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.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.