Determinate the runoff of a watershed is a challenge due to the complexity of representing all “inlets” and “outlets” involved in a rainfall–runoff model. Therefore, methodologies applied for this purpose should have a good representation of the variables that most influence in this process. One of the models used to calculate the design flow is the (USDA in Urban Hydrology for Small. Technical release, no 55 (TR-55). Soil Conservation Service. Washigton, DC, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Urban+Hydrology+for+Small+watersheds#1, 1986), which considers the analysis changes in soil coverage, time of concentration (tc), and recurrence period (T). In this way, this study sought to evaluate the hydrological behavior of a watershed with an increase in soil waterproofing. These modifications were correlated with the variation of runoff coefficients (CN), modifications of the periods of recurrence indicated by the literature, and different equations of the time of concentration. Its application was carried out in the Ribeirão do Suru watershed, Santana de Parnaíba, SP, Brazil. The CN {75; 80; 85; 90} increased 3.14, 5.61, 10.90 and 15.85%, respectively. In the most critical situation, runoff was 15.85% higher in estimated CN. The variation of precipitation as a function of T (2, 5, 10, 25, 50, 100 and 500) and application of 11 time of concentration methods designed 132 hydrographs and flow values that were statistically treated in T of Student and in the Analysis of Variance (ANOVA). Except for Bransby Willians associated Cinematic Method, Dooge with Johnstone and CTH with Tsuchyia, the pairs showed degrees of correlation below 59%. The greatest correlation was observed in Jonhstone with Dooge (90%), followed by the Kinematic Method with the Soil Conservation Service Method (83%) and with Dodge (74%). As a result, it was possible to demonstrate the behavior of the SCS parameters to minimize subjectivities and revealing how each parameter impacts the flow of the watershed. Finally, the sensitivity attributed to T was the highest among the three analyzed.

Anthropogenic influences on a global scale have caused negative impacts on the environment, among the most prominent being the increase in the concentration of carbon dioxide (CO2). In this study, the objective was then to estimate the potential of carbon flux (CO2 flux) in the riparian vegetation of the Jacareí–Jaguari reservoir; using the digital processing of orbital images of the CBERS 4A system. To determine the CO2 flux, vegetative indices were used: Normalized Difference Vegetation Index (NDVI); Photochemical Reflectance Index (PRI) and the scaled Photochemical Reflectance Index (sPRI), associating them with land use and occupation classifications from the MapBiomas collection, determining the histogram of each class for CO2 flux analysis, revealing CO2 flux between −0.136 and 0.4049. The lower values of CO2 flux in the reservoir are due to the decrease in vegetative classes, indicating the need for (re)planting and plant conservation, confirming the importance of areas with an ecosystem function, of carbon sink.

Modelos Digitais de Elevação (MDEs) são modelos matemáticos que repdroduzem uma superfície topográfica a partir coordenadas cartesianas “x e y”, com atributos altimétricos “z”, sendo que “z” representa a variação de uma superfície. Os MDEs apresentam várias aplicações, podem ser utilizados na gestão de recursos hídricos e ambientais. Neste âmbito, o objetivo deste trabalho foi comparar diferentes sistemas sensores, SRTM (1, 2 e 3), COPERNICUS DEM, TopoData, ASTER GLOBAL DEM 2 e o ALOS PALSAR, por intermédio de um sistema binômio formado pelas variáveis altimétricas, verificando a dispersão e precisão através dos dados de MDE, também pela análise física do perfil altimétrico decorrente do talvegue de maior extensão no município de São Carlos. A análise consistiu também na avaliação estatística dos MDE com Teste T de Student e Análise de Variância (ANOVA). Seguindo com os Mapas de Orientação de Vertente verificando quais foram as vertentes predominantes, pelos Perfis Altimétricos dos MDEs demonstrando pequenas diferenças de cota e conforme o par analisado, diferenças posicionais. A melhor correlação ocorreu entre os sensores SRTM, enquanto a pior foi dada pelos sensores da família TANDEM/TERRASAR-X. Quanto às comparações físicas (visuais) e estatísticas dos dados de MDE, afirma-se a pertinência quanto à similaridade dos sensores SRTM 30, TopoData e NASADEM, a maior diferença entre ASTER GDEM e TANDEM/TERRASAR-X. Diferença ocorre devido ao processo de constituição do MDE, principalmente pela resolução espacial de cada.