The development of the National Research Council of Canada's benefit cost guideline for storm drainage and flood control infrastructure presented at the WEAO 2021 Collection Systems Committee Fall Webinar, October 28, 2020

1. National Guideline Development for
Benefit-Cost Analysis of
Storm Drainage Infrastructure
Robert Muir | Dillon Consulting (formerly with City of Markham)
Fabian Papa | FP&P HydraTek
WEAO | Collection Systems Committee Fall Webinar
28 October 2020

2. 2
 What is it?
 A means of assessing a return on investment (ROI)
 A means of comparing competing alternatives
 A basis for rational investment decision-making
 Who can use it?
(for storm infrastructure investment decisions)
 Municipalities, large and small
 Funding agencies
 e.g., Infrastructure Canada via its
Disaster Mitigation Adaptation Fund (DMAF)
(Minimum Benefit-Cost Ratio of 2:1 Required)
Benefit-Cost Analysis

3. 3
 Guidelines on Undertaking a Comprehensive Analysis of
Benefits, Costs and Uncertainties
of Storm Drainage Infrastructure in a Changing Climate
 Anticipated completion: Q1 2021
 Project mandate considers Direct & Indirect, Tangible Benefits
associated predominantly with Flood Damage reduction
 Other studies being conducted in parallel to address matters relating
to Intangible benefits (e.g., environmental, social)
 To be integrated separately
National Guideline Development

4. 4
Q: Which approach to use?
A: It depends…
• Application (e.g., project or initiative)
• Available data/information
• Scale of project or initiative
• Degree of risk (and/or vulnerability)
• Criticality of asset(s) considered
Approaches to Benefit Estimation
 Top-Down Analysis
 aka “area-based”
 Uses aggregated insurance loss
data for damage estimation
 Modest effort required
 Bottom-Up Analysis
 aka “object”-based (e.g., bldgs.)
 Uses damage-frequency curves
for damage estimation
 Requires a lot of data and
analysis effort
$
$$$$$
Can test reasonability of estimates
and/or calibrate bottom-up damage
estimates by seeking convergence
between approaches or comparing
against historical event damages

5. 5
Expected Annual Damages (EAD)
 Insured losses (+ loss expenses)
most commonly reported
(blue columns in graphic)
 Overall losses is a more
meaningful representation of
economic impact incurred
(green columns in graphic)
 Our research suggests that the
ratio of Overall-to-Insured losses
ranges from 1.0 to >2.5
Weigthed Average ≈ 2.0
 Adjustments to consider:
 Wealth (e.g., GDP)
 Peril type

6. 6
Expected Annual Damages (EAD)
$0.0
$0.2
$0.4
$0.6
$0.8
$1.0
$1.2
$1.4
$1.6
$1.8
0 0.2 0.4 0.6 0.8 1
Damages
Millions Probability of Occurrence in any Given Year
Gumbel Extreme Value Probability Distribution
$0.0
$0.2
$0.4
$0.6
$0.8
$1.0
$1.2
$1.4
$1.6
$1.8
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Damages
Millions
Year
Insurance Industry Loss Data
Area Under Curve: EAD = $291,600
Calculation example using Ontario insured loss data (IBC)
For conservatism, additional area to
the right of the actual distribution
(red line) added to EAD calculation
(in this case)

7. 7
Scaling to Level of Initiative
 Relative losses by
province show strong
correlation to proxy
measures such as GDP,
population, etc.
 May reasonably use
proxies as basis for
scaling damages
 Caution: Assumes
consistency in
standards of living,
development form, etc.
(Ok across Canada)

8. 8
National Flood Adaptation Funding
Simple Example 1
What level of funding should be allocated at a
national level to reduce flood-related damages?

9. 9
 Assumptions:
 Investment funding to occur over 20-year period
 Benefits enjoyed over 100-year service life of investment
 Target Benefit-Cost Ratio of 6:1  Say effectively only 4:1
 Investment profile assumed (for numerical exploration and illustration only):
Simple Example 1:
National Flood Adaptation Funding
100 years
20 years
Investment (costs)
Avoided Damages (benefits)
Why? Mitigation measures likely
to be only partially effective

10. 10
 Some (very) rough math:
Simple Example 1:
National Flood Adaptation Funding
Insurance Data
EAD
A
Damages over
100 Years
B = 100 × A
BCR
C
Adaptation Funding
D = B ÷ C
Annual Funding over
20 Years
E = D ÷ 20
Munich Re – Overall $1.35B $135B 4 $34B $1.7B
Munich Re – Insured $0.70B $70B 4 $17.4B $0.9B
CatIQ – Urban Flood $0.82B $82B 4 $20.5B $1.0B
Infrastructure Canada has identified 59 eligible DMAF projects
Total value of projects = $4.0B spread over 10 years (i.e., $0.4B/year)
How much can afford to be spent
(per year, for 20 years)
IGNORES:
• Discounting
• Project Effectiveness
• Growth/Wealth
• Climate Change

11. 11
City-Wide & Project-Scale Funding
Simple Example 2
How much should I be spending in my City
and/or for each of my projects?

12. 12
Simple Example 2:
City-Wide & Project-Scale Funding
Canada EAD = $1.35B
Ontario EAD = $512M
38% of Canada’s Population
2.54% of Ontario’s Population
Markham EAD = $12.5M
39% of Markham’s Flood Reports
West Thornhill EAD = $4.9M
Munich Re Overall Losses

13. 13
 Some more (very) rough math:
 Some things to keep in mind:
 Projects will not be 100% effective at eliminating risks/damages
 Increases in O&M costs with new infrastructure (vs. replacements)
 Time value of money considerations
Simple Example 2:
City-Wide & Project-Scale Funding
Insurance Data
EAD
A
Damages over 100
Years
B = 100 × A
Adaptation Budget
C
Benefit-Cost Ratio
D = B ÷ C
City-Level (Markham) $12.5M $1,250M $368M 3.4
Study-Level (West Thornhill) $4.9M $488M $112M 4.4
Project-Level (West Thornhill Phase I) $0.76M $76M $19.5M 3.9
Good Return on Investment
May reduce effective
Benefit-Cost Ratio
IGNORES:
• Discounting
• Project Effectiveness
• Growth/Wealth
• Climate Change

14. 14
Calibrating Sewer Damage Estimates
Simple Example 3
Testing the reasonability of benefit estimation.

15. 15
Sewer Back-Up Losses
CatIQ Data on Insured Losses
(Overall Losses > Insured Losses)

16. 16
 Insured losses vary by region
(tens of thousands of closed
claims)
 Average claim amounts may be
used to refine bottom-up,
property-scale damage estimates
(see paper example)
 Overall losses > Insured losses
Accounting for Regional Variations

17. 17
Layering In Uncertainties, etc.
Migrating from Simple Examples to Richer Analyses
Future climate? Time value of money? Growth?

18. 18
Layering in Uncertainties to Estimation of Benefits
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
2021
2023
2025
2027
2029
2031
2033
2035
2037
2039
2041
2043
2045
2047
2049
2051
2053
2055
2057
2059
2061
2063
2065
2067
2069
2071
2073
2075
2077
2079
2081
2083
2085
2087
2089
2091
2093
2095
2097
2099
2101
2103
2105
2107
2109
2111
2113
2115
2117
2119
2121
2123
2125
2127
2129
Benefits
(Millions)
Year
Investment Period
(10 Years)
 Recall Example 1
 Estimation of
Appropriate National
Flood Adaptation
Funding
 Benefit stream adjusted
to account for:
 Economic Growth
 Climate Change
 Calculations adjusted for
time value of money
 Target BCR = 2.0

19. 19
 Base Case:
 $1.8 million
per year
to achieve
BCR of 2
 Sensitivity
Analysis
 Inputs
modified
by 25%
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
Discount
Rate
Funding
Period
Benefit-Cost
Ratio
(BCR)
Expected
Annual
Damages
(EAD)
Effectiveness
Factor
Time
Horizon
Economic
Growth
Rate
Climate
Change
Growth
Rate
Annual
Funding
Allocation
(millions)
Ranked by range of uncertainty

20. 20
$1,436
25%
$2,312
75%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
$0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 $3,500 $4,000 $4,500 $5,000
Cumulative
Distribution
Frequency Annual Funding Allocation (millions)
 Monte Carlo Analysis
Layering in Uncertainties to Estimation of Benefits
$2,305
$1,427
50%
Conf.
Band
Average $1,817

21. 21
 Additional Considerations:
 Damages may increase non-linearly with increasing rainfall and flood flows
 Flood flows can be poorly correlated with rainfall volumes
 Synthetic depth-damage curves not
calibrated to observed losses
 Uncertainty analysis should be
proportional to the risks
 Graphical representations of vulnerability
(e.g., impact of stress tests)
 et cetera…
Layering in Uncertainties to Estimation of Benefits

22. 22
 Infrastructure investments to reduce flood damages can be evaluated at various
spatial scales (national to local).
 Benefits (avoided damages) have not been assessed closely in the past, but can
be estimated based on reported insured and estimated overall losses.
 National & regional reported losses can be scaled to cities and project areas to
estimate ‘top-down’ benefits.
 Regional sewer back-up losses can be used to support property-by-property
‘bottom-up’ benefit evaluations (e.g., average claim amount =$22,300).
 Many factors affect appropriate funding – several not typically considered
(discount rate, investment period) can have a greater effect on results than
commonly applied factors of climate change or growth.
Conclusions

23. National Guideline Development for
Benefit-Cost Analysis of
Storm Drainage Infrastructure
Robert Muir | Dillon Consulting (formerly with City of Markham)
Fabian Papa | FP&P HydraTek
WEAO | Collection Systems Committee Fall Webinar
28 October 2020