Spatiotemporal snow cover characterization and its linkage with climate change over the Chenab river basin, western Himalayas

Abstract

Monitoring of snow cover variability is crucial because it is closely linked with mountain water resources, ecosystems, and climate change. For this, moderate-resolution imaging spectroradiometer (MODIS) daily snow cover products (SCPs, version 6) were used over the Chenab river basin (CRB) during 2001–2017. In these data, cloud cover is a significant problem that produces a discontinuity in spatial and temporal scale for long-term snow cover monitoring. Therefore, a sequential non-spectral composite methodology (with five successive steps) was applied to reduce cloud obscuration. Further, the cloud gap-filled SCPs were validated with the indirect method as well as high-resolution satellite data (Landsat-8). Results indicate that the cloud-removed SCPs show an overall efficiency of 92.8 $$ \pm $$ 1.6% with an indirect approach, while an overestimation (9.3%) was observed between Landsat and MODIS snow cover area (SCA) along with higher correlation (R = 0.99, p < 0.001). The result shows an increasing trend (0.25% $${\rm{y}}{{\rm{r}}{ - 1}}$$) of mean annual SCA during 2001–2017, while it is slightly decreasing since 2009 and was statistically insignificant. Moreover, the Snow Cover Day and nine indexes (from snow depletion curves) were derived for snow cover characterization, indicating that a shift or change in the snow accumulation period in terms of the seasonal snow cover in the recent decade. Furthermore, the linear relationships between SCA and climatic variables were established to identify the influence of snow cover distribution and its related snowmelt onset. The analysis demonstrated that the precipitation and net shortwave radiation (SWN) were increasing in the north-eastern region of the basin. However, the air temperature ($${{\rm{T}}_{\rm{a}}}$$) and wind speed showed a declining trend. Furthermore, associated uncertainty and sensitivity analyzes were performed, suggesting that the SCA is more sensitive to $${{\rm{T}}_{\rm{a}}}$$. However, it may be less susceptible to precipitation during the melt season. Overall, this finding indicates the potential importance of climatic variables on the snow cover distribution that is essential for proper management of the hydrological system.

Keywords

• climatic variability
• cloud gap-filling algorithm
• modis snow cover products
• snow cover variability
• trend analysis