Improving flood forecasting using an input correction method in urban models in poorly gauged areas |
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Authors: | Maria Clara Fava Maurizio Mazzoleni Narumi Abe Eduardo Mario Mendiondo Dimitri P Solomatine |
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Institution: | 1. Department of Hydraulic Engineering and Sanitation, S?o Carlos School of Engineering, University of S?o Paulo, S?o Carlos, S?o Paulo, Brazilmclarafava@usp.br https://orcid.org/0000-0002-8201-4339;3. Department of Earth Sciences, Program for Air, Water and Landscape Sciences, Uppsala University, Uppsala, Sweden;4. Centre of Natural Hazards and Disaster Science (CNDS), Uppsala University, Uppsala, Sweden https://orcid.org/0000-0002-0913-9370;5. Department of Hydraulic Engineering and Sanitation, S?o Carlos School of Engineering, University of S?o Paulo, S?o Carlos, S?o Paulo, Brazil https://orcid.org/0000-0003-2212-3442;6. Department of Hydraulic Engineering and Sanitation, S?o Carlos School of Engineering, University of S?o Paulo, S?o Carlos, S?o Paulo, Brazil https://orcid.org/0000-0003-2319-2773;7. Chair Group of Hydroinformatics, IHE Delft Institute for Water Education, Delft, The Netherlands;8. Water Resources Section, Delft University of Technology, Delft, The Netherlands https://orcid.org/0000-0003-2031-9871 |
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Abstract: | ABSTRACTPoorly 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. |
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Keywords: | data assimilation semi-distributed model flood modelling physically-based model SWMM |
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