Integrated Quantile Mapping and Spatial Clustering for Robust Bias Correction of Satellite Precipitation in Data-Sparse Regions

Ghazi Al-Rawas (), Mohammad Reza Nikoo, Nasim Sadra and Farid Mousavi
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Ghazi Al-Rawas: Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat P.O. Box 33, Oman
Mohammad Reza Nikoo: Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat P.O. Box 33, Oman
Nasim Sadra: School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand
Farid Mousavi: Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat P.O. Box 33, Oman

Sustainability, 2025, vol. 17, issue 18, 1-22

Abstract: Precipitation estimation is one of the main inputs of hydrological applications, agriculture, and disaster management, but satellite-based precipitation datasets often present biases and discrepancies compared to ground measurements, particularly for data-scarce regions. The present work discusses the development of a novel methodology that merges quantile mapping with machine learning-based spatial clustering, aiming at enhancing the accuracy and reliability of satellite precipitation data. Results showed that quantile mapping, by aligning the distributional properties of satellite data with in situ measurements, reduced systematic biases. On the other hand, quantile mapping could not capture the extremes in precipitation merely by relying on a simple model complexity–performance trade-off. While increasing the number of clusters enhanced capturing spatial heterogeneity and extreme precipitation events, the benefit from using more clusters was really realized up to a point, as continued improvement in metrics beyond 10 clusters was marginal. Conversely, the extra clusters further did not provide any significant reductions in RMSE or Bias. This showed that the effect of further refinement in model performance showed diminishing returns. This hybrid quantile mapping and clustering framework provides a robust tool that can be adapted for enhancing satellite-based precipitation estimates and therefore has implications for data-poor areas where accurate precipitation information is key to sustainable water resource management, climate-resilient agricultural production, and proactive disaster preparedness that supports long-term environmental and socio-economic sustainability.

Keywords: extreme precipitation; hydrological modelling; machine learning algorithms; quantile mapping techniques; satellite-derived data; spatial-temporal analysis; sustainable water management; climate resilience; environmental sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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