Abstract
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.
Highlights
A lengthy rainfall data series plays a major role in all water related studies
The Arithmetic Mean (AM) method could not be applied to any of the target stations since the average annual rainfalls at their surrounding gauges were not within the 10% range of the normal annual precipitation at the target station. This means that the annual rainfall values can be significantly different among the gauging stations even though they are located close to each other, probably due to the considerable variations in their elevations
When the correlation coefficients with the neighboring stations are each more than 0.7, the Multiple Linear Regression (MLR) method performed acceptably
Summary
A lengthy rainfall data series plays a major role in all water related studies. Consistency and continuity of rainfall data series are very important for obtaining reliable results from such studies. These rainfall data series very often contain gaps or missing values due to various reasons such as the absence of observers, problems with measuring devices, loss of records etc. The use of a rainfall data series with missing values may critically influence the statistical power and accuracy of a study. By estimating and filling the missing rainfall data, a series could be made longer to make the water related study more reliable. Diverse techniques have been proposed and adopted in filling missing data with a view to obtaining a continuous and lengthy rainfall data series
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
