Search

The traditional approach to characterize extreme rainfall events is based on using past observed data to fit a probability distribution, which is used to associate a certain probability of occurrence to a rainfall magnitude, under a stationary assumption. Observed series, however, may exhibit trends and abrupt changes, resulting from the natural hydro climatic variability and the climate change context, which are not usually considered in the stationary modelling procedure and which may affect the characterization of extreme events. In this paper, it was evaluated the presence of these temporal changes in rainfall series and their impact to estimate design quantiles, using rainfall data of 317 stations, located in São Paulo State, Brazil. The results showed that extreme rainfall events are changing in the state. Low-return period events (up to 10 years) are intensifying. Moderate- and high-return period events, on the other hand, did not present a clear spatial pattern. Trends and abrupt changes may significantly influence the estimation of design quantiles. The presence of the former in rainfall series exhibited more influence on low-return period events, showing, however, no clear relationship with high-return period design quantiles. The presence of the latter seems to influence the characterization process, affecting estimated quantiles of all evaluated return periods. Such findings highlight the need to consider the presence of trends and abrupt changes and their influence on design quantiles for a more reliable characterization. [ABSTRACT FROM AUTHOR]

This paper contributes to the study of climate change and environmental justice with a particular focus on a Global South case-study in the São Paulo Macro Metropolis of Brazil. We also aim to contribute to mandatory critical dialogue between (anticipatory) governance and environmental justice. This study focuses on the rainy seasons from 2016 to 2019. We examine the incidence of 61 extreme precipitation events, as well as 47 deaths caused by rain events, considering their location based on vulnerability indicators. The correlations among these data allow us to reveal the socio-environmental patterns within the relationships between social vulnerability and deaths caused by general rainfall and, more specifically, extreme events. Based on this, we demonstrate that current infrastructure or its lack is one of the reasons why death tolls remain due to the absence of anticipatory governance. [ABSTRACT FROM AUTHOR]