The dataset of of potential glacial lakes (PGLs) distribution in the Tibetan Plateau and its surrounding (TPS) are vector data (. SHP). The data set contains the ID, area, perimeter, volume and elevation of each PGL. The TPS region was divided into 17 subregions based on the river basins’ borders, including 8 outflow river basins, i.e., the Yellow, Yangtze, Mekong, Salween, Brahmaputra, Ganges, Indus, and Ob river basins, and 9 exorheic river basins, i.e., the Qiangtang, Hexi, Tarim, Qiadam, Junggar, Yili, Syr Darya, Amu Darya, and Mongolia river basins. This data is processed from theGlacier ice thickness distribution dataset (provided by Farinotti et al. (2019)). The grid difference between the initial DEM and the glacier ice thickness distribution was used to produce the DEM without glaciers. The overdeepenings were detected via two steps. First, we filled the depressions of the DEM without glaciers using a hydrology tool in the ArcGIS software. Second, using the filled DEM to subtract the DEM without glaciers, we ascertained the PGLs’ locations, areas, depths, and volumes. The quality of this data set depends on the quality of the original glacier thickness data, and the quality of the ice thickness dataset is the best of all similar data at present. The dataset of of potential glacial lakes distribution in the Tibetan Plateau and its surroundings can provide a new perspective from which to understand the future formation and evolution of glacial lakes in the TPS. It is anticipated that approximately 16,000 PGLs areas of greater than 0.02 km2 will be formed in the TPS, covering an area of 2253.95 ± 1291.29 km2 and holding a water volume of 60.49 ± 28.94 km3, which would contribute to a 0.16 ± 0.08 mm equivalent sea-level rise.
0 2022-04-15
Greenland digital elevation models (DEMs) are indispensable to fieldwork, ice velocity calculations, and mass change estimations. Previous DEMs have provided reasonable estimations for the entire Greenland, but the time span of applied source data may lead to mass change estimation bias. To provide a DEM with a specific time-stamp, we applied approximately 5.8×108 ICESat-2 observations from November 2018 to November 2019 to generate a new DEM, including the ice sheet and glaciers in peripheral Greenland. A spatiotemporal model fit process was performed at 500 m, 1,2, and 5 km grid cells separately, and the final DEM was posted at the modal resolution of 500 m. A total of 98% of the grids were obtained by the model fit, and the remaining DEM gaps were estimated via the ordinary Kriging interpolation method. Compared with IceBridge mission data acquired by the Airborne Topographic Mapper (ATM) Lidar system, the ICESat-2 DEM was estimated to have a maximum median difference of -0.48 m. The performance of the grids obtained by model fit and interpolation was similar, which both agreed well with the IceBridge data. DEM uncertainty rises in regions of low latitude and high slope or roughness. Furthermore, the ICESat-2 DEM showed significant accuracy improvements compared with other altimeter-derived DEMs, and the accuracy was comparable to those derived from stereo-photogrammetry and interferometry. Overall, the ICESat-2 DEM showed excellent accuracy stability under various topographic conditions, which can provide a specific time-stamped DEM with high accuracy that will be useful to study Greenland elevation and mass balance changes.
0 2022-04-15
(1) Data content: this data set is the soil profile water, heat and carbon data set of Shenzha alpine grassland from 2019 to 2020, including the daily average values of soil temperature, water content and CO2 concentration at different depths (5 cm, 10 cm, 20 cm, 40 cm, 100 cm and 150 cm)（ 2) Data sources and processing methods: the data are from field in situ observation. Among them, the data of soil temperature comes from cs109 probe, the data of soil water content comes from CS616 probe, and the data of soil CO2 concentration comes from gmm222 probe（ 3) The data quality is high, but due to the power supply problem, there is a lack of data at the end of April（ 4) It is helpful to improve the understanding of the underground carbon processes in the Tibetan Plateau.
0 2022-04-15
This data set includes evapotranspiration data set of the Tibet Plateau at the monthly scale from 1979 to 2018. The data set is based on the ERA5 net radiation and China meteorological forcing dataset (CMFD). The evapotranspiration is derived by the sigmoid generalized complementary equation, which is calibrated and verified by the observation data of 12 eddy flux sites and water balance data of 5 river basins (the source region of Yangtze river, the source region of Yellow River, the Nu River, the Yarlung Zangbo River, and the Hei River) on the Tibetan Plateau, which shows a high accuracy. The data set can be used to study the hydrological cycle and climate change in the Tibetan Plateau.
0 2022-04-15
The interannual variation data set of Lake area and water quantity in different regions of the Qinghai Tibet Plateau contains the continuous series data of 20 lakes with an area of more than 100 square kilometers in different regions from 1976 to 2019 （no data available from 1978 to 1985）. According to the October December data of Landsat series images, the seasonal variation can be reduced while the available data can be maximized. The NDWI water body index was used to extract the lake area, and the SRTM DEM was used to fit the relationship between the lake area and the change of water quantity. The data are applied to the study of lake change, lake water balance and climate change in the Qinghai Tibet Plateau.
0 2022-04-15
This data set is a 21 year (1988-2008) surface soil moisture data set in the Qinghai Tibet Plateau. The temporal resolution is yearly, the spatial resolution is 25km, and the data unit is m3 / m3. Based on the retrieval of plateau soil moisture by van der Velde et al. (2014), the data set was generated by using three-dimensional discrete transformation method to make up for the lack of value. The data has been verified by the site, and compared with the reanalysis data, it is found that the data quality is better. The data can be used to study the spatiotemporal variation of soil moisture in spring.
0 2022-04-15
China's daily snow depth simulation and prediction data set is the estimated daily snow depth data of China in the future based on the nex-gdpp model data set. The artificial neural network model of snow depth simulation takes the maximum temperature, minimum temperature, precipitation data and snow depth data of the day as the input layer of the model, The snow depth data of the next day is used as the target layer of the model to build the model, and then the snow depth simulation model is trained and verified by using the data of the national meteorological station. The model verification results show that the iterative space-time simulation ability of the model is good; The spatial correlations of the simulated and verified values of cumulative snow cover duration and cumulative snow depth are 0.97 and 0.87, and the temporal and spatial correlations of cumulative snow depth are 0.92 and 0.91, respectively. Based on the optimal model, this model is used to iteratively simulate the daily snow depth data in China in the future. The data set can provide data support for future snow disaster risk assessment, snow cover change research and climate change research in China. The basic information of the data is as follows: historical reference period (1986-2005) and future (2016-2065), as well as rcp4.5 and rcp8.5 scenarios and 20 climate models. Its spatial resolution is 0.25 ° * 0.25 °. The projection mode of the data is ease GR, and the data storage format is NC format. The following is the data file information in NC Time: duration (unit: day) Lon = 320 matrix, 320 columns in total Lat = 160 matrix, 160 rows in total X Dimension: Xmin = 60.125; // Coordinates of the corner points of the lower left corner grid in the X direction of the matrix Y Dimension: Ymin = 15.125; // Coordinates of the corner points of the grid at the lower left corner of the Y-axis of the matrix
0 2022-04-15
As an important parameter of snowpack, the snow depth can adjust changes in sea ice and plays a vital role in the climate system. This dataset provides the daily snow depth on Arctic sea ice during cold-season from 2012 to 2020 (October-April). The snow depth retrieval algorithm combines linear regression and deep learning algorithm (LSTM). Firstly, according to AMSR2 brightness temperature data and the buoy snow depth data, the optimal gradient ratio for estimating the snow depth on first-year ice (FYI) is determined, and the snow depth retrieval model on FYI is established. The input of LSTM is different gradient rates, polarization ratio (PR) of the brightness temperature at 37 GHz, and the output is snow depth on sea ice. Then, the snow depth retrieval model on multi-year ice (MYI) is determined. Finally, the snow depth on Arctic sea ice with a 25-km spatial resolution is obtained. This dataset performs well in reflecting snow depths over thick and thin sea ice, which agrees well with the NASA Operation IceBridge (OIB) data, with the root mean square error of 7.35 cm. Hence, this dataset provides accurate snow depth estimates for retrieving sea ice thickness and is conducive to analyses of Arctic mass balance and energy balance.
0 2022-04-15
We release three data products for monthly groundwater storage (GWS) changes in North America. In the first one, we provide an independent estimate for monthly GWS changes within North America in 1-degree-grids and their trends. In the second one, we give the monthly GWS changes and the trends averaged for the 5 major GWS trend anomalies in around Saskatchewan, Nevada, California, Arizona and Texas, respectively. The third data product includes the monthly GWS changes and the trends averaged for the 14 states or provinces in the US and Canada, affected by the above GWS trend anomalies, i.e., for Saskatchewan, Montana, Nevada, California, Arizona, New Mexico, Texas, Oklahoma, Kansas, Alberta, North Dakota, Minnesota, Colorado and Chihuahuas, respectively. Our estimates of monthly GWS changes and their trends can serve as alternative and beneficial input for sustainable management of groundwater resources in North America. Our data products are derived from the release-6 version of GRACE monthly level-2 data, GNSS data, two land surface models of GLDAS 2.1 for soil moisture and snow water equivalent, and satellite altimetric lake level data. Unlike previous studies, glacial isostatic adjustment (GIA) effects are eliminated by employing an independent separation approach with the aid of GNSS vertical velocity data (Wang et al., 2013). The monthly changes of those GWS anomalies are validated by well level data. The monthly GWS changes for the 14 states or provinces are basically to show compatible variations with precipitation drought intensity level variations. We find a GWS increasing trend anomaly in Saskatchewan and 4 GWS declining trend anomalies with peaks in Nevada, California, Arizona and Texas, respectively. As they are not estimated using GIA models in the correction and their comparison with available well level and drought data confirms their reliability, we suggest our data products as alternative input to groundwater resource management in the discussed areas.
0 2022-04-15
Land surface temperature (LST) is a key parameter in the study of surface energy balance. It is widely used in the fields of meteorology, climate, hydrology, agriculture and ecology. As an important means to obtain global and regional scale LST information, satellite (thermal infrared) remote sensing is vulnerable to the influence of cloud cover and other atmospheric conditions, resulting in temporal and spatial discontinuity of LST remote sensing products, which greatly limits the application of LST remote sensing products in related research fields. The preparation of this data set is based on the empirical orthogonal function interpolation method, using Terra / Aqua MODIS surface temperature products to reconstruct the lst under ideal clear sky conditions, and then using the cumulative distribution function matching method to fuse era5 land reanalysis data to obtain the lst under all-weather conditions. This method makes full use of the spatio-temporal information of the original MODIS remote sensing products and the cloud impact information in the reanalysis data, alleviates the impact of cloud cover on LST estimation, and finally reconstructs the high-quality global 0.05 ° spatio-temporal continuous ideal clear sky and all-weather LST data set. This data set not only realizes the seamless coverage of space-time, but also has good verification accuracy. The reconstructed ideal clear sky LST data in the experimental areas of 17 land cover types in the world, the average correlation coefficient (R) is 0.971, the bias (bias) is -0.001 K to 0.049 K, and the root mean square error (RMSE) is 1.436 K to 2.688 K. The verification results of the reconstructed all-weather LST data and the measured data of ground stations: the average R is 0.895, the bias is 0.025 K to 2.599 K, and the RMSE is 4.503 K to 7.299 K. The time resolution of this data set is 4 times a day, the spatial resolution is 0.05 °, the time span is 2002-2020, and the spatial range covers the world.
0 2022-04-15
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