I END - Encontro Nacional de Desastres da ABRH

Data: 25/07/2018 à 27/07/2018
Local: Porto Alegre-RS
ISSN: 2764-9040
Mais informações: https://www.abrh.org.br/iend

COM QUAL ANTECEDÊNCIA CONSEGUIMOS PREVER CHEIAS NO RIO URUGUAI USANDO UM MODELO HIDROLÓGICO DE GRANDE ESCALA?

Código

END101

Autores

GUILHERME MENDOZA GUIMARÃES, Fernando Mainardi Fan, Francisco Fernando Noronha Marcuzzo, Franco Turco Buffon, Andrea de Oliveira Germano

Tema

1 - Previsão e mapeamento de inundações

Resumo

The short and medium-range flow forecasting techniques using large-scale hydrological models have several direct applications in the management of natural disasters through warning systems. The present study is a research study on this type of system, applied in the Uruguay River Basin (RS, SC, Argentina and Uruguay). The objective of this study was to investigate the predictability of critical events at points of interest in the Uruguay river basin. We aimed to evaluate how early it is possible to estimate the peak flow in some sites susceptible to flooding in the basin. For the present research we selected municipalities that will be initially served by the Geological Service of Brazil (CPRM) warning system in the Uruguay river: Garruchos, Itaqui, Porto Lucena, São Borja and Uruguaiana. In the evaluation, the Nash-Sutcliffe Efficiency coefficient (NSE) was used. After the calibration and validation of the model, the predictability analysis was performed based on 15 flood events that occurred between 1980 and 2017 in which the forecasts of daily time-step were compared to a reference simulation. It was verified that there is an increase in the predictability from one day for up to three days, increasing as further downstream the place of interest is located. Also it was evidenced that the predictability especially in Uruguaiana city is dependent on where the floods are originated (higher Uruguay basin or Ibicuí basin). These results constitute useful information that may assist managers in decision making during critical events where forecast are provided by the proposed type of modeling.

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