A stacking ensemble model for hydrological post-processing to improve streamflow forecasts at medium-range timescales over South Korea

This study presents the potential of hydrological ensemble forecasts over South Korea for medium-range forecast lead times (1-7 days). To generate hydrological forecasts, this study utilizes a framework based on stacking ensemble learning, an emerging machine learning technique that includes a two-level structure: base-learner and meta-learner models. In particular, the present research contributes to hydrological post-processing techniques by: (1) introducing a penalized quantile regression-based meta-learner to generate probabilistic predictions, (2) considering modeled climate predictions and antecedent hydrologic conditions simultaneously for regional hydrological forecast development, and (3) quantifying the skill enhancements from the multi-model forecasts under the stacking generalization. The proposed model is evaluated in massive 473 grid cells along with nine additional simpler models to test the specific hypotheses introduced in this study. Results indicate that our proposed forecasts can be used for relatively short lead times. In addition, results demonstrate that utilizing a penalized probabilistic meta-learner and antecedent conditions contributes to the forecast skill improvements. Lastly, we find that base-model diversity outperforms increased ensemble size alone in enhancing the forecast abilities under the stacking ensemble generalization. We conclude this paper with a discussion of possible forecast model improvements from an adaptation of additional information from input and model structures under the stacking generalization.