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Reading: A Comparison of Methods of Estimating Missing Daily Rainfall Data


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A Comparison of Methods of Estimating Missing Daily Rainfall Data


H. P. G. M. Caldera ,

Lanka Hydraulics Institute, LK
About H. P. G. M.
AMIE (Sri Lanka), B.Sc. (Eng) Hons
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V. R. P. C. Piyathisse,

University of Peradeniya, LK
About V. R. P. C.
B.Sc. (Eng) Hons
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K. D. W. Nandalal

University of Peradeniya, LK
About K. D. W.

Senior Professor, Department of Civil Engineering

IntPE (SL), C.Eng, FIE (Sri Lanka), B.Sc. (Eng) Hons, MEng (AIT), Ph.D. (The Netherlands)

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The availability of a long and complete rainfall record is very important for carrying out a hydrological study successfully. However in general, the data series in these records may contain gaps for various reasons. The objective of this study is to analyse the different methods available for filling gaps in rainfall data records and propose a method suitable for a river basin situated in a mountainous area in Sri Lanka. Towards this end, daily rainfall data from ten gauging stations in the upper catchment area of BaduluOya were collected. Seven different techniques were studied to ascertain their suitability. The methods studied were the Arithmetic Mean method, Normal Ratio method, Inverse Distance Weighting method, Linear Regression method, Weighted Linear Regression method, Multiple Linear Regression method and the Probabilistic method. The data generated for the target stations were compared with actual observations made, based on error statistics, Error Standard Deviation (STD),Root Mean Square Error (RMSE) and Correlation Coefficient (CC). The results of the study showed that for target stations that have only one neighbouring station with a high correlation coefficient, the Probabilistic method and the Linear Regression method give good predictions. For stations that have relatively low correlation coefficients with the neighbouring stations, the Inverse Distance Squared method and the Normal Ratio method outperformed the others. To obtain accurate results from the Multiple Linear Regression method and the Weighted Linear Regression method, it is necessary to have a set of neighbouring stations that have fairly high correlation coefficients with the target station.
How to Cite: Caldera, H.P.G.M., Piyathisse, V.R.P.C. & Nandalal, K.D.W., (2016). A Comparison of Methods of Estimating Missing Daily Rainfall Data. Engineer: Journal of the Institution of Engineers, Sri Lanka. 49(4), pp.1–8. DOI:
Published on 22 Oct 2016.
Peer Reviewed


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