Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS

Post-processing of hydrological model simulations using machine learning algorithms can be applied to quantify the uncertainty of hydrological predictions. Combining multiple diverse machine learning algorithms (referred to as base-learners) using stacked generalization (stacking, i.e. a type of ensemble learning) is considered to improve predictions relative to the base-learners. Here we propose stacking of quantile regression and quantile...