A stochastic conceptual-data-driven approach for improved hydrological simulations

In a companion paper, Sikorska-Senoner and Quilty (2021) introduced the ensemble-based conceptual-data driven approach (CDDA) for improving hydrological simulations. This approach consists of an ensemble of hydrological model (HM) simulations (generated via different parameter sets) whose residuals are 'corrected' by a data-driven model (one per HM parameter set), resulting in an improved ensemble simulation. Through a case study involving three Swiss catchments, it was demonstrated that CDDA generates significantly improved ensemble streamflow simulations when compared to the ensemble HM. In this follow-up study, a stochastic version of CDDA (SCDDA) is developed that, in addition to parameter uncertainty, accounts for input data, input variable selection, and model output uncertainty. Using several deterministic and probabilistic performance metrics, it is shown that SCDDA results in significantly more accurate and reliable ensemble-based streamflow simulations than the CDDA, ensemble and stochastic HMs, and a quantile regression-based approach, improving the mean interval score by 26-79%.