A Minimal High-Resolution Urban Flood Model for Data-Scarce Regions

Currently urban flooding poses a growing threat to urban and semi-urban cities across India, mainly due to climate change and rapid unplanned urbanization. Many existing urban flood models require extensive hydrological, hydraulic, and infrastructure data, which are often unavailable in developing or data scarce regions. Our work addresses this challenge by proposing a minimal input modelling approach.

In this study, we develop a high-resolution urban flood simulation model using only rainfall and Digital Elevation data, significantly reducing the dependency on detailed datasets such as land use/land cover and drainage network information. The model framework is implemented in MATLAB using a matrix-optimized water redistribution algorithm combined with flood water routing. This enables efficient simulation of water accumulation and propagation across both flat and complex urban terrains, while allowing simulations over large urban areas.

The model is currently being deployed for Bengaluru urban region in India, which is often prone to flooding during monsoon season, flood inundation maps for multiple time durations were generated using artificial rainfall inputs and a 1-m high resolution DEM. By requiring minimal input data while maintaining high spatial detail, the proposed framework provides a scalable solution for urban flood hazard assessment in data limited regions, supporting early-stage risk mapping and climate resilient urban planning.