DSD-INT 2022 Academic compound flood modelling in the USA - Grimley

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Presentation by Lauren Grimley (University of North Carolina at Chapel Hill, USA), at the Delft3D User Days, during Delft Software Days - Edition 2022. Wednesday, 16 November 2022.

Software

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

Changing Coasts and Floodplains
2
Photo: Avery Smith
Photo: Avery Smith
Photo: Chuck Burton

Flood hazard estimates neglect pluvial and compound flood
mechanisms.
Storm Sewer or
Groundwater
Surcharge
Precipitation
Streamflow
Storm Surge
Photo: AP Photo/Steve Helber
3

Hindcasting flooding from Tropical Cyclones using
SFINCS
4
Houston, Texas
Hurricane Harvey (2017)
~1500 mm

Hindcast of pluvial, fluvial, and coastal flood damage in Houston,
Texas during Hurricane Harvey (2017) using SFINCS
5
- 25m grid
- 3 streamflow inputs
- 1 coastal input

SFINCS tends to overpredict peak water levels by 0.8 meters.
6
High-Water Marks

A large portion of residential damage is estimated to occur in areas
far from the mapped floodplain.
7
Within FEMA Floodplain
Across SFINCS Domain

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.

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

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

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

Model Inputs and Scenarios
12
USGS Discharge/Stage
NOAA/ADCIRC Coastal Water Levels
MRMS radar-rainfall
Ocean Weather wind

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

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

15
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?

How well is the model routing
water across the terrain?
16
Average MAE: 1.36
Average RMSE: 1.60

17
Observed minus Modeled
How well is the model routing
water across the terrain?

How well are we capturing peak water levels?
18

Is the model
predicting
flooding at
the right
locations?
19
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

20
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

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DSD-INT 2022 Academic compound flood modelling in the USA - Grimley

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

2. Changing Coasts and Floodplains 2 Photo: Avery Smith Photo: Avery Smith Photo: Chuck Burton

3. Flood hazard estimates neglect pluvial and compound flood mechanisms. Storm Sewer or Groundwater Surcharge Precipitation Streamflow Storm Surge Photo: AP Photo/Steve Helber 3

4. Hindcasting flooding from Tropical Cyclones using SFINCS 4 Houston, Texas Hurricane Harvey (2017) ~1500 mm

5. Hindcast of pluvial, fluvial, and coastal flood damage in Houston, Texas during Hurricane Harvey (2017) using SFINCS 5 - 25m grid - 3 streamflow inputs - 1 coastal input

6. SFINCS tends to overpredict peak water levels by 0.8 meters. 6 High-Water Marks

7. A large portion of residential damage is estimated to occur in areas far from the mapped floodplain. 7 Within FEMA Floodplain Across SFINCS Domain

8. 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 10 North and South Carolina Hurricane Florence (2018) ~750 mm

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

12. Model Inputs and Scenarios 12 USGS Discharge/Stage NOAA/ADCIRC Coastal Water Levels MRMS radar-rainfall Ocean Weather wind

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

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

15. 15 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?

16. How well is the model routing water across the terrain? 16 Average MAE: 1.36 Average RMSE: 1.60

17. 17 Observed minus Modeled How well is the model routing water across the terrain?

18. How well are we capturing peak water levels? 18

19. Is the model predicting flooding at the right locations? 19 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

20. 20 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

DSD-INT 2022 Academic compound flood modelling in the USA - Grimley

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