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Snow monitoring

SWE (snow water equivalent) estimation at basin scale.

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Snow monitoring at catchment scale

The snow is a crucial variable in mountain environment. The majority of precipitation during winter and early spring falls as snow in Europe over 1.000 m altitude, and remains stored in the snow until the melting season, when it returns in the hydrological cycle. The importance of snow for many human and environmental activities requires a continuous snow monitoring during winter time.

Snow monitoring is usually carried out by Avalanche Warning Institutes, which maintain a network of stations where snow depth and meteorological variables are automatically measured at specific points. Such network is usually integrated by manual observations performed by specifically trained people. The collected data are eventually used to produce the avalanche bulletin.

Current aproach

In order to spatially distribute snow cover, usual approaches expoit geostatistical techniques based on statistical correlation. However:

  • They do not account for the morphological heterogeneity typical of mountain environments

  • Require a large amount of in-situ snow measurements to obtain a reasonable correlation, with consequent high cost of sensors installation and/or organization of measurement campaigns

  • Are not suited to forecast future evolution

SnowMaps: new technology

SnowMaps is a new technology in snow modeling: thanks to a physically-based approach typical of hydrological models (Endrizzi et al. 2014), it calculates snow evolution through the solution of mass and energy conservation equations.

Each point of the domain is assigned its peculiar morphological characteristics (elevation, slope, aspect) in order to accurately derive the incoming energy fluxes (radiation, turbulent fluxes) and mass fluxes (liquid and solid precipitation).

This allows to distinguish the physical processes commanding snow evolution (accumulation, compaction and melting) and eventually to estimate snow depth and snow water equivalent.

Furthermore, this approach allows to feed the model with weather forecast and thus to obtain snow evolution prediction for the succeeding days.

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