Articles | Volume 26, issue 7
https://doi.org/10.5194/nhess-26-3505-2026
https://doi.org/10.5194/nhess-26-3505-2026
Research article
 | 
24 Jul 2026
Research article |  | 24 Jul 2026

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, Camilo Ocampo-Marulanda, Lina Maria Ceron-Aramburo, Teresita Canchala, and Tiago Alessandro Ferreira

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Cited articles

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Canchala Nastar, T., Carvajal Escobar, Y., Alfonso Morales, W., and Caicedo Bravo, E.: Estimation of missing data of monthly rainfall in southwestern Colombia using artificial neural networks, Data Brief, 26, 104517, https://doi.org/10.1016/j.dib.2019.104517, 2019. 
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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.

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