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Evaluating the Snow Cover Service Value on the Qinghai–Tibet Plateau.

Authors :
Gao, Xianglong
Feng, Qi
Liu, Wen
Deng, Xiaohong
Zhu, Meng
Zhang, Baiting
Xue, Jian
Source :
Remote Sensing; Jul2024, Vol. 16 Issue 14, p2600, 18p
Publication Year :
2024

Abstract

The Snow Cover Service Value (SCSV) is an important component of the ecological assets of the Qinghai–Tibet Plateau (QTP). Exploring the SCSVs on the QTP is the key to maintaining the functions of climate regulators and Asian water towers, and it is also an important theoretical basis for maintaining the ecological security of ecological barrier areas. Using multi-source data such as daily and monthly observation data sets and related statistical yearbooks, an evaluation framework for the SCSVs on the QTP was constructed for the first time. The results showed that the average annual SCSV of the QTP from 2001 to 2020 was 6.99 trillion yuan, and the average annual climate regulation value was 5.81 trillion yuan, which was the most important SCSV. The Inner Plateau Basin, the Brahmaputra Basin, and the Yangtze Basin had the highest SCSVs, while the Yellow Basin had the lowest unit SCSV, where it was the most vulnerable area of snow cover resources on the QTP, and the SCSV of the Yellow Basin was significantly correlated with rainfall. The correlation between the SCSV and the temperature in the Indus and Ganges Basins was significant. The freshwater supply value of the snow in the Indus Basin and Tarim Basin was found to be able to reach 30% of the whole year in June, where it would be necessary to pay attention to the risk of flooding caused by snowmelt during the melting period. Finally, this paper discussed the strategies for the protection and development of snow resources in each basin based on the spatial distribution characteristics, seasonal variation characteristics, and influencing factors of the SCSVs. The research can provide reference for the rational allocation of snow resources and ecological protection on the QTP. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
16
Issue :
14
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
178698127
Full Text :
https://doi.org/10.3390/rs16142600