Autor(es)

Fava, Maria Clara ^[1] ; Mazzoleni, Maurizio ^{[2, 3]} ; Abe, Narumi ^[1] ; Mendiondo, Eduardo Mario ^[1] ; Solomatine, Dimitri P. ^{[4, 5]}

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Sao Carlos Sch Engn, Dept Hydraul Engn & Sanitat, Sao Carlos, SP - Brazil

^[2] Uppsala Univ, Dept Earth Sci, Program Air Water & Landscape Sci, Uppsala - Sweden

^[3] Uppsala Univ, Ctr Nat Hazards & Disaster Sci CNDS, Uppsala - Sweden

^[4] Delft Univ Technol, Water Resources Sect, Delft - Netherlands

^[5] IHE Delft Inst Water Educ, Chair Grp Hydroinformat, Delft - Netherlands

• Tipo de documento: Artigo Científico
• Fonte: HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES; v. 65, n. 7 MAR 2020.
• Citações Web of Science: 0

Resumo

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of Sao Carlos, Brazil. The results show a significant improvement in the simulation accuracy. (AU)

• Processo FAPESP: 14/50848-9 - INCT 2014: INCT para Mudanças Climáticas (INCT-MC)
• Beneficiário: Jose Antonio Marengo Orsini
• Modalidade de apoio: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - Temático