Abstract
Rainfall forecasting models developed using the seasonal autoregressive integrated moving average (SARIMA) technique for spatially distributed rain gauge stations in the state of Kerala are presented in this paper. Monthly rainfall data for 113 years were considered to build models for 29 meteorological stations. As the time-series data span 11 decades, there is a high chance of non-stationarity, owing to the presence of trend and seasonality. The non-stationarities in the time-series datasets are assessed by applying the seasonal and trend decomposition using the Loess technique (STL) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test. Prior to applying this procedure, preliminary analyses on the datasets are carried out to detect and fill the missing values, examining the properties of the time-series datasets, including the tests for detecting trend and seasonality. Out of 29 stations, the results of the Mann–Kendall trend test indicated that marginal trends were present only in the rainfall datasets belonging to two stations. The partial autocorrelation function (PACF) and autocorrelation function (ACF) plots of most of the stations showed strong seasonal autocorrelation. The developed SARIMA models also indicated that the influence of the seasonal components is dominant compared to non-seasonal components for most of the stations.
Research Highlights
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The percentage of missing values in the precipitation time-series datasets ranged between 0.52% (at Thiruvananthapuram (O)) and 14.75% (at Irikkur) and were filled by applying the expectation–maximisation algorithm.
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Shapiro–Wilk test and Kolmogorov–Smirnov test were applied to check for normality of the time series. It was found that all the stations were non-normally distributed.
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Classical Mann–Kendall and Sen’s slope were applied to determine the magnitude and direction of the trend.
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STL technique and KPSS test are applied to evaluate the stationarity behaviour in the meteorological time-series datasets.
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The authors would like to acknowledge the India Meteorological Department (IMD) for the precipitation datasets.
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Kabbilawsh Peruvazhuthi: Conceptualisation, methodology, software, validation, formal analysis, investigation, resources, data curation, writing – original draft, review and editing, visualisation, supervision, project administration. Sathish Kumar D: Formal analysis, investigation, writing – original draft, review and editing, visualisation, supervision. Chithra N R: Formal analysis, investigation, writing – original draft, review and editing, visualisation, supervision.
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Kabbilawsh, P., Kumar, D.S. & Chithra, N.R. Forecasting long-term monthly precipitation using SARIMA models. J Earth Syst Sci 131, 174 (2022). https://doi.org/10.1007/s12040-022-01927-9
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DOI: https://doi.org/10.1007/s12040-022-01927-9