Accurate and timely flood forecasting is critical for sustainable water management and disaster mitigation in the low‐lying and densely populated Kendrapara District of Odisha, India. This study develops and validates an adaptive ensemble framework to enhance flood prediction by integrating the EF5 hydrological model with advanced interpolation techniques and the DREAM algorithm for parameter optimization. The framework was implemented and rigorously tested over the Mahanadi River basin, with a specific focus on the Kendrapara District. The results demonstrate a marked improvement in forecast skill compared to conventional deterministic and static ensemble methods. The AEF significantly reduced peak flow errors by ~25% and increased the Nash–Sutcliffe efficiency from 0.72 to 0.89 for lead times of up to 72 h. Furthermore, the probabilistic nature of the ensemble output provided reliable uncertainty quantification, crucial for risk‐based decision‐making. The model proved highly reliable for short‐term forecasting, maintaining accurate predictions up to 4 h ahead, though skill diminished with longer lead times due to inherent data uncertainties. This research provides a validated, operational tool for water authorities, offering a significant advancement in flood early warning systems. The proposed framework not only improves forecast accuracy but also establishes a foundation for informed decision‐making, directly contributing to the goals of sustainable water management and enhanced community resilience in the face of increasing flood risks.
Dalai et al. (Thu,) studied this question.
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