A Comparative Assessment of Advanced Bias Correction Methods for GPM IMERG in Banjir Kanal Timur Watershed - Lufira, Rahmah Dara; Suhartanto, Ery; Andawayanti, Ussy; Asmaranto, Runi; Utami, Rizki Tri (Engineering, Technology & Applied Science Research, 2026)
Reliable precipitation inputs are essential for flood risk management in the Semarang lower Banjir Kanal Timur (BKT) catchment, where tidal effects and convective storms play a significant role. This study uses monthly data (2014–2023) from three gauges and GPM IMERG Final Run to evaluate five bias correction methods: Linear Scaling (LS), Linear Regression (LR), Correction Factor (CF), Spatiotemporal (ST), and Random Forest (RF). The analysis follows a reproducible gauge–satellite workflow. Skill was assessed with Nash–Sutcliffe efficiency (NSE), Pearson correlation coefficient (R), and the ratio of RMSE to standard deviation (RSR). RF yielded the highest performance (NSE = 0.61, R = 0.78, and RSR = 0.63), indicating improved variance reproduction and association. CF and ST attained satisfactory operational skills (both NSE = 0.53, R = 0.73, and RSR = 0.69), while LS and LR yielded modest gains (NSE = 0.37 and 0.28; R = 0.70 and 0.65; RSR = 0.79 and 0.84), reflecting persistent intensity and season-dependent biases and point pixel mismatch. This study provides a decade-long, practice-oriented benchmark for bias correcting IMERG in a tidally influenced tropical urban basin and establishes a consistent monthly performance hierarchy (RF > CF ≈ ST > LS > LR). The resulting bias-corrected series enhances GPM IMERG suitability for hydrological modelling, flood forecasting, and monitoring in data-scarce tropical cities, while highlighting needs for tail-focused evaluation and temporally blocked validation to strengthen operational deployment.