The gap filling is common practice to complete hydrological data series without missing values for environmental simulations and water resources modeling in a changing climate. However, gap filling processes are often cumbersome because physical constraints, such as complex terrain and density of weather stations, often limit the ability to improve the performance. Although several studies of gap filling methods have been developed and improved by researchers, it is still challenging to find the best gap filling method for broad applications. This research explores a gap filling method to improve climate data estimates (e.g., daily precipitation) using gamma distribution function with statistical correlation (GSC) in conjunction with cluster analysis (CA). The daily dataset at the source stations (SSs) is utilized to estimate missing values at the target stations (TSs) in the study area. Three standard gap filling methods, including Inverse Distance Weight (IDW), Ordinary Kriging (OK), and Gauge Mean Estimator (GME) are evaluated along with cluster analysis based on statistical measures (RMSE, MAE, R) and skill scores (HSS, PSS, CSI). The result indicates that cluster analysis can improve estimation performances regardless of the gap filling methods used. However, the GSC method associated with cluster analysis, in particular, outperformed other methods when the performance comparison task was conducted under rain and no-rain conditions in the study area. The proposed method, GSC, therefore, will be used as a case toward advancing gap filling methods in the field.
