CLR hosted a Friday Forum webinar on February 22 titled 'Robust impact patterns: an approach to account for uncertainties in local sea-level rise vulnerability assessments', led by Jackie Yip of University of British Columbia. While sea-level rise (SLR) is an inevitable effect of climate change, there are deep uncertainties regarding when and how SLR can impact society, which act as a significant barrier to adaptation. Recent literature calls for a shift from seeking optimal adaptation options to robust options that can perform reasonably under a range of possible futures, embracing uncertainties rather than eliminating them. In response, this study develops a new approach, the Robust Impact Patterns (RIPs) method, to help decision-makers account for SLR impact uncertainties in adaptation planning. The method utilizes the pattern recognition capability of machine learning to transform thousands of local SLR impact maps into a small number of impact patterns that are robust across multiple futures, thereby processing an otherwise vast and overwhelming volume of impact information. Jackie Yip is a Consequence Analyst at Kerr Wood Leidal, with experience in a range of climate vulnerability and resilience projects. Before joining KWL, she completed her Ph.D. at the Institute for Resources, Environment, and Sustainability of UBC, where she conducted this presentation’s research in partnership with the City of Vancouver. At the organizational level, Jackie was also a technical lead at Fraser Health, where her work focused on improving the resilience of healthcare facilities in the Lower Mainland to extreme events, including flooding and extreme heat. More broadly, she has led the design of Resilient-C, an online platform connecting Canadian coastal municipalities to share knowledge and collaborate on reducing risks to coastal hazards.

1. Robust impact patterns: an approach to account for
uncertainties in local sea-level rise vulnerability assessments
J a c k i e Z . K . Yi p , P h D
I n s t i t u t e f o r R e s o u r c e s , E n v i r o n m e n t a l , a n d S u s t a i n a b i l i t y,
U n i v e r s i t y o f B r i t i s h C o l u m b i a
S u p e r v i s o r : P r o f . S t e p h a n i e E . C h a n g
I C L R F R I D A Y F O R U M
2 2 F E B R U A R Y 2 0 1 9

2. Presentation overview
2
• SLR impacts over wide range of futures
• Basis for developing robust adaptation
options
Develop a new approach
Robust Impact Patterns
(RIPs) Method
• Demonstrate applicability
Apply RIPs method to
City of Vancouver (CoV)
• Assess usability and limitation
Host expert workshop
with CoV experts
Part I Part II Part III

3. Presentation overview
3
• SLR impacts over wide range of futures
• Basis for developing robust adaptation
options
Develop a new approach
Robust Impact Patterns
(RIPs) Method
• Demonstrate applicability
Apply RIPs method to
City of Vancouver (CoV)
• Assess usability and limitation
Host expert workshop
with CoV experts
Part I Part II Part III

4. Presentation overview
4
• SLR impacts over wide range of futures
• Basis for developing robust adaptation
options
Develop a new approach
Robust Impact Patterns
(RIPs) Method
• Demonstrate applicability
Apply RIPs method to
City of Vancouver (CoV)
• Assess usability and limitation
Host expert workshop
with CoV experts
Part I Part II Part III

5. Sea-level rise (SLR)
uncertainties
• Increasing flood risk
• SLR – one of the most
certain effect of climate
change
• How much and when is
deeply uncertain
Credit: DailyMail UK
5

6. Diverse projections for global mean sea levels
6
• Broad range of sea-level rise (SLR) projections just in recent years
• From 0.98m to 6m
Upper limits of global mean SLR
projections for 2100 (m)

7. Compounding uncertainties in SLR
impact assessment
Global
Temperature
Global
mean sea-level
Local
sea-level
Socio-economic
processes
Emission
7

8. 8
SLR planning with deep uncertainties
Optimal adaptation options
(Traditional approach)
Lowest cost and
highest benefit
Single or handful of
“best” prediction(s)
Eliminate
uncertainties
Robust adaptation options
(Recommended approach)
Acceptable
performance across
multiple futures
Large number of
plausible futures
Account for
uncertainties

9. Future flood
scenarios
New approach to assess impacts for SLR adaptation planning
Flood impact
maps
GIS & Socio-
economic models
Robust impact
patterns
Economic
Social
Environmental
Self organizing maps
(SOMs)
9

10. • Pattern recognition
(e.g. facial recognition)
• Example: satellite imageries
of tropical storms in Indian
Ocean
• Spatial variations are
preserved
Machine-learning algorithm:
Self-organizing maps (SOMs)
10

11. 11
Applying the RIPs Method at the City of Vancouver
336
Future flood
scenarios
STAGE I
Develop future flood
scenarios
14
Impact types for each
scenario
GIS
STAGE II
Geospatially model
flood impacts
16
Robust impact patterns
for 14 impact types
STAGE III
Identify robust
impact patterns
SOMs

12. 336 future coastal flood scenarios
Stage
damage
functions
Power
outage
extent
Population
and land-use
distribution
Sea-level
rise
Storm return
period
Flood event with
no adaptation
action
1:50
0m
Current land-use
Optimistic
HAZUS default
MCM
Pessimistic
Status Quo
Compact
Sprawl
1m
2m
3m
4m
5m
6m
1:500
1:10000
12

13. 336 future coastal flood scenarios
Stage
damage
functions
Power
outage
extent
Population
and land-use
distribution
Sea-level
rise
Storm return
period
Flood event with
no adaptation
action
1:50
0m
Current land-use
Optimistic
HAZUS default
MCM
Pessimistic
Status Quo
Compact
Sprawl
1m
2m
3m
4m
5m
6m
1:500
1:10000
13
#
##
#
#
#
# #
#
Murrin
Camosun
Kidd #1
Sperling
Mainwaring
Dal Grauer
George Dickie
Mount Pleasant
Cathedral Square
Worst-case outage scenario of B2 flood scenarioPessimistic outage scenario for 1:500 year
storm with 2m SLR

14. 14
14 Flood Impacts Modelled
ECONOMIC
SOCIAL
ENVIRONMENTAL
Business disruptions:
1. Primary sectors
2. Secondary sectors
3. Tertiary sectors
Direct building damage:
4. Residential
5. Commercial
6. Governmental
Potentially affected:
7. Vulnerable population
8. Schools
9. Health care facilities
10. Social services
11. Transportation points
12. Emergency services
13. Debris generated 14. Sewage backup damage potential

15. Business Disruption (Tertiary sectors)
• Temporary closures due to disruptions induced by flood event
• Disruptions from building damage and power outage
• Based on business disruption model in Chang (2008)
TEMPORARY
BUSINESS
CLOSURE
Sector
Building
damage
Power outage
15

16. 16 Patterns of Business Disruption (Tertiary sectors)
16

17. Type of SLR scenarios represented by each pattern
Grouping Robust Impact Patterns
16 robust patterns of business disruption (tertiary sectors)
Groups by sea-level rise ranges
A: 4-6m
B: 2-4m
C: 0-2m
A
B
C
A
B
C 17

18. Business Disruption (Tertiary sectors) – Group A, B, C
A (4-6m SLR) B (2-4m SLR) C (0-2m SLR)
~4100 closures ~2100 closures ~ 900 closures
Hotspots:
Downtown 44%
Strathcona 9%
West End 8%
Grandview 8%
Marpole 6%
Hotspots:
Downtown 38%
Marpole 13%
Strathcona 9%
Kerrisdale 8%
Oakridge 6%
Hotspots:
Downtown 33%
Fairview 13%
Marpole 12%
Sunset 12%
Kitslano 7%
A
B C
18

19. Business Disruption (Tertiary sectors) – Hotspots
A (4-6m SLR) B (2-4m SLR) C (0-2m SLR)
~4100 closures ~2100 closures ~ 900 closures
Hotspots:
Downtown 44%
Strathcona 9%
West End 8%
Grandview 8%
Marpole 6%
Hotspots:
Downtown 38%
Marpole 13%
Strathcona 9%
Kerrisdale 8%
Oakridge 6%
Hotspots:
Downtown 33%
Fairview 13%
Marpole 12%
Sunset 12%
Kitslano 7% 19
Downtown
Strathcona
Marpole
Kerrisdale
Oakridge
Fairview
(Granville Island)

20. 20
Business Disruption (Tertiary sectors) – Group A (4-6m SLR)

21. 21
Business Disruption (Tertiary sectors) – Group A (4-6m SLR)

22. SLR
Land-use
22
Business Disruption (Tertiary sectors) – Group C (0-2m SLR)

23. Potentially Affected Social Service Facilities
• Disruption due to inundation and/or power outage
Homeless
shelters
Free-meal
locations
Non-market
housing
Childcare and
pre-school
centres
Community
centres
Senior centres
23

24. A (4-6m SLR) B (2-4m SLR) C (0-1m)
~ 380 ~ 150 ~ 110
Hotspots:
Downtown 29%
Grandview 18%
Strathcona 18%
West End 9%
Marpole 8%
Hotspots:
Downtown 39%
Marpole 20%
Fairview 9%
Mt Pleasant 7%
Oakridge 6%
Hotspots:
Downtown 26%
Fairview 23%
West Point Grey 21%
Mt Pleasant 12%
Marpole 8%
A
B C
24
Social Services Disruption – Group A, B, C

25. A (4-6m SLR) B (2-4m SLR) C (0-1m)
~ 380 ~ 150 ~ 110
Hotspots:
Downtown 29%
Grandview 18%
Strathcona 18%
West End 9%
Marpole 8%
Hotspots:
Downtown 39%
Marpole 20%
Fairview 9%
Mt Pleasant 7%
Oakridge 6%
Hotspots:
Downtown 26%
Fairview 23%
West Point Grey 21%
Mt Pleasant 12%
Marpole 8% 25
Social Services Disruption – Change in hotspots
0-1m SLR 2-3m SLR 6m SLR

26. Sewage Backup Damage Potential Index
• Common flood hazard with potentially severe impacts
• First attempt index to account for risk factors inside and outside homes
• Relative level of damage expected from sewage backup in ground related homes at
the city block level
SEWAGE
BACKUP
DAMAGE
POTENTIAL
Type of drainage
system connected
Power outage
Sewage pump
installed
Flood depth and
distance
Number of ground
related homes
Number of pre-70s
homes
26

27. 27
Sewage Backup Damage Potential Index – Group A, B, C
A: 4-6m B: 2-4m C: 0-2m
17
Figure 5.24 The 16 RIPs of sewage backup damage potential index (SBDPI). The shading shows the damage potential per hectare, while the total
damage potential in each RIP is labeled above it.
A
C B
1
2
3
4
1 2 3 4
SBDPI per hectare
A
B C

28. A (4-6m SLR) B (2-4m) C (0-1m)
~160 ~110 ~90
Hotspots:
Hastings Sunrise 18%
Kerrisdale 12%
West Point Grey 7%
Marpole 7%
Dunbar Southlands 7%
Hotspots:
Hastings Sunrise 19%
Kerrisdale 11%
Dunbar Southlands 11%
Kitsilano 11%
West Point Grey 8%
Hotspots:
Hastings Sunrise 17%
Kitsilano 11%
Dunbar Southland 8%
Grandview 8%
West Point Grey 7%
A
B C
28
Sewage Backup Damage Potential Index – Group A, B, C

29. Hastings Sunrise
Marpole Kerrisdale
West Point Grey
Kitsilano
Sunset
29
Sewage Backup Damage Potential RIP of 2-3m SLR
#
##
#
#
#
# #
#
Murrin
Camosun
Kidd #1
Sperling
Mainwaring
Dal Grauer
George Dickie
Mount Pleasant
Cathedral Square
Worst-case outage scenario of B2 flood scenario

30. 30
Part III: Evaluating the utility of the RIPs method from
potential users’ perspective – how?
BC Hydro
CoV Planning
CoV Development & Permits
CoV Sustainability
CoV Emergency Management
Consultant
NRCan
EXPERT WORKSHOP
• 15 experts
• Involved in SLR planning
OR Problem expert
SURVEY
• 10 specific ways to use RIPs
to support SLR adaptation
planning

31. 31
Part III: Evaluating the utility of the RIPs method from
potential users’ perspective – how?
BC Hydro
CoV Planning
CoV Development & Permits
CoV Sustainability
CoV Emergency Mngm.
Consultant
NRCan
EXPERT WORKSHOP
• 15 experts
• Involved in SLR planning
OR Problem expert
SURVEY
• 10 specific ways to use RIPs
to support SLR adaptation
planning
GROUP DISCUSSIONS
• Why
• Examples

32. 32
Expert workshop: survey results (10 ways to use the RIPs)
Options development
Access to resources and stakeholder
support

33. 33
Expert workshop: group discussion results
Communicate SLR risk to stakeholders
UNANIMOUSLY AGREED
Why?
• Relevant and understandable to
multiple types of stakeholders
• Raise awareness with neighborhood-
specific impacts
• Educate about cascading effects
• Communicate compounding impacts
• Easier way to convey the worse case
scenario
“Helps to diversify the risk. Not just people that live near water that are affected.
Helps to put it on people's radar.”
[Participant #2, CoV Planning]
“Practically, the worse case scenario is something we don't want to make
even more terrible and this tool could support this“
[Participant #7, Consultant]

34. SUMMARY
• Impact assessment approach
Efficient way to consider SLR
impacts across wide range of
futures. Consider uncertainties in
early stages
• City of Vancouver application
Demonstrated applicability and
value of including indirect impacts
• Useful communication tool
Multiple ways to support
adaptation planning – most useful
for risk communication
• Future modifications
Needs modifications to further
support developing adaptation
options
34

35. THANK YOU
35
S P E C I A L T H A N K S T O T H E S E O R G A N I Z A T I O N S
I N - K I N D S U P P O R T R E S E A R C H F U N D I N G