This is Tibet Plateau (TP) 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 TP temperatures is r = 0.61). The dataset can be used to study the temperature variability over the TP and some regions of the TP during the past millennium (1000-2000 AD).
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.
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.
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02℃) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements. The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems.
To understand the potential impact of projected climate changes on the vulnerable agriculture in Central Asia (CA) in the future, six agroclimatic indicators are calculated based on the 9km-resolution dynamical downscaled results of three different global climate models and a high-resolution projection dataset of agroclimatic indicators over CA is produced. These indicators are growing season length (GSL, days), biologically effective degree days (BEDD, ℃), frost days (FD, days), summer days (SU, days), warm spell duration index (WSDI, days), and tropical nights (TR, days). The periods are 1986-2005 and 2031-2050. The spatial resolution is 0.1°. As all the indicators except WSDI are defined with absolute temperature thresholds and particularly sensitive to the systematics biases in the model data, the quantile mapping (QM) method is applied to correct the simulated temperature. Results show the QM method largely reduces the biases in all the indicators. GSL, SU, WSDI, and TR will significantly increase over CA and FD will decrease. However, changes in BEDD are spatially heterogeneous, with the increases in northern CA and the mountainous areas and decreases in the southern and middle part of the plain areas. This dataset can be applied for assessing the future risks in the local agriculture for climate changes and will be beneficial to adaption and mitigation actions for food security in this region.
This data set includes precipitation data from a total of nine ground-based precipitation observation stations located in the Yadong River Valley in the middle of the Himalayas. The observation data was collected by the Hobo tumbler rain gauge developed by Onset company and exported through supporting data reading software. Accumulated counts, the rain gauge tipped once, indicating that 0.2 mm of precipitation was recorded, and the default value of -999 was used when no precipitation event occurred. We screened the collected data and eliminated abnormal values to ensure its quality. This data set has made some progress in the analysis of precipitation characteristics, satellite data verification and model simulation evaluation in this area and two academic papers have been published, which provides strong support for the analysis of precipitation characteristics in the high-altitude valleys of the Himalayas lacking ground observation data.
The observation data set of field meteorological stations in Central Asia and Western Asia (2019-2020) includes the monthly meteorological data of 12 field meteorological stations in Kazakhstan (5 stations), Kyrgyzstan (1 station), Tajikistan (3 stations), Uzbekistan (1 station) and Iran (2 stations), involving 21 observation indicators: Monthly average temperature (TA), monthly average pressure (PA) Monthly average relative humidity (RH), monthly total rainfall (PR), monthly average wind speed (WS), monthly average wind direction (WD), 0cm monthly average soil temperature (TS1), 5cm monthly average soil temperature (TS2), 10cm monthly average soil temperature (Ts3), 15cm monthly average soil temperature (ts4), 20cm monthly average soil temperature (ts5), 40cm monthly average soil temperature (TS6) 60cm monthly average soil temperature (ts7), 100cm monthly average soil temperature (ts8), monthly total solar radiation (SR), monthly total reflected radiation (GR), monthly total ultraviolet radiation (UVR), monthly total net radiation (NR), monthly total photosynthetic effective radiation (PAR), monthly total soil heat flux (HF) and monthly total sunshine duration (SD). The 12 field stations cover farmland, forest, grassland, desert, desert, wetland, plateau, mountain and other different ecosystem types. The data length starts from October 2019 to December 2020. The original meteorological data collected by the ground meteorological observation station is obtained after format conversion after screening and review, and the data quality is good. Central Asia has diverse climate types, fragile ecological environment and frequent meteorological disasters. The establishment of this data set provides data support for long-term research in the fields of ecological environment monitoring, disaster prevention and reduction, climate change and ecological environment in Central Asia. At present, it has been applied in the research of ecological environment monitoring in Central Asia.
LI Yaoming LI Yaoming
LI Hu, PAN Xiaoduo, LI Xin, GE Chunmei, RAN Youhua
Based on the historical daily maximum temperature data and reanalysis data set of stations, a daily maximum temperature statistical downscaling model based on first-order autoregressive and multiple linear regression models is developed. Driven by the IPCC cmip6 scenario data of the global climate model (cnrm-cm6-1), the statistical downscaling model predicts the number of five heat wave indexes (heat wave events) of 65 stations in Central Asia from 2015 to 2100 (HWM), heat wave frequency (HWF), heat wave intensity (HWM), maximum duration of heat wave (HWD), heat wave amplitude (HWA)). Finally, the heat wave change scenario data sets of 65 stations in Central Asia under four emission scenarios (ssp126, ssp245, ssp370, ssp585) from 2015 to 2100 were obtained.
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
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
This data set includes grid emission inventories of sulfur dioxide, nitrogen oxides and PM2.5 in 2019 in China's third polar region (Tibet, Xinjiang, Yunnan and Qinghai). The emission inventory comes from the emission inventory database of the research group of Professor Wang Shuxiao of Tsinghua University. The emission inventory is processed into a 1km * 1km grid dataset by using ArcGIS software technology. The basic data of emission calculation is calculated by the emission factor method based on public data collection, satellite observation data and literature collection. The data are from the data of the National Bureau of statistics and the statistical yearbook of other industries, and its quality can be guaranteed. The data can be used for further study of climate and air quality in the third polar region.
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.
WU Qingru WU Qingru
The data set of light absorbing impurities in snow and ice in and around the Qinghai Tibet Plateau include black carbon and dust concentration data and their mass absorption cross sections from 9 glaciers (Urumqi glacier No.1, Laohugou glacier No.12, xiaodongkemadi glacier, renlongba glacier, Baishui River glacier No.1, and golubin glacier, Abramov glacier, syekzapadniyi glacier and No. 354 glacier in Pamir region) . The black carbon data is obtained by DRI 2015 model thermo-optical carbon analyzer, and the dust data is obtained by weighing method. The sampling and experimental processes are carried out in strict accordance with the requirements. The data can be used for the study of snow ice albedo and climate effect.
Data content: 2019 planting structure data set of issyk Lake Basin. Data source and processing method: divide 2019 into three time periods, splice the sentry 2 data with the least cloud amount and the highest quality in each time period into a complete map, obtain the phase III sentry 2 remote sensing image of the Aral Sea basin, calculate the NDVI value of the phase III image, and then combine the obtained cultivated land data and field sampling data to classify it with random forest algorithm, Finally, the planting structure type on each plot is obtained. Data quality: the spatial resolution is 10m × 10m, time resolution of year, kappa coefficient of 0.8. Data application results: it can be used for crop yield estimation and water resource utilization efficiency calculation.
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: soil moisture data of Aral Sea Basin in 2019. Data source and processing method: from NASA, the daily soil moisture data are added to obtain the sum of soil moisture in each month, and then divided by the number of days to obtain the average value of soil moisture in each month. Data quality: spatial resolution of 0.25 ° × 0.25 °, the time resolution is monthly, and the value of each pixel is the average value of monthly soil humidity. Data application achievements and prospects: 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 data set is the monthly runoff data of nijnii hydrological station, the main stream of the upper reaches of Amu Darya River in Central Asia from 1967 to 2017. The station is located on the main stream of the border between Tajikistan and Afghanistan. The data is from Tajikistan hydrometeorological Bureau. The data are processed according to the country's hydrological observation specifications and quality control process. The data period is 1967-2017. The hydrological station is located at 37.193121 ° n, 68.590218 ° e, 328m above sea level, and the unit of runoff is m3 / s. The data can be used for scientific research and water conservancy engineering services such as water resources assessment in Central Asia mountainous areas.
This data includes the land cover data of Central Asia, South Asia and Indochina Peninsula in the from 1992 to 2020 with a spatial resolution of 300mLand cover data includes 10 primary categories, which are combined from the secondary categories of the original data. The data source is the surface coverage product CCI-LC of ESA, where the spatial distribution of cropland, built-up land, and water for the land cover data from 1992 to 2020. Combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 500 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), the training sample dataset of land cover interpretation were built from the consistent areas of multiple products. The Google Earth Engine and random forest algorithm were used to correct the cropland, built-up land, and water of temporal CCI-LC data. Using the high resolution images in Google Earth at 2019 and 2020, the accuracy of change areas of cropland, built-up land, and water was validated by the stratified random sampling. A total of 3,600 land parcels were selected from 1,200 land parcels of the three land cover types, indicating that the accuracy of our corrected product increased in the range of 11% to 26% for the change areas compared to the CCI-LC product.
This dataset was captured during the field investigation of the Qinghai-Tibet Plateau in June 2021 using uav aerial photography. The data volume is 3.4 GB and includes more than 330 aerial photographs. The shooting locations mainly include roads, residential areas and their surrounding areas in Lhasa Nyingchi of Tibet, Dali and Nujiang of Yunnan province, Ganzi, Aba and Liangshan of Sichuan Province. These aerial photographs mainly reflect local land use/cover type, the distribution of facility agriculture land, vegetation coverage. Aerial photographs have spatial location information such as longitude, latitude and altitude, which can not only provide basic verification information for land use classification, but also provide reference for remote sensing image inversion of large-scale regional vegetation coverage by calculating vegetation coverage.
LV Changhe, ZHANG Zemin