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Using innovative remote sensing data of the seasonal snowpack to improve snow hydrological modeling in the Ötztal Alps (Austria)

Authors

Warscher,  Michael
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Rottler,  Erwin
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Strasser,  Ulrich
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Warscher, M., Rottler, E., Strasser, U. (2023): Using innovative remote sensing data of the seasonal snowpack to improve snow hydrological modeling in the Ötztal Alps (Austria), XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1235


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017354
Abstract
We assess the potential of new remote sensing data products developed within the AlpSnow project (2020-2023), a science activity within ESA's Alpine Regional Initiative, to improve snow-hydrological modeling in the high alpine environment of the Rofental (Ötztal Alps, Austria). The satellite data products are optimized for scientific and operational applications and include snow covered area, surface albedo, grain size, snow water equivalent, snow depth, snowmelt area extent and liquid water content. The study area covers an elevation range of 1890 - 3770 m a.s.l. and is part of LTSER and INARCH research initiatives. In a first experiment, we force the snow hydrological model openAMUNDSEN with station-based meteorological data to evaluate and optimize the simulation results using the AlpSnow products with a focus on snowmelt and runoff timing. Specific targets of model optimizations are precipitation regionalization and the parameterizations for lateral snow redistribution, liquid water content, and albedo. In a second experiment, added values of assimilating the remote sensing products in the model are analyzed. We validate results against in-situ snow hydrological observations recorded by the station network in the research basin.