Computational Modeling and Simulation of Surface Waterflood in Mountainous Urban Watersheds with the MOHID Platform: Case Study Nova Friburgo, Brazil

Wagner R. Telles (), Diego N. Brandão (), Jader Lugon (), Pedro P. G. W. Rodrigues () and Antônio J. Silva Neto ()
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Wagner R. Telles: Santo Antônio de Pádua, Universidade Federal Fluminense
Diego N. Brandão: Rio de Janeiro, Centro Federal de Educação Tecnologica Celso Suckow da Fonseca
Jader Lugon: Instituto Federal Fluminense
Pedro P. G. W. Rodrigues: Universidade do Estado do Rio de Janeiro UERJ, Instituto Politécnico
Antônio J. Silva Neto: Universidade do Estado do Rio de Janeiro UERJ, Instituto Politécnico

A chapter in Towards Mathematics, Computers and Environment: A Disasters Perspective, 2019, pp 125-143 from Springer

Abstract: Abstract Different countries in Latin America (LA) have suffered severely in recent years with hydrological disasters. The Emergency Events Database (EM-DAT), created by the World Health Organization (WHO), shows that in LA, only, more than 26 million people have been affected by these disasters, with a total death toll above six thousand in the time period from 2007 until 2016. Hydrological disasters are defined as hazards caused by both freshwater and saltwater abnormal behavior. In the present work, we are particularly interested in hydrological disasters caused by waterflood. The use of the MOHID (Modelagem Hidrodinâmica—Hydrological Modeling) computational platform is presented for the simulation the models of water flow and runoff in mountainous urban watersheds. The mathematical model based on partial differential equations used to represent the physical phenomena of interest is described, and the importance of computational modeling and simulation as auxiliary tools for disaster risk reduction is discussed. Simulations results are presented for a real case study with data from the Bengalas River watershed, in Nova Friburgo region, a city located on the mountains of Rio de Janeiro state, Brazil. In 2011 this geographical area went through a major natural disaster caused by heavy rainfall.

Date: 2019
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DOI: 10.1007/978-3-030-21205-6_7

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