Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3489-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-3489-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2026
Research article |
| 23 Jul 2026
| 23 Jul 2026
Hourly-scale modeling of storm transitions in Southern Brazil with Markov Chains
Camilo Ocampo-Marulanda
CORRESPONDING AUTHOR
Graduate Program in Biometrics and Applied Statistics, Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, 52171-900, Brazil
Water Resources, Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, 760032, Cali, Colombia
Jefferson Vieira Santos
Graduate Program in Biometrics and Applied Statistics, Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, 52171-900, Brazil
Julian David Mera-Franco
Water Resources, Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, 760032, Cali, Colombia
Alvaro Avila-Diaz
Earth System Science Program, School of Sciences and Engineering, Universidad del Rosario, Bogotá D.C., Colombia
Tiago Alessandro Espinola Ferreira
Graduate Program in Biometrics and Applied Statistics, Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, 52171-900, Brazil
David Henriques da Matta
Federal University of Goiás (UFG), Institute of Mathematics and Statistics (IME), Goiânia, 74001-970, Brazil
Antonio Samuel Alves da Silva
Graduate Program in Biometrics and Applied Statistics, Department of Statistics and Informatics, Federal Rural University of Pernambuco, Recife, 52171-900, Brazil
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The CAMELS-COL dataset is a valuable resource for studying water issues in Colombia. It includes weather, geological, soil, and land cover data. Covering 1981–2022, it provides precipitation, temperature, and streamflow records from 347 basins. This helps researchers understand catchment responses and climate change impacts. By being publicly available, it supports studies on hydrology, water management, and fosters collaboration.
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Short summary
After the devastating floods that struck southern Brazil in May 2024, this study set out to better understand how storms behave hour by hour – a topic still missing from the scientific literature. The results show that storms in the mountains are shorter but more intense, while those in lowland areas last longer. These insights can help improve flood warnings and guide smarter risk management.
After the devastating floods that struck southern Brazil in May 2024, this study set out to...
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