region contains 10 extreme precipitation index long time series data from 47 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, 15 stations are from 1925 to 2005, and 32 stations are from 1936 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
Temperature-humidity index (THI) was adopted to evalulate the climate suitability for the Green Silk Road. The relative humidity isone of the basic parameters to calculate THI. Refering to theTHI model of Tanget al. (2008), the multi-year average of relative humidity is calculted based on the observation data (1981-2017) of weather stations provided by National Meteorological Information Center. The multi-year average values were interpolated into the raster dataset at the resolution of 11km×1km by Kriging method based on GIS software. The climate suitability evaluation results calculated based on this dataset could highlight regional differences.
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2018. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
The gridded desertification risk data of Iranian plateau in 2019 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in Iranian plateau in 2019.
The major deserts in China include the Taklamakan Desert, Gurban Tunggut Desert, Qaidam Desert, Kumtag Desert, Badain Jaran Desert, Tengger Desert, Ulan Buh Desert, Hobq Desert, MU US Desert, Hunshandake Desert, Hulunbuir Sands, and Horqin Sands. All the desert boundaries were derived from Google Earth Pro® via manual interpretation. We delineated the desert boundaries using the Digital Global Feature Imagery and SpotImage (2011, 10 m resolution) collections of Google Earth Pro®, whose spatial resolution is finer than 30 m. The acquisition time of most images was in 2011.
LI Guoshuai LI Xin
The sand drift potential (DP, in vector units (VU)) is calculated by DPi=∑U^2 [U-Ut]*fu where i represents 16 directions: N, NNE, NE, NEE, E, EES, ES, ESS, S, SSW, WS, WWS, W, WWN, NW and NNW; U is the effective sand-moving wind speed at the standard height of 10 m; Ut is the threshold wind velocity, i.e., the minimum wind velocity at the standard height to cause sand particle rolling; and fu is the fraction of time when the wind speed is higher than Ut. The 2 m s-1 bin is adopted in the effective sand-moving wind (sand-moving wind >6 m s-1 at the height of 10 m) directions, corresponding to the mean wind speeds of 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 34 m s-1, to sum all the above results to obtain the final DP in the wind direction. The divisor used in calculating the frequency of effective sand-moving winds from different directions is the total hour number of Julian years (8760 hours for common years or 8784 hours for leap years). The wind speed and wind direction data from 2000 to 2008 were hourly estimates of 10 m u-component of wind and 10 m v-component wind with a horizontal resolution of 0.25°×0.25° generated with the fifth generation of ECMWF atmospheric ReAnalysis of the global climate (ERA5).
Guided by the theory of plate tectonics, paleogeography, petroliferous basin analysis and sedimentary basin dynamics, we have collected a large number of data and achievements of geological research and petroleum geology in recent years, including strata, sedimentation, paleontology, paleogeography, paleoenvironment, paleoclimate, structure, oil and gas (potash) geology and other basic materials, especially paleomagnetism, Paleogene Based on the data of detrital zircon and geochemistry, combined with the results of typical measured stratigraphic sections, the lithofacies and climate paleogeographic pattern of Cretaceous were restored and reconstructed, and two lithofacies paleogeographic maps of early and late Cretaceous of Pan tertiary and two climate paleogeographic maps of early and late Cretaceous of Pan tertiary were obtained, aiming at discussing the influence of paleogeography, paleostructure and paleoclimate In order to reveal the geological conditions and resource distribution of oil and gas formation, and provide scientific basis and technical support for China's overseas and domestic oil and gas exploration deployment.
1) Data content: Paleomagnetic data, magnetic index data, major element percentage data and chemical weathering index can establish the paleomagnetic age framework of the Dahonggou section and restore the precipitation change and chemical weathering history in geological history. 2) Data sources and processing methods The data source is experimental data. Paleomagnetic data: a cylindrical sample of 2x2x2cm was drilled with a small gasoline drill and measured with a low-temperature superconducting magnetometer in a magnetic shielding room. Magnetic data: the samples collected in the field were ground into fine particles by mortar and put into 2x2x2 non-magnetic plastic box, and tested by kappa bridge susceptibility meter, pulse magnetometer and rotating magnetometer. Mass percentage content and chemical weathering index data of major elements in the whole sample and particle size fraction: firstly, the whole sample and particle size fraction sample were pretreated with acetic acid and hydrogen peroxide to remove carbonate and organic matter, and then pressed into a round cake with a diameter of about 4cm and a thickness of about 8mm by a pressure apparatus, and finally XRF fluorescence analysis was carried out. 3) Data quality The sample collection and experimental processing are carried out according to strict standards, and the data quality is reliable. 4) Data application achievements and Prospects Three SCI papers were published using this set of data, one of which is Ni.
The data set is a 2015 heat wave risk data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of year. Heat wave risk refers to the probability or loss possibility of harmful consequences caused by the interaction between heat wave hazard (possible heat wave events in the future), heat wave exposure (total population, livelihood and assets in the area where heat wave events may occur) and heat wave vulnerability (the tendency of the disaster bearing body to suffer adverse effects when affected by heat wave events) . The risk assessment method of heat wave is "hazard-exposure-vulnerability". The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei YIN Cong
The data set is a 2015 heat wave hazard, exposure and vulnerability data set in Dhaka, Bangladesh, with a spatial resolution of 30m and a temporal resolution of yearly. Heat wave hazard is an index to measure the severity of heat wave event, which is expressed by surface temperature; heat wave exposure refers to the degree that human, livelihood and economy may be adversely affected, which is expressed by nighttime lighting data, and population density. The population older than 65 and younger than 5 years old constitute vulnerable groups; heat wave vulnerability is a measure of increased / reduced risk in the environment. The distance from road / hospital and ambulance station / water body, NDVI, impervious layer and slum area are used to represent the vulnerability of high temperature heat wave. The data set has been proved by experts, which can provide support for regional high temperature heat wave risk assessment.
YANG Fei YIN Cong
The data of farmland distribution on the Qinghai-Tibet Plateau were extracted on the basis of the land use dataset in China (2015). The dataset is mainly based on landsat 8 remote sensing images, which are generated by manual visual interpretation. The land use types mainly include the cultivated land, which is divided into two categories, including paddy land (1) and dry land (2). The spatial resolution of the data is 30m, and the time is 2015. The projection coordinate system is D_Krasovsky_1940_Albers. And the central meridian was 105°E and the two standard latitudes of the projection system were 25°N and 47°N, respectively. The data are stored in TIFF format, named “farmland distribution”, and the data volume is 4.39GB. The data were saved in compressed file format, named “30 m grid data of farmland distribution in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for farmland ecosystem management on the QTP.
LIU Shiliang SUN Yongxiu LI Mingqi
The Grassland Degradation Assessment Dataset in agricultural and pastoral areas of the Qinghai-Tibet Plateau (QTP) is a data set based on the 500m Global Land Degradation Assessment Data (2015), which is evaluated according to the degree of grassland degradation or improvement. In this dataset, the grassland degradation of the QTP was divided into two evaluation systems. At the first level, the grassland degradation assessment was divided into 3 types, including no change type, improvement type and degradation type. At the second level, the grassland degradation assessment on the QTP was divided into 9 types, among which the type with no change was class 1, represented by 0. There were 4 types of improvement: slight improvement (3), relatively significant improvement (6), significant improvement (9) and extremely significant improvement (12). The degradation types can be divided into 4 categories: slight degradation (-3), relatively obvious degradation (-6), obvious degradation (-9) and extremely obvious degradation (-12). This dataset covers all grassland areas on the QTP with a spatial resolution of 500m and a time of 2015. The projection coordinate system is D_Krasovsky_1940_Albers. The data are stored in TIFF format, named “grassdegrad”, and the data volume is 94.76 MB. The data were saved in compressed file format, named “500 m grid data of grassland degradation assessment in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The file volume is 2.54 MB. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for grassland ecosystem management and restoration on the QTP.
LIU Shiliang SUN Yongxiu LIU Yixuan
This project is based on the gsflow model of USGS to simulate the surface groundwater coupling in Zhangye basin in the middle reaches of Heihe River. The space-time range and accuracy of the simulation are as follows: Simulation period: 1990-2012; Simulation step: day by day; The spatial scope of simulation: Zhangye basin; The spatial accuracy of simulation: the underground part is 1km × 1km grid (5 layers, the total number of grids in each layer is 150 × 172 = 25800, among which the active grid 9106); the surface part is based on the hydrological response unit (HRU) (588 in total, each HRU covers an area of several square kilometers to dozens of square kilometers). The data include: surface infiltration, actual evapotranspiration, average soil moisture content, surface groundwater exchange, shallow groundwater level, simulated daily flow of Zhengyi gorge, simulated monthly flow of Zhengyi gorge, groundwater extraction and river diversion
This data set includes apatite and zircon (U-Th) / He ages, apatite fission-track (AFT) ages of the Yalong River thrust belt, which will be continuously updated in the future. The first part is the apatite and zircon He and apatite fission-track data from the Yunongxi fault, a branch fault in the hinterland of the Yalong River thrust belt. The second part of the data is from the Jinping Shan-Muli fault, a branch of the Yalong River thrust belt, including apatite and zircon He ages data. The data results are concentrated, which well constrain the evolution of the Yalong River thrust belt and provide a high-quality chronological basis for exploring its role in the process of plateau expansion.
1) Data content: species list of amphibious and reptile in Tibet, including class, order, family, genus, species; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Tibet from 2010 to 2019, and recording the species composition and distribution range of amphibians and reptiles in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples, longitude, latitude and altitude information are checked to ensure the quality of distribution data; 4) Data application results and prospects: We selected amphibians and reptiles as model species for study. we obtained data on population size and distribution range, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.
The birds along elevation gradients in Gangrigabu Mountains were investigated by point count method. With a 400-meter elevational gradient, elevation zones were set up in the survey area. Five elevation zones were built in the north slope from TongMai Town to Galong Temple in Bome County, and 8 elevation zones were built in the south slope from Jiefang Bridge to Galongla in Medog County. So that we can make clear about the pattern and maintenance mechanism of bird diversity along elevation gradients in this region. The data of bird diversity and distribution will be used to further explore the key scientific issues such as the impact of climate change on bird diversity and adaptation strategies, and the response and protection strategies of bird species diversity under the global climate change.
From October to November 2020, we used both live traps and camera traps to collect mammal diversity and distributions along the elevational gradients at the Yarlung Zangbo Grand Canyon National Nature Reserve. We set trap lines for small mammals inventory, with a total of 8000 live trap nights. We collected 526 individuals and1052 tissue samples of small mammals during the field sampling. We also retrived images of 130 camera traps placed between May 2020 and October 2020. We obtained 4218 pictures of wild animals,25 species of large and medium mammals were recorded.. The camera traps were reset in the same locations after renew batteries and memory cards. Small mammal data consist of richness, abundance, traits, environmental gradients etc, and could be used to model relationship between environmental gradients and traits concatenated by richness matrix. Camera trap data could inventory endangered species in the region, and provide information to identify biodiversity hotspots and conservation priorities.
Based on the MODIS satellite remote sensing data, the overall vegetation coverage (VC) of the China-Mongolia-Russia Economic Corridor was calculated. The traditional VC formula selects the normalized difference vegetation index (NDVI) as a variable. For the reduction of deviation caused by soil background and the impacts of the atmosphere, the enhanced vegetation index (EVI) instead of NDVI is adopted in the calculation process of VC data set. The original data is the enhanced vegetation index data in the Terra MODIS Vegetation Index Data Version 6 (MOD13A3) with the resolution of 1 km. The MOD13A3 dataset is of higher quality than the source data because it filters the outliers or missing measurements of the MODIS satellite data. The China-Mongolia-Russia Economic Corridor is an area with high risk of desertification. At present, the development of desertification in the corridor extends along the main road between China and Mongolia, and the desertification is the most serious in densely populated urban areas. The regional desertification information can be extracted effectively from the vegetation coverage data, which will provide ecological and environmental data support for the disaster risk prevention and safe operation of transportation and pipelines.
The China-Mongolia-Russia Economic Corridor is confronted with security problems related with global warming, mostly including the increasingly serious of degradation of permafrost and land desertification. On one hand, frozen soil degradation has caused frequent disasters such as debris flow, flood, ice and snow damage along the China-Mongolia-Russia transportation and pipeline, which will cause water and soil erosion followed by exposed pipes in frozen soil, in particular in summer. On the other hand, desertification will drive the ecological environment more vulnerable with the compound hazards of soil erosion and sandstorms occurring frequently. Therefore, this dataset will hopefully provide basic climate data for the research on the climate change and its impacts on permafrost and desertification for the China-Mongolia-Russia Economic Corridor. The original data is extracted from ERA5- Land surface climate reanalysis data (ERA5 – Land) (source: https://cds.climate.copernicus.eu). We adopted the inverse distance weight (IDW) method to interpolate the original data with the spatial resolution of 10 km. Based on this dataset, the spatial and temporal distribution pattern of climatic factors are outlined over the past 40 years for the corridor.
Main railway lines of China-Mongolia-Russia Economic Corridor: Manzhouli-Chita; Hohhot-Erlian-Ulaanbaatar; Suifenhe-Vladivostok/Khabarovsk; Erlian-Zamen Uud; Dalian-Harbin; Harbin-Manzhouli; Jining-Erlian; Changchun-Huichun; Zamen Udda-Ulaanbaatar-Sukhbaatar; Zabaikalsk-Chita; Novosibirsk-Ulan-Ude; Ulan-Ude-Chaktu-Darhan-Bayan Gol-Ulaanbaatar-Bayantar-Gobi Sumber-Joy Er-Sinshanda-Zamyn-Uud-Erenhot-Jining-Yanggao-Zhangjiakou-Langfang-Tianjin Port; Inner Mongolia-Erenhot-Zamyn-Uud-Joyel-Ulaanbaatar-Dalkhan-A Letan Bragg-Chaktu-Ulan-Ude; Naushki-Ulan-Ude; Changchun-Hunchun; Sino-Russian oil pipeline: The first and second lines of the Sino-Russian crude oil pipeline (Linyuan-Daqing-Lindian-Nehe-Nenjiang-Dayangshu-Uerqi-Jagedaqi-Mohe-Songling-Jingsong-Xinlin-Tahe-Walagan- 22nd Station-Xing'an Town-Skovorodino (Siberia-Pacific Crude Oil Pipeline System) East Siberia-Pacific Pipeline ((Daqing-Taishe 1, 2) Taishet-Skovorodino-Magdagazi-Khabarovsk-Perevoznaya-Kozimino) Sino-Russian crude oil pipeline (Taishet-Lensk-Olyekminsk-Ardan-Tenda-Skovorodino-Mohe-Qiqihar-Daqing) Sino-Russian Far East pipeline (Tashet-Lensk-Olyekminsk-Ardan-Tenda-Khabarovsk-Vladivostok)