Estimativa do Número da Curva (CN) e sua adaptação ao contexto das paisagens mineiras

• Ano de defesa: 2023
• Autor(a) principal: Cláudia Campos de Albuquerque
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; Brasil; Programa de Pós-Graduação em Análise e Modelagem de Sistemas Ambientais; UFMG
• Palavras-chave em Português: número da curva; recursos hídricos; modelos hidrológicos; unidades de paisagem; ; álgebra de mapas; Modelagem de dados – Aspectos ambientais; Recursos hídricos – Desenvolvimento – Minas Gerais; Paisagens – Minas Gerais; Planejamento regional – Minas Gerais
• Link de acesso: http://hdl.handle.net/1843/65998

Water resources are essential for maintaining life and earth dynamics. To understand the dynamics between water resources and land use in different landscapes, spatial analyzes are valuable tools. One of the widely used methodologies is the Soil Conservation Service (SCS), which is based on the Curve Number (CN) coefficient, among other parameters. However, the method and estimation of the CN coefficient was developed for a specific landscape context in the United States and is not necessarily suitable for other areas. Adapting the CN coefficient to the context of the state of Minas Gerais can help in the study and characterization of hydrology in the state's landscapes, helping to represent water resources in the context of the landscape. This work estimated the CN tabulated by the SCS and using map algebra refined the coefficient for the specific context of Minas Gerais, based on spatial data that are directly related to the hydrodynamics of the region. As a result, a tabulated NC map and a refined NC map adapted for the state of Minas Gerais were created, whose data were validated with hydrological information from the Rio Doce basin. The NC estimate for high values indicates locations of high surface runoff and low infiltration. The CN tabulated above 80 represents 84% of the total area of the state of Minas Gerais. Estimates for the refined CN show that 65% of the state's total area has a CN above 80. Tabulated and refined CN values were compared and validated. This study highlights the importance of adapting existing methodologies to the conditions of local landscapes, contributing to the achievement of global goals such as Sustainable Development Goals SDG 6 (Ensure availability and sustainable management of water and sanitation for all), SDG 11 (Make cities and inclusive, safe, resilient and sustainable human settlements) and SDG 15 (Protect, restore and promote the sustainable use of Earth's ecosystems, sustainably manage forests, combat desertification, halt and reverse land degradation and halt the loss of biodiversity).