A Comparative Evaluation of Bias Correction Techniques for Improving GPM-IMERG Precipitation Data in the Welang Watershed, Indonesia
Received: 1 August 2025 | Revised: 11 September 2025 | Accepted: 21 September 2025 | Online: 2 December 2025
Corresponding author: Ery Suhartanto
Abstract
Precise precipitation information underpins hydrological modeling, water resource planning, and hazard mitigation; yet, gauge coverage in many Indonesian catchments is sparse. The GPM-IMERG product provides 0.1°/30-min rainfall estimates, however, systematic biases limit its operational value. Five benchmark correction techniques were evaluated: Linear Scaling (LS), Linear Regression (LR), Genetic-Algorithm-based Correction Factor (GA-CF), Local Intensity Scaling (LOCI), and Power Transformation (PT) against daily observations from seven gauges in the Welang Watershed (2001–2020). LS delivered the most consistent improvement (NSE = 0.87, R = 0.92, RSR = 0.36), reducing the residual error by 30% relative to the next-best method. LR, GA-CF, and LOCI enhanced seasonal patterns (NSE ≈ 0.85), while PT provided complementary gains for moderate events but remained sub-optimal for extremes. The refined IMERG series met the accuracy thresholds proposed for reservoir operations, providing a readily deployable rainfall input for data-scarce, topographically complex tropical watersheds.
Keywords:
satellite precipitation, bias correction, GPM IMERG, hydrological modeling, Welang watershedDownloads
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Copyright (c) 2025 Ery Suhartanto, Ussy Andawayanti, Muhammad Nurjati Hidayat, Rahmah Dara Lufira, Rizki Tri Utami
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