This data set is the water resources data of the Qinghai Tibet Plateau from 1990 to 2010, which is the sum of renewable surface and groundwater resources. The data is in vector format and the spatial resolution is in the scale of prefecture level administrative units. The data is obtained by checking the results of VIC (variable injection capacity) hydrological model. The simulated water resources are the sum of the surface runoff and underground runoff in the output results of hydrological simulation. The simulation results are verified by comparing with the runoff data of the measured stations. According to the statistics of water resources at the provincial level in China water resources bulletin, a correction coefficient α is introduced at the provincial level, so that the product of water resources and α in the hydrological model simulation province is equal to the statistics of water resources. Then the amount of water resources in the administrative unit is the product of the total amount of water resources and α.
DU Yunyan YI Jiawei
Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐ seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multi- ple controlling variables, including near‐surface air temperature downscaled from re‐ analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to avail- able existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5– 65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.
Tibetan Plateau, located in southwest China, is one of the key areas affecting the Asian monsoon, and it is also an early warning area and sensitive area for global climate change. As the main parameter of surface energy balance, surface temperature represents the degree of energy and water exchange between earth and atmosphere, and is widely used in climatology, hydrology and ecology. The study of land-atmosphere interaction in Qinghai-Xizang Plateau urgently needs long time series and all-weather surface temperature data set with high temporal and spatial resolution. However, the frequent cloud cover characteristics in this area limit the use of the existing satellite thermal infrared remote sensing surface temperature data set. The generation method of this data set is an integrated method of thermal infrared and passive microwave surface temperature based on the time component decomposition model of surface temperature. This method was originally applied to Northeast China and its adjacent areas, and subsequently extended to western China including the Qinghai-Xizang Plateau. The main input data of this method are Aqua MODIS,Aqua AMSR-E,GCOM-W1 AMSR2 and other data, and the auxiliary data include leaf area index (LAI) products provided by satellite remote sensing, surface cover type data and so on. This method makes full use of the steady and unstable components of surface temperature provided by satellite thermal infrared remote sensing and passive microwave remote sensing, as well as the spatial correlation of surface temperature. The obtained all-weather surface temperature has good accuracy and image quality. The time span of the dataset is from 2003 to 2018, the time resolution is 2 times a day, and the spatial resolution is 1 km, which is expected to provide data support for related applications.
ZHANG Xiaodong Zhou Ji LIU Shaomin
This data is the aridity index (AI) under the rcp4.5 scenario. AI data is the ratio of precipitation to potential evapotranspiration. This data is calculated by the average of 14 models. These 14 modes are canesm2; ccsm4; cnrm-cm5; csiro-mk3-6-0; giss-e2-r; hadgem2-cc; hadgem2-es; inmcm4; ipsl-cm5a-lr; miroc5; miroc-esm-chem; miroc-esm; mpi-esm-lr; mri-cgcm3. The spatial resolution is 2 * 2 degrees, and the temporal resolution is from January 2020 to December 2099. This data set can be used to analyze the future dry and wet change scenarios in the Great Lakes region of Central Asia, as well as the dry and wet past and pattern in other regions of the world under the future scenarios.
The continuous snow cover area in time and space is one of key elements to study of land surface energy and water exhange, mountain hydrology, land surface model, numerical weather forecast and climate change. However, the large number of clouds causes data gaps in the snow cover area from optical remote sensing. The MODIS observations of Terra and aqua, FY-2E and FY-2F VISSR are used to obtain fractional snow cover (subpixel snow cover) which is less affected by the cloud, and the snow cover of the remaining cloud pixels is supplemented according to the time series information. Finally the cloudless daily snow fraction is obtained. This data set includes the daily fractional snow cover at 5 km spatial resolution in the Tibetan Plateau and China.
The gridded desertification risk data in Central-Western Asia 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 1km 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 Central-Western Asia.
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2018, and provided a long and dense lake observations between the 1970s and 2018. We found that the number of lakes, with area larger than 1 km2, has increased to ~1400 in 2018 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
This dataset includes component temperatures measured by the thermal infrared (TIR) radiometers at the Mixed Forest and Sidaoqiao stations between 22 July, 2014 and 19 July, 2016. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, two TIR radiometers (SI-111, Apogee Instruments Inc., USA) connected to a data logger (CR800, Campbell Scientific Inc., USA) measured component temperatures of the sunlit canopy and shaded canopy. TIR radiometers were mounted horizontally at 5 m height on iron rods just south and north of a tree and pointed to its canopy. The distance from the sensor to the canopy was ~1 m. At the Sidaoqiao station, two SI-111 TIR radiometers connected to a CR800 data logger measured component temperatures of the soil and shrub. The first sensor pointed from 2 m height under a viewing zenith angle of 45° to bare soil; the second sensor was mounted at 1-m height and pointed horizontally into the shrub canopy.
MODIS 250-meter forest coverage is a key parameter that accurately reflects the overall coverage of forests. Forests serve as special “transformation” roles in lithosphere, biosphere, soil circles and the atmosphere, to assess the global carbon balance of ecosystems and Regional contributions and responses provide the foundation.Currently, MODIS satellite data products are an important source of data for inversion of forest cover.With 18 key nodes as the research area, based on the MOD44B data from 2000 to 2016, the forest coverage data of different regions were tailored and estimated, and the MODIS 250-meter forest coverage data of key nodes in 2000-2016 was obtained.
1. Data source: MODIS/Terra Vegetation Indices 16-day L3 Global 250m SIN Grid V006 products (2000-2017) Download address https://search.earthdata.nasa.gov/ 2. Data name: (1) resize is automatically generated in the batch cropping process, which means that it has been extracted by mask and the data range after processing is xinjiang provice; (2) seven digits represent the time of data acquisition, the first four digits are years, and the last three digits are days of the year.For example, "2000049" means that the year of data acquisition is 2000 and the specific time is the 49th day of that year. (3) 250m represents the ground resolution, i.e. 250 meters; (4) 16_days represents the time resolution, that is, 16 days; (5) NDVI represents data type, namely normalized vegetation index; 3. Data time range: 2000049-2017353, data interval of 16 days; 4..Tif file and.hdr file . Tif file is the original NDVI data with the same name. HDR file is the mask data that supports normal use of. 5. To analyze the ecological effects of cryosphere
The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
XU Ziwei LIU Shaomin
This data includes future population and GDP estimates based on the SSP2 scenario at the Mekong basin grid scale. The data comes from the global population projection data with a spatial resolution of 5 minutes (about 10km) and the GDP projection data with a spatial resolution of 0.5 degrees (about 50km) provided by the ISIMIP. The method of spatial interpolation is used to get 0.25-degree population projection data from 5-min population projection, and 0.5-degree GDP projection data is downscaled to obtain the 0.25 degree GDP data. The data provided by ISIMIP has passed the data with good quality control, and has not been further verified after data interpolation. The data can be used for the socio-economic impact assessment of climate change and extreme climate events in the Mekong River Basin.
The most complete Early Cenozoic strata in the Simao Basin are located in Xiaojinggu Town, Jinggu County, which mainly includes the sedimentary strata of the Mengyejing Formation, the Denghei Formation and the Mengla Formation. Due to the tectonic uplifting of the mountain in the late Cenozoic, the syncline structure caused the top of the Mengyejing Formation, the Denghei Formation and the Mengla Formation to be exposed to the surface. However, a complete sedimentary profile containing the middle and lower part of the Mengyejing Formation could not be obtained due to vegetation cover and village construction. The chronological study of sedimentary strata in the Simao Basin is mainly concentrated in the Mengyejing Formation with potassium salt. However, there still has significant controversy about the deposition time of this group at this stage. Recently, a continuous and complete high-resolution sequence (361.86 m in thickness) of the Mengyejing Formation was obtained through the continuous drilling. Among them, the Mengyejing Formation (0-353.3 m) is mainly a set of purple-red muddy silt and mudstone combination, while the underlying Mangang Formation (353.3-361.86 m) is a set of gray-white sandstone.
The thick Cenozoic sediments deposited in Yunnan are ideal achieves used to explore the history of local deformation process affected by the collision of the Indian-Eurasian plate as well as the evolution of the Indian monsoon in the Cenozoic. However, due to the lack of precise age control, the early Neogene strata in Yunnan are poorly constrained. The Qujing Basin in the northern part of Yunnan Province preserves thick and continuous Cenozoic sediments, which can be divided into the Xiaotun Formation, the Caijiachong Formation and the Ciying Formation from bottom to top. Through the combination of the field outcrop profile and the borehole core, the research team obtained the stratified stratum of the Xiaotun Formation and the Caijiachong Formation with a total thickness of 251 m in the Qujing Basin. The U-Pb geochronology of the top volcanic tuff layer (35.49 ± 0.78 Ma), Caijiachong mammal fossil group (late Eocene) as well as magnetic stratigraphy collectively reveals that the age at the bottom of the Xiaotun Formation is 46.2 Ma, the top of the Caijiachong Formation should be < 36.2 Ma, and the epoch line of the two groups is 41.2 Ma. However, due to the weak influence of tectonic activities in the late Cenozoic and the small deformation of the formation, the terrain in the middle of the basin is relatively flat, resulting in the inability to obtain the top of the continuous Caijiachong Formation and the upper Ciying Formation samples. A total of 320.1 meter core covering the entire Ciying Formation and the Caijiachong Formation was obtained through the continuous drilling mission carried out in the center of the basin. Among them, the overall lithology of the core of the Ciying Formation (0-216.3 m) is dominated by gray mudstone and siltstone, and several layers of coal seams are intercalated; while the lower Caijiachong Formation (216.3-305.5 m) is grayish and grayish green mudstone. The lithology of the Xiaotun Formation (305.5-320.1 m) is mainly dominated by red mudstone.
This is the daily temperature observation data set of 6 points in Xiaodong Kemadi, 4 points in Yangbajing, and 4 points in Hariqin during 2012-2015.
The data set includes the vertical profile of water quality and the multi-parameter data of surface water quality of Selincho Lake during the investigation of the sources of rivers and lakes from June to July of 2017. The main water quality parameters measured are dissolved oxygen, conductivity, pH, water temperature, etc. YSI EXO2 water quality multi-parameter measuring instrument is calibrated according to lake surface elevation and local pressure before each measurement. The time interval of measurement is set at 0.25s, and the speed of putting in is slow, so he high continuity of data acquisition is guaranteed. The original data obtained include the measured data exposed to air above the water surface, which are eliminated in the later processing.
Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
HE Jie YANG Kun
Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.
HUANG Wei ZHAO Hong