Evaluating Linear Scaling and Random Forest Bias Correction of GPM-IMERG V7 Rainfall in Moluccas Islands Watersheds
Received: 17 December 2025 | Revised: 4 February 2026 | Accepted: 11 February 2026 | Online: 31 March 2026
Corresponding author: Ery Suhartanto
Abstract
Reliable, high-resolution daily rainfall data are essential for hydrological assessments and flood-risk management in tropical island watersheds; however, rain-gauge networks in the Moluccas Islands are sparse and unevenly distributed. This study assesses the daily performance of the GPM-IMERG Version 7 (Final Run) precipitation product. It develops bias-correction strategies for six coastal watersheds (Dodaga, Sangaji, Kobe, Inggol, Wai Ruhu, and Wai Batumerah) using daily gauge observations from 2022 to 2025 as reference. A conventional mean-based Linear Scaling (LS) approach is implemented as a benchmark and compared with a non-parametric Random Forest (RF) regression model developed using matched IMERG-gauge pairs and evaluated against gauge observations over 2022-2025. Model skill is measured at the daily level using the Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (R), and the RMSE-to-standard-deviation ratio (RSR). LS shows poor daily performance (NSE −0.53 to 0.18; R 0.12-0.49; RSR 0.91-1.40), indicating that mean adjustment alone is insufficient to address event-scale errors and intensity-related biases. In contrast, RF significantly enhances agreement with gauge observations across all watersheds (NSE 0.85-0.90; R 0.96-0.98; RSR 0.32-0.39). These results suggest that RF-corrected IMERG V7 offers a reliable daily rainfall input suitable for hydrological applications in data-limited tropical islands. Additionally, it provides a correction workflow that can be adapted to similar coastal watersheds.
Keywords:
GPM-IMERG V7, bias correction, linear scaling, random forest, Moluccas islands watershedsDownloads
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Copyright (c) 2026 Hayatuddin Tuasikal, Pitojo Tri Juwono, Moh. Sholichin, Ery Suhartanto
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