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Abstract Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father’s Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions.
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This content will become publicly available on March 1, 2026
Improving Manning's n in Flood Models Using 3D Point Clouds, Flume Experiments, and Deep Learning
Abstract Friction is one of the cruxes of hydrodynamic modeling; flood conditions are highly sensitive to the Friction Factors (FFs) used to calculate momentum losses. However, empirical FFs are challenging to derive, causing flood models to rely on surrogate observations (such as land cover) and introducing uncertainty. This research presents a laboratory‐trained Deep Neural Network (DNN), developed using flume experiments, to estimateManning's nbased on Point Cloud (PC) data. The DNN was deployed on real‐world lidar PCs to directly estimateManning's nunder regulatory and extreme storm events, showing improved modeling capabilities in both 1D and 2D hydrodynamic models. For 1D models, the lidar estimates decreased differences with values assigned by experts through engineering judgment. For 1D/2D coupled models, the lidar values produced better agreement with flood extents obtained from airborne imagery, while better matching flood insurance claim data for Hurricane Harvey. In both 1D and 1D/2D coupled models, lidar resulted in better agreement with validation gauges. For these reasons, the lidar values ofManning's nwere found to improve both regulatory models and forecasts for extreme storm events, while simultaneously providing a pathway to standardize the estimation of FFs. Changing from land cover to lidar estimates significantly affected fluvial and pluvial flood models, while surge flooding was generally unaffected. Downstream flow conditions were found to change the impacts of FFs to fluvial models. This manuscript introduces a reliable, repeatable, and readily accessible avenue for high‐resolution friction estimation based on 3D PCs, improving flood prediction, and removing uncertainty from hydrodynamic modeling.
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- Award ID(s):
- 2324629
- PAR ID:
- 10588005
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 61
- Issue:
- 3
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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