Resumo A concentração de gases de efeito estufa (GEE) na atmosfera tem aumentado acentuadamente desde a revolução industrial, o que levou a intensificação do efeito estufa e consequentemente vem causando o aquecimento global. A análise espacial de tendências permite observar as mudanças no comportamento e determinar em quais regiões uma determinada variável vem sofrendo mudanças ao longo do tempo. Diante disso, o objetivo do presente trabalho foi analisar as tendências temporais da precipitação e da temperatura média no Brasil, utilizando o método Contextual Mann-Kendall (CMK), utilizando dados espacialmente distribuídos elaborados pelo Climatic Research Unit (CRU), entre os anos 1961 e 2011. A umidade relativa e a evapotranspiração foram analisadas no intuito de auxiliar na interpretação dos resultados da precipitação e temperatura. Os resultados mostraram tendências não significativas em mais de 70% do território brasileiro em todos os meses na precipitação, porém a temperatura média apresentou tendência positiva significativa em grande parte do Brasil ao longo de todo ano. Em geral, a evapotranspiração apresentou um comportamento diretamente proporcional à temperatura, enquanto que a umidade relativa apresentou comportamento inversamente proporcional. A continuidade dessas tendências poderá resultar em impactos na agricultura e no ciclo hidrológico, e consequentemente para a fauna e flora e para a população.

This study consists of hydrological simulations of the Muriaé river watershed with the topography-based hydrological model (TOPMODEL) and available stream gauge and rain measurements between 2009 and 2013 for two subbasins, namely Carangola and Patrocínio do Muriaé. The simulations were carried out with the Climate Prediction Center morphing method (CMORPH) precipitation estimates and rain gauge measurements integrated into CM-ORPH by the Statistical Objective Analysis Scheme (SOAS). TOPMODEL calibration was performed with the shuffled complex evolution (SCE-UA) method with Nash-Sutcliffe efficiency (NSE). The best overall results were obtained with CMORPH (NSE ~ 0.6) for both subbasins. The simulations with SOAS resulted in an NSE ~ 0.2. However, in an analysis of days with highlevel stages, SOAS simulations resulted in a better hit rate (23%) compared to CMORPH (10%). CMORPH simulations underestimated the flows at the flood periods, which indicates the importance to use multi-sensor precipitation data. The results with TOPMODEL allow an estimate of future discharges, which allows for better planning of a flood warning system and discharge measurement schedule.

This study comprises the hydrological modeling of the Muriaé river basin. Hydrologic simulations were performed with the TOPMODEL hydrological model, with precipitation measurements and discharge estimation from the Brazilian Hydrometeorology Network (RHN). It was also used satellite precipitation estimates with the CMORPH method, and the integrated precipitation analysis between the precipitation measured by the telemetry and the estimated by satellite through objective statistical analysis (SOAS). The calibration and validation of the TOPMODEL model were performed for hydrological events between 2016 and 2018. The calibration of the TOPMODEL model with the above precipitation data series was evaluated using the Nash-Sutcliffe coefficient (NSE), which ranged from 0,7 and 0,9. Validation of the TOPMODEL model with independent series resulted in NSE from-0,8 to 0,3. This result is largely due to the small number of hydrological events since the beginning of telemetry measurements at the Muriaé river basin. TOPMODEL was also used to simulate flows in series with annual period between 2009 and 2013. Calibration and validation with annual series resulted in NSE ~ 0.6. Notably, CMORPH simulations tend to underestimate flow rates, while with SOAS the performance was better, especially for flood periods. Therefore, the results suggest the applicability of the TOPMODEL model for hydrological simulations of the Muriaé river basin, with the best results obtained when the modeling started in a drought period and the rainfall data represented the spatial variability of the rainfall.

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