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Snow multivariable data assimilation for hydrological predictions in Alpine sites

Authors :
Piazzi, G.
Thirel, G.
Campo, L.
Gabellani, S.
Stevenin, H.
aucun
CIMA Research Foundation
Hydrosystèmes et bioprocédés (UR HBAN)
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
Dipartimento Territorio
Regione Autonoma Valle d'Aosta
Hydrosystèmes et Bioprocédés (UR HBAN)
CIMA RESEARCH FOUNDATION SAVONA ITA
Partenaires IRSTEA
Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)
DIPARTIMENTO TERRITORIO AMBIENTE E RISORSE IDRICHE REGIONE AUTONOMA VALLE D'AOSTA ITA
Source :
19th EGU General Assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, EGU General Assembly 2017, EGU General Assembly 2017, Apr 2017, Vienna, Austria. 18 p, Geophysical Research Abstracts, EGU General Assembly 2017, Apr 2017, Vienna, Austria. pp.1
Publication Year :
2017
Publisher :
HAL CCSD, 2017.

Abstract

International audience; This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model strengthened by a multivariable DA framework for hydrological purposes. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state.

Details

Language :
English
Database :
OpenAIRE
Journal :
19th EGU General Assembly, EGU2017, proceedings from the conference held 23-28 April, 2017 in Vienna, EGU General Assembly 2017, EGU General Assembly 2017, Apr 2017, Vienna, Austria. 18 p, Geophysical Research Abstracts, EGU General Assembly 2017, Apr 2017, Vienna, Austria. pp.1
Accession number :
edsair.dedup.wf.001..5c66936477c511c654ce010c8103ffdb