Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3505-2026
© 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-26-3505-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Use of nonlinear principal components of CHIRPS precipitation data and ocean–atmospheric variables for streamflow forecasting in an area of scarce data – case study: Tocaría river basin – Orinoquia Colombiana
Jhon Derly Sarria-Ospina
CORRESPONDING AUTHOR
Research Group TERRANARE, Faculty of Natural Sciences and Engineering, Fundación Universitaria de San Gil, Yopal, 850001, Colombia
Camilo Ocampo-Marulanda
Programa de Pós-graduação em Biometria e Estatística Aplicada, Departamento de Estatística e Informática, Universidade Federal Rural de Pernambuco, Recife, 52171-900, Brazil
Water Resources, Engineering and Soil Research Group (IREHISA), School of Natural Resources and Environmental Engineering, Universidad del Valle, Cali, 760032, Colombia
Lina Maria Ceron-Aramburo
Research Group TERRANARE, Faculty of Natural Sciences and Engineering, Fundación Universitaria de San Gil, Yopal, 850001, Colombia
Teresita Canchala
Environmental Engineering Program, Faculty of Engineering, Universidad Mariana, Pasto, 520002, Colombia
Tiago Alessandro Ferreira
Programa de Pós-graduação em Biometria e Estatística Aplicada, Departamento de Estatística e Informática, Universidade Federal Rural de Pernambuco, Recife, 52171-900, Brazil
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Camilo Ocampo-Marulanda, Jefferson Vieira Santos, Julian David Mera-Franco, Alvaro Avila-Diaz, Tiago Alessandro Espinola Ferreira, David Henriques da Matta, and Antonio Samuel Alves da Silva
Nat. Hazards Earth Syst. Sci., 26, 3489–3504, https://doi.org/10.5194/nhess-26-3489-2026, https://doi.org/10.5194/nhess-26-3489-2026, 2026
Short summary
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.
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Short summary
A forecasting framework was developed to river flow prediction in data-scarce tropical regions. The study extracted hidden rainfall patterns from satellite precipitation records and integrated them with large-scale ocean–atmosphere climate signals into a statistical model, resulting in improved predictive accuracy. These findings can support more effective water-resources management during dry periods and strengthen early warning systems in regions with limited hydrometeorological monitoring.
A forecasting framework was developed to river flow prediction in data-scarce tropical regions....
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