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

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 (typica...

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Bibliographic Details
Published in:Hydrological sciences journal 2020-05, Vol.65 (7), p.1096-1111
Main Authors: Fava, Maria Clara, Mazzoleni, Maurizio, Abe, Narumi, Mendiondo, Eduardo Mario, Solomatine, Dimitri P.
Format: Article
Language:English
Subjects:
Catchment area
Catchment models
Catchments
Computer simulation
Data
Data assimilation
Data collection
Flood forecasting
flood modelling
Flood predictions
Floods
Heavy rainfall
Hydrologic data
Hydrologic models
Hydrology
Model updating
Nonlinear systems
physically-based model
Procedures
Rain
Rainfall
River discharge
semi-distributed model
SWMM
Urban catchments
Water levels
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Summary: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 São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.
ISSN:0262-6667
2150-3435
DOI:10.1080/02626667.2020.1729984