Abstract
Abstract. Physically based snow models provide valuable information on snow cover evolution and are therefore key to provide water availability projections. Yet, uncertainties related to snow modelling remain large as a result of differences in the representation of snow physics and meteorological forcing. While many studies focus on evaluating these uncertainties, no snow model comparison has been done in environments where sublimation is the main ablation process. This study evaluates a case study in the semi-arid Andes of Chile and aims to compare two snow models with different complexities, SNOWPACK and SnowModel, at a local point over one snow season and to evaluate their sensitivity relative to parameterisation and forcing. For that purpose, the two models are forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing. Results indicate large uncertainties depending on forcing, the snow roughness length z0, albedo parameterisation, and fresh snow density parameterisation. The uncertainty caused by the forcing is directly related to the bias chosen. Even though the models show significant differences in their physical complexity, the snow model choice is of least importance, as the sensitivity of both models to the forcing data was on the same order of magnitude and highly influenced by the precipitation uncertainties. The sublimation ratio ranges are in agreement for the two models: 36.4 % to 80.7 % for SnowModel and 36.3 % to 86.0 % for SNOWPACK, and are related to the albedo parameterisation and snow roughness length choice for the two models.
Highlights
Snow models provide valuable information on snow cover evolution and are key to quantify runoff and provide accurate water availability projections
Snow models are key to quantifying runoff and providing accurate water availability projections
The two models were run over the 2017 snow season, at local point, and forced with (i) the most ideal set of input parameters, (ii) an ensemble of different physical parameterisations, and (iii) an ensemble of biased forcing
Summary
Snow models provide valuable information on snow cover evolution and are key to quantify runoff and provide accurate water availability projections. With different complexities in the representation of different snow processes, from empirical to physically based approaches, have been developed to simulate snow depth changes. The use of the energy balance equation, coupled with snow models, enables a more complete understanding of snow physical processes and are essential for understanding the interaction between snow cover evolution and climate change. Based snow models have different complexities in their physical representations, from a single layer approach In a snow model intercomparison study, Etchevers et al (2004) highlighted the importance of parameterisation choice, especially regarding the net longwave and Published by Copernicus Publications on behalf of the European Geosciences Union
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