The data include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.
0 2022-04-19
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.
0 2022-04-19
This data set contains the high-resolution tropospheric nitrogen dioxide vertical column concentration pomino v2.1 data in East Asia from 2012 to 2020. It is a new version of the data after bug fix of v2.0.1, which provides an important data basis for studying the spatial distribution characteristics and temporal change trend of tropospheric nitrogen dioxide in China. Based on the tropospheric nitrogen dioxide slant column concentration provided by KNMI, the pomino tropospheric nitrogen dioxide vertical column concentration is calculated through the tropospheric AMF retrieval algorithm developed by ourselves. The comparison with the ground-based observation data shows that the tropospheric nitrogen dioxide column concentration of pomino can better capture the day-to-day variation trend, and has better correlation with the ground-based observation data. At present, the data has been used for scientific research by many universities and scientific research institutions at home and abroad. In the future, the data set will provide more comprehensive data support for scientific research projects on the Qinghai Tibet Plateau.
0 2022-04-19
The data set is based on the NPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the net primary productivity of the ecosystem. Data was derived from Le Quéré et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.
0 2022-04-19
Based on the data of Gaogao No. 1 and No. 2 in China from 2019 to 2020, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermal melting lakes and ponds and the distribution data of thermal melting landslides. The data set can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan Anduo section of Qinghai Tibet highway is self-made based on the domestic gaogao-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from Gaogao No. 2 data; Then, ArcGIS is used for manual editing. During the production process, the operators are required to strictly abide by the operation specifications, and a special person is responsible for the quality review.
0 2022-04-19
The Tibetan Plateau, featuring the most extensive lake distribution in China, has seen rapid expansion of most its lakes. These lakes are important nodes for regional water and energy cycles, and highly sensitive to climate change. It is therefore imperative to unravel lake water storage changes under climate variation and change to improve the understanding of mechanisms of the interactions between regional hydrology and climate and their changes. This developed data set provides water level, hypsometric curves, and lake storage changes for 52 large lakes across the TP from 2000 to 2017, comprising traditional altimetry water levels and a unique source of information termed as the optical water levels derived from tremendous amounts of Landsat archives using Google Earth Engine. Field experiments agree with the theoritical analysis that the uncertainty of optical water level is 0.1 - 0.2 m, comparable with that of altimetry water level. The uncertainty of altimetry water level is represented by the standard deviation of water levels obtained from effective footprints of the same cycle, which is included in the dataset. This dataset is applicable in water resource and security management, lake basin hydrological analysis, water balance analysis and the like. For instance, it has great potential in monitoring lake overflow flood.
0 2022-04-19
The data involved three periods of geodetic glacier mass storage change of three Rongbuk glaciers and its debris-covered ice in the Rongbuk Catchment from 1974-2016 (unit: m w.e. a-1). It is stored in the ESRI vector polygon format. The data sets are composed of three periods of glacier surface elevation difference between 1974-2000，2000-2016 and 1974-2006, i.e. DHPRISM2006-DEM1974（DH2006-1974）、DHSRTM2000-DEM1974（DH2000-1974）、DHASTER2016-SRTM2000（DH2016-2000）. DH2006-1974 was surface elevation change between ALOS/PRISMDEM(PRISM2006) and DEM1974, i.e. the DEM1974 was subtracted from PRISM2006, DH2006-1974 =PRISM2006 – DEM1974. The PRISM2006 was generated from stereo pairs of ALOS/PRISM on 4 Dec. 2006. The earlier historical DEM (DEM1974, spatial resolution 25m) was derived from 1:50,000 topographic maps in October 1974(DEM1974,spatial resolution 25m). The uncertainty in the ice free areas of DHPRISM2006-DEM1974 was ±0.24 m a-1. DHSRTM2000-DEM1974（DH2000-1974）was surface elevation change between SRTM DEM(SRTM2000) and DEM1974. The uncertainty in the ice free areas of DHSRTM2000-DEM1974 was ±0.13 m a-1. DHASTER2016-SRTM2000（DH2016-2000）was the surface elevation change between ASTER DEM2016 and SRTM DEM(SRTM2000). The uncertainty in the ice free areas of DHASTER2016-SRTM2000 was ±0.08 m a-1. Glacier-averaged annual mass balance change (m w.e.a-1) was averaged annually for each glacier, which was calculated by DH2006-1974/DH2000-1974/DH2016-2000, glacier coverage area and ice density of 850 ± 60 kg m−3. The attribute data includes Glacier area by Shape_Area (m2), EC2000-1974/EC2016-2000/EC2006-1974, i.e. Glacier-averaged surface elevation change in each period(m a-1), MB2000-1974/ MB2016-2000/MB2006-1974, i.e. Glacier-averaged annual mass balance in each period (m w.e.a-1), and MC2000-1974/ MC2016-2000/MC2006-1974,Glacier-averaged annual mass change in each period(m3 w.e.a-1), Uncerty_EC is the maximum uncertainty of glacier surface elevation change（m a-1）、Uncerty_MB, is the maximum uncertainty of glacier mass balance（m w.e. a-1），Uncerty_MC, is the maximum uncertainty of glacier mass change（m3w.e. a-1）。 MinUnty_EC，is the minimum uncertainty of glacier surface elevation change，MinUnty_MB，is the minimum uncertainty of glacier mass balance（m w.e. a-1），MinUnty_MC is the minimum uncertainty of glacier mass change（m3 w.e. a-1.The data sets could be used for glacier change, hydrological and climate change studies in the Himalayas and High Mountain Asia.
0 2022-04-19
Based on 11 well-acknowledged global-scale microwave remote sensing-based surface soil moisture products, and with 9 main quality impact factors of microwave-based soil moisture retrieval incorporated, we developed the Remote Sensing-based global Surface Soil Moisture dataset (RSSSM, 2003~2020) through a complicated neural network approach. The spatial resolution of RSSSM is 0.1°, while the temporal resolution is approximately 10 days. The original dataset covered 2003~2018, but now it has been updated to 2020. RSSSM dataset is outstanding in terms of temporal continuity, and has full spatial coverage except for snow, ice and water bodies. The comparison against the global-scale in-situ soil moisture measurements indicates that RSSSM has a higher spatial and temporal accuracy than most of the frequently-used global/regional long-term surface soil moisture datasets. In addition, although RSSSM is remote sensing based, without the incorporation of any precipitation data or records, its interannual variation generally conforms with that of precipitation (e.g., the GPM IMERG precipitation data) and Standardized Precipitation Evapotranspiration Index (SPEI). Moreover, RSSSM can also reflect the impact of human activities, e.g., urbanization, cropland irrigation and afforestation on soil moisture changes to some degree. The data is in ‘Tiff’ format, and the size after compression is 2.48 GB. The relevant data describing paper has been published in the Journal ‘Earth System Science Data’ in 2021.
0 2022-04-19
1) The optical depth, vertical mass concentration and extinction coefficient of dust, sulfate, organic carbon, black carbon and sea salt aerosols and total aerosols were measured; 2) Data source: numerical simulation, processing method: Based on CALIPSO satellite vertical observation and global aerosol model, it is generated by four-dimensional local ensemble transformation Kalman filter assimilation method; 3) The data quality is good; 4) It can also be used to study the spatiotemporal distribution of aerosols and their spatial and temporal characteristics of precipitation and their assimilation.
0 2022-04-19
This dataset covers the 2017 sulfur dioxide, nitrogen oxides, PM2.5 emissions grid list of Pan-third polar regions (South Asia: Nepal, Bhutan, India, Pakistan, Bangladesh, Sri Lanka, Maldives; Central Asia: Turkistan, Kyrgyzstan, Uzbekistan, Tajikistan, Kazakhstan, Afghanistan; Josiah: Iran, Iraq, azerbaijan, Georgia, Armenia, Turkey, Syria, Jordan, Israel, Palestine, Saudi Arabia, yemen, bahrain, Qatar, Oman, united Arab emirates, Kuwait, Lebanon, Cyprus). The emission inventory is derived from the data set publicly available in IIASA network. By using ArcGIS software technology, the emission inventory is processed into a GRID data set of 50km*50km, whose quality can be guaranteed. The data can be used by modelers to further study climate and air quality in the third polar region.
0 2022-04-19
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