Light-absorbing impurities from snow and ice in Tibetan Plateau and its surroudings (2020)

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.

0 2021-12-27

Forage supply and supplementary feeding demand data in northern Tibet and Sanjiangyuan areas (2020)

In August 2020, the forage supply and supplementary feeding of herdsmen in northern Tibet and Sanjiangyuan areas of Tibet were investigated. Northern Tibet includes 204 samples. The research areas include Dangxiong County of Lhasa City, seni District of Naqu City, Baqing County, Suo County, such as County, Jiali County, bango County, Ando County, NIMA County, Cuoqin County, Gaize County, Gar County, Ritu County, Pulan county and Zada county. The research indicators include contracted grassland area, grazing forbidden area, grass storage balance grassland area, number of livestock, etc. There are 224 survey samples of herdsmen in Sanjiangyuan area of Qinghai. The survey areas include Maqin County, Gande County, Maduo County, Jiuzhi County, Bama County, dari County of Golog Prefecture and paoqian County, Zaduo County, Yushu county and Chengduo County of Yushu prefecture. The research indicators include the quantity of purchased feed and self-produced feed raised by livestock.

0 2021-12-17

The desertification risk map of the Arabian Peninsula in 2020

The gridded desertification risk data of The Arabian Peninsula in 2021 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 500m 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 the Arabian Peninsula in 2021.

0 2021-12-12