1.
Academic compound flood modelling in the USA
Assessing Compound Flooding from Tropical Cyclones in Texas and the Carolinas
Lauren Grimley, PhD Candidate
UNC Flood Hazards Lab | Department of Earth, Marine and Environmental Sciences
Delft3D User Days
November 16, 2022

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Changing Coasts and Floodplains
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Photo: Avery Smith
Photo: Avery Smith
Photo: Chuck Burton

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Flood hazard estimates neglect pluvial and compound flood
mechanisms.
Storm Sewer or
Groundwater
Surcharge
Precipitation
Streamflow
Storm Surge
Photo: AP Photo/Steve Helber
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4.
Hindcasting flooding from Tropical Cyclones using
SFINCS
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Houston, Texas
Hurricane Harvey (2017)
~1500 mm

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Hindcast of pluvial, fluvial, and coastal flood damage in Houston,
Texas during Hurricane Harvey (2017) using SFINCS
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- 25m grid
- 3 streamflow inputs
- 1 coastal input

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SFINCS tends to overpredict peak water levels by 0.8 meters.
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High-Water Marks

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A large portion of residential damage is estimated to occur in areas
far from the mapped floodplain.
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Within FEMA Floodplain
Across SFINCS Domain

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T HE UN IV E RSIT Y OF N ORT H CAROLIN A AT CHAP E L HILL 8
Extreme rainfall from topical
cyclones can led to significant
uninsured losses.
Areas with high rates of flooded
structures are particularly
vulnerable populations.

9.
Continued work in Houston
• Model improvements
• Incorporate channel
bathymetry
• Subgrid application
(Deltares)
• Using model results to assess
climate vulnerability and
household adaptation

10.
Hindcasting pluvial flooding from Tropical Cyclones
using SFINCS
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North and South Carolina
Hurricane Florence (2018)
~750 mm

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Validating SFINCS from inland to the coast
Ocracoke
80m grid w/ 4m subgrid

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Model Inputs and Scenarios
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USGS Discharge/Stage
NOAA/ADCIRC Coastal Water Levels
MRMS radar-rainfall
Ocean Weather wind

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Surge Only Surge + Streamflow Surge + Streamflow + Wind + Rain
The contribution of different flood mechanisms varies from
inland to the coast

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The contribution of different
flood mechanisms varies
from inland to the coast
New Bern, NC
Jacksonville, NC

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Model Validation
How well is the model routing water across the terrain?
How well is the model capturing peak water levels?
Is the model predicting flooding at the right locations?

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How well is the model routing
water across the terrain?
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Average MAE: 1.36
Average RMSE: 1.60

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Observed minus Modeled
How well is the model routing
water across the terrain?

18.
How well are we capturing peak water levels?
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19.
Is the model
predicting
flooding at
the right
locations?
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Miss
False Alarm
Correct Non-
forecasts
“Hit” if modeled
max depth
> 0.15 m
Probability of detection = 0.91
False alarm rate = 0.49
Success ratio = 0.51
Critical success index = 0.48

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Observations:
- Wind is an important driver of flooding in estuarine communities
- Assessing models for predicting pluvial flooding is challenging but
important when considering damage estimation
Final Steps:
- Representing channel bathymetry + finer subgrid resolution
- Publish!
Future Work:
- Estimate damages from TCs under future conditions
- Create database of historical flooding
- Use to inform risk and vulnerability assessments
Email: Lauren.Grimley@unc.edu
Twitter: @_LaurenGrimley