This study analyzed the correlation between climate indices—El Niño–Southern Oscillation (NINO34), Southern Oscillation Index (SOI), Antarctic Oscillation (AOC), Sea Surface Temperature in the southwestern Atlantic (ISSTRG2 + RG3), South Atlantic Subtropical High (SASH), Pacific Decadal Oscillation (PDO), and Madden–Julian Oscillation (MJO)—and precipitation in Rio Grande do Sul (RS) during 45-day subseasonal periods from 2006 to 2022. Precipitation data from 670 rain gauges were categorized into three clusters: cluster 1, located in western RS, displayed the lowest precipitation variation; cluster 2, in eastern RS, exhibited the greatest variability; and cluster 3, situated in northern RS. ENSO demonstrated the strongest positive correlation with precipitation during spring in clusters 1 and 3 (0.65–0.79), while PDO also correlated positively, especially in summer and spring. AOC exhibited negative correlations, most pronounced in spring. Significant inter-index correlations were identified, including a high positive correlation between SASH and AOC (0.7) and a high negative correlation between NINO34 and SOI (−0.73). Within clusters, NINO34 and PDO showed low positive correlations with precipitation (0.24–0.32), while SOI demonstrated low negative correlations (−0.21 to −0.30). Seasonal analysis revealed that NINO34 influenced summer and spring precipitation, correlating with above-average rainfall during El Niño events. SASH and PDO also showed positive correlations with summer and spring rainfall, with PDO’s positive phase associated with a 25% increase in precipitation. These findings provide valuable insights into the complex interactions between global climatic indices and regional precipitation patterns, enhancing the understanding of subseasonal climate variability in RS and supporting the development of more accurate climate prediction models for the region.

Resumo O módulo de elasticidade do concreto é uma propriedade importante para os profissionais envolvidos na indústria da construção civil, uma vez que seu valor é determinante para o controle das deformações. Este trabalho avaliou o módulo de elasticidade de três classes distintas de concreto (C20, C30 e C40) produzidas com dois tipos litológicos de rochas, basalto e dolomito, de diferentes jazidas da região do Triângulo Mineiro. Como parte do estudo experimental, foram moldadas 324 amostras cilíndricas de 10 cm × 20 cm. Os valores de módulo de elasticidade obtidos foram comparados com seis formulações propostas em quatro normas, institutos e códigos do concreto: ABNT NBR 6118 versão 2007 e 2014, ACI 318, EUROCODE 2 and FIB Model Code, Ibracon 2003. Dentre as formulações propostas pelas normas, as indicadas pela FIB Model Code (2010) e ABNT NBR 6118 (2014) apresentaram valores mais próximos aos resultados experimentais deste estudo.

This study evaluates the diagnostic capability of the Fourier Decomposition Method (FDM) in comparison with the Fast Fourier Transform (FFT) for diagnosing subseasonal precipitation variability in Rio Grande do Sul (RS), southern Brazil. Time series of 45-day moving averages derived from gridded precipitation estimates from the CHIRPS v2.0 dataset were analyzed across three spatially distinct regional clusters representing different hydroclimatic regimes in the state. Rain gauge observations were used exclusively for validation purposes. The analysis considered large-scale and intraseasonal climate indices, including Niño3.4 (NINO34), the Pacific Decadal Oscillation (PDO), the Madden–Julian Oscillation (MJO),the Southern Annular Mode (SAM/AAO), the Southern Oscillation Index (SOI), the South Atlantic Subtropical High index (SASHI), and sea surface temperature anomalies in the southwestern South Atlantic (SST23). Non-stationarity was assessed using the KPSS test, followed by spectral decomposition using both FFT and FDM within a conservative statistical inference framework. Statistical significance was evaluated using an effective sample size to account for serial autocorrelation and corrected for multiple testing using a false discovery rate approach. Under these stringent conditions, only climate–precipitation relationships that remain statistically supported were retained. The results indicate that spectral reconstruction enhances the detection of multiscale and transient variability relative to fixed-basis approaches, particularly at intraseasonal timescales. Signals associated with large-scale Pacific variability emerge as persistent low-frequency coherence in clusters dominated by synoptic-scale processes, whereas intraseasonal and regional atmospheric influences—linked to the MJO and South Atlantic circulation—are preferentially resolved in specific clusters when FDM is employed. By integrating spatial regionalization with multiscale spectral analysis, this study advances a robustness-based framework for assessing precipitation–climate relationships under non-stationary conditions. The findings underscore the relevance of adaptive spectral methods for regional climate diagnostics and for improving the interpretation of subseasonal precipitation variability in southern Brazil.

Understanding precipitation variability is essential for assessing climate dynamics and their impacts on agriculture, water resources, and infrastructure. This study analyzes subseasonal precipitation patterns in Rio Grande do Sul, Brazil, using 45-day accumulated intervals over a 17-year period (2006–2022), a timescale critical for understanding drivers of extreme events like the catastrophic floods of 2024. A total of 138 precipitation fields were generated from 670 spatial points. Spatial analysis revealed median precipitation values ranging from 130 to 329 mm/45 days, with the northeast showing the highest accumulations and the southwest showing the driest conditions. Temporal variability was marked by abrupt anomalies, with median peaks up to 462 mm and minima of 33 mm. Significant temporal autocorrelation (lag-1, 45 days) was identified in the central and northern regions, while lag-2 (90 days) showed inverse patterns in the south (correlation coefficient ≈ −0.45). Principal component analysis (KMO = 0.909; Bartlett’s χ² = 187,990.945; p < 0.05) identified seven dominant modes, with PC1 explaining 26% of total variance and highlighting extremely wet anomalies (e.g., SPI > 2.0). Correlation with the Oceanic Niño Index revealed heterogeneous responses to ENSO phases, with strong El Niño episodes (2009, 2015–2016) associated with precipitation peaks up to 966 mm/45 days. These results underscore the importance of subseasonal scales for understanding climate anomalies and support the development of regional forecast strategies and water management policies under increasing climate variability.

O uso e ocupação do solo, associado à escolha de materiais de edificações provoca diferentes níveis de acúmulo de calor no tecido urbano. Entretanto, pouco se sabe sobre a magnitude que cada variável possui sobre a formação do microclima urbano. Sendo assim, o objetivo deste estudo é analisar quais as variáveis microclimáticas possuem maior contribuição no campo térmico de áreas industriais e residenciais em um contexto de clima tropical. Para isso, foi simulada a interação microclimática entre os elementos do tecido urbano em uma área residencial e industrial de Uberlândia-MG, com apoio do software ENVI-met. Por fim, os dados de saída foram agrupados utilizando o método de Clusterização e sintetizados através da Análise Fatorial por Componentes Principais (PCA). Portanto, os resultados apontaram que para ambas as áreas, a Umidade Relativa, Absoluta e Temperatura do Ar são determinantes na formação do microclima. Entretanto, em áreas residenciais (LCZ3) o Fator de Visão do Céu apresentou um peso mais elevado na variância dos componentes principais. E em áreas industriais (LCZ8) a velocidade do vento descreveu um importante percentual na variância das duas dimensões principais. Desta forma, nota-se que no contexto tropical, as variáveis apresentaram diferentes pesos na formação do campo térmico em função da diferença do uso e ocupação do solo das cidades, o que nos leva a reflexão sobre a adequação de políticas públicas e bases normativas.