Arango-Londoño et al. (2026) Satellite-Based Daily Precipitation Bias Correction in a Tropical Mountainous Region Using Functional Generalized Additive Mixed Models: A Case Study in Valle del Cauca, Colombia

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Identification

• Journal: Climate
• Year: 2026
• Date: 2026-09-09
• Authors: David Arango-Londoño, Delia Ortega-Lenis, Mauricio Alejandro Mazo-Lopera, Johan Steven Aparicio, Paula Moraga
• DOI: 10.3390/cli14090188

Research Groups

• University of [Institution Name], Department of Hydrology and Climate Science
• IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales), Colombia

Short Summary

This study proposes a Functional Generalised Additive Mixed Model (FGAMM) to correct daily satellite-derived precipitation estimates in data-scarce tropical regions. The FGAMM achieves significant improvement over existing methods, reducing the mean cross-validation RMSE by 0.68 mm/day.

Objective

• To develop an accurate method for correcting systematic biases in satellite-derived precipitation estimates in complex terrain under bimodal tropical regimes influenced by ENSO.

Study Configuration

• Spatial Scale: Departmental scale (Valle del Cauca, Colombia)
• Temporal Scale: 2012–2020

Methodology and Data

• Models used: Functional Generalised Additive Mixed Model (FGAMM), Linear Regression, SVM, Random Forest
• Data sources: CHIRPS satellite product, IDEAM station-level observations

Main Results

• The FGAMM achieves a mean cross-validation RMSE of 0.68 mm/day (95% bootstrap CI: 0.61–0.75) in the Valle del Cauca dataset.
• Significant improvement over linear regression, SVM, and Random Forest models.

Contributions

• Original contribution to the field of satellite-derived precipitation correction, particularly in complex terrain under bimodal tropical regimes influenced by ENSO.
• Development of a functional formulation targeting systematic biases relevant for water-balance applications.

Funding

• This research was funded by [Project Name], [Program Code] and [Reference Number].

Citation

@article{ArangoLondoño2026SatelliteBased,
  author = {Arango-Londoño, David and Ortega-Lenis, Delia and Mazo-Lopera, Mauricio Alejandro and Aparicio, Johan Steven and Soto, Diego and Moraga, Paula},
  title = {Satellite-Based Daily Precipitation Bias Correction in a Tropical Mountainous Region Using Functional Generalized Additive Mixed Models: A Case Study in Valle del Cauca, Colombia},
  journal = {Climate},
  year = {2026},
  doi = {10.3390/cli14090188},
  url = {https://doi.org/10.3390/cli14090188}
}