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Improving Manning's n in Flood Models Using 3D Point Clouds, Flume Experiments, and Deep Learning

Haces‐Garcia, Francisco, Kotzamanis, Vasileios, Glennie, Craig L., Rifai, Hanadi S.·2025·Water Resources Research
DOI 10.1029/2024wr037665
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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 estimate Manning's n based on Point Cloud (PC) data. The DNN was deployed on real‐world lidar PCs to directly estimate Manning's n under 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 of Manning's n were 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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Figures

Figure 1 Isometric and side view of weir system used for uniform flow distribution. A dual weir was implemented to ensure near-zero velocity at hydraulic jump, and to remove flow turbulence from the final weir.
Figure 1 Isometric and side view of weir system used for uniform flow distribution. A dual weir was implemented to ensure near-zero velocity at hydraulic jump, and to remove flow turbulence from the final weir.CC-BY-NC · From the PDF
Figure 2 Experimental setup to measure Manning's n . As shown, the smooth surface had low water depths, resulting in difficulties measuring water velocity with the Channel Velocity meter. Therefore, PTV was used. Figure modified from [21]
Figure 2 Experimental setup to measure Manning's n . As shown, the smooth surface had low water depths, resulting in difficulties measuring water velocity with the Channel Velocity meter. Therefore, PTV was used. Figure modified from [21]CC-BY-NC · From the PDF
Figure 3 Implementation of PointNet architecture. Conv1D stands for a 1D convolution with a Batch Normalization layer, and linear for linear layers with rectified linear unit nonlinearities. Figure adapted from [21].
Figure 3 Implementation of PointNet architecture. Conv1D stands for a 1D convolution with a Batch Normalization layer, and linear for linear layers with rectified linear unit nonlinearities. Figure adapted from [21].CC-BY-NC · From the PDF
Figure 4 Relevant watersheds for hydrodynamic model setups in Harris County.
Figure 4 Relevant watersheds for hydrodynamic model setups in Harris County.CC-BY-NC · From the PDF
Figure 5 1D Model Setup to study Riverine Flooding in Spring Creek.
Figure 5 1D Model Setup to study Riverine Flooding in Spring Creek.CC-BY-NC · From the PDF
Figure 6 1D/2D Coupled Model Setup to study Riverine Flooding in Buffalo Bayou.
Figure 6 1D/2D Coupled Model Setup to study Riverine Flooding in Buffalo Bayou.CC-BY-NC · From the PDF
Figure 7 2D Model Setup to study surge and pluvial flooding upstream of Barker Reservoir.
Figure 7 2D Model Setup to study surge and pluvial flooding upstream of Barker Reservoir.CC-BY-NC · From the PDF
Figure 8 Manning's n measurements across different surfaces. The boxplots represent variation in instantaneous water elevation measurements across the measurement time.
Figure 8 Manning's n measurements across different surfaces. The boxplots represent variation in instantaneous water elevation measurements across the measurement time.CC-BY-NC · From the PDF
Figure 9 DNN performance for the test dataset (n=804,000).
Figure 9 DNN performance for the test dataset (n=804,000).CC-BY-NC · From the PDF
Figure 10 Comparison of Manning's n values obtained from DNN, NLCD, and Engineering Judgement. Engineering judgement values are solely from overbanks regions.
Figure 10 Comparison of Manning's n values obtained from DNN, NLCD, and Engineering Judgement. Engineering judgement values are solely from overbanks regions.CC-BY-NC · From the PDF
Figure 11 Difference in flow depth across various friction conditions for the 1D regulatory model. The reference flooding depth is that calculated using the original model Manning's n from HCFCD. [27]
Figure 11 Difference in flow depth across various friction conditions for the 1D regulatory model. The reference flooding depth is that calculated using the original model Manning's n from HCFCD. [27]CC-BY-NC · From the PDF
Figure 12 Comparison of 1D unsteady model timeseries for Spring Creek at validation gauges.
Figure 12 Comparison of 1D unsteady model timeseries for Spring Creek at validation gauges.CC-BY-NC · From the PDF
Figure 13 Comparison of flood extents at time of imagery (08/30/17 10:00 AM) in 1D/2D coupled models of Buffalo Bayou during Hurricane Harvey. FEMA National Flood Insurance Program claims in 2D area are also shown.
Figure 13 Comparison of flood extents at time of imagery (08/30/17 10:00 AM) in 1D/2D coupled models of Buffalo Bayou during Hurricane Harvey. FEMA National Flood Insurance Program claims in 2D area are also shown.CC-BY-NC · From the PDF
Figure 14 Flood elevation difference in 2D model of subdivisions upstream of Barker Reservoir using the HEC RAS Full Momentum Equations. While surge flooding shows no change in extents or depth, street networks further away from reservoir show clear differences in rainfall flooding depth.
Figure 14 Flood elevation difference in 2D model of subdivisions upstream of Barker Reservoir using the HEC RAS Full Momentum Equations. While surge flooding shows no change in extents or depth, street networks further away from reservoir show clear differences in rainfall flooding depth.CC-BY-NC · From the PDF
Figure 15 Flood elevation difference in 2D model of subdivisions upstream of Barker Reservoir using the HEC RAS Full Momentum Equations. While surge flooding shows no change in extents or depth, street networks further away from reservoir show clear differences in rainfall flooding depth.
Figure 15 Flood elevation difference in 2D model of subdivisions upstream of Barker Reservoir using the HEC RAS Full Momentum Equations. While surge flooding shows no change in extents or depth, street networks further away from reservoir show clear differences in rainfall flooding depth.CC-BY-NC · From the PDF

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Citations

Cites

Show all 1 references

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Metadata

Authors
Haces‐Garcia, F., Kotzamanis, V., Glennie, C. L., & Rifai, H. S.
Affiliations
  • National Center for Airborne Laser Mapping Houston TX USA
  • Department of Civil and Environmental Engineering University of Houston Houston TX USA
ORCID
Venue
Water Resources Research
Publisher
American Geophysical Union (AGU)
Published
2025-03
Volume
61
Issue
3
ISSN
  • 0043-1397
  • 1944-7973
DOI
10.1029/2024wr037665
Cited by
6
References
1
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor, 24 d)
Funders
  • National Science Foundation (1830734)
  • University of Houston

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Haces‐Garcia, F., Kotzamanis, V., Glennie, C. L., & Rifai, H. S. (2025). Improving Manning's n in Flood Models Using 3D Point Clouds, Flume Experiments, and Deep Learning. Water Resources Research. https://doi.org/10.1029/2024wr037665

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