Forecasting the next decade of mean annual rainfall based on historical rainfall data and CHIRPS data using RStudio
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Date
2024
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Faculty of Science, University of Kelaniya Sri Lanka
Abstract
Rainfall is a very crucial event that significantly influences environmental conditions. To address adverse environmental scenarios, rainfall analysis is essential. This study aims to predict annual rainfall patterns for the next 10 years across 9 rainfall stations in Sri Lanka and visualize these predictions through graphs. Daily rainfall data from 1992 - 2022 were collected from the Department of Meteorology in Colombo, Sri Lanka. And, the CHIRPS data from the Climate Hazard Centre at the University of California, Santa Barbara, were utilized as a parallel data source to ensure the accuracy of the statistical tests performed. The collected rainfall data were processed to calculate annual rainfall values for each rainfall station in Sri Lanka. Annual rainfall values for the past 30 years were statistically analyzed by undertaking simple exponential smoothing statistical tests using RStudio software and whereby line scatterplots were derived for next 10 years for each rainfall station. This simple exponential smoothing is a time series forecasting method used to predict future values based on past data. According to the results, the highest average annual rainfall increment can be expected in Ratnapura rain gauge station which is 4247 mm. Satellite-based rainfall data corroborated these findings, predicting an average annual rainfall as 3968 mm for the same rain gauge station. Conversely, the lowest average annual rainfall is expected at the Hambantota rain gauge station, indicating 839 mm/year based on rain gauge data. According to the CHIRPS database, it is 1106 mm/year. However, the methodology does not account for major climate phenomena such as El Nino Southern Oscillation (ENSO) and the Madden-Julian Oscillation in the Indian ocean. This means, Sri Lanka may experience an increment in annual rainfall from 2023 to 2033 due to alterations that have already happened in climate. Hence, it is very important to be aware that responsible parties undertake necessary actions to tackle potential adverse situations in the future.
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Keywords
CHIRPS, Forecasting, Rainfall, RStudio, Simple exponential smoothing
Citation
Perera W. W. A. M. R.; Weerasinghe V. P. A.; Nawarathna D. A. G. S. K. (2024), Forecasting the next decade of mean annual rainfall based on historical rainfall data and CHIRPS data using RStudio, Proceedings of the International Conference on Applied and Pure Sciences (ICAPS 2024-Kelaniya) Volume 4, Faculty of Science, University of Kelaniya Sri Lanka. Page 158