A two-stage variational mode decomposition and support vector regression is designed to reduce the influence of boundary effects without removing or correcting boundary-affected decompositions. The proposed model significantly reduces the boundary effect consequences, saves modeling time and computation resources, barely overfits the calibration samples, and forecasts monthly runoff reasonably well compared to the benchmark models.