Impact of snow on vegetation green-up dynamics on the Tibetan Plateau: Integration of survival analysis and remote sensing data

• Published in: Agricultural and forest meteorology Vol. 362; p. 110377
• Main Authors: Xu, Jingyi, Tang, Yao, Xu, Jiahui, Chen, Jin, Shu, Song, Ni, Jingwen, Zhou, Xiaoqi, Yu, Bailang, Wu, Jianping, Huang, Yan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2025
• Subjects: alpine vegetation; China; Climate change; forests; meteorology; phenology; regression analysis; shortwave radiation; snow; Snow depth; snowpack; soil water; Survival analysis model; temperature; Tibetan Plateau; Vegetation green-up date; China; Vegetation green-up date; Climate change; Tibetan Plateau; Snow depth; Survival analysis model
• ISSN: 0168-1923
• DOI: 10.1016/j.agrformet.2024.110377

•A survival analysis model was established to examine the impact of snow variation on satellite-derived vegetation green-up date (GUD) on the Tibetan Plateau under comprehensive influences of various meteorological and environmental factors.•Incorporating snow depth significantly enhances the explanatory power of the survival analysis model and improves GUD estimation of vegetation GUD.•Snow has a more pronounced impact on vegetation GUD than precipitation.•Snow had uneven effects on vegetation green-up throughout the year, hindering growth before the 156th day and accelerating it thereafter. Snow cover variation significantly impacts alpine vegetation dynamics on the Tibetan Plateau (TP), yet this effect under climate change remains underexplored. This study uses a survival analysis model to assess the influence of snow on vegetation green-up dynamics, while controlling for key temperature and water availability factors. This analysis integrates multi-source data, including satellite-derived vegetation green-up dates (GUDs), snow depth, accumulated growing degree days (AGDD), downward shortwave radiation (SRAD), precipitation, and soil moisture. Our survival analysis model effectively simulated GUD on the TP, achieving an R of 0.62 (p < 0.01), a root mean square error (RMSE) of 11.20 days, and a bias of −1.41 days for 2020 GUD predictions. It outperformed both the model excluding snow depth and a linear regression model. By isolating snow's impact, we found it exerts a stronger influence on vegetation GUD than precipitation in snow-covered areas of the TP. Furthermore, snow depth effects varied seasonally: a 1-cm increase in preseason snow depth reduced green-up rates by 8.48% before 156th day but increased them by 4.74% afterward. This indicates that deeper preseason snow cover delays GUD before June, but advances it from June onward, rather than having a uniform effect. These findings highlight the critical role of snow and underscore the need to incorporate its distinct effects into vegetation phenology models in alpine regions.