Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool. (November 2022) - Hah, David; Quilty, John M.; Sikorska-Senoner, Anna E.
Abstract: Recently, the conceptual data-driven approach (CDDA) was proposed to correct residuals of ensemble hydrological models (HMs) using data-driven models (DDMs), followed by the stochastic CDDA (SCDDA) that used HM simulations as input to DDMs within a stochastic framework - both approaches improved ensemble HMs' simulations. Here, a new SCDDA is introduced where CDDA uncertainty is estimated (instead of DDM uncertainty in the original SCDDA). Using nine HM-DDM combinations for daily streamflow simulation in three Swiss catchments, the new SCDDA improved CDDA's mean continuous ranked probability score up to 15% and performed similarly without a snow-routine in a snowy catchment, suggesting that SCDDA may account for missing processes in HMs. The stochastic framework can convert unreliable ensemble models into more reliable (stochastic) models at the cost of simulation sharpness. The coverage probability plot is proposed as a diagnostic tool, predicting SCDDA's out-of-sample reliability using validation set data (CDDA simulations and observations). Highlights: A new version of the stochastic conceptual-data-driven approach (SCDDA) is proposed. SCDDA and benchmarks applied for streamflow simulation in three Swiss catchments. Three hydrological models and three data-driven models explored within SCDDA. Coverage probability plots (CPPs) used as a diagnostic tool. CPP predicts SCDDA's out-of-sample reliability using validation set data.