Estimation of rainfall and stream flow missing data for Terengganu, Malaysia by using interpolation technique methods

Abstract

Missing data is a serious problem in many climatological time series. Daily rainfall and stream flow datasets with no missing values are required for efficient estimation for application purposes. In order to estimate any missing observations in data, interpolation techniques are often used. This study focuses on comparing a few selected methods in the estimation of missing rainfall and stream flow data. The interpolation techniques studied were the Arithmetic Average (AA) method, Normal Ratio (NR) method, Inverse Distance (ID) method and Coefficient of Correlation (CC) method. However, in the case when there is no information from neighboring stations, the mean on the same day and month but at different years is taken as estimation of the missing value on that particular date. Twenty years of daily rainfall and stream flow data at 12 stations located at Terengganu were used for this study. In testing to verify which method is the best in evaluating missing values at the target station using information from the nearby stations (in the radius range of 10 km to 50 km), several percentages of missing values were considered. The validation of the best estimation methods is done based on the estimation error; with tests such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (R) tests.