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Multi-temporal scale of urban rainfall in the Eastern Northeast based on observed data and gridded products: physiographic factors and changes in land use and occupation
(2026-02-01)
da Silva, Luis Felipe Francisco Ferreira
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de Oliveira Romão, William Max
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Lyra, Gustavo Bastos
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da Silva, Elania Barros
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da Silva Costa, Micejane
Rainfall is one of the most important meteorological variables in the daily lives of urban populations. The city of Maceió, the capital of Alagoas, located in the eastern part of the Northeast of Brazil (ENEB), has 50 neighborhoods and a population of approximately one million people, with few studies on the subject. The objectives were: (i) to validate the CHIRPS product; (ii) to identify the preferential rainfall periods in Maceió via GIS; (iii) to map areas for the installation of in situ stations in the city with the aim of supporting the prevention of hydrometeorological disasters; and (iv) creation of a theoretical-conceptual rainfall model. The statistical indicators (R², ρ, BIAS, MAPE and RMSE) were used to validate the gridded precipitation product CHIRPS from 11 CEMADEN rain gauge stations. Monthly rain occurrence maps via Spline tension were developed by QGIS (Quantum GIS) software. The HAND model was applied at neighborhood level for the assessment of urban floods. Waterborne disease data were obtained from SINAN, Natural Disaster data via S2iD from the period 2000 to 2023, and the NDVI and EVI indices in the years 2015 and 2022 were evaluated in the study. All stations were monotonically positive (ρ > 0.65) and significant (p-value < 0.001), indicating that CHIRPS is able to capture rainfall variability despite the influences of the coast, Lagoa Mundaú, and topography. Most stations showed underestimation (negative BIAS) and lower errors (MAE and RMSE). Spatially, the increase in rainfall on the coastal plateau is due to the interaction of the wind regime with the relief, driven by the circulation of breezes and the influence of trade winds. The preferential rainfall period occurs between 04:00 am and 07:00 am. The HAND model identified very high and high susceptibility, mainly on the coast, in areas adjacent to Lagoa Mundaú, and in neighborhoods crossed by rivers and urban canals, and low susceptibility in densely populated neighborhoods. Waterborne diseases together with transformations via NDVI and EVI indicated that rainfall amplifies risk scenarios for the most vulnerable and densely populated populations. In light of this, it is perceived that the rainfall patterns in Maceió are due to the interaction of physiographic and/or anthropogenic factors and meteorological systems – theoretical-conceptual model – which requires improvements in infrastructure and an active monitoring system.
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Analysis of drought and extreme precipitation events in Thailand: trends, climate modeling, and implications for climate change adaptation
(2025-12-01)
de Oliveira-Júnior, José Francisco
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Mendes, David
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Porto, Helder Dutra
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Cardoso, Kelvy Rosalvo Alencar
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Neto, José Augusto Ferreira
Thailand is under threat from climate change, where extreme climate events are expected to intensify and increase in the coming decades. The objective is to assess extreme drought and rainfall events in Thailand based on climate modeling through an ensemble for future projections of extreme climate indices. The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. The projections were based on three scenarios: historical (20th century); intermediate forcing (RCP 4.5) and high forcing (RCP 8.5). The results of the climate indices pointed to significant regional differences in Thailand. Historically, the CDD indicated 35 consecutive dry days in the northern (N) and northeastern (NE) parts of Thailand, whereas the southern region showed CDD values of fewer than 10 consecutive dry days. In the R4.5 scenario, a meridional pattern emerged in CDD, increasing from east (E) to west (W). In the R8.5 scenario, the number of consecutive dry days increased across the entire country. The WSDI stood out in both the R4.5 and R8.5 scenarios, with an increase in the duration of warm spells in Thailand. The CSU did not perform satisfactorily in the scenarios adopted. Historically, the CWD indicated consecutive wet days in the N and NE, whereas in the R4.5 and R8.5 scenarios, this was observed only in the Central and Southern regions. Historically, the maximum number of consecutive rainy days varied in the NE and South via WW. In the R4.5 and R8.5 scenarios, there was a significant increase in the maximum number of consecutive rainy days across Thailand. Projections based on climate indices indicate that Thailand needs to adopt mitigation measures across its regions to neutralize the impacts of extreme drought and rainfall events on socioeconomic sectors, particularly in tourism, industry, agricultural production, and food security for its population.
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Impact of the El Niño on Fire Dynamics on the African Continent
(2024-01-01)
de Oliveira-Júnior, José Francisco
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Mendes, David
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Szabo, Szilard
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Singh, Sudhir Kumar
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Jamjareegulgarn, Punyawi
Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge.

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