Analysis of hydrological extremes in the Guaíba hydrographic region: an application of extreme values theory

• Autor(a) principal: Vieira, Sabrina Antunes
• Data de Publicação: 2022
• Outros Autores: Osório, Daniela Montanari Migliavacca, de Quevedo, Daniela Muller
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista Brasileira de Ciências Ambientais (Online)
• Texto Completo: https://www.rbciamb.com.br/Publicacoes_RBCIAMB/article/view/1317
• topic: eventos intensos; valor extremo generalizado; probabilidade; projeções.; intense events; generalized extreme value; probability; projections.

Knowing the behavior of extreme hydrological phenomena is essential so that the impacts resulting from these natural events are minimized. Rio Grande do Sul has frequently been hit by extreme events such as droughts and floods, and these events are associated with several consequences, such as energy or water rationing, urban flooding and damage to hydraulic structures. In this context, the analysis of historical series extremes of hydrometeorological data through the Extreme Values Theory ​​(EVT) is one of the ways to determine the variability due to climate change, enabling the modeling of extreme events. EVT makes it possible to know the frequency with which extreme events occur, allowing extrapolation beyond the historical series, generating occurrence probabilities of such an event. Therefore, the purpose of this work was to apply the Extreme Values Theory ​​in hydrological the data historical series of flow and precipitation in the Guaíba hydrographic region and to carry out occurrence probabilities of intense events return, helping in the planning of the hydrographic watersheds that are in this region, as well as to verify whether the EVT has return periods similar to the climate projections of CMIP5 models. The results demonstrate that the values of flow and precipitation, in the historical series used, have already presented changes regarding the volume and frequency of extreme events occurrence and, in the future, for some stations, values ​​can be expected both above and below the extremes already observed in the historical series.