1.A daily, 0.05° Snow depth dataset for Tibetan Plateau (2000-2018)

Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.

YAN Dajiang, MA Ning, MA Ning, ZHANG Yinsheng

   POLES TPDC

doi: 10.11888/Snow.tpdc.271743 14574 444 Requestable 2022-04-18

2.Basic geographic dataset of resources and environment in Central and Western Asia Region

Basic Geographic Data Set of Resources and Environment in Central and Western Asia Region, includes six parts: administrative divisions map, topographic and geomorphological map, river system maps, precipitation map, temperature map and potential evapotranspiration map. The precipitation and temperature datasets are interpolated based on the ground observations, while the potential evapotranspiration dataset is calculated based on the Penman-Monteith equation. The precipitation, temperature and potential evapotranspiration datasets are resampled from the original 0.5° CRU dataset by using the linear interpolation method in ArcGIS software. This dataset is made based a large number of gauge observations with good quality control and homogeneity check. The results of the related studies (Deng and Chen, 2017; Li et al., 2017; Li et al., 2016) suggested that this dataset is applicable and satisfactory for the climatological studies. 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.

   POLES TPDC

doi: 10.11888/Geogra.tpdc.270491 6379 406 Protection period 2022-04-18

3.Meteorological observation stations distribution map of the Heihe River Basin

This data set includes the information of 21 conventional meteorological observation stations in Heihe River Basin and its surrounding areas, of which Wutonggou and Quixote stations have been cancelled in the 1980s, and other stations have operated since the establishment of the station. Station name, longitude and latitude 1. Mazong mountain 97.1097 41.5104 2. Yumen town 97.5530 39.8364 3. Wutonggou 98.3248 40.4697 4. Jiuquan 98.4975 39.7036 5. Jinta 98.9058 39.9988 6. Dingxin 99.5117 40.3080 7. Gaotai 99.7907 39.3623 8. Linze 100.165 39.1385 9. Sunan 99.6178 38.8399 10. Yeniugou 99.5830 38.4167 11. Tole 98.0147 39.0327 12. Ejina Banner 101.088 41.9351 13. Guaizi Lake 102.283 41.3662 14. Zhangye 100.460 38.9124 15. Shandan 101.083 38.7746 16. Folk music 100.826 38.4376 17. Alxa Right Banner 101.429 39.1407 18. Yongchang 101.578 38.1771 19. Qilian 100.238 38.1929 20. Gangcha 100.111 37.2478 21. Menyuan 101.379 37.2513 22. Gekkot 99.7063 41.9183 23. Jiayuguan 98.2241 39.7975

National Meteorological Information Center

   POLES TPDC HEIHE

doi: 10.11888/Meteoro.tpdc.270588 23118 788 Open Access 2020-06-05