Abstract:
The Karakoram and Himalayan (KH) cryosphere (in terms of snow and glaciers) plays a significant role in managing the ecosystem and supporting livelihood and economic development. The KH region experiences large variations in snow cover and glaciers in a warming environment. The potential adverse effect of changing cryosphere causes a cascading implication on water availability and generates a condition of water stress in the future. Other than this, the change in water storage with increasing temperature can create drought-like conditions and affect people living in the region. Therefore, monitoring cryospheric changes and their interaction with climate are essential to understanding present climate sensitivity and future water availability. The melting of the cryosphere provides water for the region where the livelihood of millions of people depends upon meltwater of snow and glacier during the summer season. However, the continuous monitoring of snow cover at a large spatial extent is challenging due to harsh climatic conditions and rugged topography. For this, the use of remote sensing presented a great advantage in snow cover monitoring.
This thesis uses satellite observations for spatio-temporal snow cover monitoring at basinal and regional scales. The cloud blocks are the major limitation of optical remote sensing data that hinders the original snow cover information in the high-mountain terrains. To overcome this limitation, a spatially distributed cloud removal methodology is developed to ensure that all the necessary physical-based considerations and topographical variations are correctly incorporated in the method. This non-spectral sequential methodology includes a combination of multi-sensor data, temporal filter, nearest neighborhood filter, zonal snowline filter, and multiday-backward replacement filter. The cloud-gap-filled snow cover outcomes are validated with a direct and indirect approach to assess the accuracy of the methodology over the Chenab River basin. The results suggested that the spatially distributed cloud removal methodology can bridge the gap between regional observation-based snow cover and cloud blocks. After analyzing the methodology performance, the snow cover distribution is assessed at spatio-temporal scale along with topographical parameters. Further, we have established a relationship between snow cover and essential climatic drivers.
