Projecting groundwater flood risk in a lowland karst system under future climates

Abstract Future flood dynamics in a lowland karst catchment draining into Galway Bay, Ireland, have been assessed under climate-change scenarios. Bayesian neural network model (BNN) was calibrated on 1980–2015 observations of rainfall, tides, and turlough flood volumes, yielding correlations of R = 0.95 (training) and R = 0.78 (overall). Projections driven by CORDEX under RCP 4.5 and 8.5 for 2016–2100 reveal ensemble-mean rainfall increases of 1.2 mm decade−1 and 2.5 mm decade−1, respectively, corresponding to flood-volume growth rates of 5 × 10⁶ m3 decade−1 and 1.1 × 107 m3 decade−1. Wavelet coherence indicated high-frequency coupling (> 0.7) between rainfall and floods under RCP 8.5 versus < 0.5 under RCP 4.5. Extreme-event analysis showed a 40% rise in joint 95th -percentile rainfall and flood-volume events under RCP 8.5 (p < 0.05). Generalized extreme-value fits to annual maxima for 2018–2037 versus 2080–2099 project that a historical 100-year storm becomes 1-in-16-year event under RCP 8.5. 10-year rolling 90th -percentile analysis revealed rapid intensification of upper-tail floods under RCP 8.5 than RCP 4.5. These findings demonstrated that high-emission pathways substantially amplify flood magnitudes and frequencies and underscores the utility of integrated statistical and machine-learning frameworks for robust flood-risk assessment and climate-adaptation planning.