Identification of extreme rainfall events and disasters triggered by rain in the city of Petrópolis-RJ

The municipality of Petrópolis/RJ/Brazil is prone to extreme rainfall events that cause damage and direct and indirect economic losses. In order to verify the increase or not of these events, this study evaluates the temporal pattern of rainfall in the municipality (1976-2022), identifying whether t...

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Detalhes bibliográficos
Autores: Fardin, Sara Carolina Soares Guerra, Aguilar-Muñoz, Viviana, Dias, Leonardo Freire, Bastos, Beatriz Justen Mussi Tanus e, Cunha, Ana Paula Martins do Amaral
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:Brasil
Recursos:Universidade de Brasília (UnB)
Repositorio:Sustentabilidade em Debate (Online)
Idioma:inglés
portugués
OAI Identifier:oai:ojs.pkp.sfu.ca:article/49463
Acesso em linha:https://periodicos.unb.br/index.php/sust/article/view/49463
Access Level:acceso abierto
Palavra-chave:extremos de precipitação
Petrópolis-RJ
desastres relacionados à chuva
ocorrência de desastres
extreme precipitation
Petropolis-RJ
Disasters related to precipitation
Occurrence of disasters
Extreme precipitation
Descrição
Resumo:The municipality of Petrópolis/RJ/Brazil is prone to extreme rainfall events that cause damage and direct and indirect economic losses. In order to verify the increase or not of these events, this study evaluates the temporal pattern of rainfall in the municipality (1976-2022), identifying whether the event that occurred in 2022 can be considered an extreme event, as well as the relationship between rainfall and impact intensity, in terms of damage and losses triggered. The accumulated rainfall over 24 hours (RX1), over 5 days (RX5), the 95th and 99th percentiles (R95 and R99), the Rainfall Anomaly Index (RAI) and trend analyses using the Mann-Kendall method, as well as loss and damage data were then calculated. The results did not indicate a trend towards an increase in precipitation extremes, although they did confirm the February 2022 event as an extreme event, which stood out as the largest within the historical series analysed.