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Assessment of temporal homogeneity of long-term rainfall time-series datasets by applying classical homogeneity tests

Author

Listed:
  • P. Kabbilawsh

    (National Institute of Technology Calicut)

  • D. Sathish Kumar

    (National Institute of Technology Calicut)

  • N. R. Chithra

    (National Institute of Technology Calicut)

Abstract

The word "inhomogeneity" in a time-series analysis is defined as those changes that occur in the time-series datasets due to non-climatic factors. Rainfall datasets without the presence of inhomogeneity are needed for conducting accurate hydro-climatological studies. The homogeneity of long-term rainfall (exceeding 100 years) datasets encompassing Kerala's entire state has not been checked. This research article bridges the gap by conducting homogeneity tests on every calendar month, monthly, seasonal, and annual scale from 1901 to 2013. The classical absolute homogeneity tests, such as the Standard normal homogeneity test (SNHT), Pettitt test, Buishand range (BR) test, and Von Neumann ratio (VNR) test at a 95% confidence level were applied for this purpose. Based on the results obtained, the rainfall datasets are further classified as "useful", "doubtful", and "suspect". The time-series datasets were generated by arranging the monthly rainfall data at each calendar month, seasonal and annual scale. The results indicated that more than 80% of the time-series datasets were homogeneous and accepted the null hypothesis; SNHT (88.50%), Pettit's test (88.12%), BR test (87.35%) and VNR test (82.15%). The research article presents two major findings. Firstly, it identifies cases where a data series deemed homogeneous by one test is considered non-homogeneous by other tests, emphasizing the importance of using multiple tests for homogeneity analysis. Secondly, the study suggests that researchers and practitioners should carefully consider the homogeneity test type and temporal scale/arrangement of the rainfall data while selecting and analyzing it for various applications.

Suggested Citation

  • P. Kabbilawsh & D. Sathish Kumar & N. R. Chithra, 2024. "Assessment of temporal homogeneity of long-term rainfall time-series datasets by applying classical homogeneity tests," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 16757-16801, July.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:7:d:10.1007_s10668-023-03310-0
    DOI: 10.1007/s10668-023-03310-0
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    References listed on IDEAS

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    1. Kamal Ahmed & Nadeem Nawaz & Najeebullah Khan & Balach Rasheed & Amdadullah Baloch, 2021. "Inhomogeneity detection in the precipitation series: case of arid province of Pakistan," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(5), pages 7176-7192, May.
    2. J. Drisya & D. Sathish Kumar & Thendiyath Roshni, 2021. "Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3653-3672, March.
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