Among many indicators reflecting changes in climate and environment, the stable isotope index of ice core is an indispensable parameter in ice core record research, and it is one of the most reliable means and the most effective way to restore past climate change. Meanwhile, ice core accumulation is a direct record of precipitation on the glacier, and high-resolution ice core records ensure continuity of precipitation records. Therefore, ice core records provide an effective means of restoring changes in precipitation. Stable isotopes from ice cores drilled throughout the TP have been used to reconstruct climate histories extending back several thousands of years. This dataset provides data support for studying climate change on the Tibetan Plateau.
0 2022-06-20
Different forms of precipitation (snow, sleet, and rain) have divergent effects on the Earth’s surface water and energy fluxes. Therefore, discriminating between these forms is of significant importance, especially under a changing climate. We applied a state-of-the-art parameterization scheme with wet-bulb temperature, relative humidity, surface air pressure, and elevation as inputs, as well as observational gridded datasets with a maximum spatial resolution of 0.25◦, to generate a gridded dataset of different forms of daily precipitation (snow, sleet, and rain) and their temperature threshold across mainland China from 1961-2016. The annual snow, sleet, and rain amount were further calculated. The dataset may benefit various research communities, such as cryosphere science, hydrology, ecology, and climate change.
0 2022-06-17
In recent years, branched chain glycerol dialkyl glycerol tetraethers (brGDGTs) derived from microbial cell membrane lipids are sensitive to environmental parameters (temperature and pH, etc.) and are widely used in the quantitative reconstruction of paleoenvironment. Based on the surface sediments of lakes in the Qinghai Tibet Plateau and brGDGTs in the surface sediments of other lakes published in China, we developed a new brGDGT-air temperature calibration. By collecting the annual suspended particulate matter of Dagze Co and analyzing brGDGTs, the distribution of brGDGTs in different water column layers were reconstructed. Combined with the modern observation results and the new calibration, using the results of brGDGT in the sediments of Xiada Co, the atmospheric temperature changes in the western Qinghai Tibet Plateau in the past 2000 years are reconstructed. This result provides an important theoretical reference for the reconstruction of temperature by brGDGTs in the future.
0 2022-06-15
This data set is an upgraded version of "China snow depth long time series data set (1978-2012)". The long time series data set of snow depth in China (1979-2021) adopts longitude and latitude projection, and the data is floating-point. Data sets are stored by year. Each year is a compressed package, and each compressed package contains daily snow depth files. The daily snow depth is stored in a TXT file named "yyyyddd.txt", where yyyy stands for year, DDD stands for Julian date, and the unit of snow depth is cm. For example, 2005001 Txt represents this ASCII file to describe the snow cover in China on the first day of 2005. The ASCII code file of the data set is composed of a header file and the main content. The header file consists of 6 lines of description information, such as the number of rows, the number of columns, the coordinates of the x-axis center point, the y-axis center point, the grid size, and the label value of the no data area. The main content is a two-dimensional group composed of the number of rows and columns. The unit of snow depth is cm. Because the space described by all ASCII files in the data set is nationwide in China, the header files of these files are unchanged. Now the header files are excerpted as follows (where xllcenter, yllcenter and cellsize are in degrees): Ncols 321 Nrows 161 Xllcenter 60 Yllcenter 15 Cellsize 0.25 NoData_ Value -1
0 2022-06-15
This data set is the global high accuracy global elevation control point dataset, including the geographic positioning, elevation, acquisition time and other information of each elevation control point. The accuracy of laser footprint elevation extracted from satellite laser altimetry data is affected by many factors, such as atmosphere, payload instrument noise, terrain fluctuation in laser footprint and so on. The dataset extracted from the altimetry observation data of ICESat satellite from 2003 to 2009 through the screening criteria constructed by the evaluation label and ranging error model, in order to provide global high accuracy elevation control points for topographic map or other scientific fields relying on good elevation information. It has been verified that the elevation accuracy of flat (slope<2°), hilly (2°≤slope<6°), and mountain (6°≤slope<25°) areas meet the accuracy requirements of 0.5m, 1.5m, and 3m respectively.
0 2022-04-15
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
0 2022-04-15
The sand drift potential (DP, in vector units (VU)) is calculated by DPi=∑U^2*[U-Ut]*fu where i represents 16 directions: N, NNE, NE, NEE, E, EES, ES, ESS, S, SSW, WS, WWS, W, WWN, NW and NNW; U is the sand-moving wind speed at the standard height of 10 m; Ut is the threshold wind velocity, which is the minimum wind velocity at the 10 m height to cause sediment particles in saltation; and fu is the fraction of time when the wind speed is higher than Ut. The 2 m s-1 bin is adopted in each sand-moving wind direction, corresponding to the mean wind speeds of 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33 and 34 m s-1, and the sum of these bins is the final DP in the wind direction. Note that these mean wind speeds have been expressed in knots by the approximate conversion (1 knot = 0.5144 m s-1) to ensure the valid classification of wind energy (low energy, <200 VU; intermediate energy, ≥200 VU and <400 VU; high energy, ≥400 VU) developed by Fryberger. The divisor used in calculating the frequency of effective sand-moving winds from different directions is the total hour number of Julian years (8760 hours for common years or 8784 hours for leap years). The wind speed and wind direction data from 1950 to 2021 were hourly estimates of 10 m u-component of wind and 10 m v-component wind with a horizontal resolution of 0.1°×0.1° generated with the ERA5-Land dataset.
0 2022-06-07
Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).
0 2022-06-06
The long-term sequence data set of lake areas on the Tibetan Plateau contains area data of 364 lakes with areas greater than 10 square kilometers from 1970s to 2013. Based on Landsat images, Landsat data in October are mainly used, and one data is taken every three years to reduce seasonal variation and make the available data reach the maximum. The data set is extracted by the NDWI Water Index, and each lake undergoes manual visual inspection and edition. The data set can be used to study lake change, lake water balance and climate change on the Tibetan Plateau. Data type: Vector data. Projection: WGS84.
0 2022-05-24
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
0 2022-05-17
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