City of Waterloo Flood Risk Factors - Historical Design of Sanitary Sewer and Overland Flow Paths Help Define Neighbourhood-scale Flooding Risk During Extreme Rainfall

This post summarizes risk factors affecting urban flooding and explores the example of flood risk in the City of Waterloo, Ontario.

Two key factors explain basement flooding risks in many urban areas:

1) sanitary sewer design practices, and
2) overland flow design practices.


Virtually all urban properties have gravity-drained sanitary sewer connections to the municipal sanitary sewer systems, and this collects wastewater from homes as well as infiltrated groundwater from foundation drains in most pre-1980 areas and occasionally direct rain and melt water inflows thorough illicit collections to the home plumbing and drainage systems and ultimately the municipal sanitary sewer system.  Because of this connection, any surcharging of municipal sanitary sewer systems during extreme weather can back-up into low-lying floor drains, flooding basements.

So the capacity of the municipal sanitary sewer system will partially define basement flooding risk. Design standards in Canada have evolved over time as described in a previous post. While each municipality is a little different, we can consider 1975 as a year in which systems became fully-separated, with no more foundation drain connections that serve to overwhelm the system with infiltration and, more importantly, provide a pathway for illicit inflow connections, like from rooftops or other property drains (in York Region we once even found an outside kitchen sink connected to foundation drains - it was near the garden and used to rinse vegetables!).

Overland drainage began being considered in urban drainage design in the late 1970's - the former Town of Markham's design standards recognized overland 'major' system design requirements in 1978, under the guidance of University of Ottawa's Dr. Paul Wisner. Many other municipalities in Canada adopted dual minor-sewer/major-overland drainage design standards throughout the 1980's. Historical development grading and old subdivisions that did not integrate overland flow are prone to flood stresses due to i) water entry into building openings via windows, doorways, recessed walkouts/stairs, and reverse-sloped driveways, ii) storm sewer surcharge that backs up into foundation drains and through basement walls and under flood slabs, and iii) sanitary inflows into maintenance hole lids (e.g., at roadway locations with deep ponding over the lids pick-holes and edge). The insurance industry refers to overland flooding pluvial flooding, an unheard of term in Canadian engineering design (this is to distinguish between urban overland flooding and 'fluvial' riverine flooding that occurs in valleys).

Show me !

The City of Waterloo has an extensive Open Data portal that includes information on sanitary sewer installation date. This GIS data has been used to characterize neighbourhood flood risk according to era of construction and engineering design practices.

Overland flow risks can be mapped in many ways with increasing complexity on aspects of:

i) Input Data - e.g., elevation model detail and conditioning as input to the hydrologic and hydraulic analyses can be based on coarse provincial datasets (raster cell sizes suitable for macro-scale neighbourhood assessments), local datasets such as detailed 3D breaklines used for other image rectification (raster cell size of a metre or two for master drainage planning), to LiDAR datasets (to generate sub-metre cell size for fine-scale lot-by-lot, or gutter-by-gutter analyses),

ii) Defining Risk Zones / Hazard Area - e.g., this can involve the simple delineation of flow accumulation paths and definition of sinks (ponding areas), to setting of buffers around flow paths based on drainage area size (a surrogates for hydrology and hydraulics but good for screening), or more advanced flow spread calculations (i.e., applying hydrologic and hydraulic principles) to identify risk zones.

Data to the above can include province of Ontario processed topographic data (through Land Information Ontario (LIO)), including a conditioned elevation model and flow direction raster grid that has been used to map overland flow paths and spread across much of the province.


Simple Flow Path and Ponding (Sink) Delineation: My City-wide Storm System Master Plan for the City of Stratford in 2004 was one of the first applications of major drainage system / overland / pluvial flood risks using ESRI's Spatial Analyst and the emerging hydrology tools (that would later become the familiar ArcHydro tools), and first introduced by the University of Texas as an extension to ArcView 3. The following map illustrates the assessment of overland flow path drainage issues and ponding issues. No base data was available for the analysis and the elevation model was derived from half-metre AutoCAD contours to generate a 2-metre DEM raster for analysis. The integrated GIS-modelling approach was subsequently presented at the 2004 AWRA conference in Nashville, Tennessee.

Major Overland Pluvial Flood Risk
Stratford City-wide Storm System Master Plan - Major Overland Flow / Pluvial Flood Risks Based on GIS-based Flow Paths Delineation and Ponding Areas using ArcView GIS Spatial Analyst Extension.
Buffered Flow Paths and Ponding (Sink) Delineation: A similar approach was taken in Markham, Ontario in 2013 to conduct a screening-level identification of properties in close proximity to flow paths or within potential ponding areas. This was shown in a previous post. The images below illustrate some of the outcomes that were subsequently aggregated over catchments to identify areas for detailed study. In this example 3D breaklines from a recent orthophoto rectification were used to generate the DEM raster within the city - this was integrated with a more-coarse elevation model outside of the city boundaries to ensure a complete watershed delineation. The final DEM was refined after extensive manual editing of the 3D breaklines and reprocessing of overland flow paths and ponding areas/sinks.

Overland flow / pluvial flooding risk defined by buffers on overland flow path as a function of drainage area. 

Overland flow / pluvial flooding risk defined by buffers on overland flow path and ponding with building pluvial flooding risk risk estimated by proximity to flow buffer or to ponding area..
Hydrologic-Hydraulic-Based Overland Flow Paths: Analysis of City of Toronto overland flood risks was completed in 2015 using a pre-conditioned provincial DEM - as it is conditioned it cannot be used to generate ponding limits. Simplified rational method hydrology was applied considering individual cell-by-cell time-to-peak and individual 100-year design rainfall intensities, along with a standard runoff coefficient. Overland hydraulics to define flow spread were applied on a derived vector-based overland flow network that considered 100-year flow along each overland reach and flow spread defined by longitudinal slope and uniform flow conditions for a typical roadway cross section. The presentations below illustrates the overland flood hazard / flow spread that was then used to explain the location and density of reported basement flooding during recent extreme rainfall events.

Refined Hydrologic-Hydraulic-Based Overland Flow Paths: The Toronto-based overland risk mapping approach was refined using SOLRIS land use classification to derive cell-by-cell weighted rational method runoff coefficients, for a more precise hydrology. This was required as both rural and urban areas across south-west and central Ontario were assessed. The analysis was completed in 2016 as summarized in a previous post. The result is an overland drainage network with over 800,000 flow segments (reaches) with an individual 100-year design flow rate and flow spread. A snapshot of the analysis is shown below.
Ontario Overland Flow / Major Drainage / Pluvial Flood Risk Assessment

This last overland flood risk analysis approach is used to help assess City of Waterloo flood risks. The map below shows flow paths in the western part of the city and and highlights buildings (in red) that intersect the overland flow path - in this analysis flow paths with 3 hectares of contributing drainage area (i.e., 30,000 square metres or more) are shown. The presence of modern stormwater management and drainage design, as suggested by the municipal stormwater ponds in the western-most areas, would mitigate the possible impact of these overland flow paths by capturing and controlling the release of major flow during extreme events. In addition, modern minor systems in these modern, post-1980 subdivisions may be designed to capture and convey runoff generated by extreme rainfall.

City of Waterloo - Example Overland Flow Risk (Urban Major Drainage / Pluvial Flood Risk) - Buildings along Flow Path Highlighted (Surface Flooding and Sanitary Inflow Risk)
Multiples of the 100-year flow spread are shown for catchments of 3 to 1000 hectares. For larger areas, only the flow centreline is shown and those assessing valley-feature overland flood risk should refer to regulated floodplain limits that are determined through more advanced hydrologic and hydraulic analyses.

The next map shows installation date of sanitary sewers with pre-1975 sewers shown red (highest risk for infiltration and inflow stresses during extreme weather), 1975-1989 sewers shown in orange, and post-1990 sewers shown in green.
City of Waterloo - Sanitary Sewer Installation Date  (Inflow and Infiltration Risk) - Pre-1975 sewers (red), 1975-1989 sewers (orange), 1990 and newer sewers (green).
The map suggests that sanitary sewer replacement has occurred in the older core ares to the east (new green sewers surrounded by older red sewers).

This next map illustrates the intersection of overland flow path attributes onto sanitary sewer features that they intersect. Specifically the drainage area is assigned to each sewer segment it crosses and the sum of the intersected overland flow is aggregated to each segment and then weighted by the age of the sewer - post 1990 sewers have the area reduced by a factor of 5 considering modern drainage design and low infiltration and inflow stresses in modern fully-separated systems, while 1975 to 1990 sewers have the area sum divided by a factor of 2 considering lower fully-separated systems stresses. This is an approximate screening method, of course, but consistent with industry understanding of risk factors based on more detailed studies. The width of the red highlighting surrounding sanitary sewer segments illustrated thee age-factored sum of intersected flow area.

City of Waterloo - Overland Flow Impact on Sanitary Sewer Systems - Intersection of Major Drainage Flow Path Areas To Sanitary Sewer Segments, Factored by Age of Construction.

Red highlighted areas are or interest for further study. It is clear that in some core areas with predicted flood risks, sanitary sewer replacement has already occurred (i.e., newer green sewers in eastern areas), meaning that some flood risks may have already been mitigated.

The last map adds average age of dwelling construction in census areas. Clearly, the is a strong correlation to the sanitary sewer age risk factor and overland drainage design risk factor and the average age of construction. It is interesting to note that the broad, census-area neighbourhood risk does not account for local sanitary sewer upgrades, nor does it help identify individual properties that are at risk of significant overland flooding, as those buildings are isolated to the major overland flow path hazard area.

City of Waterloo - Urban Flood Risk Factors and Average Age of Dwelling Construction

Catastrophic Losses in Canada - Have Flood Damages Increased Significantly Or Have Changing Data Sources Affected Trends?

Disaster Losses Are Up
Catastrophic loss trends have been reported regularly in Canada, often in relation to flood damages. These have often linked to climate change effects as well as other factors that may include aging infrastructure (not a significant factor in our mind), or urbanization and intensification (the true overriding factor in many urban centres). This post looks at how trends have changed in relation to changes in data sources.

GDP Adjusted Losses are Down
A blog post by the Institute for Catastrophic Loss Reduction (ICLR) discusses loss trend reporting by the Insurance Bureau of Canada. ICLR discusses but dismisses the calls for adjusting losses for growth, which is commonplace in Munich RE NatCatSERVICE analysis and reporting, and which is promoted by may others (this includes my paper in the Journal of Water Management Modelling which evaluated losses adjusted for net written premiums, and Roger Pielke Jr.'s work, such as reported here in Five Thirty Eight - see charts to the right - that also calls for evaluating trends considering GDP growth).

The ICLR notes "Normalizing disaster loss data to include such factors as growth in population, economic activity and building stock is not a simple undertaking. Further, there are many problems with using simple measures like GDP or insurance premium growth as a normalizer. For these and other reasons, I don’t want to go ‘there’ at this point ...".

So ICLR is content to us the following chart that does not include GDP adjustments:

Catastrophic Losses Flood Damages Canada
Losses in Canada Unadjusted for GDP Growth - 1983-2007 Data per IBC Survey, 2008- Data per CatIQ.

The ICLR notes a change in the data source for the above graph: "Bureau data begins at 1983. From that year to 2007, IBC uses data it collected itself through various company surveys conducted immediately after significant natural disaster events. It also uses various data from Property Claim Services (PCS), Swiss Re, Munich Re and Deloitte. After 2007, the Bureau only uses data from Catastrophe Indices and Quantification Inc. (CatIQ)."

How does the change in data affect reported losses? We can look at how the increase in losses has been reported, for example by the ICLR in 2016:

Catastrophic Loss Trends in Canada. Effects of change in data source on reported losses pre 2008.
Below the ICLR chart, the timing of the change in data is shown. This indicates that the change in reported annual losses from $400M average up to 2008 to $1B average after corresponds to the change in data source in 2008.

More recently the Intact Centre on Climate Adaptation (ICCA) has reported trends in losses on TVO's The Agenda as shown in the chart below:

Intact Centre on Climate Adaptation cites changes in insurable claims on TVO (chart shown), with ICLR's noted change in data sources added below (IBC data up to 2007 and CatIQ data from 2008 onward).

Again, the change in data is added below the ICCA chart. The lower losses of $200-500M up to 2008 and higher losses typically over $1B from 2009 onward correspond to this change in data source.

Adjusting for data sources or for GDP does not really change priorities for flood risk and catastrophic loss reduction. Better characterization of the GDP-adjusted trend can give us insight into the effectiveness of past mitigation efforts, more-resilient design standards that are common in modern practice. Without such GDP adjustment, one would think that everything is built as disaster-prone as it was in the past. Also, understanding the cause of the trend in losses will help focus adaptation or mitigation efforts in the proper place - if increases are explained by GDP growth as opposed to changes in extreme weather (shown to not be a factor) efforts will be placed on adaptation infrastructure built to old, less-resilient design standards as opposed to mitigation (e.g., GHG reduction).

A more wordy comment has been added to the ICLR blog post.


A paper Trend Analysis of Normalized Insured Damage from Natural Disasters, published in:
Climatic Change, 113 (2), 2012, pp. 215-237, by Fabian Barthel and Eric Neumayer, Department of Geography and Environment and The Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science explores "Normalized" / GDP adjusted damages, exploring trends for different types of events.

As noted in their abstract:

"As the world becomes wealthier over time, inflation-adjusted insured damages from natural disasters go up as well. This article analyzes whether there is still a significant upward trend once insured natural disaster loss has been normalized. By scaling up loss from past disasters, normalization adjusts for the fact that a hazard event of equal strength will typically cause more damage nowadays than in past years because of wealth accumulation over time. A trend analysis of normalized insured damage from natural disasters is not only of interest to the insurance industry, but can potentially be useful for attempts at detecting whether there has been an increase in the frequency and/or intensity of natural hazards, whether caused by natural climate variability or anthropogenic climate change."

The following charts from the paper show an increase in deflated (non-normalized) damage losses over time, and virtually no change in normalized losses.

Global deflated insured losses from natural disasters
Global normalised insured losses from all disasters
Similarly, the following charts illustrate normalized trends for convective storm events (4165 disasters) showing a decrease, all storms including winter and other storms but excluding tropical cyclones (4369 disasters) showing a decrease, and for tropical cyclones (874 disasters) showing an increase.

Global normalized insured losses from convective events
Global normalized insured losses from all storm events except tropical cyclones

Global normalized insured losses from tropical cyclones


The Government of Canada has reported that the majority of loss increases have been due to growth (more exposed people, assets and wealth), and that climate change 'may' be having an effect - this contrast many media and insurance industry comments. The true driver of increased losses was reiterated in the just-released Canada in a Changing Climate: National Issues Report (see post: Canadian loses have been normalized for growth and show a moderate increase over time - the report notes that earlier data may be incomplete, which would affect the normalized trend as well (more complete older data could decrease the trend).

Economics of Flood Damage Claims - Large Events Dominate Overall Losses - Should Effective Mitigation Strategies Focus On "The Big Ones"

What type of meteorologic events dominate flood damage claims? Is it from many frequent small storms or a few infrequent black-swan events? Understanding what size of events cause the most losses can help us focus on the most effective flood loss mitigation measures - this is essential for achieving high returns on investment in flood mitigation strategies. By reducing flood losses in an economically efficient manner, high benefit-cost ratios can be achieved.

This post summarizes the distribution of FEMA's flood damage claims and explores what type of storm events - big or small - govern extreme weather losses.

FEMA summarizes the number of payout claims and the total value of payouts for significant flood events, i.e., those with 1500 or more payouts. The total value of payouts, adjusted for inflation to 2018 dollars, is plotted below against the total number of claims in the events between 1978 and 2017:

FEMA Average Claim Amount for Various Event Sizes (Number of Claims) - Small Significant Flood Events with a Minimum of 1500 Claims Per Event

The median number of payouts is 4115 with a median payout amount of $29,700, adjusted to 2018 dollars. This is over a total of 116 flood events from 1978 to 2017.

Larger storm result in more extensive flood damages and numbers of payout claims as shown on the following chart that labels some of the largest tropical storm / hurricane events:
FEMA Average Claim Amount for Various Event Sizes (Number of Claims) - Small and Large Significant Flood Events with a Minimum of 1500 Claims Per Event
How do the larger events affect the flood damage and payout values? The average flood claim payout of $36,200 is above the median value reflecting the skew in catastrophic event distribution - the right tail of rare black-swan events in the probability distribution of events pulls the average above the median.

Five of the 116 event have claim counts that are over ten to forty times the median number of claims. That is, Hurricane Ike and Irene had over 40,000 claims compared to the median count of just over 4000 claims. And Hurricane Harvey had over 160,000 claims. The losses are greater for these larger events with the best-fit line showing average claim values of over $50,000 to over $120,000 for these largest significant events. What effect do these claim counts have on weighted claim amount - they increase the claim-count-weighted average loss to $60,600 - more than double the median claim amount per event that is not weighted by the number of claims in each event.

So when looking at the economic losses associated with a significant flood event, we need to consider the size of the event. And when we develop strategies and best practices for flood resilient communities and flood risk mitigation, striving for significant damage reduction and return on investment in averted flood damage losses, we must also consider what events cause the most damages. Canada's Disaster Mitigation and Adaptation Fund (DMAF), for example, requires return on investment (ROI) evaluations for eligible risk reduction projects. It would appear that to achieve meaningful flood damage reduction ROI we must target solutions toward events leading to the most damages

Looking at FEMA's significant flood events, data show that 3 of 116 events account for 54% of the total inflation-adjusted damages. Those 3 events are Hurricane Harvey, Superstorm Sandy and Hurricane Katrina. And the top 20 events, each with total event claims of over $500M, account for 81% of the total claims. So it is clear that to reduce the bulk of flood damages we have to consider how to increase resiliency in existing communities during the largest storm events. If we target flood risk reduction for the small catastrophic events, the smaller 97 events, we will be addressing only 20% of the total claim value. So the 80/20 rule, the Pareto principle, does apply to flood damage reduction.

FEMA Inflation Adjusted Significant Flood Event Payout Distribution - Pareto Distribution and the 80/20 Rule

The impact on a few recent large events on damages helps show sample-bias in catastrophic event losses as explored in a previous post. That is, up to 2004 prior to Hurricane Katrina, the distribution of losses based on the 1978-2004 sample of events did not consider the true 'population' distribution of flood events that includes very extreme, right-tail events. As Fleming demonstrated in "Yep, We're Skewed", short samples with high skew underestimate losses of the true population of events.


For statistics geeks:

Could it be that the common chorus of explaining recent floods losses as being due to climate change may in fact be explained simply by statistics and larger sample sizes overcoming short sample biases (underestimation)?

Could it be that growth in high risk areas is driving flood damages higher? AON Benfield's review of Hurricane Harvey suggests that growth in at-risk areas explains some flood impacts:

 "Given the volume of water, local infrastructure across southeast Texas was simply unable to handle such an enormous amount of rainfall in a short amount of time. This led to major water run-off that quickly accumulated across a very large area. With so much residential and commercial growth throughout this part of the state – combined with abundant concrete and poor absorbing clay soil –this only worsened the flood impact."

Solutions to flood risk mitigation therefore cannot only be local infrastructure solutions to convey enormous amounts of water but rather land use planning policies to direct development and redevelopment away from high flood risk areas. As AON Benfield notes " Hurricane Harvey’s rainfall reached the 1,000-year rainfall return period based on many time intervals during the course of a number of hours and days.", and it is not cost effective, or technically feasible, to have local infrastructure convey the runoff from events of this magnitude.


Canada Connection (for those appreciate tree-sauce, skatey-punchy, and noble antler cows):

CatIQ claim datasets have been used to evaluate flood damages across Canada according to the size of the flood event (i.e., related to the number of claims). A similar pattern of increasing damages with increasing event size and distribution is apparent in the CatIQ datasets. In contrast to the FEMA claims noted above for many hurricane events, the CatIQ data reflect basement flooding claims primarily, as overland flooding has not been insured in the past and is not widely held. What is the magnitude of these Canadian claims? Aviva Canada provided this summary of claim trends and magnitude:

"In 2014, water damage claims accounted for 44% of dollars paid out on all Aviva Canada property damage claims, compared with 39% in 2004. The average cost per residential water damage claim has increased significantly – going from $11,709 in 2004 to $16,070 in 2014, a 37% increase."

So basement flooding damages are significantly less than FEMA's large scale catastrophe claims. CatIQ data shows that for larger events (those with higher claim counts) the average claim amount does increase above the Aviva Canada values noted above. Comprehensive benefit-cost analysis used to develop ROI rankings for flood mitigation projects would apply the lower range of typical damages to frequent to moderate events and the higher damages/claim amounts to the frequent events, factored by their probabilities. The most frequent storms, typically 5 to 10 year return period events as in a recent study by Atkins for the US EPA do not generate damages.

Watershed-Scale Flood Damage Reduction Using LID BMPs - Does Green Infrastructure for New Development and Redevelopment Significantly Reduce Existing River Flood Risks

River Flood Loss Avoidance (Deferred Damages) with
Green Infrastructure / Low Impact Development BMPs
in New Development and Redevelopment in the US.
A study by Atkins for the U.S. EPA evaluates flood damage reduction across North America using Low Impact Development (LID) Stormwater Best Management Practices (BMPs). The 2015 report "Flood Loss Avoidance Benefits of Green Infrastructure (GI) for Stormwater Management" is available at this link and evaluates benefits of implementing LID BMPs in new development and redevelopment over 20 years.

Flood Losses Avoided in the Year 2040 for Various Zero Damage Thresholds
What is the value of estimated flood damage reduction from 2020 to 2040 by constructing GI / LIDs? Savings range from $63M to $136M per year in 2040, depending on whether river flood damages are assumed to start above 10 year events or more frequently above 5 year events.

Over the 20 year implementation period the deferred flood losses increase from $0 in 2020 to an average of $100 million (2011 dollars) for 5 and 10 year zero damage thresholds.

Deferred Flood Losses with LID BMP Implementation
for Stormwater Management in New Developments
After the first 10 year s of implementation the flood losses avoided averages approximately $50 million. The table to the right indicates the increase in flood reduction benefits over the green infrastructure implementation period.

Key question: is the losses avoidance significant and is there value in implementing green infrastructure to achieve river flood damage reduction?

To explore whether deferred damages are significant, lets compare the average loss reduction over the 20 year implementation with average losses in North America. Munich RE's NatCatService estimates losses in North America for meteorologic events and hydrological events. The chart at right shows that over the past 10 year from 2008 to 2017 the average losses per year we approximately $75 billion in 2017 dollars. (we could net out non-US areas like Canada to make this more apples to apples).

Munich RE Catastrophic and Relevant Event Overall Losses
Inflation Adjusted and Normalized 
The deferred flood losses over the 20 year period with LID implementation are $50 million in 2011 dollars - according to the Bureau of Labor Statistics consumer price index, the dollar experienced an average inflation rate of 1.42% per year. Therefore the annual LID loss reduction amount in 2017 is 8.8% higher than deferred losses in 2011, or $54 million (2017 USD).

Deferred annual flood losses of $54 million represents only 0.07 % out of $75 billion in overall annual losses. This suggests that near-term river flood damage reduction is not a core benefit of green infrastructure implementation.

The cost of expanded green infrastructure implementation is not assessed in the Atkins study as noted here:

"The costs of GI implementation are not included in this document. Nevertheless, new development and redevelopment already require stormwater management expenditures, either on-site or downstream; therefore, GI could be used to meet those requirements fully or partially for little or no
additional cost compared to overall construction costs. This study does not assume retrofitting of existing imperviousness. Retrofitting, in addition to implementation on new development and redevelopment, would be expected to generate more flood loss avoidance benefits but would incur
additional costs."

Given that green infrastructure stormwater controls are assumed to be included in the base cost of development or redevelopment, the micro-sized river flood loss reduction benefit comes at no addition cost, which could indicate good 'value' - however given the almost insignificant percentage of overall flood damages averted, one could question whether river flood risk reduction should be prescribed as a low impact development benefit at all. It would appear that core benefits of green infrastructure are instead the environmental ones related to water quality improvements and erosion risk reduction.

Today it is popular to promote green infrastructure citing its multi-faceted triple-bottom-line (TBL) benefits on many aspects of the environment. It would appear that riverine flood risk reduction is not one of those benefits to consider in the TBL assessment. Many groups and Canadian municipalities have noted impacts of green infrastructure on existing utilities and foundations as a dis-benefit / adverse impact of green infrastructure measures that typically infiltrate runoff into the ground. Many of these negative impacts were recently summarized in the Ontario Society of Professional Engineers' (OSPE) comments on Ontario's draft Watershed Planning Guidance which had promoted the role of green infrastructure for flood control :

The OSPE comments note:

"While green infrastructure has recognized roles in achieving watershed outcomes, including
water balance and water quality management in greenfield developments, the above statement
is inconsistent with numerous studies that discount the flood-control benefits of green
infrastructure. Numerous studies have demonstrated that green infrastructure does not provide
a flood risk reduction benefit."

To support this statement, OSPE cited numerous Master Plans, Master Drainage Plans, Class Environmental Assessment studies, Best Practice documents, local university research, and municipality and water industry comments on the Ontario Ministry of the Environment and Climate Change's draft Low Impact Development guideline.