In 2017 Hurricanes Harvey, Irma, and Maria caused catastrophic damage to coastal regions in the Gulf of Mexico, Caribbean Sea, and Atlantic Ocean, and the 2018 season further contributed to coastal and inland damage with Hurricanes Florence and Michael. Recent forecasts suggest that these high intensity seasons may be representative of a “new normal,” with a greater number of Category 3-5 storms making landfall in the U.S. (Klotzbach and Bell, 2018). Theserecent hurricane seasons have emphasized the need for coastal engineers, scientists, and stakeholders to seek innovative solutions to improve coastal resiliency and effectively mitigate damage during extreme events. In order to mitigate damage, it is critical to better understand the wave transformation during overland flow conditions as well as to identify relationships between wave loading and structural response. document the vulnerability of coastal residences to damage during Hurricane Ike (2008) on the Bolivar Peninsula, TX and Hurricane Irma (2016) in Key West and Big Pine Key, FL. Results identified the need to objectively characterize structural damage as well as to better understand overland wave propagation and transformation in the presence of macroroughness elements such as buildings and rigid vegetation. Natural shorelines (mangroves) were identified as effectively withstanding storm surge flooding and riding waves associated with Hurricane Irma, and further prevented damage to inland structures, showing the parcel scale benefits of natural and nature based features. While natural and nature-based features have potential to serve as sustainable coastal engineering solutions, their engineering performance as well as limitations must be quantified.

1. Uncertainty in
Hurricane Damage
Assessment and
Prediction:
Field Observations in the Florida
Keys after Hurricane Irma
Tori Tomiczek
United States Naval Academy
26 March 2019
MUMS Program Coupling Uncertain
Geophysical Hazards Workshop
Raleigh, North Carolina
LA Times

2. 2
Outline
• Introduction and motivation
• Case Study: effects of shoreline characteristic on
hurricane damage in the Florida Keys
• Uncertainty in damage assessment metrology
• Uncertainty in models of overland waves and surge
• Uncertainty in effects of nature-based features
• Conclusions and discussion

3. 3
Introduction
Crystal Beach, TX
3

4. 4
Introduction
Crystal Beach, TX

5. 5
A new normal?
• Require sustainable, resilient solutions for coastal communities to predict
hazard conditions, mitigate damage, and hasten recovery.
NOAA (2019)
Rank Event Year Cost (billions, 2019 USD) Deaths
1 Hurricane Katrina 2005 165.0 1833
2 Hurricane Harvey 2017 127.5 89
3 Hurricane Maria 2017 91.8 2981
4 Hurricane Sandy 2012 72.2 159
5 Hurricane Irma 2017 51.0 97
6 Hurricane Andrew 1992 49.4 61
9 Hurricane Ike 2008 35.7 112
14 Hurricane Michael 2018 25.4 49
17 Hurricane Florence 2018 24.0 53
…
………
……

6. 6
Duration 30 August-16 September, 2017
Keys Landfall Cudjoe Key, 10 September, 2017, 1310 UTC, Category 4
Central Pressure 914 mBar (min)*; 929 mBar (Keys landfall)
Wind Speeds 185 mph (maximum)**; 130 mph (Keys landfall)
Storm Surge 3 m (Florida Keys)
Effects Catastrophic damage in Barbuda, USVI, Caribbean,
middle Florida Keys, >146 deaths
US Property Damage $53.4 billion***
* 2nd most intense of 2017 (behind Hurricane Maria)
** Strongest of 2017
*** 5th costliest in US History
Hurricane Irma

7. 7
Florida Keys: structural consistency, shoreline variability
Revetments
Mangroves
191 m
Revetments
Sand
Mangroves
DOC
Pre-1950
1950-1960
1960-1970
1970-1980
1980-1990
1990-2000
2000-2010
2010-2017

8. 8
Hazard Intensity Measures
• ADCIRC/SWAN hindcast courtesy Coastal
Emergency Risks Assessment (CERA)
Key West Big Pine Key
Wind Velocity (m/s) 44.8-49.2 49.3-53.6
Inundation Depth (m) 1.23-2.14 1.53-2.75
Significant Wave Height (m) 0-1.83 0.92-2.74
Storm Surge Elevation Significant Wave Height Wind Velocity (1-min)

9. 9
• NEU-USNA Collaborative Effort
• July 2017- present
• Key West and Big Pine Key
• Investigate relationship between shoreline resiliency, structural
vulnerability, and shoreline management
• October Survey: 262 residential structures, 332 shorelines
Parcel Scale Damage Assessments

10. 10
Bulkhead: cracks, undercutting,
structural collapse
Sandy Beaches:
erosion
Mangrove: broken
branches, loss of
foliage, regrowth
Revetment:
rocks displaced
• 4 point damage scale from 0
(no visible damage) to 3
(totally destroyed)
• Based on field observations,
permitting data
Shoreline Archetypes and Shoreline Damage

11. 11
Standardizing Damage Assessment

12. 12
Standardizing Damage Assessment

13. 13
• 56 surveyors, 12
shorelines
• 95 % Confidence
Intervals > 0.5 DS
• Larger variation for
intermediate damage
states
Mangrove Sandy Revetment Bulkhead
Standardizing Damage Assessment

14. 14
Shoreline Damage
mangrove
bulkhead
sandy beach
revetment
1
2
3
0
Damage
State

15. 15
Structural Damage

16. 16
Component-based Structural Damage Assessments

17. 17
1
2
3
4
0
Damage
State
Structural Damage

18. 18
fb
lhsm
wave crest
elevation
fb=freeboard
DS= damage state
Fragility Relationships: Relate Hazard and Structural Damage (?)

19. 19
Structures with mangrove
shorelines: lower DS for
higher wave crest
elevations above LHSM
Fragility Relationships: Relate Hazard, Shoreline Type, and Damage

20. 20
Model pfb pηwave pShoreline AIC
Shoreline --- 0.0028 1.32 x 10-23 161
Structure 0.041 --- 4.89 x 10-24 271
Statistical Significance and AIC for
Empirical Multinomial Fragility Models
• Shoreline Damage, Structural Damage as
ordinal response variables
• Shoreline type (mangrove vs. other) as a
categorical predictor variable
Multinomial Logistic Regression

21. 21
Interconnectivities between shoreline type, hazard, and social
perceptions
• Mixed mode interviews
• Perceived impact of mangroves, seawalls,
and beaches, on social and ecological
systems during Hurricane Irma
“Mangroves are the only thing
keeping the island from eroding”
“Without mangroves, the impact of the
storm would have been much worse”
“90% of beaches were swept away”

22. 22
Uncertain future for NNBF
• Natural and nature-based features (NNBF) may
mitigate overland flow and resulting inland
damage during storm events- but by how much?
• Some previous studies in the field and lab,
but questions remain:
• Need to quantify effects of mangroves, other
vegetation on wave and surge
transformation for improved models, design
• Need to identify breakpoints- when will
NNBF “fail”?
• Need to identify where and when NNBF are
appropriate
• Start with a simple case- laboratory model

23. 23
Field Characterization of Mangrove Shorelines
• Field study to characterize mangrove prop root density, average diameter,
elastic modulus, canopy characteristics

24. 24
Laboratory Investigation of Parcel Scale Mangrove Effects
Ohira et al. (2013)
Parameter Key West
(1:1)
Model
(1:16)
Material Red
mangrove
PVC + Galv.
Steel
dtrunk (m) 0.11 – 0.28 0.013
droot(m) 0.01 – 0.06 0.0025
Nroots 12-24 22
hroot(m) 1.0 – 2.0 0.125

25. 25
Laboratory Investigation of Parcel Scale Mangrove Effects
FShielded
(N)
FUnshielded
(N)
FN/FS
200.0 272.2 0.735
26.5% force reduction with
4 rows of mangroves (10.9
m prototype scale)
Shielded Unshielded

26. 26
Conclusions:
• Field, laboratory observations can provide benchmark data
to quantify uncertainty, validate computational models
• Areas to improve models: overland flow, wave force
estimation (fatigue), resolving vegetation/local effects
• Measurement uncertainty- all data is biased

27. 27
Thank you for your kind attention!