Spatial Interpolation for Missing Rainfall Data in Northern Region of Peninsular Malaysia

Abstract

This study is aimed to estimate missing rainfall values for daily rainfall data from 30 selected rainfall stations. The daily rainfall data were obtained from the Department of Irrigation and Drainage Malaysia (DID) for the periods of 1999 to 2019. The missing values throughout the 20 years period were estimated using spatial interpolation methods. These methods include arithmetic average (AA), normal ratio (NR), inverse distance (ID) and coefficient of correlation (CC) weighting methods. The methods consider the distance between the target and the neighbourhood stations as well as the correlation between them. In determining the best spatial interpolation method, three tests for evaluating model performance have been used namely similarity index (S-index), mean absolute error (MAE) and root mean square error (RMSE). The homogeneity test using Standard normal homogeneity (SNHT), Buishand range (BR), Pettitt and Von Neumann (VNR) ratio are conducted to test the homogeneity of the rainfall data. The results show that the ID method is more efficient than the others method and 85% of the rainfall stations were homogenous based on this method. This study is important as it can be used to fill in the missing value rainfall data so that the conclusions that can be drawn from the data is valid.