Snow Cover Phenology Change and Response to Climate in China during 2000–2020

Abstract

Snow cover phenology (SCP) is critical to the climate system. China has the most comprehensive snow cover distribution in the middle and low latitudes and has shown dramatic changes over the past few decades. However, the spatiotemporal characteristics of SCP parameters and their sensitivity to meteorological factors (temperature and precipitation) under different conditions (altitude, snow cover classification, or season) in China are insufficiently studied. Therefore, using improved daily MODIS cloud-gap-filled (CGF) snow-cover-extent (SCE) products, the spatiotemporal characteristics (distribution and variation) and respond to climate of snow cover area (SCA), snow cover start (SCS), snow cover melt (SCM), and snow cover days (SCD) are explored from 2000 to 2020. The results show that in the past 20 years, snow cover in China has demonstrated a trend of decreasing SCA, decreasing SCD, advancing SCS, and advancing SCM, with SCM advancing faster than SCS. The greatest snowfall occurs in January, mainly in northeastern China, northern Xinjiang, and the Tibet Plateau. Spatially, the slope of SCP was mainly within ±0.5 day/year (d/y) Statistics indicated that the area proportion where SCD is significantly reduced is greater than increased; SCD, SCS, and SCM are shortened or advanced in three snow-covered area classifications. Moreover, compared with precipitation, the significantly correlated regions (6–47.2% more than precipitation) and correlation degree (1.23–8.33 times precipitation in significantly correlated snow cover classification) between temperature and SCP in different seasons are larger. For stable snow-covered areas (SSA), SCD are mainly affected by spring temperature below 1500 m and mainly by autumn temperature above 1500 m; the precipitation is more affected in autumn. The correlation of SCP with temperature and precipitation has obvious spatial and seasonal differences and shows characteristic variation with altitude. These results can provide important data support for climate prediction, hydrological research, and disaster warning.

The Arabian Sea and Bay of Bengal play a key role in extreme precipitation during the snow season over the Tibetan Plateau

“…In this study, November to March of the next year was defined as the snow season (Hao et al, 2021; Zhao et al, 2022). First, we used meteorological station data to validate the grid data among the CMFD, CPC and HAR v2 datasets.…”

Response of the Normalized Difference Vegetation Index (NDVI) to Snow Cover Changes on the Qinghai–Tibet Plateau

“…The significance of changes in slope was tested using the F-test, where we considered whether the trend was significant at the 95% confidence level, i.e., the trend was not significant when p ≥ 0.05, whereas the trend was significant when p < 0.05. Pearson's correlation coefficients were calculated for correlation analysis between the snow cover phenology and climatic and vegetation factors [14]. Pearson's correlation coefficient measures the closeness of the correlation between two series by the product of their deviations from the respective means.…”

“…Several previous studies have investigated the relationship between snow and vegetation [14][15][16][17], as well as the effects of dynamic changes in snow and vegetation [6] on the QTP, but it is still unclear how various factors might affect snow and vegetation on the Plateau. In particular, few studies have investigated the positive and negative feedback between changes in vegetation and snow cover in different plateau areas and the factors (such as temperature, precipitation, solar radiation, soil moisture, etc.)…”

Decline of Late Spring and Summer Snow Cover in the Scottish Highlands from 1984 to 2022: A Landsat Time Series

“…The normalised snow difference index (NDSI) uses the fact that snow reflects strongly in the visual wavelengths and absorbs strongly over the shortwave infrared (SWIR) wavelengths, helping to distinguish snow from other visually reflective features such as clouds or rocks. This index, or close variants thereof, is very widely used to detect snow cover in satellite imagery [41,[51][52][53][54][55][56][57] and was computed as:…”