Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/58398
Título: Daily rainfall erosivity as an indicator of natural disasters applied to the mountainous region of Rio de Janeiro, Brazil: current scenario and future projections
Título(s) alternativo(s): Erosividade da precipitação diária como um indicador de desastres naturais aplicado na região serrana do estado do Rio de Janeiro, Brasil: cenário atual e projeções futuras
Autores: Mello, Carlos Rogério de
Thebaldi, Michael Silveira
Alvarenga, Livia Alves
Avanzi, Junior Cesar
Correa, Sly Wongchuig
Palavras-chave: Erosividade diária
Desastres naturais
Regiões serranas brasileiras
Índices de alertas de precipitação
Mudanças climáticas
Daily rainfall erosivity
Natural disasters
Brazilian mountainous regions
Rainfall warning system
Climate change
Nova Friburgo (RJ)
Petrópolis (RJ)
Teresópolis (RJ)
Data do documento: 6-Out-2023
Editor: Universidade Federal de Lavras
Citação: ALVES, G. J. Daily rainfall erosivity as an indicator of natural disasters applied to the mountainous region of Rio de Janeiro, Brazil: current scenario and future projections. 2023. 65 p. Tese (Doutorado em Recursos Hídricos)–Universidade Federal de Lavras, Lavras, 2021.
Resumo: Natural disasters result from extreme natural events that cause significant impacts on the social, economic, and environmental balance. Thus, alert indices to prevent or minimize the impacts caused by natural disasters have become one of the most significant challenges of the twenty-first century. In this context and considering that some indices based only on precipitation have been shown to be inefficient, the rainfall erosivity, calculated as a function of the energy dissipated by the impact of drops on the surface, has great potential for application in studies related to landslides and floods. Thus, daily rainfall erosivity (Rday) are promising indices to be applied as alerts of the occurrence of natural disasters, also allowing us to analyze the behavior of these events in the face of climate change. Therefore, this study aimed to i) model the Rday through a seasonal model for the mountainous region of the state of Rio de Janeiro (RSERJ), one of the regions most affected by natural disasters in Brazil; ii) adapt thresholds of the Rday indices that classify the events according to their observed impacts based on catastrophic events that have occurred in the last two decades; iii) apply the adjusted seasonal model to calculate Rday considering two greenhouse gas emission scenarios (RCP 4.5 and 8.5) and the regionalized HadGEM2-ES climate model for the 5 km scale throughout the twenty-first century; iv) map the maximum daily rainfall erosivity (Rmaxdia) to evaluate the susceptibility of the region, according to the established thresholds throughout the century; and v) spatially analyze the frequency of occurrence of Rday values causes of natural disasters considering future projections. The adjusted model showed a satisfactory result, allowing its application as an estimator of the seasonality of the Rday at RSERJ. Events that resulted in Rday > 1,500 MJ.ha-1. mm. h-1. day-1 presented this region's highest number of deaths. The mapping of Rmaxdia showed that the entire RSERJ presented values classified as causing major natural disasters in the last 30 years and is still highly susceptible to the occurrence of major natural disasters throughout the twenty-first century, with intensification from 2040 to 2071. The urban areas of Nova Friburgo and Petrópolis showed the highest frequency of events in the range 1,000 < Rmaxdia < 1,500 MJ.ha-1.mm.h-1.day-1. The period between 2011 and 2040 presented the lowest frequency of events, with a concentration of Rmaxdia < 1,000 MJ.ha-1.mm.h-1.day-1. The Rday indices were promising indicators of natural disasters, being more effective than those generally used, based only on rainfall quantity (mm) and intensity (mm.h-1).
URI: http://repositorio.ufla.br/jspui/handle/1/58398
Aparece nas coleções:Recursos Hídricos - Doutorado (Teses)



Este item está licenciada sob uma Licença Creative Commons Creative Commons