A model setup for mapping snow conditions in high-mountain Himalaya

Saloranta, Tuomo; Thapa, Amrit; Kirkham, James D. ORCID:; Koch, Inka; Melvold, Kjetil; Stigter, Emmy; Litt, Maxime; Møen, Knut. 2019 A model setup for mapping snow conditions in high-mountain Himalaya. Frontiers in Earth Science, 7, 129.

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Seasonal snow cover is an important source of melt water for irrigation and hydropower production in many regions of the world, but can also be a cause of disasters, such as avalanches and floods. In the remote Himalayan environment there is a great demand for up-to-date information on the snow conditions for the purposes of planned hydropower development and disaster risk reduction initiatives. We describe and evaluate a snow mapping setup for the remote Langtang Valley in the Nepal Himalayas, which can deliver data for snow and water availability mapping all year round. The setup utilizes (1) robust and almost maintenance-free in-situ instrumentation with satellite transmission, (2) a freely available numerical snow model, and (3) estimation of model key parameters from local meteorological and snow observations as well as from freely available climatological data. Novel features in the model include the estimation of melt parameters and solid precipitation from passive gamma-radiation based snow sensor data, as well as improved parameterization and estimation of melt water refreezing (36% of total melt) within, and sublimation/evaporation (57 mm yr−1) from the snow pack. Evaluation of the model results show a reasonable fit with snow cover data from satellite images. As many of the high-mountain regions in central and eastern Nepal show high correlation (>0.8) with the estimated snow line elevation in the Langtang catchment, the results may provide a first-order approximation of the snow conditions for these areas too.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 2296-6463
Additional Keywords: seasonal snow, modelling, Himalaya, snow water equivalent, hydropower
Date made live: 20 Jun 2019 09:00 +0 (UTC)

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