Abstract
Mexico City is facing problems of flooding in some areas at certain times of the year, causing important losses and damages on properties and residents including some casualties. Therefore, it is important to carry out a flood risk assessment in the catchment of Mexico City and estimate damages of probable flood events. However, limited data of observed discharges and water depths in the main rivers of the city are available, and this represents an obstacle for the understanding of flooding in Mexico City. The premise of this study is that with the limited data and resources available, the catchment can be represented to an acceptable degree by the construction of a deterministic hydrological model of the Mexico City basin. The objective of the developed tool is to provide an efficient support to management of the flood processes by predicting the behavior of the catchment for different rainfall events and flood scenarios. The capability of a model based on MIKE SHE modeling system for the Mexico City catchment was evaluated by comparing the observed data and the simulation results during a year after a careful development based on the most important parameters for characterizing the processes. Significant and operational results (>0.75 for Nash Sutcliffe coefficient) have been obtained on one of the major sub-catchments of the Mexico basin. These results demonstrate the interest to implement a deterministic hydrological model for assessing flood risks in a dense urban environment where data availability is limited. The developed model can be used for assessing the risks and designing some protection measures.
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Acknowledgments
The authors would like to thank the Surface Water department of the National Water Commission of Mexico CONAGUA and the Institute of Geography and Statistics INEGI for the data provided for this study.
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Vargas, R.B., Gourbesville, P. (2016). Deterministic Hydrological Model for Flood Risk Assessment of Mexico City. In: Gourbesville, P., Cunge, J., Caignaert, G. (eds) Advances in Hydroinformatics. Springer Water. Springer, Singapore. https://doi.org/10.1007/978-981-287-615-7_5
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DOI: https://doi.org/10.1007/978-981-287-615-7_5
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