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.
Highlights
Rainfall data are one of the most important measurement variables in hydrological and environmental modelling
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 inverse distance (ID) method is more efficient than the others method and 85% of the rainfall stations were homogenous based on this method
Summary
Rainfall data are one of the most important measurement variables in hydrological and environmental modelling. Rainfall data is important in assessing the water quality. Studies involving the use of continuous series data are always faced with the problem of missing value. The existing data series are not enough to perform a good and meaningful analyses and often contain a large number of missing values [1]. Lack of data and inhomogeneity problem are due to rainfall station relocation, instrument malfunction and network reorganization [2]. In hydrologic modelling, developing a method to get an accurate estimation of rainfall are very crucial. In order to get an accurate result in analyses, the rainfall data must be complete, homogeneous and have a good quality
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
