Abstract
Snow cover has significantly changed due to global warming in recent decades, causing large changes in the vegetation ecosystem. However, the impact of snow cover changes on the spring phenology of different vegetation types in Northeast China remains unclear. In this study, we investigated the response of the start of the growing season (SOS) to different snow cover indicators using partial correlation analysis and stepwise regression analysis in Northeast China from 1982 to 2015 based on multiple remote sensing datasets. Furthermore, we revealed the underlying mechanisms using a structural equation model. The results show that decreased snow cover days (SCD) and an advanced snow cover end date (SCED) led to an advanced SOS in forests. Conversely, an increased SCD and a delayed SCED led to an advanced SOS in grasslands. The trends of SCD and SCED did not exhibit significant changes in rainfed cropland. The maximum snow water equivalent (SWEmax) increased in most areas. However, the proportion of the correlation between SWEmax and SOS was small. The impact of snow cover changes on the SOS varied across different vegetation types. Snow cover indicators mainly exhibited positive correlations with the SOS of forests, including deciduous broadleaf forests and deciduous coniferous forests, with positive and negative correlations of 18.61% and 2.58%, respectively. However, snow cover indicators mainly exhibited negative correlations in the SOS of grasslands and rainfed croplands, exhibiting positive and negative correlations of 4.87% and 13.06%, respectively. Snow cover impacted the SOS through the “temperature effect” in deciduous broadleaf forests, deciduous coniferous forests, and rainfed croplands, while it affected SOS through the “moisture effect” in grasslands. These results provide an enhanced understanding of the differences in snow cover changes affecting SOS in different vegetation types under climate change in Northeast China.
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