<![CDATA[Rainfall is one of the most important hydrological parameters; however, the availability of complete rainfall data is often constrained by missing records at observation stations. Therefore, rainfall estimation methods are required to maintain data continuity. This study aims to analyze the performance of several rainfall estimation methods and to evaluate the effects of distance and the number of rain gauge stations on the estimation results. This research employs a quantitative approach using monthly rainfall data from 2014 to 2023 obtained from five rain gauge stations located in Badung regency, Bali. The estimation methods applied in this study include the Inverse Square Distance method, Normal Ratio method, Arithmetic Mean method, and the United States Weather Bureau method. Data processing and analysis were conducted using Microsoft Excel. The performance of the estimation methods was evaluated using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the correlation coefficient (R). The results indicate that all estimation methods produce very good correlation with the observed rainfall data, with correlation values ranging from 0.907837 to 0.999341. However, the correlation tends to decrease as the distance between rain gauge stations increases, and the addition of more stations does not necessarily improve the correlation linearly, but instead shows a fluctuating pattern. This study is expected to provide a reference for selecting appropriate rainfall estimation methods based on the spatial characteristics of rain gauge stations]]>
