OPERATIONAL HYDROLOGICAL - HYDRODYNAMIC FLOOD FORECASTING SYSTEM FOR URBAN AREAS IN ESPÍRITO SANTO, BRAZIL

Código

V-END_II-EBHE0191

Autores

Danieli Mara Ferreira, Andrei Chadanowicz, Grenda Izabeli Menezes da Silva, ARLAN SCORTEGAGNA ALMEIDA, Maria Fernanda Dames dos Santos Lima, José Eduardo Gonçalves, Ivna Carla Herzog Mação Campos, Deysilara Figueira Pani, LUCAS FUENTES SILVEIRA

Tema

12 - Inundações graduais e bruscas;

Resumo

Flood forecasting in urban watersheds requires accurate streamflow prediction together with spatial information on flood extent to support emergency response. This study presents an operational hydrological?hydrodynamic flood forecasting system developed by SIMEPAR for the Espírito Santo State Agency for Water Resources (AGERH), currently implemented for five flood-prone municipalities in southeastern Brazil. The system integrates the GR4J rainfall-runoff model with two-dimensional HEC-RAS simulations, automatically converting daily streamflow forecasts forced by ECMWF ensemble precipitation forecasts into flood inundation maps with lead times of up to 15 days. The operational workflow, fully automated through Python routines, includes data acquisition, hydrological forecasting, hydraulic simulations, post-processing, and the estimation of exposed buildings and critical infrastructure. The hydrological models achieved average Nash?Sutcliffe Efficiency (NSE) and Kling?Gupta Efficiency (KGE) values of 0.71 and 0.84, respectively. During the flood event of 6 January 2026 in Cachoeiro de Itapemirim, the forecast peak discharge issued two days in advance differed by approximately 3% from the observed peak, while the automated workflow successfully generated flood inundation maps and impact-oriented geospatial products. The results demonstrate that the proposed framework provides a reliable and scalable solution for operational flood forecasting, supporting early warning and disaster risk reduction through the integration of hydrological modeling, hydraulic simulation, and automated geospatial processing.