2025/10/31 by John Bagiliko, Bagiliko, John, David Stern +9
Earth and Planetary Sciences · Environmental Science · #Applications (stat.AP) #FOS: Computer and information sciences #Flood Risk Assessment and Management #Hydrology and Drought Analysis #I.2 #J.2 #Precipitation Measurement and Analysis
paper · pdf · doi:10.48550/arxiv.2510.27456
openalex publication_date 2025/10/31 · openalex created_date 2025/11/05 · openalex updated_date 2026/07/28
In data-sparse regions, satellite and reanalysis rainfall estimates (SREs) are vital but limited by inherent biases. This study evaluates bias correction (BC) methods, including traditional statistical (LOCI, QM) and machine learning (SVR, GPR), applied to seven SREs across 38 stations in Ghana and Zambia. We introduce a constrained LOCI method to prevent the unrealistically high rainfall values produced by the original approach. Results indicate that statistical methods generally outperformed machine learning, though QM tended to inflate rainfall. Corrected SREs showed high capability in detecting dry days (POD ≥ 0.80). The ENACTS product, which integrates numerous station records, was the most amenable to correction in Zambia; most BC methods reduced mean error at >70% of stations. However, ENACTS performed less reliably at an independent station (Moorings), highlighting the need for broader validation at locations not incorporated into the product. Crucially, even after correction, most SREs (except ENACTS) failed to improve the detection of heavy and violent rainfall (POD ≤ 0.2). This limits their utility for flood risk assessment and highlights a vital research gap regarding extreme event estimation.