A multi-hybrid model approach optimizing discharge forecasts in karst catchment under climate change

• A novel hybrid model was proposed for daily rainfall-runoff modelling. • The hybrid model enhanced rainfall–runoff simulations in the karst catchment. • The hybrid model remarkably improved the extreme discharge simulation performance. • Slow-flow conceptual models’ components affect future discharge simulation. Rainfall-runoff modelling in karst catchments is challenging due to complex hydrogeological and climatic conditions. Conventional hydrological models can struggle to simulate the nonlinear dynamics. To address this challenge, this study proposes a multiple hybrid modelling approach to enhance daily rainfall-runoff simulations in the karst Ljubljanica River catchment in Slovenia. This approach integrates the Technische Universität Wien (TUW) and the Génie Rural à 5 paramètres Journalier (GR5J) lumped conceptual models, which consider the snow processes, with the symbolic regression- genetic programming (SR-GP) data-driven model. The hybrid model, TUW-CemaNeige GR5J-SR-GP, was fortified by grey wolf optimisation (GWO) for calibration, ensemble empirical mode decomposition (EEMD) for data decomposition, and recursive feature elimination (RFE) for feature selection. Rainfall-runoff modelling was conducted for the observed and projected datasets under the Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) climate change scenarios. The hybrid model improved baseflow simulation performance by 41 % (TUW) and 36 % (CemaNeige GR5J), enhanced monthly peak discharge simulation performance by 5 % and 13 %, and yielded notable improvements in simulating low and high flows under the RCP4.5 and RCP8.5 scenarios. The algebraic equations of the SR-GP model and sensitivity analysis highlighted the influence of the slow-flow components on discharge simulations. The hybrid modelling approach is a promising alternative for compensating for the limitations of the stand-alone models in karst catchments for efficient water resources management.