Unravelling the impact of spatial discretization and calibration strategies on event-based flood models

Accurate streamflow simulation is crucial for effective hydrologic forecasting and water resource management. This study introduces a nested discretization scheme aimed at refining catchment delineation based on the spatial heterogeneity of its characteristics. The scheme aims to align with the assumption of spatial homogeneity within a hydrologic model, enhancing simulation accuracy. Investigating the impact of discretization, the study evaluates lumped, semi-lumped, and semi-distributed conceptual model structures, both in continuous and event-based simulations of flood events in Jagdalpur and Wardha basins of India. Results indicate superior performance by continuous semi-distributed and semi-lumped models (efficiency > 0.77), followed by continuous lumped models (efficiency > 0.68) during both calibration and validation periods at both basins. Event-based models, particularly semi-distributed and semi-lumped, exhibit higher median efficiency (> 0.71 at Jagdalpur and > 0.67 at Wardha) compared to their lumped counterparts (0.57 at Jagdalpur and 0.27 at Wardha), showcasing their proficiency in capturing spatial variability. However, a marginal performance increase in semi-lumped models with increased spatial discretization is observed, accompanied by a significant rise in computational time. This research contributes insights into the trade-offs associated with the proposed discretization scheme and emphasizes the balance between model complexity and efficiency for optimal streamflow simulations.