Claudio José Cavalcante Blanco

This study analyzes trends in water availability using the surface water balance at the sub-basin scale in the Amazon biome. The analysis considered a reference period (2009–2021) and a future period (2022–2100). Two climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were established: SSP2-4.5 and SSP5-8.5. Precipitation projections were obtained from the Global Climate Model (GCM) GFDL-ESM4, while temperature projections were estimated using the mean of a five-model ensemble (MM-5), composed of KIOST-ESM, FGOALS-g3, BCC-CSM2-MR, NESM3, and GFDL-ESM4. Streamflow was simulated using a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) recurrent neural network. Reference evapotranspiration was estimated using the Hargreaves–Samani equation, calibrated for local conditions, and regional crop coefficients were applied to improve evapotranspiration estimates. Trend analyses were performed using the Mann–Kendall test and Sen’s slope estimator for water balance and components. The calibration of the Hargreaves–Samani equation improved the estimation of reference evapotranspiration, whereas the original formulation tended to overestimate ETo under humid tropical conditions. The results also indicate a significant increasing trend in evapotranspiration (Z = 7.92), accompanied by decreasing precipitation (Z = − 3.96), reductions in specific discharge (Z = − 3.61), and a pronounced decline in the water balance (Z = − 4.77), particularly under the SSP5-8.5 scenario.

Climate change and alterations in land use and land cover directly impact hydroelectric power generation (Devkota et al. 2025), especially in regions projected to experience reduced rainfall (Kemar...

O objetivo foi mapear e estimar a probabilidade diária de inundação/alagamento em Belém-PA, sede da COP30, considerando o efeito combinado chuva-maré. As séries de dados de maré e chuva são de 2003 a 2024. A probabilidade diária foi calculada através da divisão do número de eventos ocorridos em determinado dia do ano pelo total de anos. O mapeamento foi elaborado via software QGIS, que foi alimentado com dados de geográficos, imagens de satélite, shapefile dos bairros e hidrografia de Belém. As ocorrências de alagamentos/inundações foram validadas por fotografias e imagens da internet. Analisando-se os resultados, 352 dias apresentaram probabilidades de ocorrências de inundações/alagamentos entre 10% e 100% distribuídas em 18 bairros de Belém. Nesse caso, bairros com áreas de baixada foram os mais atingidos, pois possuem um sistema de drenagem deficiente. Logo, a instalação de comportas com um sistema inteligente de acionamento, poderia evitar a entra das águas da maré durante os eventos de chuva, conservando o sistema de drenagem para conter as águas da chuva. Assim, a construção de sistemas de comportas inteligentes nos canais que cortam a cidade é uma necessidade urgente para aliviar o sofrimento das populações menos favorecidas, que moram nas baixadas/periferias de Belém do Pará.

This study aims to simulate streamflow using machine learning in a basin located in the Brazilian Amazon under two future climate scenarios from CMIP6, and analyze the impacts of climate change on streamflow until 2100 through trend analysis. A Nonlinear Auto Regressive Recurrent Neural Network with Exogenous Inputs (NARX) was trained to project streamflow under the SSP2-4.5 and SSP2-4.5 and SSP5-8.5 scenarios. Precipitation projected by the Global Circulation Models (GCMs) GFDL-ESM4, FGOALS-g3 e CESM2 was used as input to the model. The maximum streamflow simulated by NARX model in the reference period were underestimated. This underestimation was attributed to a systematic error in the precipitation projected by the GCMs, characterized by the delay in the peak of maximum. Therefore, the empirical quantile mapping EQM method was applied to correct the bias in the simulated streamflow using observed data from the reference period. The Mann-Kendall method was used to analyze future streamflow trends. The results show that the overall performance of the simulations was classified as good, with Kling-Gupta (KGE) values ranging from 0.73 to 0.77 in both scenarios. This performance was also reflected in the cumulative distribution function (CDF) curves of streamflow, which showed good agreement with the observed streamflow distribution. The Mann–Kendall test results for the simulated streamflow under the SSP2-4.5 scenario indicate stability in the streamflow regime. In contrast, for SSP5-8.5, a stronger signal appears mainly in GFDL-ESM4, which shows a significant decreasing trend ( p = 0.0071) with Sen’s slope = − 0.313, indicating possible streamflow reduction. FGOALS-g3 ( p = 0.5120) and CESM2 ( p = 0.1527) showed no significant trends, although their slopes suggest opposite tendencies (0.0566 and − 0.1533, respectively). The MME also showed no significant trend ( p = 0.1029), but its negative slope (− 0.1488) suggests a general tendency toward decreasing streamflow.

Belém, the capital of the state of Pará and the host city of COP30 (30th United Nations Climate Change Conference), located in the Brazilian Amazon, faces a risk to its potable water source due to pollution. Thus, the study aims to model the dispersion of pollutants, in this case, phosphorus, within the Utinga source, which is formed by the artificial lakes Ígua Preta and Bolonha. The Saint-Venant equations were solved via a hydrodynamic model formulated with the finite element method. Pollutant dispersion simulation followed the SUPG/θ numerical method for spatial and temporal discretization, respectively, of the advection-diffusion-reaction equation in two dimensions. The results indicate that, due to the low flow dynamics of the lakes, the highest phosphorus concentration is found near the entry of Lake Ígua Preta. This is a result of the waters from the Guamá River, which receive a significant portion of the city of Belém's sewage, only 2.38% of which is treated. A good legacy of COP30 would be a sewage collection and treatment system for Belém, which would minimize the pollution of the waters of the Guamá River and, consequently, the waters of the Utinga source benefiting the region's population.