A Bare‐Earth GoogleDEM to Simulate Flooding in New Delhi, India - Yinxue Liu; Paul Bates; Jeffrey Charles Neal; Louise Slater; Jie Zhao; Zvika Ben‐Haim (Water Resources Research, 2025)
Abstract Accurate flood mapping in urban environments remains a critical yet challenging task. The primary challenge lies in the accuracy of widely available topographic data, a key constraint when employing hydrodynamic models for large‐scale inundation mapping. Recent advances in Very High‐Resolution satellite Photogrammetry Digital Elevation Models offer a promising solution to mitigate this limitation. However, effective preprocessing is imperative before integrating these data sets into flood inundation modeling, as the presence of artifacts such as buildings and trees can lead to inaccurate simulations. Here, we evaluated the potential of using the 0.5 m‐resolution GoogleDEM for flood inundation modeling using New Delhi, India—a densely populated urban area with pronounced flood risk—as a case study. We first examined the feasibility of extracting flood defenses from GoogleDEM. We then developed an iterative morphological filter to generate a bare‐earth GoogleDEM. Lastly, we assessed the flood inundation accuracy of our processed GoogleDEM by simulating a flood event with a 25‐year return period flood. We find that the errors of GoogleDEM‐derived flood defense heights are mostly within 1 m, but the automated extraction of defenses remains challenging. Our proposed approach reduced the artifact‐related errors of the original GoogleDEM by 85% (RMSE). The error of the simulated water surface level in the bare‐earth GoogleDEM (with flood defenses) was reduced by 97% compared to the original GoogleDEM, down to 0.16 m. This study presents a comprehensive evaluation of integrating cutting‐edge DEM data to enhance flood mapping accuracy, particularly in complex urban areas that are otherwise extremely data‐poor.