Comparing Areal Rainfall Estimation Methods in the Ayung Watershed, Bali, Indonesia: A Comprehensive Analysis
Issued Date
2024-01-01
Resource Type
Scopus ID
2-s2.0-85207506608
Journal Title
2024 10th International Conference on Smart Computing and Communication, ICSCC 2024
Start Page
212
End Page
217
Rights Holder(s)
SCOPUS
Bibliographic Citation
2024 10th International Conference on Smart Computing and Communication, ICSCC 2024 (2024) , 212-217
Suggested Citation
Dharmayasa I.G.N.P., Putri P.I.D., Sugiana I.P., Jindal R., Surakit K., Thongdara R. Comparing Areal Rainfall Estimation Methods in the Ayung Watershed, Bali, Indonesia: A Comprehensive Analysis. 2024 10th International Conference on Smart Computing and Communication, ICSCC 2024 (2024) , 212-217. 217. doi:10.1109/ICSCC62041.2024.10690592 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101878
Title
Comparing Areal Rainfall Estimation Methods in the Ayung Watershed, Bali, Indonesia: A Comprehensive Analysis
Corresponding Author(s)
Other Contributor(s)
Abstract
This study investigates areal rainfall estimation methods in the Ayung Watershed, Bali, a critical source of water for the region. Understanding areal rainfall patterns is crucial for flood risk assessment, water resource management, and infrastructure design. Three methods are evaluated: arithmetic average, Thiessen polygon, and Inverse Distance Weighting (IDW). Rainfall data from eight stations across the watershed (2004-2018) reveals a distinct wet-dry season pattern. All three methods yielded consistent results for areal rainfall estimation, with IDW showing a slight advantage in accuracy. However, the arithmetic average method offers a simpler and faster computational approach, making it suitable for initial analyses. Spatial interpolation methods like Thiessen polygon and IDW provide a more comprehensive picture of spatial rainfall distribution, crucial for detailed hydrological studies and disaster mitigation strategies. Integrating these methods with advancements like the Internet of Things (IoT) can further enhance areal rainfall estimation for improved water resource management.