Ĭampana NA, Tucci CE (2001) Predicting floods from urban development scenarios: case study of the Dilúvio Basin, Porto Alegre, Brazil. Bachelor Thesis, University of Twente, Netherlandīansal N, Muhua M, Gairola A (2015) Causes and impact of urban flooding in Dehradun.

–case-study-of-hyderabad.pdfĪwakimjan I (2015) Urban flood modelling recommendations for Ciudad Del Plata. Īhmed Z, Rao DRM, Reddy K (2013) Urban flooding-case study of Hyderabad. The model results identify 17% of total area is liable to floods out of which 9% area indicates high risk, 52% area shows medium risk and remaining 35% area falls under low risk of flooding.Īgilan V, Umamahesh NV (2017) What are the best covariates for developing non-stationary rainfall intensity–duration–frequency relationship? Adv Water Resour 101:11–22. HEC-RAS, a freely available 2-D hydraulic model with integration to GIS is used to generate depth of flood inundation over underlying terrain and risk maps of flood inundation are developed for different rainfall scenarios.

This paper presents a simple sophisticated approach to analyze extreme rainfall events based on past critical events and synthetic hyetographs developed from IDF curves for a part of Hyderabad, India. A vast number of numerical models have evolved over the past few years which are capable of flood mapping most of them are commercial, rigorous and need extensive dataset to generate precise results. Thus, modeling of urban floods becomes a complex process. The difficulty associated with urban floods is unpredictable flow conditions in urban environment due to rapid alterations in topography and unavailability of extensive raw dataset. Prevention of such events is not possible but with advancement of technology, flood-vulnerable areas can be identified through 2-D modeling of critical rainfall events.

In recent times, severe floods in urban areas are occurring more frequently owing to uncontrolled urbanization and climate change and it will continue to grow in upcoming years. Flood is the most common natural disaster upsetting the highest population of the world.

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