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Understanding the Influence of Snow Cover in Issuing Streamflow Forecasts for High-Mountainous Basins in the Himalayan Region

Author(s): Shairik Sengupta; Rajarshi Das Bhowmik

Linked Author(s): Shairik Sengupta, Rajarshi Das Bhowmik

Keywords: Himalayan river basin; Stream flow forecast; Hydrological modelling; Teleconnection; Climate change impact

Abstract: The Himalayan mountains are the source of numerous rivers that together are the lifelines of some of the most densely populated and fast developing regions on earth. While these rivers are glacier fed, most of the water flowing through them comes from the Indian monsoon system. Monsoonal rain introduces a strong seasonality in river discharge, while the glacial input is of utmost importance to maintain the base flow. The objectives of the current study are i) to understand the influence of hydroclimate variation on snow cover for a high-mountainous basin, ii) to investigate historical drivers of river discharge using reconstructed snow-cover records. Towards this, the study examines spatiotemporal variation in satellite-based estimates of snow cover. A wavelet analysis is carried out to link teleconnections (multiple large-scale atmospheric and oceanic circulations) with snow cover. Based on the statistical relationship between the snow cover and Sea Surface Temperature indices along with other hydroclimate variables, historical snow cover is reconstructed for the watershed. Finally, the study considers a lumped hydrological model to investigate the different controls of river discharge. Basin characteristics, teleconnections, hydroclimate, and snow are considered as primary drivers. A rigorous validation is carried out to eliminate data over-fitting issues in model development. Initial results suggest that there exists a significant inter-seasonal variation in snow cover on the high latitudes that has the potential to substantially influence river discharge. Hence, an accurate short-range forecasting of snow covers based on teleconnection is the key to a skilful flow forecast.

DOI: https://doi.org/10.3850/IAHR-39WC252171192022930

Year: 2022

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