Geoestatística aplicada à estimativa e espacialização de dados de vazão

• Ano de defesa: 2022
• Autor(a) principal: Camila Dourado Machado
• Orientador(a): Fabio Verissimo Goncalves
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Fundação Universidade Federal de Mato Grosso do Sul
• País: Brasil
• Palavras-chave em Português: Espacialização de dados
• Link de acesso: https://repositorio.ufms.br/handle/123456789/5141

Given the need to know the signatures that define the quality and quantity of water available in each water body, the great problem for hydrologists and other professionals who work with water resources is the obtaining of flow data. Solving this problem of lack of data is not a simple task, mainly due to the great differences of behavior between hydrological regions. The regionalization techniques include several types of methodology, from mathematical and statistical models, multivariate regressions with explanatory variables, to geostatistical models that consider spatial proximity as a reference. Not only is the prediction of data for locations without information necessary, but when we speak of agility and reliability, the systematization of data in flow maps is of great value. Through geostatistics we can perform not only the regionalization for locations without data, but also the spatialization of the data. This research is justified in the need to solve the problem of the lack of flow data and to create reliable and agile tools to estimate and spatially visualize this information. We propose here a new approach on the study of the use of kriging as methodology of regionalization of flows and tool of data spatialization. We will focus this work on studying the behavior of this estimator to generate specific yield maps, for maximum, minimum and average flows, with the main objective being the elaboration of rasters that serve as a tool for support and decision support in the management of water resources.