Regionalização de vazões na bacia hidrográfica do Baixo Rio Grande-UPGRH-GD8

• Ano de defesa: 2018
• Autor(a) principal: Matias, Fernando Justino Nascimento
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Qualidade Ambiental
• Palavras-chave em Português: Ecologia; Recursos hídricos - Desenvolvimento - Aspectos ambientais; Bacias hidrográficas; Água - Manejo; Modelagem hidrológica; Planejamento de recursos hídricos; Análise de cluster; Algoritmo de Ward; Disponibilidade hídrica; Hydrological modeling; water resources planning; Cluster analysis; Ward algorithm; Water availability.; CNPQ::ENGENHARIAS::ENGENHARIA CIVIL::ENGENHARIA HIDRAULICA::HIDROLOGIA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/23383; http://dx.doi.org/10.14393/ufu.di.2018.859

The hydrological flow regionalization is a technique used to estimate water availability at non-monitored area or with a lack of observed data. It is based on measurements of hydrological parameters collected in a specific basin. This technique is a useful tool in the process of granting rights of water used, construction of buses, among others. Regionalization allows the adjustment of regression models in regions considered homogeneous, using the physical and climatic characteristics of the river basin. The aim of the present research was to propose the hydrological models for estimation of maximum and minimum reference flow, using hydrological regionalization in the Water Resources Planning and Management Unit of Baixo Rio Grande river basin. Data were obtained from National Water Agency, 11 fluviometric stations were used. The mean minimum flow of 7 consecutive days was determined with 10-year return period, maximum daily average and flow with 90 and 95% of permanence time. Two hydrologically homogeneous regions were identified by the hierarchical method using Euclidean distance (dissimilarity), and Ward algorithm. The Gumbel probability distribution (maximum and minimum) was used and it was possible to adjust regression equations in each region considered homogeneous. Modeling results showed that the watershed area was the physical characteristic that produced the best adjustments for flow estimation. The comparison was basin on the models developed in this study and with Digital Atlas methodologies of the Waters of Mines and Surface Deflúvios of Minas Gerais from efficiency indicators widely used in hydrological modeling, showing very good performance of the models proposed in this work, in the estimation of reference flow and annual maximum daily average.