Improving the degree-day method for sub-daily melt simulations with physically-based diurnal variations |
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| Institution: | 1. Dept. of Atmospheric & Environmental Sciences, Univ. at Albany, Albany, NY, USA;2. Robert D. Clark Honors College, University of Oregon, Eugene, OR, USA;3. Dept. of Geography, Univ. of Zurich, Switzerland;4. Dept. of Civil and Environmental Engineering, Imperial College London, London, UK;5. Univ. Grenoble Alpes, CNRS, IRD, Institut des Géosciences de l''Environnement (IGE), Grenoble, France;6. Freshwater Biological Laboratory, Dept. of Biology, University of Copenhagen, Copenhagen, Denmark;7. Instituto de Investigaciones Geológicas y del Medio Ambiente, Universidad Mayor de San Andres, La Paz, Bolivia;8. Depto. de Ingenieria Civil y Ambiental, Escuela Politecnica Nacional, Quito, Ecuador;9. Dept. of Geosciences, Univ. of Fribourg, Switzerland |
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| Abstract: | This paper proposes a new extension of the classical degree-day snowmelt model applicable to hourly simulations for regions with limited data and adaptable to a broad range of spatially-explicit hydrological models. The snowmelt schemes have been tested with a point measurement dataset at the Cotton Creek Experimental Watershed (CCEW) in British Columbia, Canada and with a detailed dataset available from the Dranse de Ferret catchment, an extensively monitored catchment in the Swiss Alps. The snowmelt model performance is quantified with the use of a spatially-explicit model of the hydrologic response. Comparative analyses are presented with the widely-known, grid-based method proposed by Hock which combines a local, temperature-index approach with potential radiation. The results suggest that a simple diurnal cycle of the degree-day melt parameter based on minimum and maximum temperatures is competitive with the Hock approach for sub-daily melt simulations. Advantages of the new extension of the classical degree-day method over other temperature-index methods include its use of physically-based, diurnal variations and its ability to be adapted to data-constrained hydrological models which are lumped in some nature. |
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