Preprints
https://doi.org/10.5194/egusphere-2025-3694
https://doi.org/10.5194/egusphere-2025-3694
29 Sep 2025
 | 29 Sep 2025
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).

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

Abstract. Accurate streamflow forecasting is critical for mitigating the impacts of hydrological extremes and guiding sustainable water resource management, particularly in poorly gauged tropical catchments. This study presents a hybrid forecasting framework that integrates Neural Network Seasonal Autoregressive Integrated Moving Average using exogenous variables (NN-SARIMAX) models with nonlinear principal components (NLPCs) derived from CHIRPS precipitation data, and large-scale ocean–atmosphere indices (macroclimatic variables, MVs). Four monthly models were developed and tested for the Tocaría River basin in the Colombian Orinoquía region: (1) a baseline SARIMA (4,0,4) (0,0,3)12 model; (2) SARIMAX with exogenous MVs; (3) NN-SARIMAX with NLPCs; and (4) a hybrid NN-SARIMAX combining both MVs and NLPCs. The hybrid model achieved the best performance with an R2 of 0.78 during the validation period. These results underscore the effectiveness of integrating local precipitation variability and large-scale climatic drivers to enhance forecast accuracy under data-scarce conditions. The proposed methodology offers a transferable approach for operational forecasting in ungauged or sparsely monitored basins, contributing to early warning systems, drought preparedness, and adaptive water governance in vulnerable tropical regions.

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Jhon Derly Sarria-Ospina, Camilo Ocampo-Marulanda, Lina Maria Ceron-Aramburo, Teresita Canchala, and Tiago Alessandro Ferreira

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Jhon Derly Sarria-Ospina, Camilo Ocampo-Marulanda, Lina Maria Ceron-Aramburo, Teresita Canchala, and Tiago Alessandro Ferreira

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The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes Chris Funk et al. https://doi.org/10.1038/sdata.2015.66

Jhon Derly Sarria-Ospina, Camilo Ocampo-Marulanda, Lina Maria Ceron-Aramburo, Teresita Canchala, and Tiago Alessandro Ferreira

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Short summary
A method was developed to improve river flow predictions in tropical regions with limited measurements. By combining local rainfall patterns with global climate signals through a hybrid machine learning model, the forecasting accuracy was enhanced. This approach can support water managers in making more informed decisions during dry periods. The proposed method offers a straightforward way to enable early warnings in data-scarce regions.
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