Snow Cover Response to Climatological Factors at the Beas River Basin of W. Himalayas from MODIS and ERA5 Datasets

• Published in: Sensors
• Main Authors: Sunita, Pardeep Kumar Gupta, George P. Petropoulos, Hemendra Singh Gusain, Vishakha Sood, Dileep Kumar Gupta, Sartajvir Singh, Abhay Kumar Singh
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: snow cover area (SCA); climatological factors; Himalayas; permafrost; European Centre for Medium-Range Weather Forecasts (ECMWF); Chemical technology; TP1-1185
• Online Access: https://doi.org/10.3390/s23208387; https://doaj.org/article/b11435b1914442fb84239f8755fb3759

Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management.