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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Improving flood forecasting using an input correction method in urban models in poorly gauged areas

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Autor(es):
Fava, Maria Clara [1] ; Mazzoleni, Maurizio [2, 3] ; Abe, Narumi [1] ; Mendiondo, Eduardo Mario [1] ; Solomatine, Dimitri P. [4, 5]
Número total de Autores: 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
Número total de Afiliações: 5
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