An adjoint/variational scheme is developed to assimilate streamflow observations at multiple locations in the distributed hydrologic model MOBIDIC, that is operationally employed. The assimilation system is tested through several hindacst experiments in the Arno river basin, including both high flow events and false alarms. Flood forecasting skill significantly improves. Skill dependence on forecast lead-time is clearly quantified.

VARIATIONAL ASSIMILATION OF MULTIPLE RIVER FLOW DATA IN OPERATIONAL FLOOD FORECASTING / Ercolani, Giulia; Castelli, Fabio. - ELETTRONICO. - (2016), pp. 521-524. (Intervento presentato al convegno XXXV Convegno di Idraulica e Costruzioni Idrauliche tenutosi a Bologna nel 14-16 Settembre 2016).

VARIATIONAL ASSIMILATION OF MULTIPLE RIVER FLOW DATA IN OPERATIONAL FLOOD FORECASTING

Ercolani giulia
;
Castelli Fabio
2016

Abstract

An adjoint/variational scheme is developed to assimilate streamflow observations at multiple locations in the distributed hydrologic model MOBIDIC, that is operationally employed. The assimilation system is tested through several hindacst experiments in the Arno river basin, including both high flow events and false alarms. Flood forecasting skill significantly improves. Skill dependence on forecast lead-time is clearly quantified.
2016
Atti del XXXV Convegno di Idraulica e Costruzioni Idrauliche
XXXV Convegno di Idraulica e Costruzioni Idrauliche
Bologna
14-16 Settembre 2016
Ercolani, Giulia; Castelli, Fabio
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1108731
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