Analysis of precipitation data in Rio de Janeiro city using Extreme Value Theory

• Augusto J. M. da Fonseca Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ) http://orcid.org/0000-0003-1480-5814
• Fabio Porto National Laboratory for Scientific Computing (LNCC)
• Mariza Ferro National Laboratory for Scientific Computing (LNCC) / Federal Fluminense University (UFF) http://orcid.org/0000-0003-0191-582X
• Eduardo Ogasawara Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)
• Eduardo Bezerra Federal Center for Technological Education of Rio de Janeiro (CEFET/RJ)

DOI: https://doi.org/10.5753/sbbd_estendido.2022.21864

Abstract

Every year the city of Rio de Janeiro is hit by heavy rains that cause several disasters. To be able to reduce potential damages that may arise due to extreme rainfall, it is important to make accurate estimates of such events. In this paper, we present preliminary work on using Extreme Value Theory (EVT) to model rainfall data in Rio de Janeiro municipality. We use twenty-five years of historical data coming from rain gauges spread throughout the city. We investigate the behavior of both approaches in EVT to identify extreme values, namely, Block Maxima and Peeks-over-Threshold. After determining the best sampling distributions given the available data, we present an analysis of their goodness of fit and of their corresponding return period plots.

Keywords: Climate extremes, Extreme Value Theory, Precipitation, Rio de Janeiro

References

Ban, N., Rajczak, J., Schmidli, J., and Schär, C. (2020). Analysis of alpine precipitation extremes using generalized extreme value theory in convection-resolving climate simulations. Climate Dynamics, 55:61-75.

Davison, A. and Huser, R. (2015). Statistics of extremes. Annual Review of Statistics and Its Application, 2(1):203-235.

dos Reis, C. J., Souza, A., Graf, R., Kossowski, T. M., Abreu, M. C., de Oliveira-Júnior, J. F., and Fernandes, W. A. (2022). Modeling of the air temperature using the extreme value theory for selected biomes in Mato Grosso do Sul (Brazil). Stochastic Environmental Research and Risk Assessment.

Lima, A. O., Lyra, G. B., Abreu, M. C., Oliveira-Júnior, J. F., Zeri, M., and Cunha-Zeri, G. (2021). Extreme rainfall events over Rio de Janeiro State, Brazil: Characterization using probability distribution functions and clustering analysis. Atmospheric Research, 247:105221.

Luiz-Silva, W.and Oscar-Júnior, A. (2022). Climate extremes related with rainfall in the state of Rio de Janeiro, Brazil: a review of climatological characteristics and recorded trends. Nat Hazards.

Lyra, G., Correia, T., Oliveira-Júnior, J., and Zeri, M. (2018). Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro, Brazil. Theoretical and Applied Climatology, 134.

Silva, W. and Dereczynski, C. (2014). Caracterização climatológica e tendências observadas em extremos climáticos no estado do Rio de Janeiro. Anuário do Instituto de Geociências UFRJ, 2(37):123-138.

Back A. and Bonfante, F. M. (2021). Evaluation of generalized extreme value and gumbel distributions for estimating maximum daily rainfall. Brazilian Journal of Environmental Sciences, 56(4):654-664.