National Guideline Development for Benefit-Cost Analysis of Storm Drainage Infrastructure

The following paper was presented at the WEAO 2020 Collection Systems Committee Fall Webinar on October 28, 2020. The presentation made is included after the paper. Paper download: download pdf

The complete guidelines can be downloaded here: https://nrc-publications.canada.ca/eng/view/object/?id=27058e87-e928-4151-8946-b9e08b44d8f7

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National Guideline Development for Benefit-Cost Analysis of Storm Drainage Infrastructure

Robert J. Muir, M.A.Sc., P.Eng., Dillon Consulting Limited, Fabian Papa*, M.A.Sc., M.B.A., P.Eng., FP&P HydraTek Inc. 

*FP&P HydraTek Inc., 216 Chrislea Road, Suite 204
Vaughan, Ontario L4L 8S5

INTRODUCTION

      Flooding is a matter of significant importance to Canadians and is a high priority for all levels of government involved in its management and mitigation.  Economic impacts to communities and businesses can be significant in terms of the direct and indirect costs, and affected industries have a heightened interest in mitigating insured losses.  Governments and flood management agencies responsible for upgrading infrastructure to reduce these impacts recognize that decision-making on funding levels and project prioritization must be supported by sound economic analysis to assess upgrade costs and the benefits of avoided losses. While rigorous benefit-cost analysis frameworks have been developed and applied in other jurisdictions worldwide, application in Canada has been limited with the exception of regionally-significant river projects.

      To support decision making on urban infrastructure investment, the National Research Council of Canada (NRC) is developing guidelines on the assessment of benefits, costs and uncertainties relating to storm drainage infrastructure in a changing climate. The guideline development involves the thorough review of: (i) industry practices in the preparation of benefit-cost analyses; (ii) data sources and methods for the estimation of direct and indirect damages (i.e., potential benefits); (iii) the impact of a changing climate on system performance and vulnerability analysis; (iv) analysis methods to assess performance; (v) life-cycle costs of storm drainage infrastructure; and (vi) methods of comparing infrastructure alternatives. These foundational components of the study are used to develop guidelines to assist municipalities and other agencies with storm drainage responsibilities to prepare sound economic assessments in order to identify worthwhile projects and prioritize investments. The guidelines are intended to be flexible for applicability across Canada’s vast geography and range of project and municipality scales, with their focus being on direct and indirect tangible costs and benefits, primarily related to flooding. Recognizing that storm drainage infrastructure can provide other additional intangible co-benefits, NRC has engaged others to consider social and environmental aspects.

      This paper will focus on the component of the work concerned with the available data sources and methods for estimating damages, noting that the reduction of damages due to infrastructure investment represents the benefits to society and the numerator in the benefit-cost ratio, a metric which is used to assess funding applications by certain agencies. This will include a discussion of high-level “top-down” and local “bottom-up” approaches, as well as the importance to ensure that the results from each approach tie together reasonably well, rather than to produce divergent outcomes.  A description of these data sources and application of the approaches in case studies is provided in the following sections.  Each approach has its place in risk management frameworks and assessments, and each has different needs in terms of the level of effort and investment required to derive meaningful and actionable results. The details derived from the project’s findings in relation to this topic will be presented and documented.

      The following sections present methodologies for i) estimating benefits (i.e., deriving top-down insured and overall flood losses and bottom-up direct damages that represent potential benefits of flood mitigation infrastructure) and ii) estimating costs (i.e., capital and operating costs of mitigation works).  Also presented are the results of analyses applying these methodologies in case studies i) examining infrastructure investment budgeting across Canada and in an example municipality (City of Markham), and ii) examining works to control basement flooding.  The analyses consider flood control benefits derived from reported losses and from synthetic depth-damage curves.  They consider completed project costs and derived unit costs, as well as estimated costs based on expected benefit-cost ratios for infrastructure works.  Conclusions, including considerations for setting public policy and funding priorities for infrastructure investments are provided.

METHODOLOGY

Application of Benefit-Cost Analysis in Water Resources

      The WEAO 2019 Technical Conference paper titled An Economic Analysis of Green v. Grey Infrastructure (Muir and Papa, 2019) presented the history of benefit-cost Analysis (BCA) in water resources including example applications in Canada.  While analysis considering monetized or quantified costs and benefits is mandatory for new regulations based on Treasury Board of Canada Secretariat guidelines, and regional riverine flood management projects have applied BCA for decades, application to urban infrastructure projects has been limited.

      More recently, Infrastructure Canada’s Disaster Mitigation Adaptation Fund (DMAF) established a minimum benefit-cost ratio of 2:1 for eligible projects (Infrastructure Canada, 2018). Benefits represented averted damages and could include any quantifiable socio-economic and environmental damages.  However, no guidance was provided on the economic damages to be considered, whether direct or indirect, insured or uninsured, nor were resources for assessing benefits or costs (e.g., through typical case study examples available).

      A review of BCAs conducted for several proposed DMAF urban drainage flood mitigation projects indicated a range of top-down and bottom-up methods to estimate damages that were then equated to potential benefits.  National Resources Canada (NRCan) and Public Safety Canada (PSC) (2017) draft guidelines on flood vulnerability functions described three methods for estimating tangible damages as follows:

 1.       The first entails an examination of the floodplain immediately after the water recedes. If such estimates were available for every flood over a period of many years, a damage-frequency curve could be created;

2.         An alternative method is to determine the damage caused by three or four recent floods whose hydrologic frequency can be determined and a smooth damage frequency curve plotted through these points; however, for most floodplains, changes in land use with calendar time prevent direct usage of a damage-frequency relationship from historical damages; and

3.         The third method entails hydrologically determining various flood elevations for specific flood frequencies and deducing synthetically the damages that would occur given these flood events. This analysis provides a synthetic damage-frequency curve from which one can estimate average annual damages for a given study area.”

 The DMAF projects reviewed included examples of Method #1 whereby recent damages and program costs were enumerated, Method #2 whereby top-down annualized damages were scaled to a local municipality or project area, and Method #3 whereby the bottom-up assessment of property-by-property frequency event losses were integrated and aggregated to derive project-wide annual damages.

      The DMAF project review revealed several limitations in data sources and analysis methods that support NRC’s current guideline development.  For example:

i)                the loss estimate for sewer back-up in bottom-up assessments was often overestimated considering historical losses;

ii)              bottom-up loss estimates using synthetic depth-damage curves were not calibrated to actual incurred losses from insurance industry data or disaster relief funding;

iii)            the quantitative assessment of climate change impacts was not assessed (with the exception of sea level rise impacts);

iv)             some losses were double-counted (both top-down and bottom-up);

v)               the integration of event damages to derive annualized losses was not consistently calculated;

vi)             there was inconsistent consideration of overall losses beyond insured losses;

vii)           operation and maintenance costs were not evaluated in most assessments;

viii)         present value discounting of costs and benefits was not considered in most assessments;

ix)             effects of growth on future damages and potential benefits was not considered in any assessments; and

x)               financing costs were not considered in any assessments.

     In addition, avoided losses were assumed to be equal to total losses which is a reasonable approximation for large riverine mitigation projects but less reasonable for urban flood mitigation projects where infrastructure upgrades are not likely to be 100% effective given other causes.  Later sections present losses based on the statistical analysis of recorded losses that may be used to derive top-down estimates used to assess adaptation funding needs at a national, regional or local scale, and be used to calibrate bottom-up estimates derived with synthetic depth-damage curves.

Estimation of Potential Benefits and Adaptation Funding (Total Cost)

      Appropriate investment in flood mitigation infrastructure is of interest at the national and local levels as overall programs and strategies should be adequately funded to be effective.  The required overall program investment cost can itself be supported through BCA, to ensure that sufficient funding is secured for worthwhile projects (that also demonstrate value through individual BCAs).  In the case of flood reduction benefits, the Office of the Parliamentary Budget Officer reviewed past damages at a national scale to guide financial assistance budgeting (2016). From 2005 to 2014, flood losses including insurance payments plus disaster assistance for uninsured losses totaled $12,505M (2014 CAD), or $1.25B per year – this may be considered NRCan/PSC Method #1, representing national top-down damages.  Using JBA Risk Management flood models and exposure data (i.e., building replacement cost) from Brookfield RPS, the Insurance Bureau of Canada (IBC) estimated Canada-wide property-by-property losses and total future annual losses of $2.43B – this may be considered NRCan/PSC Method #3.  Such historical and projected modelled losses represent potential benefits of risk reduction investments.

      More recently, the Federation of Canadian Municipalities (FCM) and IBC estimated the “investment in municipal infrastructure and local adaptation measures needed to reduce the impacts of climate change in Canada” (FCM and IBC, 2019).  Adaptation costs for 34 communities to address a range of perils including flooding were compiled and scaled across the country according to GDP.  Analysis determined that an average annual investment in municipal infrastructure and local adaptation measures of $5.3B was required.  FCM and IBC recommended sustained federal funding of a minimum of $1B per year for twenty years (IBC, 2019).  The total adaptation cost over twenty years is $106B with a minimum of $20B of federal funding.  With a cited benefit-cost ratio of 6:1, the annualized benefits would be six times the adaptation cost (i.e., $636B in total and $6.36B per year, assuming a 100 year service life of infrastructure investments and excluding ongoing operation and maintenance costs as well as the time value of money).

      The above examples illustrate the wide range of potential annual benefits considering recent reported losses ($1.25B), projected model losses ($2.43B), and losses based on scaled adaptation costs and an assumed benefit-cost ratio ($6.36B).  A disclaimer in the 2016 Parliamentary Budget Officer report notes that “actual losses from catastrophic events may differ from the results of simulation analyses” (i.e., the projected model losses that consider empirical methods and the experience of scientists and specialists). Furthermore, the “the accuracy of predictions depends largely on the accuracy and quality of the data used by Library of Parliament.”  Given the wide range of potential benefits, and the uncertainty in modelled values, it is reasonable to expect that that modelled losses could be calibrated to reported losses.  A subsequent section presents estimates of potential benefits based on the statistical analysis of various types of reported losses.  These values may be used to estimate the range of national to local benefits of adaptation considered in top-down assessments, and to calibrate modelled loss estimates derived in bottom-up assessments.

Estimation of Adaptation Costs

      The cost of infrastructure investments to achieve adaptation benefits is estimated throughout the planning and design process that identifies, prioritizes, refines and implements projects, sometimes within an overall strategy or flood control program.  Various sources of reliable information exists, particularly in relation to traditional (e.g., grey) infrastructure elements for which there is a long history, and the volume and quality of data for more recent green infrastructure elements is steadily improving.  The focus of this paper is on selected components relating to the estimation of benefits and, as such, the discussion relating to costs is limited to this section.  Whatever the case, the estimation of both costs and benefits should be normalized to their discounted present values and/or annual equivalents thereof for purposes of project evaluation and comparisons amongst alternatives.

Potential Adaptation Benefits (Avoided Damages) Based on Reported Losses

      Muir and Papa (2019) analyzed IBC’s Ontario water damage loss and loss expenses to derive annualized losses in Ontario, equivalent to NRCan/PSC Method #2.  Province-wide losses, or expected annual damages (EAD), were scaled to the City of Markham, representing a top-down approach to estimate potential flood control program benefits.  Avoided damages were assumed to be equivalent to insured losses, recognizing that overall losses are higher than insured losses but that mitigation measures may only be partially effective.

      As IBC losses are not adjusted for GDP and do no identify losses beyond insured losses, other data sources may be considered to derive EAD values. Munich RE is a leading global provider of reinsurance, primary insurance and insurance-related risk solutions that compiles GDP-adjusted insured and overall losses.  National EAD for insured losses and overall losses were derived using historical losses from 1980 to 2017 obtained through Munich RE’s NatCatSERVICE.  Figure 1 illustrates theses value for hydrological and meteorological events, normalized for GDP growth, and expressed in 2017 USD.

 

FIGURE 1. MUNICH RE INSURED AND OVERALL LOSSES IN CANADA 1980 – 2017 (Munich Re, 2018)

      Values were converted to 2017 CAD and a Gumbel (extreme value) probability density function was derived to yield damage-frequency values, as per NRCan/PSC Method #2. The EAD for insured losses was calculated by integrating the event losses, resulting in a value of $0.695B as illustrated in Table 1.  The method conservatively sets 1-year damages to the derived 2-year damages.  While 1-year damages are expected to be less than 2-year damages, this approach can help compensate for the fact that losses from smaller events are not reported.

TABLE 1. MUNICH RE CANADIAN INSURED HYDROLOGICAL AND METEOROLOGICAL LOSSES, 2017 CAD – GUMBEL EXTREME VALUE DISTRIBUTION AND EXPECTED ANNUAL DAMAGE (EAD)  

     Overall losses have been analyzed in a similar manner.  The EAD for overall losses is $1.347B as calculated from event damages shown in Table 2.  While there is variability from event-to-event, the ratio of overall-to-insured losses is 1.94 (i.e., $1.34B ÷ $0.695B), indicating that uninsured losses are comparable to insured ones.

TABLE 2. MUNICH RE CANADIAN OVERALL HYDROLOGICAL AND METEOROLOGICAL LOSSES, 2017 CAD – GUMBEL EXTREME VALUE DISTRIBUTION AND EXPECTED ANNUAL DAMAGE (EAD)  

        The regional distribution of losses including those associated with particular flood event types can be assessed using the Catastrophe Indices and Quantification Inc. (CatIQ) loss database.  CatIQ delivers detailed analytical and meteorological information on Canadian natural and man-made catastrophes on a subscription basis to serve the needs of the insurance / reinsurance industries, public sector and other stakeholders.  National EAD for urban flood events was derived based on insured loss and loss expenses obtained through CatIQ.  This event type categorization is unique from CatIQ’s broad Flood and Water peril classifications and is intended to represent those events with a significant proportion of sewer back-up/water damage within reported personal property damages. It is noted that events may be characterized by multiple peril types, including Hail, Windstorm, Winterstorm, and Fire; some water damages may occur primarily as a result of other perils not related to extreme rainfall conditions (e.g., due to power interruption disrupting sump pump operation, or wind damage to rooftops allowing water entry). 

Based on the foregoing, some Flood and Water peril events may therefore be characterized by very limited water damage, and would represent damages that cannot be mitigated through storm infrastructure upgrades intended to address extreme rainfall conditions.  Those events are excluded for the purpose of urban flood event analysis, and are identified as those with a minimum of 30% sewer back-up/water in overall personal property damages.  As sewer back-up/water losses have been discretized only since 2013 in the CatIQ database, the time series of losses represents only part of the overall CatIQ dataset that begins in 2008, specifically 2013-2018.  After filtering the data as discussed above, the derived damage-frequency values and EAD are summarized in Table 3. 

TABLE 3. CATIQ CANADIAN URBAN FLOOD LOSSES, CAD – GUMBEL EXTREME VALUE DISTRIBUTION AND EXPECTED ANNUAL DAMAGE (EAD) 

 

Sewer back-up/water losses represent a component of urban flood event losses. On average these losses represent 70% of total event losses for the urban flood events.  The component of total losses beyond sewer back-up/water include other personal property, personal non-property, commercial and automobile line of business losses. Table 4 summarizes derived damage-frequency values and EAD in Canada and in provinces where CatIQ data is available.  The average sewer back-up loss is shown to vary across provinces and may be used to calibrate losses derived from bottom-up damage assessments that apply NRCan/PSC Method #3.  For example, where EAD is calculated by evaluating event losses for a range of return period events in Method #3, the average EAD across many properties within a study area or region should be comparable to the Event Average Sewer Back-up Loss in Table 4.   

TABLE 4. CATIQ CANADIAN SEWER BACK-UP/WATER LOSSES, CAD – GUMBEL EXTREME VALUE DISTRIBUTION AND EXPECTED ANNUAL DAMAGE (EAD)  

      To summarize, national EAD values have been derived for various loss types representing potential adaptation benefits.  These are:

      i)                Munich RE Hydrological and Meteorological Events (1980-2017)

a.      EAD_MROL = Overall Losses $1.347B ($2017 CDN)

b.     EAD_MRIL = Insured Losses $0.695B ($2017 CDN) 

ii)              CatIQ Urban Flood Events (2013-2018)

a.      EAD_CIUF = Loss and Loss Expenses $0.821B (CDN) 

iii)            CatIQ Sewer Back-Up/Water (2013-2018)

a.      EAD_CISB = Loss and Loss Expenses $0.376B (CDN)

 Case studies in the Results section illustrate how national and provincial losses may be scaled to local areas, such as municipalities or project areas to support local damage estimation and mitigation program budgeting, and to calibrate modelled national-level damages. These will illustrate a top-down approach, based on the NRCan/PSC Method #2, to derive losses, and will illustrate how they may be applied to calibrate bottom-up damage estimates (i.e., NRCan/PSC Method #3).

Assessment of Regional and Local Flood Damages

      National and regional EAD values may be scaled to more local areas based on economic, demographic, housing and infrastructure factors that have been shown to be closely correlated at a provincial scale (i.e., GDP, population, dwelling count, and sewer infrastructure length and value) and that have also been shown to be correlated to provincial long-term losses in Ontario.  Such factors are commonly cited as influencing damages and therefore global values are often normalized by these factors, such as by exposed population and GDP (Formetta, 2019).   Klotzbach et al. (2018) examined damage trends as a function of population, housing units, and wealth (GDP) in the continental US.  The assessment of disaster assistance trends in Canada’s Office of the Parliamentary Budget Officer (PBO, 2016) adjusted historical payouts by GDP growth and acknowledged that when population increases “losses will be greater” and that as GDP per capita increases “this increases losses in a natural disaster.”

TABLE 5. REGIONAL ECONOMIC, DEMOGRAPHIC, HOUSING AND INFRASTRUCTURE INDICATORS 

      A case study in the Results section illustrates how the above provincial losses may be scaled to a municipality or project area to support the calibration or verification of bottom-up damage estimates based on NRCan/PSC’s Method #3.

Potential Adaptation Benefits (Avoided Damages) Based on Synthetic Depth-Damage Curves

      Synthetic depth-damage curves have been applied in Canada for many decades to assess bottom-up, property-by-property flood damages (i.e., NRCan/PSC Method #3).  Curves have been updated and applied over large areas to prioritize riverine flood risk reduction efforts (Government of Alberta’s Provincial Flood Damage Assessment Study (IBI Group, 2015), Toronto Flood Risk Ranking (Toronto and Region Conservation Authority, 2019)) and to support the economic evaluation of risk reduction alternatives (e.g., City of Calgary’s Flood Mitigation Options Assessment (IBI Group and Golder Associates, 2017)).  The latter study noted that neither “groundwater inundation nor flood damage estimates were fully validated or calibrated to historic events, due to lack of data to complete such analysis” and furthermore that “analysis conducted in this Study concluded that available flood insurance data does not lend itself to any type of uniform recalibration of depth-damage curves or flood damage modelling for a variety of reasons…”  Depth-damage curves have been more recently used to assess urban pluvial and sewer back-up damages as well (Sakshi, 2019).  In the City of Surrey analysis, where buildings are subject to both overland and storm sewer back-up, the damage value from overland flooding overrides the back-up damages.  Limitations of the analysis were noted to include the quality of the input data, the presence of a direct connection to the stormwater main (sewer), and the lack of calibration of the hydraulic model with the economic loss model.  Despite the lack of historical calibration, it appears that damages estimated through synthetic depth-damage curves are effective in the identification and ranking of regional riverine flood management priorities and in the relative ranking of riverine flood mitigation alternatives.  In an urban setting, such curves may also assist in the prioritization of risk areas for further study (EPCOR, 2019), and be used to estimate relative changes as a result of climate change effects on extreme weather statistics. 

The Need for Calibration of Depth-Damage Curves

     Where more than a relative assessment of damages is required for prioritization, calibration of depth-damage curves is recommended such that cumulative losses broadly reflect recorded insured losses and estimated overall losses.  Research in jurisdictions worldwide has shown that damages estimated using synthetic depth-damage curves may not represent actual reported losses without calibration.  An evaluation of loss methods in Australia noted “most of the synthetic methodologies prepared for Australia are not calibrated with empirical loss data or express the magnitude of damage in absolute monetary values” (Nafari, 2018).  Nafari evaluated two depth-damage curve methods (Geoscience Australia (GA) Depth-Damage Function and FEMA/USACE Depth-Damage Function (USACE)) and has shown that observed losses in Australia may be overestimated by a factor of almost 100%, considering a February 2012 flood event.  A calibrated depth-damage function, called FLFA_rs, predicted damages within the 95% confidence interval of the reported losses.  A case study in Denmark (Olsen et. al, 2015) concluded that insurance data can be used to calibrate inundation modelling even though estimating individual damages is a challenge: 

“…it is shown that with the present data we can establish clear relationships between occurrences of claims and hazard maps on a basis of integrated hazard simulation and vulnerability assessment. This suggests that insurance data can be valuable for calibrating inundation modelling in terms of frequency and location of flooding, even when acknowledging that it is difficult to accurately identify the flooded properties, in particular for the low hazard category. The estimation of damage costs for individual claims remains a challenge in this study. Our results suggest that the main variation in per claim costs can, perhaps, be better described using socioeconomic variables in models of the value at risk in different households, rather than simple rainfall event variables.”

      The Flood Damage Assessment, Literature review and recommended procedure (Olesen et al., 2017) identified the need for detailed hydraulic information for bottom-up assessments:

 “The most complex damage model is the micro-scale damage model, where flood loss is evaluated on an object level. To use this model, detailed information about type and use of single buildings and elements is needed. The model can therefore only be applied if such data exist for the investigated area. The highly detailed model requires a great amount of data, and is therefore only recommended if the level of detail of the hydraulic simulation can match that of the damage assessment.”

 The report also recommended the use of average unit damages, independent of flood depth where “the available data does not suggest that the consideration of further flood characteristics adds more information to the study,” recommended unit cost approaches for pluvial flooding, and recommended the use of “specific stage-depth damage curves for the investigated area.” 

      A comparison of national recorded annual flood losses ($1.25B) and projected model losses ($2.43B) suggests that calibration of model losses is warranted.  While these model losses were not based on conventional depth-damage curves, the empirical methods could be refined such that the estimated total aggregated property damages are broadly in line with the observed losses.  Calibration of losses on a property-by-property basis is not expected, but rather on the total losses.    

RESULTS 

     The following case studies illustrate i) the application of top-down Method #2 damage estimates at a national and municipal scale to inform national and local adaptation program funding, and ii) the calibration of bottom-up Method #3 damages for an urban infrastructure upgrade project using historical provincial sewer back-up/water damage data. 

National Flood Adaptation Funding 

     National investments in disaster mitigation should be sufficient to address expected damages through cost-effective projects.  This case study evaluates Canada-wide adaptation funding requirements considering historical expected damage values, estimated benefit/damage ratios that consider the cost effectiveness of mitigation projects, and estimated benefit-cost ratios considering recent DMAF project funding applications and international experience. 

     Damages to be addressed were presented in the Methodology section, including EAD_MROL and EAD_MRIL based on Munich RE Hydrological and Meteorological Events data and EAD_CIUF based on CatIQ Urban Flood Events data.  While the expected benefit-cost ratios vary from project-to-project in an adaptation program, the overall ratio can be estimated considering a review of international and national programs.  The ECONADPAT program reported a benefit-cost ratio of 4.1:1 for “hard flood control” (i.e., traditional grey infrastructure) based on a wide survey of international programs (Kuik et al., 2016).  A survey of Canadian DMAF projects that required a minimum ratio of 2:1 revealed ratios from 5.5:1 to 17:1.  The FCM and IBC suggested that a ratio of 6:1 could be achieved in a national adaptation program.   For this case study, the potential benefit-cost ratio is assumed to be 6:1, while the actual benefit-cost is 4:1, considering that mitigation measures may be only partially effective. 

     Table 6 summarizes damages, benefits and adaptation budget values based on various EAD values and the assumed benefit-cost ratio.  The service life of adaptation infrastructure over which benefits are achieved is assumed to be 100 years.  Total adaptation funding and annual funding over a 20-year investment period is shown. 

TABLE 6.  NATIONAL DAMAGES AND ADAPTATION FUNDING  

      Infrastructure Canada has identified 59 eligible DMAF-eligible project costs (Infrastructure Canada, 2020) totaling $4.0B, with program spending over 10 years, or $0.4B/year on average.  Based on expected damages and benefits, annual funding of $0.87-1.7B, about two to four times DMAF annual project investments, could be justified over twenty years based on expected benefits of hard flood control projects. 

City-Wide and Project-Scale Flood Control Program Funding 

     Municipalities in Canada have pursued system-wide evaluation of risks and investment in adaptation upgrades to address recurring flood damages. The City of Markham’s Flood Control Program represents a system-wide flood risk reduction strategy that focuses on storm sewer upgrades, but that also includes culvert upgrades and floodplain reclamation / on-line storage (land purchase and use reassignment).  The program cost is estimated to be up to $368M (City of Markham, 2019). 

     City-wide benefits from infrastructure upgrades can be estimated by scaling national damages to Ontario using the Table 5 scaling percentage (38%), and to Markham based on the ratio of Ontario and Markham populations of 2.45% (Muir and Papa, 2019) as follows: 

i)                EAD_MROL-Ontario = EAD_MROL × 0.38 = $512 M

ii)              EAD_MROL-Markham = EAD_MROL × 0.0254 = $12.5 M 

     Benefits in the Markham study and project implementation areas can be scaled from city-wide values based on many approaches depending on available data, including insurance risk ratings obtained through the city’s insurer, hydrodynamic computer model surcharge risks (Xu and Muir, 2018), or based on city-records of reported flooding.  The fractions of the West Thornhill study area and the Phase 1 project area flood reports to city-wide reports are 39% and 6.1%, respectively. 

TABLE 7.  CITY-WIDE, STUDY AREA AND PROJECT-LEVEL DAMAGES AND EFFECTIVE BENEFIT-COST RATIO FOR SEWER UPGRADES  

      Table 7 shows the study and project area costs.  Construction in the study area is 45% complete and the example project area upgrades were completed in 2016.  The potential benefit-cost ratios for the city-wide program, study area and project area range between 3.4:1 and 4.4:1.  While a few projects may be 100% effective at eliminating flood risk (e.g., floodplain reclamation), sewer upgrades may only be partially effective, resulting in lower effective ratios on a project and city-wide basis.  Where additional operation and maintenance costs are added with new infrastructure, those ongoing costs would reduce the effective ratio as well.  As Markham’s program involves primarily replacement of infrastructure, no new additional operation and maintenance costs are expected.

      For new infrastructure, the operation and maintenance cost of storm sewers is expected to be on average 0.05% and up to 0.2% of pipe value per year based on National Water and Wastewater Benchmarking Initiative costs.  That would add 5 to 20% to upgrade costs over a 100 year period, reducing the effective benefit-cost ratio.  Present value discounting of future benefits would also reduce the ratio, given that costs are incurred up front and benefits are realized up to 100 years into the future.  By discounting at 3% per year, total discounted benefits are 32% of total benefits.  While this may effectively reduce the benefit-cost ratio by approximately one-third, the resulting ratio may still lie above 1, making the investment worthwhile.  Other factors affecting the ratio include growth that may increase benefits over time and financing costs that may reduce benefits.  The opportunity cost for Flood Control Program funding in the City of Markham was 2.9% in 2018 (City of Markham, 2018).

 Sewer Back-Up Damage Estimate Calibration

      A municipal sewer upgrade project estimated damages and potential benefits considering bottom-up damage estimates derived from hydraulic model results.  The analysis cited an average cost of a flooded basement of $43,000 in Ontario based on the reporting by the University of Waterloo's Intact Centre on Climate Adaptation (ICCA, 2019).  Analysis of CatIQ claim data from 2013 to 2018, including several extreme events, indicates a lower average value of approximately $18,500 in Ontario, however, as shown in Table 4.  Table 8 illustrates EAD values assuming an average damage claim of $18,500 per flood event. 

TABLE 8.  MUNICIPAL SEWER BACK-UP DAMAGE ESTIMATES  

       Applying the estimated average loss of $43,000 results in an EAD of $15,700,400 which increases the numerator in the project benefit-cost ratio by 230%.  Of course, the benefits (numerator) should be tempered somewhat as the effectiveness of the investment is likely to be less than 100% efficient such that all basement flooding risk is completely eliminated, but rather that the risk profile is changed for the better.  As the municipality also considered estimated losses of up to $100,000, reported losses and potential benefits are considerably lower than estimated values.  While average event values are $18,500, more extreme events have been shown to have a higher average loss compared to smaller, more frequent events.

CONCLUSIONS 

     Reported flood damage losses have been analyzed and can support the estimation of overall adaptation funding requirements (i.e., mitigation infrastructure investment) across Canada.  Approved DMAF disaster mitigation projects as of January 2020 have an estimated total cost of $4.0B.  To mitigate national expected annual damages over the next 100 years, adaptation funding for hard flood control measures of between $17B and $34B is warranted based on an expected benefit/cost of 4:1 for such projects (ignoring any discounting effects).  Current DMAF funding of $2B over a 10 year period of 2018-2028 could therefore be expanded to support more extensive adaptation efforts provided that eligible projects with suitable benefit-cost ratios are available. With an estimated value of wastewater and stormwater infrastructure in Canada of $368B, an investment of $17-34B represents 5-9% of current asset value.  As most assets were installed over a long period covering the past 70 years, upgrades to infrastructure can be expected to occur over decades.  The case study above demonstrates how Method #2 EAD estimates can be effectively applied, leveraging reported historical flood damages, both insured and overall to guide decision making on adaptation funding. 

     Investment in adaptation infrastructure includes large regional works including diversions and dams and also extensive municipal infrastructure upgrades.  The level of funding for city-wide flood control programs can be reviewed considering expected benefits and the effective benefit-cost ratio at various scales including city-wide, high risk study areas, and individual project areas.  The City of Markham Flood Control Program has been shown to have a potential benefit-cost ratio of up to 3.4:1, with higher risk areas exhibiting even higher ratios.  The case study above demonstrates a top-down approach leveraging Method #2 EAD estimates that are scaled down to provincial, municipal, study and project area levels using suitable proxies.  Such scaling is readily and reliably calculated using economic, demographic, development and infrastructure statistics, as well as records data characterizing localized flood risks (e.g., municipal flood reports or hydraulic system performance results). 

     The average cost of a flood incident is an important statistic in BCA of adaptation projects.  Analyses that consider property-scale damages have often used high-level estimates of costs that do not consider local records that have previously not been available in sufficient detail or duration.  Analysis of CatIQ datasets have provided provincial-scale average damage estimates that may be used to calibrate bottom-up Method #3 analysis damage estimates.  In Ontario, the average CatIQ sewer back-up claim per event is approximately $18,500.  This value provides a lower damage estimate than higher values that have been regularly applied in DMAF funding applications across Canada. To promote greater consistency in the estimation of flood damages and potential benefits, reported losses should be considered.  This would support a more reliable prioritization of projects based on relative benefits and would support more reliable budget screening based on absolute benefit-cost ratios that can be achieved.

BILBIOGRAPHY 

City of Markham (2018) 2018 First Quarter Investment Performance Review - Markham http://www2.markham.ca/markham/ccbs/indexfile/Agendas/2018/General/gc180507/2018%20Q1%20Investment%20Report%202.pdf

City of Markham (2019) Flood Control Program and Stormwater Fee Update. https://pub-markham.escribemeetings.com/filestream.ashx?DocumentId=14144 

EPCOR (2019) Stormwater Integrated Resource Plan – Capital and Operational Plan Alternatives (May 2019 Utility Committee report) https://www.epcor.com/products-services/drainage/flood-mitigation/Documents/EPCOR_SIRP_May2019_Report.pdf 

Formetta, G. (2019) Empirical evidence of declining global vulnerability to climate-related hazards, Global Environmental Change, Vol. 57 https://www.sciencedirect.com/science/article/pii/S0959378019300378 

IBI Group (2015) Provincial Flood Damage Assessment Study https://drive.google.com/open?id=1NNXxTrCKFVchjhcP6SJfJL-TS2WLcGuq 

IBI Group and Golder Associates (2017) Report, Flood Mitigation Options Assessment. https://drive.google.com/open?id=1I3LyK7rwbT1qtnLhQhqo9RzhXQ7j6Bho 

Infrastructure Canada (2018) Disaster Mitigation and Adaptation Fund - Applicant’s Guide, Strengthening the Resilience of Canadian Communities, https://www.infrastructure.gc.ca/alt-format/pdf/dmaf-faac/dmaf-faac-guidelines-flat-e.pdf

Infrastructure Canada (2020) Investing in Canada Plan Project Map, per Download Map Data (January 17, 2020) https://www.infrastructure.gc.ca/gmap-gcarte/index-eng.html 

Insurance Bureau of Canada and Federation of Canadian Municipalities (2019) Investing in Canada’s Future: The Cost of Climate Adaptation http://assets.ibc.ca/Documents/Disaster/The-Cost-of-Climate-Adaptation-Summary-EN.pdf 

Insurance Bureau of Canada (2019) New report shows urgent need for climate adaptation investment http://www.ibc.ca/on/resources/media-centre/media-releases/new-report-shows-urgent-need-for-climate-adaptation-investment 

Intact Centre on Climate Adaptation (2019)Weathering the Storm: Developing a Canadian Standard for Flood-Resilient Existing Communities. https://www.intactcentreclimateadaptation.ca/wp-content/uploads/2019/01/Weathering-the-Storm.pdf 

Kuik, O. et al. (2016) Assessing the economic case for adaptation to extreme events at different scales https://econadapt.eu/sites/default/files/docs/Deliverable%205-1%20approved%20for%20publishing_1.pdf 

Klotzbach, P.J., Bowen, S.G., Pielke Jr., R., and Bell, M. (2018) Continental U.S. Hurricane Landfall Frequency and Associated Damage Observations and Future Risks, American Meteorological Society, Articles July, 2018, https://journals.ametsoc.org/doi/pdf/10.1175/BAMS-D-17-0184.1 

National Resources Canada (NRCan), and Public Safety Canada (PSC) (2017). Canadian Guidelines and Database of Flood Vulnerability Functions, Draft.
http://hazuscanada.ca/sites/all/files/nrc-canadianguidelines-final_2017-03-30_draft.pdf 

Office of the Parliamentary Budget Officer (2016) Estimate of the Average Annual Cost for Disaster Financial Assistance Arrangements due to Weather Events https://www.pbo-dpb.gc.ca/web/default/files/Documents/Reports/2016/DFAA/DFAA_EN.pdf 

Olesen, L., Löwe, R., and Arnbjerg-Nielsen, K (2017) The Flood Damage Assessment, Literature review and recommended procedure  https://drive.google.com/open?id=1-9hUAhILCxi_N_-vt4XbJWmZtB5E0P6L 

Olsen, A. S., Zhou, Q., Linde, J.J, Arnbjerg-Nielsen K (2015) Comparing Methods of Calculating Expected Annual Damage in Urban Pluvial Flood Risk Assessments https://drive.google.com/open?id=1OOw7Ooia5FeinIleQDuKLYnH_RwoFXPd 

Muir, R. and Papa, F. (2019) An Economic Analysis of Green v. Grey Infrastructure, WEAO 2019 Technical Conference https://drive.google.com/open?id=1-DjFrp4KRfdjMAqGL091Bpb4oE0RbSVV 

Nafari, R. H. (2018) Flood Damage Assessment in Urban Areas https://drive.google.com/open?id=16sQza0QFZfnqoCMwt_nfZ3zT9Qc55heO 

Sakshi, S. (2019) Risk and Return on Investment Tool (RROIT) https://trieca.com/app/uploads/2019/03/3-1000-1030am-Sakshi-Saini-RROIT_for_trieca_public.pdf 

Toronto and Region Conservation Authority (2019) Toronto Flood Risk Ranking https://drive.google.com/open?id=1-5YWDEXDkbwKosjNydVqpJ23RLqaK8xx 

Xu, L and Muir, R. (2018) Wastewater Collection System Performance Under Climate Change – Safety Factors and Stress Tests for Flood Risk Mitigation, WEAO 2018 Technical Conference. https://drive.google.com/open?id=1FZhM7DF5DLNm5Y0X3b3pn79jIk6yWFU7

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Presentation:

National Guideline Development for Benefit-Cost Analysis of Storm Drainage Infrastructure from Robert Muir