The statistical distribution of the daily Snow Water Equivalent (SWE) is investigated for a network of gauging stations in the Alpine part of Lombardia region, in the central Italian Alps. An event based data analysis is carried out using a 14 year long data set dating back to 1989. SWE is estimated when the new snow depth is greater than 6 cm. The SWE sample average in time is shown to be related to physiographic attributes of the gauging area, thus not being homogeneous in space. The values of SWE scaled by their average, or index value, instead show well approximated homogeneity of the second order moment, or coefficient of variation, in space. This suggests the use of a regional approach for frequency estimation of SWE. The frequency of occurrence of the normalized values of SWE is evaluated and tentatively accommodated by four probability distributions, often adopted in statistical modeling of hydrological variables. The Lognormal distribution shows the best performance. Single site distribution fitting is then carried out using the regional distribution, providing satisfactory results.

The distribution of daily Snow Water Equivalent in the Central Italian Alps

BOCCHIOLA, DANIELE;ROSSO, RENZO
2007-01-01

Abstract

The statistical distribution of the daily Snow Water Equivalent (SWE) is investigated for a network of gauging stations in the Alpine part of Lombardia region, in the central Italian Alps. An event based data analysis is carried out using a 14 year long data set dating back to 1989. SWE is estimated when the new snow depth is greater than 6 cm. The SWE sample average in time is shown to be related to physiographic attributes of the gauging area, thus not being homogeneous in space. The values of SWE scaled by their average, or index value, instead show well approximated homogeneity of the second order moment, or coefficient of variation, in space. This suggests the use of a regional approach for frequency estimation of SWE. The frequency of occurrence of the normalized values of SWE is evaluated and tentatively accommodated by four probability distributions, often adopted in statistical modeling of hydrological variables. The Lognormal distribution shows the best performance. Single site distribution fitting is then carried out using the regional distribution, providing satisfactory results.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/551761
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