http://hdl.handle.net/11422/58 Fri, 10 Apr 2026 06:30:12 GMT 2026-04-10T06:30:12Z http://hdl.handle.net/11422/29010 Title: Relação entre florações de algas e ocorrência de névoas e nevoeiros marítimos durante o verão na Baía de Guanabara Author(s)/Inventor(s): Pinto, Ana Beatriz de Souza Advisor: Palmeira, Ana Cristina Pinto de Almeida Abstract: The study of interactions between algal blooms and the formation of fog and mist in Guanabara Bay aims to deepen the understanding of how environmental and meteorological variables may influence visibility reduction events in this region, which holds significant environmental and socioeconomic importance in the state of Rio de Janeiro. Variables investigated included sea surface temperature (SST), salinity, visibility, tidal conditions, and climate indices, along with algal bloom data derived from Sentinel-2 and Sentinel-3 satellite images. Days selected for analysis were focused on periods when fog or mist was observed at the Galeão and Santos Dumont airports and availability of cloud-free satellite imagery. Analyses involved Pearson correlation calculations among variables, followed by the application of Principal Component Analysis (PCA) to identify multivariate interactions and complex patterns that would not be detected by simple correlation methods. The results indicated that although direct correlations among variables were generally weak, there were signs that algal blooms, interacting with factors such as SST and salinity, may contribute to fog formation, particularly in the inner bay areas, where oceanic water circulation is less intense. The application of PCA reinforced these observations, also indicating that La Niña events appear to intensify the frequency and duration of fog events at Santos Dumont Airport, suggesting a nonlinear interaction between oceanic indices and local variables. The findings highlight the importance of integrated approaches in analyzing complex atmospheric phenomena and suggest the need for future investigations that include continuous monitoring, additional data on water quality, and characterization of algal species in blooms for a more detailed understanding of these interactions Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/11422/29010 2025-01-01T00:00:00Z http://hdl.handle.net/11422/28978 Title: Técnicas de inteligência computacional aplicadas à modelagem chuva vazão Author(s)/Inventor(s): Oliveira, Mayara Villela de Advisor: França, Gutemberg Borges Abstract: Rainfall-runoff modeling is fundamental for water resource management; however, its inherently non-linear nature and the influence of large-scale climatic phenomena represent a significant methodological challenge for traditional hydrological models. This work aimed to develop and evaluate a monthly flow forecasting model for the DO 3 Hydrographic District, outlet at Naque Velho, using computational intelligence techniques. The methodology adopted explored the potential of Artificial Neural Networks (ANNs) coupled with a Genetic Algorithm (GA). This was used for the global search of an optimal set of synaptic weights, aiming to overcome the vulnerability of the backpropagation algorithm to local minima and ensuring greater robustness and accuracy in network training. The results demonstrated that the model achieved median performance, converging in less than 20 generations, with a final fitness of 0.79. Flood events were fully captured within the upper predicted range, confirming that the model adequately incorporated the effect of antecedent precipitation and surface runoff memory; however, it showed superior performance during the dry season. The complete coverage of extremes (COV_ext=1) demonstrates the system's ability to handle hydrologically critical episodes without inflating average predictions. The model consolidates a hybrid approach capable of representing the uncertainty and seasonal variability of the basin. Its structure allows for a more faithful modeling of hydrological behavior, maintaining statistical robustness and operational simplicity Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Sat, 01 Mar 2025 00:00:00 GMT http://hdl.handle.net/11422/28978 2025-03-01T00:00:00Z http://hdl.handle.net/11422/28976 Title: Compound dry hot fire extreme events in the Pantanal Author(s)/Inventor(s): Belém, Liz Barreto Coelho Advisor: Santos, Renata Libonati dos Abstract: In recent years, the Pantanal biome, the largest continuous wetland in the world, located within the Upper Paraguay River Basin (UPRB), has been severely affected by vegetation fires associated with the intensification of hydroclimatic extremes. The combination of severe droughts, heatwaves, and anthropogenic pressures culminated in 2020 in the most extreme fire event ever recorded in the region, when more than 30% of the biome was consumed by fire, generating substantial socio-economic and environmental impacts. More recently, in June 2024, at the onset of the fire season, the Pantanal registered a record burned area of approximately 440,000 hectares, about 50 times the historical average for the month. This episode reflected exceptional climatic conditions, with an estimated return period of 35 years, within a broader context in which 2024 became the warmest year ever recorded both globally and in Brazil. In the Pantanal, these anomalies manifested as extreme drought, persistent heatwaves, and meteorological conditions highly conducive to fire spread, resulting in an exceptionally prolonged fire season from June to October. Over recent decades, the Pantanal has experienced an increase in the frequency and intensity of hydroclimatic extremes, driven by interactions among regional climate change, alterations in the hydrological regime, and anthropogenic pressures. These processes have modulated fire dynamics, favoring the occurrence of large-scale fires. Within this context, this thesis adopts an integrated approach to investigate how hydroclimatic extremes, particularly severe droughts, heatwaves, and deficits in moisture transport, modulate the occurrence, severity, and expansion of fires in the Pantanal, while also advancing the development of methods for burned-area monitoring and mapping. The first research axis investigates the atmospheric mechanisms and the occurrence of compound dry–hot events that culminated in the extreme fires of 2024, the warmest year ever recorded globally. Through the integration of satellite data, climate indicators, and synoptic analyses, 2024 is identified as the most severe drought within the 1980–2024 period, characterized by exceptional precipitation and soil-moisture deficits, the absence of the flood pulse, and the occurrence of persistent heatwaves. These factors acted simultaneously and synergistically, generating highly fire-prone conditions and elevating fire-danger indices. The second research axis deepens understanding of large-scale atmospheric processes that modulate critical droughts in the biome by analysing moisture transport and its anomalies during years of extreme drought associated with high fire activity. The application of the Lagrangian model FLEXPART allowed the identification of changes in the relative contributions of terrestrial and oceanic moisture sources, revealing that persistent deficits in moisture transport play a crucial role in the onset, duration, and severity of droughts. These patterns help explain the interannual variability of fire activity and reinforce the influence of remote atmospheric processes on regional hydroclimatic dynamics. The third research axis develops and validates a harmonized burned-area product for the Pantanal covering the 2014– 2024 period, based on the integration of the Landsat-8/9 and Sentinel-2 constellations. The method incorporates automatic sampling guided by VIIRS detections and morphological filtering to generate consistent training samples, resulting in classification accuracy exceeding 99.6% and performance metrics above 0.99. The resulting annual and monthly maps document the spatial and temporal patterns of fire activity across the biome, providing a robust foundation for ecological, climatic, and environmental management studies. By integrating physical diagnostics, atmospheric-process analyses, and methodological advances in remote sensing, this thesis contributes to a comprehensive understanding of the relationship between hydroclimatic extremes and fire in the Pantanal. The results underscore the importance of incorporating the compound nature of these events into mitigation and adaptation policies, while also providing an analytical framework and a set of tools to improve operational monitoring and integrated fire management under a scenario of accelerated climate change Publisher: Universidade Federal do Rio de Janeiro Type: Tese Sun, 01 Mar 2026 00:00:00 GMT http://hdl.handle.net/11422/28976 2026-03-01T00:00:00Z http://hdl.handle.net/11422/28968 Title: Avaliação de 6 sistemas de reanálise oceânicas no campo de correntes na margem continental brasileira com base nos dados do Programa Nacional de Boias PNBOIA Author(s)/Inventor(s): Silva, Felippe Galdino Advisor: Cirano, Mauro Abstract: Global ocean reanalysis models are an important tool for understanding and studying the oceans, making evaluation studies of these models at the regional scale essential. This study evaluated six global ocean reanalysis models at six points along the Brazilian continental margin, focusing on the current field, in comparison with observational data collected by ADCPs installed on buoys from the Brazilian National Buoy Program (PNBOIA). In addition, the bathymetry of each model was compared with the high-resolution global bathymetry model ETOPO. The bathymetric analysis showed that all models represent the continental shelf region well, as well as the isobath patterns at depths of up to 200 m; however, the lower-resolution models exhibited larger differences relative to the reference model in regions with strong topographic gradients and had difficulty representing small islands and promontories. To analyze model performance relative to the observed data, the statistical metrics of Pearson correlation, bias, root mean square error, and Skill Score were used. The main results indicate that there is no single model that performs best for all analyzed regions, and that spatial resolution proved to be important but not decisive for achieving better performance. In total, 60 parameters were analyzed, and the Australian model BRAN presented the best results in 29 instances, followed by the GLORYS12V1 model (25 instances) Publisher: Universidade Federal do Rio de Janeiro Type: Dissertação Wed, 17 Sep 2025 00:00:00 GMT http://hdl.handle.net/11422/28968 2025-09-17T00:00:00Z