Modelagem e monitoramento hidrológico das bacias hidrográficas dos córregos Botafogo e Cascavel, Goiânia – GO

• Ano de defesa: 2015
• Autor(a) principal: Pereira, Tatiane Souza Rodrigues
• Orientador(a): Formiga, Klebber Teodomiro Martins
• Banca de defesa: Formiga, KlebberTeodomiro Martins, Siqueira, Eduardo Queija de, Souza, Saulo Bruno Silveira e, Côrtes, Jussana Milograna
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Engenharia do Meio Ambiente (EEC)
• Departamento: Escola de Engenharia Civil - EEC (RG)
• País: Brasil
• Palavras-chave em Português: GLUE; DREAM; Modelagem hidrológica; Calibração monobjetivo
• Palavras-chave em Inglês: GLUE; DREAM; Hydrological modeling; Monobjective calibration
• Área do conhecimento CNPq: ENGENHARIA DE AGUA E SOLO::CONSERVACAO DE SOLO E AGUA
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/4743

The impermeabilization of Brazil watersheds have generated socio-economic and environmental impacts innumerable, which has resulton a dare for public administrations. This because most of the towns do not have an efficient drainage system, this causes the water seeps quickly into water streams that are not prepared to support this new level of streamflow, mainly towards the extreme rainfall events. Thus, the hydrological modeling appears like an important support tool for planning and management. However this tool is subject to limitations that must be ascertained through calibration and model validation in real field conditions. This calibration process has been the focus of research and discussion in the last 50 years, and the most currently accepted models are based on uncertainty those analysis to determine the parameters. This study aims at the assessthe development and calibration of the model rainfall-runoff UFGModel1.1 as well as analyze the uncertainty applied to the simulation of streamflow. For this purpose, we used methodologies of uncertainty analysis by means of the Monte Carlo methods (MC) and Markov Chain Monte Carlo (MCMC), which form the basis of the GLUE and DREAM algorithms, respectively, for estimating uncertainties.The results demonstrate that DREAM performs better than the calibrating the GLUE. The various sets of parameters found for both algorithms were considered great for model validation, enabling high efficiency of this precipitation events for medium at very strong with relatively narrow uncertainty limits for DREAM and great for GLUE. The parameter that showed lower sensitivity in the model was the Manning channel.