GIS-BASED SPATIAL BIAS ADJUSTMENT OF RADAR-DERIVED RAINFALL ESTIMATES DURING STORM DISSIPATION IN CENTRAL THAILAND

Apichaya KANGERD , Nattapon MAHAVIK

DOI: 10.21163/GT_2026.211.13

ABSTRACT: Thailand lies in a tropical monsoon region and is frequently affected by the decay of tropical storms during the rainy season, with 2–5 storms typically occurring each year. Accurate quantitative precipitation estimates (QPE) are therefore essential for assessing storm-related rainfall and associated flood risks. Radar-based rainfall estimation is particularly suitable but is prone to systematic bias arising from the radar reflectivity–rainfall (Z–R) relationship. This study develops a Geographic Information System (GIS)-based analytical approach to evaluate and compare Z–R relationships and to reduce bias between radar-estimated and gauge-observed rainfall. The analysis was conducted across lowland and mountainous areas in northern and central Thailand using data from the Phitsanulok C-band weather radar and 89 rain gauge stations during Tropical Storm Son-Tinh (2018). This study integrates radar data with Geographic Information Systems (GIS) to systematically compare multiple Z–R relationships alongside spatial bias correction, and to evaluate differences in rainfall estimation accuracy between lowland and mountainous areas. Three Z–R relationships Marshall–Palmer (MP), Rosenfeld Tropical (RF), and Summer Deep Convection (SD) were employed to generate event-based radar rainfall estimates. Spatial bias correction was conducted using the Inverse Distance Weighting (IDW) method, and accuracy was assessed through five-fold cross-validation. The results indicate that uncorrected radar rainfall estimates generally underestimate actual precipitation, whereas the IDW-based correction significantly reduces the Mean Field Bias (MFB) and improves estimation accuracy across diverse terrains. Among the three Z–R relationships, the Marshall–Palmer equation yielded the lowest errors, with a root mean square error (RMSE) of 17.517 mm and a mean absolute error (MAE) of 13.405 mm. The event-based spatial adjustment demonstrates that integrating an appropriate Z–R relationship with GIS-based bias correction substantially enhances radar QPE reliability, particularly in regions with complex topography. This framework offers practical value for hydrological applications and flood risk management in tropical monsoon regions.

Keywords: Quantitative Precipitation Estimation (QPE); Bias correction; GIS-based analysis; Tropical Storm Son-Tinh.

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