Comparison of Spatial Rainfall Interpolation by Using Statistical Methods at Thailand’s Eastern Coast Basin
Issued Date
2025-01-01
Resource Type
eISSN
2651088X
Scopus ID
2-s2.0-86000791916
Journal Title
Suranaree Journal of Social Science
Volume
19
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Suranaree Journal of Social Science Vol.19 No.1 (2025)
Suggested Citation
Wimala S., Jirakajohnkool S., Konisranukul W., Nakhapakorn K. Comparison of Spatial Rainfall Interpolation by Using Statistical Methods at Thailand’s Eastern Coast Basin. Suranaree Journal of Social Science Vol.19 No.1 (2025). doi:10.55766/sjss-1-2025-253620 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/106749
Title
Comparison of Spatial Rainfall Interpolation by Using Statistical Methods at Thailand’s Eastern Coast Basin
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Corresponding Author(s)
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Abstract
Background and Objectives: Accurate rainfall estimation is essential for effective water resource management. This is especially true for regions with varying geographical features. The Eastern Coast Basin of Thailand experiences significant rainfall variation due to topographical influences. Conventional point-based rainfall measurements often fail to provide comprehensive spatial coverage, requiring the use of spatial interpolation techniques. By estimating the daily rainfall data in the Eastern Coast Basin using Geographic Information Systems (GIS), this study aims to compare the accuracy of three statistical interpolation methods: Inverse Distance Weighting (IDW), Kriging, and Co-Kriging. The objective is to determine the most effective method for improving rainfall data accuracy in regions with limited ground-based measurement stations. Methodology: The study employs a quantitative research method utilizing daily rainfall data from 20 automatic telemetering stations collected between May and October 2017. The IDW, Kriging, and Co-Kriging techniques were implemented using GIS-based analysis. The performance of these methods was evaluated using statistical validation metrics, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Additionally, spatial distributions of interpolated rainfall were visually assessed to identify patterns and discrepancies. Comparative analysis was conducted to determine the most suitable method for estimating rainfall within the study area. Main Results: The findings indicate that IDW produced the lowest MAE and RMSE values, making it the most accurate method for spatial rainfall estimation in this region. The results align with previous studies which found that IDW was the most effective method for rainfall interpolation in the Chern River Basin of Thailand’s Chaiyaphum Province. The study also reveals that while Kriging and Co-Kriging can capture spatial variability more effectively in some cases, they tend to introduce greater estimation errors when applied to areas with limited rainfall measurement stations. Discussions: The study highlights the importance of selecting appropriate interpolation methods based on data availability and regional characteristics. IDW's superiority in this study suggests that weighing nearby station data based on proximity is an effective strategy in this specific basin. However, the limitations of each method must be considered. While Kriging and Co-Kriging offer advantages in capturing spatial trends, they require a denser network of measurement stations to minimize errors. The research underscores the need for expanding rainfall measurement networks and integrating remote sensing data, such as weather radar, to enhance spatial rainfall estimation. Future studies should explore hybrid approaches that combine multiple interpolation techniques for improved accuracy. Conclusions: This study concludes that IDW is the most suitable spatial interpolation method for daily rainfall estimation in the Eastern Coast Basin due to its lower estimation errors compared to Kriging and Co-Kriging. The findings emphasize the importance of selecting appropriate interpolation techniques based on regional data density and topographical variations. Further research should focus on integrating additional data sources, such as satellite and radar-based rainfall estimates, to enhance interpolation accuracy. Policymakers and hydrologists should consider refining rainfall measurement networks to improve the reliability of spatial rainfall estimation models, ensuring better water resource planning and disaster management in rainfall-dependent regions.