Micro-macro–scale flood modeling in ungauged channels: Rain-on-grid approach for improving prediction accuracy with varied resolution datasets

Abstract

Flood risk arises from the interplay of climatic variability, urbanization, and mitigation measures. While climatic patterns exhibit variability that may either exacerbate or mitigate flood risk across regions, urban development continues to decrease the distance between human settlements and flood-prone areas, intensifying vulnerability. This also necessitates the utilization of datasets with diverse resolutions. Although several studies have performed flood forecasting using advanced models, challenges remain in addressing specific limitations such as (a) improving the accuracy of micro–macro-scale model transitions when employing varied resolution datasets, and (b) enhancing predictive capabilities for ungauged channels. This study aims to address these challenges within the context of a case study, applying a rain-on-grid approach to link micro- and macro-scale flood predictions in a data-scarce environment. The study investigated the impact of grid size and simulation time steps for daily rainfall data on computation time and model accuracy through Geo-HECRAS. The results highlighted significant impacts on the accuracy of hydrological simulations due to variations in spatial resolution and simulation time steps. Volume accumulation error decreased from 1.49 % to 0.25 % in micro-scale scenarios and from 0.85 % to 0.006 % in macro-scale scenarios when transitioning from higher-resolution grids (5 m and 30 m) to coarser grids (10 m and 50 m) with a finer simulation time step of 15 min. While finer grids improve spatial detail, the findings suggest that coarser grid resolutions, when combined with finer temporal scales, can achieve reduced errors and optimized computational efficiency for both micro and macro-scale modeling. This approach enhances the accurate representation of flood dynamics over broader spatial scales, ensuring the reliability of predictive models. It supports the development of flood mitigation strategies and resilient infrastructure tailored to both regional patterns and site-specific hydrological conditions.