A stochastic conceptual-data-driven approach for improved hydrological simulations. (March 2022)

• Record Type: Journal Article
• Authors: Quilty, John M.; Sikorska-Senoner, Anna E.; Hah, David
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Ensemble -- Stochastic -- Streamflow simulation -- Data-driven model -- Hydrological model -- Uncertainty; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105326
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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%. Key Points: New stochastic conceptual-data-driven approach for hydrological simulation. Compared against several benchmarks for streamflow simulation in 3 Swiss catchments. New approach gives more accurate and reliable simulations than benchmarks.