Hydrological Modelling Of Snow Cover In
The Large Upper Po River Basin:
Winter 2004 Results And Validation With
Snow Cover Estimation From Satellite
Author(s)
D. Rabuffetti, A. Salandin & R. Cremonini
Abstract
The study of the hydrological budget of mountainous river basins requires the
understanding of the annual cycle of snow accumulation and melting. In fact a
deep knowledge of snow cover distribution and dynamic offers several
possibilities to improve water resource management and exploitation. The
implemented model reproduces an energetic budget of the snowy mantle at small
scales using DEM resolution. The use of a distributed model accounts for the
high time-space variability of meteorological factors, such as precipitation, air
temperature and solar radiation, whose fields at soil level are reconstructed from
spot measurements through interpolation procedures based on the topography of
the river basin. The modelled variables are the snow water equivalent (SWE) and
the discharge generated by the snowmelt while the mechanics of the snowy
mantle like thickness and density are not considered. At the same time the
simplified degree day method, that uses only air temperature as an index to melt
is also implemented and compared. The propagation of melting water inside the
snowy mantle is modelled through the conceptual linear reservoir approach; the
outflow from snowy mantle is propagated as superficial run-off to the closing
section of the river basin through the Muskingum-Cunge hydrological model.
The basin of the river Po closed at the section of Ponte Becca is studied in the
simulation; it covers a surface of approximately 38000 km2. The simulation
ranges from winter 2004 to spring 2005. The modelled snow cover over the
catchment is compared with estimation from satellite, derived using the
Normalized Difference Snow Index (NDSI), concurring to the validation of the
model.
Keywords: snow, energy budget, degree day, satellite images, model validation.
Keywords
snow, energy budget, degree day, satellite images, model validation.
Related Book
Other papers in this volume
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