Avaliação dos efeitos da incorporação das incertezas da curva-chave e do coeficiente de rugosidade de Manning na modelagem hidrodinâmica de eventos de cheias naturais

• Ano de defesa: 2023
• Autor(a) principal: Luan Marcos da Silva Vieira
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; ENG - DEPARTAMENTO DE ENGENHARIA SANITÁRIA E AMBIENTAL; Programa de Pós-Graduação em Saneamento, Meio Ambiente e Recursos Hídricos; UFMG
• Palavras-chave em Português: Inferência Bayesiana; Inundações; Modelagem hidrodinâmica; Propagação de incertezas; Análise de risco; Curva-chave; Engenharia sanitária; Recursos hídricos - Desenvolvimento; Meio ambiente; Hidrologia; Aspereza de superficie; Inferência bayesiana; Modelagem; Hidrodinâmica; Processo decisório; Avaliação de riscos - Modelos matemáticos; Incerteza; Probabilidade
• Link de acesso: http://hdl.handle.net/1843/77887

Effects of hydrological disasters related to the occurrence of floods cause severe socioeconomic damage around the globe. In this scenario, mathematical modeling of floods plays a key role in supporting risk management, water resource management, and decision-making processes. Nevertheless, the complexity inherent in the attempt to represent the physical phenomenon incorporates different degrees of uncertainty to the process, which can lead to erroneous predictions and damage potentialization. A relevant example is the incorporation of uncertainties in the estimation of flow via the rating curve in the hydrodynamic modeling of flood propagation. The combination of uncertainties for these different steps is reflected in flood mappings and, consequently, in the effectiveness of flood management plans. It was thus sought, with the development of this thesis, to deepen the understanding of the contribution of uncertainties in this process. For this, a probabilistic hydraulic model was developed, which considers both the uncertainties of the rating curve and the uncertainties of the Manning coefficient in flood modeling. In a first moment, the uncertainties of the rating curve were quantified by a Bayesian approach method, which relies on information from the physical knowledge of the cross section and the river stretch to elicit a priori distributions. In a second step, the a posteriori estimated flows were propagated through a Bayesian probabilistic hydraulic model that updated the uncertainty about the roughness coefficient. For the application of the methodology a stretch between the Acorizal and Cuiabá river gauging stations, located in the interior of Mato Grosso, was selected. The results demonstrated the adequacy of the proposed methodology for uncertainty quantification and proved that the first-order autoregressive error model adopted was appropriate to represent the modeling residuals. However, in relation to the proposed probabilistic hydraulic model, an inferior performance was identified in the estimation of the elevations and flows associated with extreme values, evidencing a general tendency to overestimate the simulated elevations and flows. In addition, uncertainties related to the variability of the rating curve and Manning's roughness coefficient were found to have an impact on the predictive uncertainty of flood elevations, resulting in a widening of the credibility intervals of both parametric and total uncertainty.