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Predicting the streamflow regimes using climate dynamics is important in water resource management. However, in Indonesia, there are few studies targeting climate indices and streamflow. A previous study found difficulty in developing a statistical prediction model for this relationship due to its non-linear nature. This study attempted to address that gap by applying multiple regression (MR) models using second- and third-order polynomial functions to show the non-linear relationship between climate and flow regime indices. First, a correlation analysis was performed to check the variable relationships. There was a good and significant correlation of El Niño Southern Oscillation (ENSO) with the flow regime indices. Secondly, MR models were developed with the same-time variables. The developed model showed that the Indian Ocean Dipole (IOD) had the effect of strongly increasing the high flow in La Niña phases. Finally, time-lagged MRs were developed aiming at forecasting. Lagged MR models with six-month leading climate indices demonstrated a relatively good correlation with the observed data (mostly R &gt; 0.700) with moderate accuracy (root mean square error = 44–51%). It suggests that the forecasting of flow regime may be possible using ENSO and IOD indices.

The Relationship between River Flow Regimes and Climate Indices of ENSO and IOD on Code River, Southern Indonesia