This data set includes daily, annual and multi-year surface mass balance data from Antarctic ice cap poles, ice (snow) cores / snow pits, automatic weather station altimeters and ground penetrating radar observations. The data come from published literature, data reports and international data sharing platform. After quality control, the most perfect data set of daily, annual and multi-year resolution of surface mass balance of Antarctic ice sheet has been formed. Its middle-aged resolution data span the past 1000 years. The data set is mainly used in glaciology, climatology, hydrology and other disciplines, especially in the quantitative analysis of the temporal and spatial changes of Antarctic surface mass balance, climate model validation, driving ice sheet model and snow granulation model, etc.
WANG Yetang
In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.
LUO Geping
The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 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, there are 15 time series from 1925 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.
YAO Junqiang, CHEN Jing, LI Jiangang
The data content mainly includes the main and micro data of the whole rock of some magmatic rocks in the Hoh Xil Lhasa plate of the Qinghai Tibet Plateau. The samples were mainly distributed in Hoh Xil lake, South Qiangtang guoganjianian, Dugur, and Gangdise Nasongduo and Saga counties. There are more than 300 major and trace elements in the samples, including olivine leucite, quartz monzonite, diorite and granite, which are of great significance to the study of the lithospheric evolution of the Qinghai Tibet Plateau. Data mainly come from published articles or being accepted. XRF spectroscopy was used to determine the major elements and ICP-MS was used to determine the trace elements. The data quality is highly reliable, and the testing units include the State Key Laboratory of Guangzhou Institute of geochemistry, Chinese Academy of Sciences, etc. The data are published in high-level journals, including geology, BSA bulletin and Journal of petroleum.
TANG Gongjian, WANG Jun, QI Yue, ZHOU Jinsheng, DAN Wei
This is Northern Hemispheric (NH) annual near-surface temperature dataset during the past millennium with a 2° spatial resolution, which is produced using the paleoclimate data assimilation approach with EnSRF method, MPI-ESM-P model and 396 multi-proxies from the PAGES2k Consoritum. This dataset agrees well with several observational temperature datasets during the instrumental period, and has a similar level of reliability as the Twentieth Century Reanalysis which assimilates surface pressure observations. In addition, the dataset shows a high level of agreement with previous proxy-based reconstructions (average correlation of annual mean NH temperatures is r = 0.61). The dataset can be used to study the temperature variability over the NH and some regions of the NH during the past millennium (1000-2000 AD).
FANG Miao, LI Xin, CHEN Hans , CHEN Deliang
Based on the medium resolution long time series remote sensing image Landsat, the data set obtained six periods of ecosystem type distribution maps of the Qinghai Tibet Plateau in 1990 / 1995 / 2002 / 2005 / 2010 / 2015 through image fusion, remote sensing interpretation and data inversion, and made the original ecological base map of the Qinghai Tibet Plateau in 25 years (1990-2015). According to the area statistics of various ecosystems in the Qinghai Tibet Plateau, the area of woodland and grassland decreased slightly, the area of urban land, rural residential areas and other construction land increased, the area of rivers, lakes and other water bodies increased, and the area of permanent glacier snow decreased from 1990 to 2015. The atlas can be used for the planning, design and management of ecological projects in the Qinghai Tibet Plateau, and can be used as a benchmark for the current situation of the ecosystem, to clarify the temporal and spatial pattern of major ecological projects in the Qinghai Tibet Plateau, and to reveal the change rules and regional differences of the pattern and function of the ecosystem in the Qinghai Tibet Plateau.
ZHAO Hui, WANG Xiaodan
The Qinghai Tibet Plateau is known as "the third pole of the Earth". The long-term and large-scale observation data of permafrost is of great significance to understand the changes and effects of Permafrost on the Qinghai-Xizang Plateau (QXP). Especially in such a cold and anoxic area, the extreme shortage of data resources greatly limits the development, improvement and validation of various remote sensing inversion algorithms, as well as the earth system simulation and scientific research of the QXP. In the past few decades, our research team has established a synthesis network in the permafrost region of the QXP. For the first time, the database systematically integrates the long-time series observation data of 6 automatic meteorological stations, 12 active layer sites and 84 boreholes. In the process of data collection and processing, all observation data have been strictly controlled. The data set will be released to scientists with multi-disciplinary backgrounds (e.g., cryosphere, hydrology, ecology and meteorology), which will greatly promote the validation, development and improvement of hydrological model, land surface process model and climate model of the QXP.
Zhao Lin, ZHAO Lin, ZHOU Defu, ZOU Defu, ZOU Defu, Wu Tonghua, Du Erji, DU Erji, Liu Guangyue, LIU Guangyue, Xiao Yao, Li Ren, Pang Qiangqiang, Qiao Yongping, WU Xiaodong, SUN Zhe, Xing Zangping, Zhao Yonghua, Shi Jianzong, Xie Changwei, Wang Lingxiao, Wang Chong, CHENG Guodong
The data set records the water quality evaluation results of the monitoring sections of the Yangtze River, Yellow River and Huangshui (2010-2012). The data is collected from Yushu ecological environment bureau. The data set contains 18 files, which are: water quality assessment of national control section of Yangtze River in April 2010, water quality assessment of national control section of Yangtze River in May 2010, water quality assessment of national control section of Yangtze River in September 2010, water quality assessment of national control section of Yangtze River in October 2010, etc. the data table structure is the same. There are seven fields in each data table Field 1: monitoring section Field 2: classification of water environment functional areas Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period of last year
Ecological Environment Bureau of Yushu Prefecture
The data set records the main distribution of sudden geological disasters in Qinghai Province from 2011 to 2018. The data are collected from the Department of ecological environment of Qinghai Province. The data set contains seven tables, which are: the main distribution of sudden geological disasters in 2011, 2012, 2014, 2015 and 2016 Distribution statistics table, 2017 Qinghai Province sudden geological disasters distribution table, 2018 Qinghai Province sudden geological disasters distribution table, the data table structure is the same. Each data table has five fields, such as the statistical table of the main distribution of sudden geological disasters in Qinghai Province in 2016 Field 1: county (city) Field 2: landslide Field 3: collapse Field 4: debris flow Field 5: loess collapsibility
Department of Ecology and Environment of Qinghai Province
The data set records the operation of the pollution source monitoring center in Haixi Prefecture of Qinghai Province from July 2018 to September 2019. The data is collected from the Department of ecological environment of Haixi Prefecture. The data set contains 42 text files, recording the weekly report of Haixi pollution source monitoring center from July 2018 to September 2019, and each file records the content of the weekly report once. Including the video monitoring system operation, online monitoring system operation, new online monitoring system construction acceptance, online monitoring system construction acceptance, online monitoring data analysis and transmission efficiency. Data coverage time range: July 16, 2018 to September 1, 2019.
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
The data set records the statistical table of groundwater level dynamic changes in various monitoring areas of Qinghai Province from 2015 to 2018. The data are recorded from the Department of natural resources of Qinghai Province, and the data set contains four data tables, which are: the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2015, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2016, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2017, and the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2018 The data table has the same structure and contains 7 fields Field 1: "geographic location" Field 2: "basic balance area (km2)" Field 3: "percentage of monitoring area (%)" Field 4: "weak descent area (km2)" Field 5: "percentage (%) of monitored area" Field 6: "strong uplift area (km2)" Field 7: "percentage (%) of monitored area"
Department of Natural Resources of Qinghai Province
The data set records the dynamic statistical data of groundwater level in the monitoring area of Golmud City, Qinghai Province from 2012 to 2018, and the statistics are classified according to the year and quantity. The data were collected from the official website of the Department of natural resources of Qinghai Province. The data set contains seven data tables, which are the dynamic statistics of groundwater level in Golmud monitoring area in 2012, 2013, 2014, 2015, 2016, 2017 and 2018, with the same structure. For example, the data table in 2012 has five fields: Field 1: year Field 2: Potassium view5 Field 3: View 4 Field 4: View 39 Field 5: Potassium view 1
ZHAO Hu
The spectral characteristics of different land use types are mainly determined by spectrograph in the surface spectral data set of Qinghai Tibet Plateau. The measured ground features are mainly divided into woodland, (Alpine) shrub, (Alpine) grassland, wetland, cultivated land and bare land. It includes the field observation points in Lhasa, Linzhi, Shigatse, Ali and Naqu. The spectral characteristics of forests were measured based on the different growth stages of vegetation; The spectral characteristics of grassland were measured based on different coverage; The spectral characteristics of cultivated land were measured based on the main crop types, rape flowers and highland barley; The measurements of wetlands were conducted on the rivers, low-lying valleys and lakes; The measurements of bare lands were conducted on the desert, Gobi and roads, which have no vegetation cover. The measurement conducted from July to August in 2019, and the data is daily observation data. The data set can provide a reference for the field verification of remote sensing interpretation.
FENG Xiaoming
This dataset is the high-resolution downscaled results of three global circulation models (CCSM4, HadGEM2-ES, and MPI-ESM-MR) from CMIP5. The regional climate model applied is the WRF model. The domain of this dataset covers the five countries of Central Asia. Its horizontal resolution is 9km. The future (reference) period is 2031-2050 (1986-2005), which includes the 10 years under 1.5-2℃ global warming. The carbon emission scenario is RCP4.5. The variances are annual mean temperature at 2m and precipitation (cumulus and grid-scale precipitation). This dataset can be used to project the climate in Central Asia.
QIU Yuan QIU Yuan
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
This dataset includes inland water data of five countries in the Great Lakes region of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), including the distribution of rivers, canals and lakes. The line and area features of each country are stored in different files. The dataset comes from the Digital Map of the World (DCW), and its main source is the Operational Navigation Map (ONC) 1:1,000,000 scale paper map series of the US Defense Survey and Mapping Agency (DMA) produced by the United States, Australia, Canada and the UK. The DCW database is the most comprehensive global geographic information system database available free of charge since 2006, although it has not been updated since 1992.
XU Xiaofan, TAN Minghong
Aiming at sustainable agriculture and food production in Central Asia, the vulnerability of land resources is investigated from the view of exploitation risk of land resources. The evaluation indices of land resources for farmland include topographic factors (such as elevation and slope), land use type, soil texture, etc. The evaluation indices of sustainable agriculture include GDP per capita, grain production per capita, growth rate of agricultural economy, urbanization rate, natural growth rate of population, soil organic matter content, etc. The evaluation indices above which can indicate the properties of land resources directly are used as the evaluation indices of land resources vulnerability. Further, the weighted average of these indices is taken as the land resources vulnerability. The land resources vulnerability is one element of land resources exploitation risk, and the weights of land resources vulnerability evaluation indices are determined with multiple linear regression when the land resources exploitation risk is evaluated. The datasets include land resources vulnerabilities in 1995s (1992-1996), 2000s (1997-2001), 2005s (2002-2006), 2010s (2007-2011), 2015s (2012-2017) and 1995-2015 with a spatial resolution of 0.5°×0.5°. It is expected to provide basic information for agricultural production and land resources exploitation in five countries in Central Asia.
LI Lanhai, HUANG Farong
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.
DAI Tie, CHENG Yueming
Coupled Model Intercomparison Project Phase 5 (CMIP5) provides a multiple climate model environment, which can be used to predict the future climate change in the key nodes in the Belts and Road to deal with the environmental and climate problems. Key nodes in the Belt and Road are taken as the study regions of this dataset. The ability of 43 climate models in CMIP5 to predict the future climate change in the study regions was assessed and the optimal models under different scenarios were selected according to the RMSE between the prediction results and real observations. This dataset is composed of the prediciton results of precipitation and near-surface air temperature between 2006 and 2065 using the optimal models in monthly temporal frequncy. The spatial resolution of the dataset has been downscaled to 10 km using statistical downscaling method. Data of each period has three bands, namely maximum near-surface air temperature, minimum near-surface air temperature and precipitation. In this data set, the unit of precipitation is kg / (m ^ 2 * s), and the unit of near-surface air temperature is K. This dataset provides data basis for solving environmental and climate problems of the key nodes in the Belts and Road.
LI Xinyan, LING Feng
Evapotranspiration over the Qinghai Tibet Plateau is calculated by etwatch, a land surface evapotranspiration remote sensing model based on multi-scale and multi-source data. Etwatch adopts the method of combining the residual term method with P-M formula to calculate evapotranspiration. Firstly, according to the characteristics of the data image, the suitable model is selected to retrieve the evapotranspiration on a sunny day; the remote sensing model is often lack of data because the weather conditions can not obtain a clear image. In order to obtain the daily continuous evapotranspiration, the penman Monteith formula is introduced, and the evapotranspiration results on a sunny day are regarded as the "key frame", and the surface impedance information of the key frame is used as the basis to construct the surface impedance Based on the daily meteorological data, the time series data of evapotranspiration are reconstructed. Through the data fusion model, the high spatial and temporal resolution evapotranspiration data set is constructed by combining the low and medium resolution evapotranspiration temporal variation information with the high resolution evapotranspiration spatial difference information, so as to generate the 8 km resolution evapotranspiration of the Qinghai Tibet Plateau Data sets (1990-2015).
WANG Xiaofeng
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
LI Guangdong
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
The data set is based on a series of microwave remote sensing data, including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), etc., which can be used as a reference for primary productivity. The data is from Liu et al. (2015), and the specific calculation method is shown in the article. The source data range is global, and Tibetan Plateau region is selected in this data set. 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.
LIU Yi
The widely definition of seasonally frozen ground include seasonally frozen layer (seasonally frozen ground regions) and seasonally thaw layer (active layer in permafrost regions). So the area extent of seasonally frozen ground occupied more than 80% land surface over Northern Hemisphere. Soil freeze/thaw cycle is one special character of seasonally frozen ground, which covers area extent, depth, time duration, variation of soil freeze/thaw. These changes in seasonally frozen ground have substantial impacts on energy, water and carbon exchange between the atmosphere and the land surface, surface and sub-surface hydrologic processes, vegetation growth, the ecosystem, carbon dioxide cycle, agriculture, and engineering constructuion, as a whole.Based on the observations from sites, CRU air temperature, we used the Stefan solution to calculate the spatial distribution of active layer thickness and soil freeze depth during 1971-2000. These results are helpful to further study the physical mechanism between seasonally frozen ground and climate change, eco-hydrology process.
PENG Xiaoqing, ZHANG Tingjun
This data set is a national high-resolution solar radiation data set covering 34 years (1983.7-2017.6), with a resolution of 10 km. The data unit is W / m2. The data set is developed by merging the global high-resolution (3 hours, 10 km) surface solar radiation data set (1983-2017) with isccp-hxg cloud products as the main input, with ground based sunshine duration derived surface solar raidation data from 2261 meteorological stations in China by using the geographic weighted regression method. The validation results show that this dataset can provide more accurate simulation of long-term variability of surface solar radiation than that of gewex-srb, cmsaf-clara-a2 and the isccp-hxg based surface solar radiation product. This data can provide favorable data support for the application and research of long-term change of hydrology in land surface process simulation.
FENG Fei, WANG Kaicun
Paleoecological and paleolimnological studies can provide a long-term perspective on changes in environmental and ecosystem processes. The sediments documented both direct and indirect impacts of climate change and human activities on aquatic ecosystems. The fossils of zooplankton remain and pigments in lake sediments can reflect community structure changes of primary producers and primary consumers. The authors reconstructed the zooplankton and algal community changes during the past 600 years using carapaces of A. tibetiana and resting eggs of D. tibetana and pigments from the sediments of Dagze Co, in the central Tibet Plateau. Using total nitrogen and total phosphorus reconstructed the nutrient changes. These results suggest that algal community structure and changes in production can be attributed to alterations in the zooplankton community, with important implications for Tibetan aquatic ecosystems.
LIANG Jie LIANG Jie
Paleoecological and paleolimnological studies can provide a long-term perspective on changes in environmental and ecosystem processes. The sediments documented both direct and indirect impacts of climate change and human activities on aquatic ecosystems. The fossils of zooplankton remain and pigments in lake sediments can reflect community structure changes of primary producers and primary consumers. The authors reconstructed the zooplankton and algal community changes during the past 600 years using carapaces of A. tibetiana and resting eggs of D. tibetana and pigments from the sediments of Dagze Co, in the central Tibet Plateau. Using total nitrogen and total phosphorus reconstructed the nutrient changes. These results suggest that algal community structure and changes in production can be attributed to alterations in the zooplankton community, with important implications for Tibetan aquatic ecosystems.
LIANG Jie LIANG Jie
This dataset includes annual mosaics of Antarctic ice velocity derived from Landsat 8 images between December, 2013 and April, 2019, which was updated in 2020 in order to produce multi-year annual ice velocity mosaics and improve the quality of products including non-local means (NLM) filter, and absolute calibration using rock outcrops data. The resulting Version 2 of the mosaics offer reduced local errors, improved spatial resolution as described in the README file.
SHEN Qiang SHEN Qiang
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-ESM2M (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 (or 6 hours), daily and yearly scales.
PAN Xiaoduo, ZHANG Lei
This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.
C.K. Shum
High Mountain Asia is the third largest cryosphere on earth other than the Antarctic and Arctic regions. The large amounts of glaciers and snow over the High Mountain Asia play an important role not only on global water cycle but also on water resources and ecology of the arid regions of central Asia. The snowline, as the lower boundary of the snow covered area at the end of melting season, its altitude changes can directly reflect the changes in snow and glaciers. The snowline altitude provides a possibility to rapidly obtain a proxy for their equilibrium line altitude (ELA) which in turn is an indicator for the glacier mass balance. In this dataset, the daily MODIS snow cover products from 2001 to 2019 are used as the main data source. The cloud removal of the daily MODIS snow cover products was firstly carried out based on the developed cubic spline interpolation cloud-removel method, and snow covered days (SCD) are extracted using the cloud-removed MODIS snow cover products. In addition, the MODIS SCD threshold for estimating perennial snow cover is calibrated using the observed data of glacier annual mass balance and Landsat data at the end of melting season. The altitude value of the snowline at the end of melting season is determined by combining the perennial snow cover area and the hypsometric (area-elevation) curve. Finally, the 30km gridded dataset of snowline altitude in the High Mountain Asia during 2001-2019 is generated. This dataset can provide data support for the study of cryosphere and climate change over the High Mountain Asia.
TANG Zhiguang, DENG Gang, WANG Xiaoru
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
This data set is the distribution data of permafrost and underground ice in Qilian Mountains. Based on the existing borehole data, combined with the Quaternary sedimentary type distribution data and land use data in Qilian mountain area, this paper estimates the distribution of underground ice from permafrost upper limit to 10 m depth underground. In this data set, 374 boreholes in Qilian mountain area are used, and the indication function of Quaternary sedimentary type to underground ice storage is considered, so it has certain reliability. This data has a certain scientific value for the study of permafrost and water resources in Qilian Mountains. In addition, it has a certain promotion value for the estimation of underground ice reserves in the whole Qinghai Tibet Plateau.
This dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. ELBARA-III horizontal and vertical brightness temperature are computed from measured radiometer voltages and calibrated internal noise temperatures. The data is reliable, and its quality is evaluated by 1) Perform ‘histogram test’ on the voltage samples (raw-data) of the detector output at sampling frequency of 800 Hz. Statistics of the histogram test showed no non-Gaussian Radio Frequency Interference (RFI) were found when ELBAR-III was operated. 2) Check the voltages at the antenna ports measured during sky measurements. Results showed close values. 3) Check the instrument internal temperature, active cold source temperature and ambient temperature. 3) Analysis the angular behaviour of the processed brightness temperatures. -Temporal resolution: 30 minutes -Spatial resolution: incident angle of observation ranges from 40° to 70° in step of 5°. The area of footprint ranges between 3.31 m^2 and 43.64 m^2 -Accuracy of Measurement: Brightness temperature, 1 K; Soil moisture, 0.001 m^3 m^-3; Soil temperature, 0.1 °C -Unit: Brightness temperature, K; Soil moisture, m^3 m^-3; Soil temperature, °C/K
BOB Su, WEN Jun
In order to investigate the variation characteristics of agricultural water resources vulnerability in Central Asia, an index system was established with 18 indicators from three components, namely exposure, sensitivity and adaptation, according to the scheme of vulnerability assessment. Based on the socio-economic, topography, land cover and soil data, agricultural water resources vulnerability were calculated using the Equal-Weights and Principal Component Analysis (PCA) method. Each original raster data is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes 1992-1996, 1997-2001, 2002-2006, 2007-2011, 2012-2017and 1992-2017with a spatial resolution of 0.5°*0.5°. It is expected to provide basic data support for agricultural water supply and demand, development and utilization analysis in five central Asian countries.
LI Lanhai, YU Shui
This data set includes the monthly average actual evapotranspiration of the Tibet Plateau from 2001 to 2018. The data set is based on the satellite remote sensing data (MODIS) and reanalysis meteorological data (CMFD), and is calculated by the surface energy balance system model (SEBS). In the process of calculating the turbulent flux, the sub-grid scale topography drag parameterization scheme is introduced to improve the simulation of sensible and latent heat fluxes. In addition, the evapotranspiration of the model is verified by the observation data of six turbulence flux stations on the Tibetan Plateau, which shows high accuracy. The data set can be used to study the characteristics of land-atmosphere interaction and the water cycle in the Tibetan Plateau.
HAN Cunbo, MA Yaoming, WANG Binbin, ZHONG Lei, MA Weiqiang*, CHEN Xuelong, SU Zhongbo
Data content: annual GDP statistics (1990-2019), quarterly cumulative GDP statistics (1990-2019) and GDP (2010-2019) Data sources and processing methods: the original macroeconomic data of China (including the third pole) from the official website of the world bank and sina.com from 1990 to 2019 are obtained through data sorting, screening and cleaning. The data are stored in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: Central Bank gold and foreign exchange reserves (2000-2020) and money supply (2000-2017) Data sources and processing methods: the original data of banks and currencies in China (including the third pole) in 2015-2019 are obtained from the official website of the world bank and sina.com, and the bank and currency data sets of China (including the third pole) from 2000 to 2020 are obtained through data sorting, screening and cleaning. The data start time is from 2000 to 2020 in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: Foreign Economic and trade_ Total import and export of goods (1952-2019) and foreign economic and trade_ Total import and export by trade (1981-2019) Data sources and processing methods: the original data of China's foreign trade and investment from 2015 to 2019 (including the third pole) were obtained from the official website of the world bank and sina.com, and the foreign trade and investment data set of China (including the third pole) from 1952 to 2019 was obtained through data sorting, screening and cleaning. The data start time is from 1952 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: national economy_ Industrial value added (monthly) (2010-2019) Data source and processing method: the original industrial economic data of China (including the third pole) from the official website of the world bank and sina.com from 2010 to 2019 are obtained through data sorting, screening and cleaning. The data start time is from 2010 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: price index_ Consumer price index (CPI) (2010-2019) Data source and processing method: obtain the economic original data of the third pole (China region) price index from the official website of the world bank and sina.com from 2015 to 2019, and obtain the economic data set of the third pole (China region) price index from 2010 to 2019 through data sorting, screening and cleaning. The data start time is from 2010 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
The distribution data of permafrost in the source area of the Yellow River is established based on the annual average ground temperature model of permafrost in the source area of the Yellow River. The annual average ground temperature of 0 ℃ is taken as the standard and boundary for dividing seasonal frozen soil and permafrost. Compared with the available permafrost maps of the source region of the Yellow River (1:3 million) and the permafrost background survey project of the Qinghai Tibet Plateau (1:1 million), the data set is based on the measured data of the Yellow River source area, which has higher consistency with the measured data, and the simulation accuracy of the permafrost distribution map is the highest. The data set can be used to verify the distribution of permafrost in the source area of the Yellow River, as well as to study the frozen soil environment.
LI Jing
1) Data content: species list and distribution data of Phrynocephalus and Eremais in Tarim Basin, including class, order, family, genus, species, and detailed distribution information including country, province, city and county; 2) Data source and processing method: Based on the field survey of amphibians and reptiles in Tarim Basin from 2008 to 2020, and recording the species composition and distribution range of Phrynocephalus and Eremias in this area; 3) Data quality description: the investigation, collection and identification of samples are all conducted by professionals, and the collection of samples information are checked to ensure the quality of distribution data; 4) Data application results and prospects: Through comprehensive analysis of the dataset, the list of species diversity and distribution can provide important data for biodiversity cataloguing in arid central Asia, and provide scientific basis for assessing biodiversity pattern and formulating conservation strategies.
PANG Hongxi
Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
MA Ziqiang
There are two types of aerosol data in the Tibetan Plateau. Aerosol type data products are the results of aerosol type data fusion by using Meera 2 assimilation data and active satellite CALIPSO products through a series of data preprocessing, quality control, statistical analysis and comparative analysis. The key of the algorithm is to judge the CALIPSO aerosol type. According to CALIPSO aerosol types and quality control, and referring to merra 2 aerosol types, the final aerosol type data (12 kinds) and quality control results were obtained. Considering the vertical and spatial distribution of aerosols, it has high spatial resolution (0.625 ° × 0.5 °) and temporal resolution (month). Aerosol optical depth (AOD) is a visible band remote sensing inversion method developed by ourselves, combined with merra-2 model data and NASA's official product mod04. The data coverage time is from 2000 to 2019, with daily temporal resolution and spatial resolution of 0.1 degree. The retrieval method mainly uses the self-developed APRs algorithm to retrieve the aerosol optical depth over the ice and snow. The algorithm takes into account the BRDF characteristics of the ice and snow surface, and is suitable for the inversion of aerosol optical thickness on the ice and snow. The results show that the relative deviation of the data is less than 35%, which can effectively improve the coverage and accuracy of the polar AOD.
GUANG Jie, ZHAO Chuanfeng
The land cover dataset of Pan third pole major cities contains 14 cities (Urumqi, Xining, Lanzhou, Dhaka, Kathmandu, Lucknow, Delhi, Lahore, Islamabad, Kabul, Dushanbe, Tashkent, Bishkek and Almaty) in 2000 / 2010 / 2017, the spatial resolution of this dataset is 30 m. It includes vegetation, cultivated land, artificial surface, water body and others. Based on globeland30, mcd12q1 and globcover2009, the consistent regions were identified and retained. The inconsistent regions were reclassified by deep learning method, and the final classification results were obtained by fusing the above regions. The data has been verified by visual interpretation. The data are applied to the study of construction land dynamics and anthropogenic influence in Pan-Third Pole cities. Data type: grid. Projection mode: UTM projection.
Xin LI, LI Xin
The coverage time of glacier runoff data set in the five major river source areas of the Qinghai Tibet Plateau is from 1971 to 2015, and the time resolution is year by year, covering the source areas of five major rivers (Yellow River source, Yangtze River source, Lancang River source, Nu River source, Yarlung Zangbo River source). The data is based on multi-source remote sensing and measured data. The glacier runoff data is simulated by using the daily scale meteorological data of five major river source areas and their surrounding meteorological stations, the global vegetation products of umd-1km, the igbp-dis soil database, the first and second glacier catalogue data, and the distributed hydrological model vic-cas coupled with the glacier module is used to simulate the glacier runoff data. The simulation results are verified by the site measured data to enhance the quality control. Data indicators include: Glacier runoff (rate of glacier runoff:%), total runoff (mm / a), snow runoff (rate of snow runoff:%), and rainfall runoff rate (rainfall runoff rate:%).
WANG Shijin
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.
YE Qinghua
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