Imputation of Missing Daily Rainfall Using Quantile Method
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Abstract
Rainfall data is essential for any study related to water resources. Daily rainfall has its specific characteristics which is continuous time series and discrete data with non-normal distribution. Generally, methods to estimate missing daily rainfall data, for examples arithmetic mean, inverse distance weighting method (IDW) still have some limitations. Such founded limitations are: 1) underestimate of average daily rainfall, 2) overestimate of non-zero rainfall events, and 3) underestimate of extreme rainfall magnitude. This study attempts to develop an imputation method for daily rainfall using quantile approach (QT) which is based on Bernoulli-Gamma distribution, and then compare to IDW method. The study results reveal that QT could yield sample statistics such as maximum, mean, and variance of estimated daily rainfall better than IDW. In addition, the 95th and 99th percentiles of rainfall depths from QT method are closer to the observed data. Therefore, QT method is capable to estimate extreme rainfall magnitude superior than IDW approach. Moreover, QT gives a higher accuracy in number of zero and non-zero rainfall events. Using QT method might be appropriate for any study that concerns with extreme rainfall events since QT would give more accurate results.
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References
[2] R. S. V. Teegavarapu and V. Chandramouli, “Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records,” Journal of Hydrology, vol. 312, no. 1–4, pp. 191–206, 2005.
[3] B. I. Lozada Garcia, G. Sparovek, P. C. Sentelhas, and L. Tapia, “Filling in missing rainfall data in the Andes region of Venezuela, based on a cluster analysis approach,” Revista Brasileira de Agrometeorologia, vol. 14, no. 2, pp. 225–233, 2006.
[4] L. R. Presti, E. Barca, and G. Passarella, “A methodology for treating missing data applied to daily rainfall data in the Candelaro river basin (Italy),” Environmental Monitoring and Assessment, vol. 160, no. 1, pp. 1, 2010.
[5] M. M. Hasan and B. F. W. Crokea, “Filling gaps in daily rainfall data: A statistical approach,” presented at 20th International Congress on Modelling and Simulation, Adelaide, Australia, December 1–6, 2013.
[6] J. Kim and J. H. Ryu, “A heuristic gap filling method for daily precipitation series,” Water Resources Management, vol. 30, no. 7, pp. 2275–2294, 2016.
[7] C. Simolo, M. Brunetti, M. Maugeri, and T. Nanni, “Improving estimation of missing values in daily precipitation series by a probability density function-preserving approach,” International Journal of Climatology, vol. 30, no. 10, pp. 1564–1576, 2010.
[8] H. Aksoy, “Use of gamma distribution in hydrological analysis,” Turkish Journal of Engineering and Environmental Sciences, vol. 24, no. 6, pp. 419–428, 2000.
[9] R. S. V. Teegavarapu, “Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods,” Hydrological Sciences Journal, vol. 59, no. 11, pp. 2009–2026, 2014.
[10] R. J. A. Little and D. B. Rubin, Statistical Analysis with Missing Data, John Wiley & Sons Inc., 1987. [11] S. Srisuttiyakorn, “Missing data analysis,” Journal of Education Studies, vol. 42, no. 1, pp. 217–223, 2014.
[12] S. Wuthiwongyothin, “Evaluating inverse distance weighting and correlation coefficient weighting methods on daily rainfall time series,” SNRU Journal of Science and Technology, vol. 13, no. 2, pp. 71–79, 2021.
[13] D. A. Mooley, “Gamma distribution probability model for Asian summer monsoon monthly rainfall,” Monthly Weather Review, vol. 101, no. 2, pp. 160–176, 1973.