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Abstract:
Snow and glacier meltwater are crucial regulators of freshwater resources in High Mountain Asia (HMA). In spite of its importance, the detailed distribution and variation of snow cover are not well known. With anticipated global warming, it is increasingly imperative to estimate the temporal and spatial distribution of snow cover. Despite various studies presenting detailed changes in snow cover in individual catchments, most large-scale assessments have been focused on annual values without seasonal variations, or have utilized moderate-resolution sensors (>250 m, such as MODIS). Moreover, global snow cover distribution products suffer from cloud cover and topographic shadows, both of which are common in high mountain areas and cause biases. To overcome this limitation, we have developed an automatic detection algorithm for snowline altitude (SLA) using high-resolution multispectral satellite imageries. SLA is a valuable variable to examine snow cover variation in HMA since it is less biased by cloud cover than snow cover extent. The proposed tool was applied to five glacierized watersheds across the Himalayas to quantify variations in seasonal and annual snow cover distribution over the past two decades, and to analyze the meteorological drivers of SLA. Our findings reveal remarkable differences in SLA among the sites in terms of decadal trends, seasonal patterns, and meteorological drivers. The application of the algorithm to a wider geographical area can offer essential insights into the spatio-temporal variability of snow cover and its drivers, which could lead to a better understanding of the future state of snow cover and high mountain hydrology.
