Area of Extreme Ontario Storms - How Over 100 Years of Storm Data Can Guide Infrastructure and Watershed Flood Resiliency Assessments and Guide Regional IDF Analysis in a Changing Climate

INTRODUCTION

      Single station Intensity-Duration-Frequency (IDF) statistics are commonly used to characterize point observations of extreme rainfall and derive synthetic design storms.  Such point data, applied in flood vulnerability assessments of urban infrastructure and natural drainage systems, is distributed across small and large sewersheds and watersheds, sometimes with an areal reduction in peak rainfall. Areal Reduction Factors (ARFs) are available based on world and North American data. Over eighty years of storm event data has been compiled by Environment and Climate Change Canada (ECCC) in “An Index To Storm Rainfall In Canada”, documenting how peak rainfall diminishes over larger areas.

      This paper reviews historical extreme storm characteristics in Ontario including peak rainfall volume and areal extent of moderate rainfall, defined by ECCC as a 50 mm volume.  This 50 mm threshold is deemed suitable to assess areas that may experience nuisance flooding and may contribute to significant downstream flooding. Available Ontario ECCC data spans from 1919 to 1984 and includes 118 extreme storms.  Ontario technical guidance for ARFs has been compared to long term Ontario data to assess the applicability of literature values in local system assessments.  The ARF is a common factor in large natural drainage system assessments and can be more widely considered in urban systems where areal-dependent reductions in RDII are apparent in large municipal wastewater systems.

      Trends in storm areal extent are compared to trends in urban development in the Greater Toronto Area, illustrating the “Expanding Bull’s Eye Effect” coined by Stradler and Ashley (2015).  The effect has been used to help explain increasing extreme weather damages over time whereby storms are the ‘arrow’ and urban areas are the ‘target / bull’s eye’.  The effect shows that damages may increase under stationary hazards (i.e., storm sizes remain the same) when the size of exposed assets increase (i.e., urban areas expand).  Trends in rainfall intensities across southern Ontario are presented to explore any correlation between intensities of various durations and return periods.  Any correlation may be used to infer project changes in extreme (e.g., 100-year) short-duration intensities related to flood risks and changes in frequent (e.g., 2-year) long duration intensities such as those predicted in climate models as “heavy precipitation”.  Frequent, annual intensity trends assessed across Canadian regions by ECCC (Cannon et. al 2024) and correlated with yearly temperatures are reviewed.

      ECCC 2024 annual intensity review assessed trends across large regions such as the Great Lakes/St. Lawrence River Basin.  This paper compares the size of this research region for trend analysis, the size of storms in ECCC’s historical storm index, and the size of typical infrastructure drainage catchments and sewershed.  The merits and limitations of assessing rainfall trends for large areas and the applicability to smaller catchment design storms are discussed.  In essence, regionalization of rainfall station trends can be viewed as an “Expanding Arrow Effect” where, opposite to the Bull’s Eye Effect the arrow (i.e., the size of the storm data characterizing the hazard) expands while the target system it affects remains the same.

      Lastly, this paper explores trends in IDF curves across a small region, the City of Markham.  Small return period statistics are compared for 18 gauges with periods of record of up to 28 years.  The comparison illustrates how regionalization, or aggregation, of single station data can result in observation of higher localized extremes.  The paper compares the localized extremes from Markham gauges with the long-term IDF statistics and 95% confidence limits for the Buttonville Airport climate station.

 100 YEARS OF STORM SIZE VS RECENT STORMS

      Assessing extreme rainfall impacts on drainage and infrastructure systems to guide risk reduction strategies requires characterization of rainfall events.  Intensity Duration Frequency (IDF) statistics represent one key characteristic that is updated regularly with new rainfall data.  While such point data on peak rainfall can characterize conditions over a small local area, the distribution of rainfall or a larger area is needed to assess impacts over watersheds and urban drainage catchments.  As summarized in the authors’ review of 2024 storms in their 2025 WEAO Conference paper (Karney et al.) real storms, peak intensities are observed to decrease over larger areas.  A review of historical storm sizes, and the areal extent of high and moderate rainfall can guide hydrologic modelling such that distributed intensities over large catchments are not realistically represented.  The authors’ related 2026 Conference paper on rainfall derived inflow and infiltration in sanitary sewer systems presents extensive data demonstrating the significant decrease in rainfall effects on collection systems as the area of the system increases.  Clearly areal reduction of storm impacts is an important design consideration.

      The Atmospheric Environment Branch of Environment Canada published An Index to Storm Rainfall in Canada (“AES Storm Index”) in 1961 following Hurricane Hazel.  The 1988 edition involved the review of almost 1000 storms from 1900 onward, identifying 118 significant Ontario storms between 1919 to 1984.  Characterization included storm centre location, maximum rainfall depth, duration, and area of 50 mm rainfall (previously 2 inches).   Figure 1 shows AES trends in maximum rainfall depth with a sample of depths added from southern Ontario storms over the next 40 years (Harrow 1989, Peterborough 2004, Toronto 2005 and 2013, Burlington 2014, and Toronto July and August 2024 shown as red triangles) and in the preceding 76 years (Toronto 1843, 1878, 1897 and 1905 shown as black diamonds; CBC 2024).  It is noted that the proliferation of rainfall gauges over recent decades as outlined in the authors’ 2025 paper can help identify peaks that were previously not monitored in less-dense rain gauge networks. 

 FIGURE 1 – SIGNIFICANT ONTARIO RAINFALL TOTAL DEPTH 1843-2024

     Many readers will be familiar with some labelled significant events, especially those recent events that had devastating impacts on highly populated communities.  The 1966 Sandusky event may be unfamiliar as it was centred in Ohio.  With 118 events from 1919 to 1984 in the AES series there have been 1.8 events per year on average demonstrates that extreme events, most over 4 inches (100 mm) are not rare.

      On event frequency, prior to 1951 the average duration between significant events was 282 days while from 1951 onward the duration was slightly longer at 314 days.  This suggests that significant events are not occurring more frequently, despite the expected increase in rainfall gauges and reporting channels over the past 100 plus years.

      Figure 2 illustrates the size of significant storms in the AES index including some recent storms.

 

FIGURE 2 – AREA OF RAINFALL OVER 2 INCHES (50 mm) 1919-2024

      Based on AES data, the median 2-inch (50 mm) rain depth area was 23,900 square miles before 1951 and 14,500 square miles after (i.e., 61,900 to 37,500 square kilometres).  This suggests that the size of significant storms is not increasing.  Five of the largest area storms are labelled and were in central and northern Ontario.  Given their remote locations, these events are not associated with significant damages and are therefore never referenced in the context of significant Ontario storms.

      To put the storm areas above into context, a large municipal wastewater collection system such as Toronto’s Ashbridges Bay wastewater treatment plant has a service area of less than 100 square miles (250 square kilometres).  A large GTA watershed like the Rouge River watershed has an area of 130 square miles (336 square kilometres).  Figure 2 illustrates that significant storms have covered vastly larger areas over the past 100 plus years – from 1951 onward the median storm size was two orders of magnitude greater than the Rouge River watershed area.

 AREAL REDUCTION FACTORS – DESIGN GUIDANCE VS DATA

      The authors’ 2025 paper compared the observed Areal Reduction Factors (ARF’s) of several 2024 Toronto-area storms with design guidance used in watershed hydrologic modelling.  Figure 3 reproduces the observed June, July and August 2024 storm ARF curves and the WMO 24 hours design curve and adds the observed AES index significant storms reported over 1919-1984.  The x-axis scale has been changed to a logarithmic scale given that the WMO guidance cited by MNRF extends up to an area of 1000 km² while the AES index storm sizes were extended to much greater areas, even over 100,000 km². 

 

FIGURE 3 – AREAL REDUCTION FACTORS FOR SIGNIFICANT ONTARIO STORMS, 2024 GTA STORMS AND WMO GUIDANCE

      Figure 3 illustrates that areal reduction of storms continues for areas beyond the WMO 1000 sq.km guidance, as would be expected.  There is considerable overlap in the short, moderate and long duration AES index storm ARFs with very similar median values of 70% for each of the three duration groups.  This characteristic of large area AES storms is contrary to WMO guidance that recommends smaller ARFs for smaller area, shorter duration storms.

      The AES index shows that significant storms can be sustained over large distances.  Designers may presume such a characteristic could simplify hydrologic analyses, assuming consistent responses in drainage and infrastructure systems when catchment areas increase.  Figure 4 explores that response in the context of wastewater collections systems and rainfall derived inflow and infiltration (RDII) rates in the Region of York.  While the authors’ parallel paper on RDII provides full background to the analyses, Figure 4 shows how 100-Year RDII flow rates, normalized relative to the highest rates observed, decline as the catchment area increases.  In comparison to 2024 storm ARF decline, the decline in RDII is more accelerated with expanding area.  While it is beyond the scope of this paper to explore this phenomenon further, factors such as the spatial and temporal variability of rainfall across the catchment, and hydraulic routing in the system could explain some of the decline.  Another explanation could be that the 100-year RDII values are derived based on a data series including small, localized storms with higher areal decline than represented in the large 2024 storms or the WMO guidance.

  

 

FIGURE 4 – AREAL REDUCTION FACTORS FOR 2024 GTA STORMS AND RDII REDUCTION (100-YEAR STORM RESPONSE)

 EXPANDING BULL’S EYE EFFECT - GTA EXAMPLE

      The previous sections illustrate that based on Environment Canada’s data significant rainfall events with large maximum depths and extensive areas have been occurring regularly in Ontario for over 100 years.  Over the past 25 years the impact of significant rainfall events on urban systems resulting in extensive property damage has been highlighted by municipalities and by the insurance industry more recently.  The House of Commons Standing Committee on Environment and Sustainable Development (2025) initiated a study in recognition of high 2024 catastrophic weather insurance losses and the need to protect Canadians.

      The increase in flood damages since the early 1980’s has been reviewed by Natural Resources Canada (NRCan) in the report Canada in a Changing Climate: National Issues (2021).  NRCan reported that: 

“Costs associated with damage from extreme weather events in Canada are significant and rising, largely due to growing exposure and increasing asset values. The scale of costs suggests that households, communities, businesses and infrastructure are not sufficiently adapted to current climate conditions and variability.”

      National Research Council’s flood cost-benefit guideline (2021) supported the above and demonstrated that observed losses across Canadian provinces was proportional to the proportion to growth and asset (e.g., GDP, population, dwelling counts and sewer length).  

      Growing exposure has been explained by the Expanding Bull’s Eye Effect (Stradler and Walker, 2015) where weather hazards are equivalent to arrows and assets are the target with a bull’s eye in the middle.  Stradler and Walker illustrate how growth in the size of the bull’s eye, e.g., represented by expanded urbanized land use, increases the exposure to hazards such as tornados or hurricanes.  They echoed NRCan’s observation stating: 

“… disaster frequency and magnitude increases may be, at least in part, attributable to the surge in people and their assets exposed to hazards, not necessarily due to changes in the climatology of hazard events themselves. That is, growing population, developed landscapes, and wealth are likely important factors in the increasing trends in disaster counts and impacts.” 

     The increase in the size of an asset “target” can be illustrated in the growth of the City of Toronto over the past 160 years.  Historical land use maps and building construction dates were used to estimate development limits in 1865, 1905, and 1945 relative to today’s development.  Figure 5 maps the August 17, 2024 storm ‘arrow’ relative to the historical and current development bull’s eyes.  As the asset bull’s eye has clearly expanded over time, the 2024 storm is expected to cause greater damage in 2024 than in 1945 or 1965 when exposure was limited with the much smaller bull’s eyes.  

 FIGURE 5 – EXPANDING BULL’S EYE EFFECT EXAMPLE – AUGUST 2024 STORM EXPOSURE OVER 1865, 1945 AND CURRENT GROWTH

CORRELATION OF INTENSITY TRENDS ACROSS VARIOUS DURATIONS, RETURN PERIODS AND AREAS

      Real storms have unique characteristics that make the creation of synthetic design storms challenging; only a select number of a natural storm characteristics can be represented in the design event.  This is due to significant variability across characteristics as shown in the AES storm charts above, all of which have unique return periods.  While there can be weak correlations across parameters (i.e., larger area storms generally have longer durations) there are more likely to be ‘bands’ of characteristics.  For Example, Figure 6 below shows the high variation in size of short and long duration 2-inch (50 mm storms), and the duration variation to 2-inch storms of the similar size. 

FIGURE 6 - ONTARIO SIGNIFICANT STORM AREA VS. STORM DURATION - 1919 to 1984

      Beyond the variability across significant storms shown above, Environment Canada data has shown that rainfall characteristics such as design intensity change over time as new climate data is considered.  A review of Engineering Climate Datasets shows that trends in IDF values are not consistent across different storm durations or return periods.  Figure 7 illustrates shifts in IDF values at 21 long-record southern Ontario when 10 more years of data were added to the v2.0 dataset.  

Chart illustrates the average ratio of Engineering Climate Dataset intensities in v3.1 dataset (records up to 2017) with intensities in the v2.0 dataset (records up to 2007) for 21 climate stations.

FIGURE 7 – AVERAGE SOUTHERN ONTARIO IDF TRENDS ACROSS DURATIONS AND RETURN PERIODS

      As shown above, 2-year rainfall intensities (green symbols) for various durations may increase or decrease while 100-year intensities for almost all durations decrease.  This indicates that the ‘extremes’ are not changing in step with the ‘means’ - this has important implications when extrapolating trends in annual maximum series (AMS) to expected trends in rare severe rainfall responsible for flood impacts (see next section).  It also has important implications when interpreting climate model projections that assess daily ‘heavy precipitation’ statistics but cannot accurately assess short-duration extreme precipitation linked to urban flooding.

      Similar analysis for 29 northern Ontario climate stations in Figure 8 below shows increases in long-duration extremes (50-year and 100-year), unlike southern Ontario where those intensities decreased.  Northern Ontario 1-hour extremes decreased while southern Ontario value increased.  One consistent trend is that the shortest duration 5-minute to 15-minute extremes decreased in both regions.  Other regions in Canada can show completely oppose trends to Ontario, e.g., Alberta showing increases in short duration extremes and decreases in all moderate and long duration intensities (i.e., 2-year to 100-year, 1-hour to 24-hour) (Muir, 2020).

FIGURE 8 – AVERAGE NORTHERN ONTARIO IDF TRENDS ACROSS DURATIONS AND RETURN PERIODS

      Broaden to extremes across all of Canada, Figure 8 shows how short and moderate duration 100-year IDF intensities changed across Canada for 226 long-record climate stations.  The two Ontario IDF charts above average station IDF data, obscuring the variability in the underlying data.  In contrast the Canada-wide Figure below shows individual station data to highlight variability.  Figure 9 clearly shows that changes in short and moderate duration intensities are not correlated, with any combination of increases and decreases in the data.

 

Chart illustrates the ratio of Engineering Climate Dataset intensities in v3.1 dataset (records up to 2017) with intensities in the v2.0 dataset (records up to 2007) for 229 climate stations.

FIGURE 9 – DIVERGING SHORT AND MODERATE DURATION 100-YEAR IDF TRENDS ACROSS CANADA

 ECCC ANNUAL SERIES AND REGIONAL TRENDS

      ECCC has completed research on 5-minute to 24-hour annual maximum rainfall in Canada using data from 1950 to 2021 (Cannon et al. 2024).  While only annual maxima were analyzed, the introduction references intense rainfall related to flood impacts: 

“Intense short-duration rain can cause flooding and the failure of buildings, roads, bridges, and other infrastructure, which, in turn, threatens public health and safety. Furthermore, flooding, primarily in residential basements, accounted for more than CAD 1 billion in average annual insurable property damage claims in Canada for the period 2009–21, which is substantially more than CAD 125–250 million in inflation and wealth adjusted losses recorded for 1983–2008 (Bakos et al. 2022).” 

     The introduction also notes that “engineers use statistics that summarize the intensity, duration, and frequency of sub hourly to daily rainfall extremes to design buildings and infrastructure” however the paper does not analyze any of the statistics that engineers use to characterize intense design rainfall.  Annual maximum rainfall observations in the research can characterize “heavy precipitation” from a broad climate perspective.  For example, the Intergovernmental Panel on Climate Change (IPCC, 2021) Sixth Assessment Report characterizes heavy precipitation based on “average annual maximum precipitation amount in a day (Rx1day)”.  The expected annual maximum daily rainfall amount in Toronto is 47 mm based on ECCC’s v3.3 dataset which is not sufficient to cause flooding in any modern municipal infrastructure system, nor widespread flooding in most historical systems[1]. 

     To offer clarity on the distinction between changes in extremes and averages, IPCC has published answers to Frequently Asked Questions for Assessment Report 6 (AR6) specifically “FAQ 11.1 | How Do Changes In Climate Extremes Compare With Changes In Climate Averages?” such as (our bold): 

“Climate change will also manifest differently for different weather regimes and can lead to contrasting changes in average and extreme conditions.”

 “At the local scale, average and extreme surface temperature changes are strongly related, while average and extreme precipitation changes are often weakly related. For both variables, the changes in average and extreme conditions vary strongly across different places due to the effect of local and regional processes.”

      The ECCC 2024 research found the following for the Great Lakes/St Lawrence Basin region that would encompass the majority of urban centres in Ontario:

     i)    there are statistically significant increasing trends in annual maximum rainfall in 5 of 11 regions             of Canada, for one or more durations,

    ii)     5 of 11 regions including Great Lakes/St Lawrence Basin do not have statistically significant                 increases in annual maximum rainfall for any durations;

    iii)    with the exception of southern Alberta, provincial and territorial regions showed statistically                 significant positive scaling for one or more durations rates (i.e., rainfall increased with higher                 summer dewpoint temperatures).

      While some may interpret heavy precipitation trends as indicative of changes that could explain increased flooding, ECCC clarifies that: 

“Results here apply only to the time and temperature dependence of average annual maximum rainfall. Observational evidence of changes in longer return period rainfall events would require a different statistical methodology …” 

EXPANDING ARROW EFFECT – A BULL’S EYE EVERY TIME

      ECCC’s 2024 paper notes that the detectability of significant trends can increase with wither the aggregation of regional data, or the use of longer records. 

“… detectability of significant trends at individual stations was poor but improved as data were aggregated to larger regions. In an update to the analysis of daily extreme precipitation by Westra et al. (2013), Sun et al. (2021) found that the use of longer records led to improvements in the ability to detect the intensification of extreme precipitation.” 

     In the authors’ 2025 WEA conference paper adopted the longer record approach to assess Toronto-area trends. It used ECCC daily rainfall records dating back to 1840 to assess changes in extreme rainfall statistics such as 100-year design intensities.  This record was 100 years longer than the data used in ECCC’s Toronto City gauge (Station ID 6158355) IDF analysis.

      ECCC’s 2024 paper adopted both the longer record and regionalization approaches, considering 15 more years in the rainfall data record and assessing trends in large regions.  The Great Lakes/St Lawrence Basin Region area adopted is approximately 1000 km long (Windsor, Ontario to Quebec City) and over 150 km wide, representing an area of about 150,000 km².  The AES index data presented earlier indicates that significant storms generally cover only a fraction of that very large region.  Consequently, regional data may not be representative of local conditions. 

     Regionalization of rainfall data did not result in increases in annual maximum rainfall in the Great Lakes/St Lawrence Basin Region.  However, there is a risk that pooling data over a large area could result in an “Expanding Arrow Effect” whereby localized increases are extrapolated across areas without such changes.  While there is merit in a precautionary approach and the incorporation of safety factors in design to account for future changes and uncertainties, the Expanding Arrow Effect could overstate risks and result in costly infrastructure overdesign. 

MICRO-REGION IDF VARIATIONS COMPARED TO LONG-TERM IDF

      Regions with multiple rain gauges will intrinsically have variability in observed rainfall and derived IDF statistics at each gauge.  This is due to the localized nature of convective storm peak intensities, as illustrated in the review of 2024 storms.  Across the City of Markham, 18 local gauges have been collecting local rainfall data as early as 2008.  Figure 10 shows the maximum and minimum 2-year IDF values across these gauges, compared to the Markham 2-year standard, the 5-year standard and the 95% confidence bands at the long-term Buttonville Airport gauge (Station ID 615HMAK).  The local IDF data up to the end of 2024 is included. 

FIGURE 10 – MARKHAM LOCAL RAIN GAUGE 2-YEAR IDF VS LONG-TERM STANDARDS

      The Markham 2-year standard, based on long-term City of Toronto data, falls within the maximum and minimum values of all the local gauges.  The long-term Buttonville Airport 95% confidence bands generally fall within the maximum and minimum local values.  It should be noted that the period of record for the Buttonville Airport gauge in ECCC’s v3.3 Engineering Climate Dataset extends from 1986 to 2016.  Given that some significant local storms occurred in 2017 and 2024 and would not have been considered in the Buttonville data, it is not unexpected that the minimum local Markham gauge values may be slightly above the Buttonville lower confidence limit for some durations.

       Figure 10 shows that the maximum 2-year statistics approached and slightly exceeded the Markham 5-year standard design intensity (i.e., the 6-hour duration).  The maximum short duration intensities for periods of 2 hours and lower fell below the 5-year standard. For the shortest durations, the maximum local values approached the city standard 2-year value. The Markham local IDF data illustrates that additional data introduces additional variability even across a relatively small ‘region’.  While maximum values exceed standard values at specific gauge points, the average values of the longest-period gauges were below the Markham standard for 5-to-30-minute durations, and close to the Markham standard for 1-to-6-hour durations.  Recognizing that the maximum rainfall depths are not sustained over wide catchments, but instead lessen over wider areas, the Markham standard IDF values are considered to be reasonable.  That is the Markham standard is similar to the average of the long-term local gauge values. 

     Over time local rainfall data may be used to review low return period values considered in design, e.g., beyond the 2-year values above.  However, given that the Markham standard is based on Toronto data extending back to 1940, it may be several decades before local data can be used to assess infrequent storm design intensities.  The above example of variability of IDF values across a small region should be considered when conducting regionalized rainfall analysis.  For example, any increase in values observed locally across a region may not be applicable across large catchments that will experience averaged values as opposed to observed peaks at gauge points.     

CONCLUSIONS 

     This paper demonstrates that extreme rainfall events in Ontario exhibit substantial variability across depth, duration, and spatial extent, with wide scatter in storm characteristics observed in both historical and recent data. The long-term AES storm index shows that large, high-depth storms with extensive areal coverage have occurred regularly for more than a century, and that neither the frequency nor the spatial scale of these significant storms has increased in a systematic way over time. Recent damaging storms in southern Ontario fall well within the historical envelope of observed storm sizes and depths, reinforcing the conclusions that extreme rainfall is not a new phenomenon in the region.

      The analysis highlights that spatial scale is a dominant factor in interpreting rainfall data and translating it into infrastructure response. Point-based IDF statistics characterize local peak intensities but do not represent the spatially average rainfall that governs watershed and urban drainage system performance. Historical storm data and recent observed events consistently show that peak rainfall intensities attenuate with increasing area, often well beyond the ranges addressed in commonly cited design guidance. Comparison of observed Ontario storm ARFs with WMO design curves indicates that real storms can sustain high rainfall over large areas in ways not fully captured by generalized guidance, showing the importance of using locally relevant data when assessing areal effects.

      The comparison between rainfall and areal reduction and observed RDII response further illustrates that system behaviour attenuates more rapidly than rainfall alone as contributing area increases. This divergence reflects the combined influence of spatial rainfall variability, temporal distribution, hydraulic routing, and system storage, and confirms that infrastructure response cannot be inferred directly from rainfall statistics without explicit consideration of scale and system characteristics. These findings show the need to avoid extrapolating small scale extremes to large catchments or systems without appropriate spatial adjustment.

     The paper also demonstrates that trends in rainfall intensity are not consistent across durations, return periods, regions, or spatial extents. Updates to Engineering Climate Datasets show that changes in frequent, long-duration rainfall intensities are often weakly correlated with changes in rare, short-duration extremes that drive urban flooding. Regional analyses across Canada further reveal that increases and decreases in extreme intensities occur in various combinations, even within the same region. This lack of coherence cautions against assuming that trends in “heavy precipitation,” as characterized by annual maximum series or daily extremes, are indicative of changes in flood-causing design storms. The increase in reported flood damages over recent decades is more consistently explained by growing exposure than by demonstrable changes in storm characteristics. The Expanding Bull’s Eye Effect provides a useful framework to interpret this pattern, whereby urban growth and increased asset density expand the target exposed to historically recurring storms. Conversely, the aggregation of rainfall data over very large regions introduces the risk of an Expanding Arrow Effect, in which localized changes are generalized across areas that do not experience the same conditions. While regionalization and long records improve statistical detectability of trends, they may also obscure the localized variability that governs infrastructure response at the scale of sewersheds, watersheds, and urban catchments. 

     Local gauge analysis within the City of Markham illustrates how additional data increases observed variability rather than converging toward a single design value. While localized peaks can exceed standard design intensities at individual gauges, spatial averaging across catchments results in responses that are more consistent with long-term regional standards. This reinforces the appropriateness of design values derived from long records, while highlighting the need for caution when interpreting short record local extremes or regionalized trend results.    

    Overall, the findings demonstrate that no single storm, statistic, or regional trend can adequately represent the range of rainfall conditions relevant to infrastructure and watershed flood response. Deterministic design storms remain a practical and necessary engineering tool, but their limitations must be understood, particularly with respect to spatial representation and scale dependence. As rainfall monitoring networks expand and datasets lengthen, there is an increasing opportunity to supplement deterministic approaches with probabilistic frameworks that explicitly account for variability, uncertainty, and spatial averaging. Continued reliance on observed data, careful attention to scale, and clear distinction between exposure-driven risk and hazard change are essential to support robust flood resilience assessments and informed updates to design practice.

Bryan Karney, Ph.D, P.Eng., University of Toronto,

Christopher Zuccaro, M.A.Sc., University of Toronto,

Robert J. Muir, M.A.Sc., P.Eng., City of Markham

* This paper was first presented at the WEAO 2026 Technical Conference, London, Ontario on April 14, 2026.  Figure 3 has been revised.

BIBLIOGRAPHY

Cannon, A.J, Jeong, D-I, Yau, K-H. (2024).  Updated Observations Provide Stronger Evidence for Increases in Subhourly to Hourly Extreme Rainfall in Canada. Journal of Climate, Volume 37. DOI: 10.1175/JCLI-D-23-0501.1 https://journals.ametsoc.org/view/journals/clim/37/12/JCLI-D-23-0501.1.xml 

CBC News (2024).  Toronto just saw record rainfall. Why wasn't it more prepared? https://www.cbc.ca/news/canada/toronto/rainfall-flood-toronto-record-1.7266064 

Conference Board of Canada (2024). Bright Future, Ep. 40: Blair Feltmate on Adapting to our Changed Climate https://www.conferenceboard.ca/insights/blair-feltmate-on-adapting-to-our-changed-climate/ 

Environment Canada, Atmospheric Environment Branch (1988). An Index to Storm Rainfall in Canada by B.Routledge, D.Carr and W.Hogg. CLI-1-88 https://publications.gc.ca/site/eng/9.880154/publication.html

House of Commons Canada, Standing Committee on Environment and Sustainable Development (2025). Minutes of Proceedings, 45^th Parliament, 1^st Session https://www.ourcommons.ca/DocumentViewer/en/45-1/ENVI/meeting-6/minutes

 Intergovernmental Panel on Climate Change (2021). Sixth Assessment Report, Working Group 1: The Physical Science Basis, Chapter 11: Weather and Climate Extreme Events in a Changing Climate, 11.4 Heavy Precipitation/ 11.4.2 Observed Trends. https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11/

 Karney, B., Zuccaro, C., Muir, R., Parhizagari, Z., Zi, J. (2025) How Big is a 100-Year Storm? A Simple Definition with a Surprisingly Complex and Elusive Reality, Considering Both Climate Change Trends and the 2024 Storm.  2025 WEAO Technical Symposium, London, Ontario, April, 2025. Paper link:   https://drive.google.com/file/d/1-CIOu4NEHgo2EnTvKbJHPqeDl5My7XCG/view?usp=drive_link; presentation link: https://drive.google.com/file/d/1oTttyYKVqbj7f4xPeYzep2774EIbasge/view?usp=sharing

 Marsalek, J, W.E. Watt (1983), Environment Canada, National Water Research Institute, Design Storms for Urban Drainage Design. https://drive.google.com/file/d/1N4xc7qJAJv0ZhY24KDVOcSrLV0NbMn72/view?usp=sharing

Muir, R. (2020).  Can We Use Daily Rainfall Models To Predict Short Duration Trends? Not Always - Observed Daily and Short Duration Trends Can Diverge https://www.cityfloodmap.com/2020/07/can-we-use-daily-rainfall-models-to.html

 National Research Council of Canada (NRC) (2021) Guidelines on Undertaking a Comprehensive Analysis of Benefits, Costs and Uncertainties of Storm Drainage Infrastructure and Flood Control Infrastructure in a Changing Climate. Appendix D | Direct & Indirect Long Time Horizon Damages, Section 3.1 Insurance Bureau of Canada Catastrophic National and Regional Losses, Figure 4: Proportion of GDP, Population, Dwellings and Sewer Infrastructure as Indicators of Expected Annual Damages (Water Perils), by Province https://nrc-publications.canada.ca/eng/view/object/?id=27058e87-e928-4151-8946-b9e08b44d8f7

Strader, Stephen & Ashley, Walker. (2015). The Expanding Bull's Eye Effect. Weatherwise. 68. 10.1080/00431672.2015.1067108. https://www.researchgate.net/publication/281294791_The_Expanding_Bull's_Eye_Effect

---

[1] Flooding in localized areas could occur where system capacity is severely limited/undersized and if the daily rainfall occurs over an extremely short period.

How BIG is a 100-year storm? A simple definition with a surprisingly complex and elusive reality, considering both climate change trends and the 2024 storms

INTRODUCTION

      The concept of “likelihood” in applied hydrology plays numerous roles in the design and performance of stormwater, wastewater, and natural drainage systems.   Likelihood is typically represented in terms of a return period, an abstract representation of how often an event of at least a particular size is expected to occur on average over an extended time.   This might sound straightforward, but within this basic concept are a variety of difficulties of both perception and reality that reward some study and reflection, especially in the context of recent extreme rainfall events that highlight system capacity limitations.

     This paper explores how suggested changes in extreme rainfall return periods have occasionally been mischaracterized compared to measured long-term data trends in Environment and Climate Change Canada’s (ECCC’s) Engineering Climate Datasets.  Trends across Canada, southern Ontario, and the Greater Toronto Area (GTA) are reviewed, including clarifications following reviews by Canada’s Minister of the Environment and CBC Ombudsman offices.  Mischaracterizations may stem from faulty intuitive perceptions or cognitive biases based on limited available data, such as statements on the rarity of multiple extreme events that fail to recognize the proliferation of rainfall monitoring stations in the GTA. This paper statistically explores the rarity of such events, considering the areal extent of storms and the independence/dependence of adjacent gauges.  It also shares examples of often-overlooked historical great rainfalls and floods, based on newspaper archives dating back to 1878 and ECCC records to 1840.  These historical records, along with 2024 event data, are used to assess trends in Toronto’s 100-year rainfall.

      While the data does not suggest increases in extreme rainfall intensities in Toronto or southern Ontario, other factors such as antecedent moisture conditions (defined by multi-day rainfall) affect storm runoff and extraneous wastewater flow responses. Thus, multi-day trends in Toronto and the western Lake Ontario basin, based on long-term ECCC records that are typically over 150 years in length, are assessed.

     The spatial and temporal variability of 2024 extreme rainfall is analyzed using event total volumes and selected short-duration intensity data for several events.  Data for 153 gauges in York Region, Toronto and Peel Region were used to characterize areal reduction factors around peak gauges, comparing with design factors and assumed values in some local hydrologic analysis.  Rainfall atlas factors from the US mid-west and observed factors in August 2024 suggest that values assumed in local studies could be unconservative. Variations in rainfall uniformity across a City and Region are also evaluated, comparing convective storms, hurricane remnants, and spring storm characteristics, highlighting the complexity of ‘real’ storms.

     Lastly, while storm events are often characterized by rain gauge total event volumes and their return periods, these statistics do not align with the return period of system peak flows.  For wastewater systems in particular, peak flows are more closely correlated with short-durations intensities. It is proposed that short-duration rainfall metrics be considered a valuable supplement to total event characterizations.   

INTUITIVE PERCEPTIONS OF PROBABILITY VS. DATA TRENDS

     Following extreme rainfall events, media commentary often demonstrates common biases in estimating an event’s rarity or severity, usually without rigorous statistical analysis. Psychologist and Nobel laureate Daniel Kahneman identified various biases in his book Thinking Fast and Slow (2013), and local examples in the context of flooding and extreme rainfall have been described by others (Muir, 2018). Politicians across Canada have routinely declared trends in severe storms, for example:

 

i)                   Prime Minister Trudeau, after the 2017 Gatineau/Ottawa River flood, stated:

"The frequency of extreme weather events is increasing, and that's related to climate change. We're going to have to understand that bracing for a 100-year storm is maybe going to happen every 10 years. Or every few years."^[1]

ii)                 Toronto Mayor Tory stated to the CBC, following a federal grant for flood relief works:

"Toronto is experiencing more severe storms, with more rain falling over a short amount of time.”^[2]

     Requests for data on storm frequency and severity have supported these claims.  In 2019, Environment and Climate Change Canada Minister Catherine McKenna responded on behalf of the PMO, writing that "the observational record has not yet shown evidence of consistent changes in short-duration precipitation extremes across the country"^[3]. The General Manager of Toronto Water responded that the City would adjust its messaging to not describe actual storms: 

“We apologize for the delay in getting back to you with regards to your question about the statement on storms being 'more severe' and occurring over a 'short amount of time' and the City's messaging. We will utilize language that describes what can happen to City infrastructure during a rainfall rather than describing the actual rainstorm.”

(Personal communication, L. Di Gironimo to R. Muir, July 29, 2019) 

     To improve media messaging, the authors of this paper have highlighted these biases in media coverage through the Financial Post^[4] and in a ‘Backgrounder’ document for the CBC Ombudsmen, to assist CBC and Radio-Canada editors scrutinize reports on extreme weather and flooding. This CBC Backgrounder was intended to improve reporting accuracy, given several recent violations of the CBC’s Journalistic Standards and Practices in 2019 and 2020 regarding extreme rainfall trends^{[5] [6] [7]}.

     To contrast statements by political figures and the media, ECCC’s long-term datasets have been used to characterize national and local trends in extreme rainfall.  These trends are reported in the National Research Council of Canada (NRC) Guidelines on Undertaking a Comprehensive Analysis of Benefits, Costs and Uncertainties of Storm Drainage Infrastructure and Flood Control Infrastructure in a Changing Climate (2021). The findings align with Minister McKenna’s statement above, namely, that there is no overall increase in extremes at 226 climate stations across Canada when the last 10 years of data area added, in southern Ontario’s local IDF statistics when comparing 1990 IDF statistics with v3.1 Engineering Climate Datasets, or in local Toronto-area extreme rainfall IDF analyses.

     Media and layperson statements suggesting changes in extreme rainfall often arise from an ‘availability bias’, a cognitive short-cut defined by Kahneman whereby judgments rely on easily recalled information rather than comprehensive data. This tendency overlooks many historical flood events that reveal no clear upward trend. For example, Toronto’s significant rainfall and flooding events stretch back over 150 years, with newspaper archives documenting numerous “great floods”. FIGURE 1 compiles sample newspaper clippings from extreme events in 1878, 1897, and 1905. The “Great Flood”^[8] of 1878 resulted in the loss of 4 lives, or 1/20,000 of the population of about 80,000^[9], equivalent to a staggering 233 lives lost in 2024 based on today’s population.

FIGURE 1 – HISTORICAL FLOOD REPORTS PER NEWSPAPER ARCHIVES (LATE 1800’s AND EARLY 1900’s) 

 

Note: [Left] “The Great Rainstorm”, The Globe and Mail, Sept. 14, 1878; [Centre] “Torrents of Water”, The Globe and Mail, July 28, 1897, [Right] “Worst Storm of Year Sweeps Over Toronto”, The Toronto Star, August 15, 1905.

     Notwithstanding the actual data trends, following the July 16^th, 2024 extreme rainfall in the GTA, claims of increasing storm frequency and severity were widely reported. For example, a post-storm City of Toronto council motion stated: “As a result of climate change, Toronto is experiencing more frequent and severe storms, resulting in flooding events that impact our road and transit network, our homes and businesses, and our infrastructure.”^[10] A Freedom Of Information (FOI) request to the City of Toronto for documentation showing more frequent and/or more severe storms in Toronto indicated that “despite a thorough search, they [Toronto Water Division] were unable to locate any records responsive to your request[11].  The following sections analyze the probability of multiple extreme events and review southern Ontario trends, including the impact of 2024 extreme rainfall on Toronto-area IDF statistics.  This analysis encourages use of the more reflective “System 2” for analytical thinking, rather than the fast and intuitive “System 1” when assessing claims of increasing severity and frequency.

PROLIFERATION OF RAINFALL GAUGES AND THE PROBABILITY OF MULTIPLE EXTREME EVENTS

     Over the past 40 years, municipalities in Ontario have invested heavily in wastewater system monitoring and capacity to accommodate high extraneous flows during wet weather.  To support this work, continuous, permanent monitoring of rainfall rates and wastewater flow rates has proliferated across southern Ontario.  For example, the City of Toronto operated 17 rain gauges in 1982^[12], equivalent to one every 36 sq.km, similar to a 6 km grid.  By 2013^[13], the City operated 35 gauges. In late 2024^[14] Toronto operated 50 gauges, one every 12.4 sq.km, similar to a 3.5 km grid.  As a result of this higher density, more localized or short-lived storms are captured, leading to apparent increases in the frequency of “extreme” events that could otherwise pass between earlier, more coarsely-spaced gauges. 

    Across the GTA, other municipalities and regions have also increased the number of permanent rain gauges as well:

●       York Region increased their number of gauges from 10 before 2008, to 18 in 2008^[15], and 44 in 2024. The combined total number of Regional, lower-tier municipality and Conservation Authority gauges in 2024 is 72.  Lower-tier municipality, the City of Markham, increased from limited gauges before 2008, to 6 in 2008, and 13 in 2024^[16].

●       Peel Region increased from 8 before 2013, 28 in 2013, and 30 in 2024^[17]. Combined with others including the City of Mississauga, and Conservation Authorities, Peel Region has 64 rain gauges in 2024. 

    Combined, Toronto, Peel Region and York Region operate a total of 186 gauges in 2024, compared to half that number in earlier years pre-2008. A total of 34 Halton Region gauges were in operation during the August 2014 Burlington severe storm, according to Conservation Halton open data sources^[18]. This brings the total number of gauges in the GTA (except for Durham) to 220.       

    TABLE 1 summarizes the number of gauges across Toronto, Peel and York reporting over 86.3 mm of rainfall, equivalent to the York Region 24-hour 100-year rainfall totals, during three 2024 events. 

TABLE 1 – RAINFALL GAUGES WITH 100-YEAR RAINFALL VOLUME DURING JUNE, JULY AND AUGUST 2024 SEVERE STORM EVENTS

Region/Municipality	2024 Rainfall Event
	June 19	July 16	August 17
Toronto (50 gauges)[19]	0	3	12
York (61 gauges)	2	0	10
Peel (42 gauges)	0	8	3

     While 100-year rainfall totals were observed during each event, the extent of extreme totals varied considerably across the events with only 2 of 153 gauges reporting over 86.3 mm on June 19^th in York Region, compared to 25 of 153 gauges on August 17^th.  It is suggested that the size of dependent 100-year gauge clusters is between 2 and 25 gauges.  Accordingly, there may be a broad range of 7 to 93 independent samples among the 186 GTA gauges.

      The insurance industry has suggested (argued?) that the frequency of extreme rainfall events has increased due to climate change effects^[20].  However, many of these claims are based on theoretical as opposed to observed data (Muir, 2018). A 2019 City of Toronto Council Member Motion referenced the Insurance Bureau of Canada in stating that the GTA has had six 100-year storms from 2005 to 2018 and that there were “a direct result of climate” and sought to pursue legal option to recoup flood-related costs from GHG emitters.

      The risk of multiple extreme events over a given period can be estimated to assess whether six 100-year storms would be rare (or not) over 12 years.  Assumptions must first be made about how statistically independent or dependent rain gauges records are.  This affects the effective number of samples observed.  Lower risks would be estimated when most rain gauges are assumed to be independent, such that in each year of observation each of the gauge clusters provides a sample.  For small clusters of 2 gauges (e.g., June 2024 storm), the 186 GTA gauges above would result in 93 samples in each of the 12 years resulting in 1116 samples.  Higher risks would be estimated with larger clusters (e.g., August 2024 storm).  With a cluster size of 25 gauges, there are 186/25 x 12 = 89 samples.

      Using the online Stat Trek tool,^[21] probabilities of multiple 100-year events were calculated for various scenarios over 12 to 21 years. Scenario 1 and 2 are based on six 100-year storms over 12 years (2005 to 2018) in the GTA, as quoted by the IBC^[22].  A review indicates the reported storms include both small areal extent (e.g., Mississauga Aug. 4, 2009 and Toronto Aug. 7, 2018) and large areal extent storms (e.g., GTA Aug. 19 2005, July 8, 2013, and Aug. 4, 2014).  The 2017 event is believed to have included Orangeville, Ontario rainfall (June 22-23, 2017) or include the Lake Ontario high water level event.  Scenario 3 is based on 12 events across southern Ontario over 21 years (2004-2024), as presented in the Nov. 2024 CVC Stormwater & Climate Change Seminar. 

TABLE 2 – PROBABILITY OF MULTIPLE STORMS ACROSS THE GTA

Gauge Cluster Size	Number of Independent Clusters (= 220 / cluster size)	Scenario 1 a	Scenario 2 b	Scenario 3 c
2 (Jun. 2024)	110	99.1% d	N/A	N/A
11 (Jul. 2024)	20	3.5% (6 events) 9.5 % (5 GTA events) e	22.1% f	53.9% g
25 (Aug. 2024)	8.8	N/A	10.1% h	N/A

Notes:

^a Extent =  Small & Large; # Years = 12; # Events = 6 (2005, 2009, 2013, 2014, 2017, 2018)
^b Extent =  Large; # Years = 12; # Events = 4 (2005, 2013, 2014, 2024)
^c Extent =  Small & Large; # Years = 21; # Events = 12 (2004x2. 2005x2, 2009, 2013, 2014, 2013, 2017x2, 2018, 2024)
^d 110x12 = 1320 samples
^e Five events excl. 2017 Orangeville rainfall outside GTA; 20x12 = 240 samples
^f 20x12 = 240 samples
^g 20x21 = 420 GTA samples, factor by 5x for S.Ont. including Stoney Creek, Peterborough, Orangeville and Elmira = 2100
^h 8.8x20 = 176 samples

     The areal extent of the storm (small vs. large) critically influences overall probability. Multiple small-area storms, each recorded by only a couple of gauges, are much more likely than multiple small plus large-areal extent storms. And simply adding up small and large-area storms can distort the true probability of multiple extreme storms affecting the region at once. Observing six 100-year events over twelve years, including small extent storm, has a notably high probability of over 99% considering small gauge clusters in Scenario 1. Six moderate-size storms with cluster sizes of 11 gauges would have only a 3.5% chance of occurring – while this is very rare, the GTA did not experience six such events over the 2005-2018 period as one event occurred in Orangeville outside the GTA. The probability of five GTA events would be higher at almost 9.5%.

     Considering large areal extent storms, the risk of 4 events such as those in 2005, 2013, 2014 and 2024 would be in the range of 10.1% to 22.1% over 12 years (see Scenario 2). When the areal extent and number of gauges increases, the probability decreases such as in Scenario 3. The number of gauges has been factored up to represent the larger area of southern Ontario in which twelve 100-year events have been reported over 21 years (2004-2024). Assuming an average gauge cluster size of 11 to represent both the small and large areal extent storms, the probability of 12 100-year events over 21 years is quite high at over 50%.

     The probability should account for both event volume and its spatial coverage. As noted by Adams and Howard (1986) in their paper Design Storm Pathology: 

Obviously, a natural hydrological event containing many characteristics cannot be fully described by statistics of only one, or at most a few, of the characteristics of the natural event. 

     The challenge of characterizing the complexities of individual storms would support the Toronto Water position to “utilize language that describes what can happen to City infrastructure during a rainfall rather than describing the actual rainstorm”.

      To this end, this paper concludes by contrasting infrastructure responses compared to rainfall inputs. Yet, practitioners must make assumptions regarding the areal extent of design storms in hydrologic analyses and so later sections evaluate storm size and areal reduction curves considering 2024 events, local hydrology studies, and industry references that may implicitly account for the probability of storm areal extent.    

SOUTHERN ONTARIO IDF TRENDS 

     In the 2021 NRC Flood Cost Benefit Guideline, IDF statistics from 1990 were compared with those from ECCC’s v3.1 dataset (up to 2017), where no overall increase in extreme rainfall intensities for 21 southern Ontario stations were found. TABLE 3 updates that analysis to 2021 (v3.3), where results show minor or insignificant increases or decreases across various durations and return periods. 

TABLE 3 – AVERAGE CHANGE IN SOUTHERN ONTARIO IDF - ECCC IDF TABLES PRE V.1 DATA (TO 1990) VS. V3.30 DATA (TO 2021)

Duration	Return Period
	2-Year	5-Year	10-Year	25-Year	50-Year	100-Year	All
5 min	-2.1%	-1.6%	-1.6%	-1.6%	-1.4%	-1.4%	-1.6%
10 min	-0.1%	0.0%	0.0%	0.2%	0.2%	0.2%	0.1%
15 min	-0.2%	0.1%	0.3%	0.5%	0.6%	0.7%	0.3%
30 min	-0.1%	0.3%	0.5%	0.6%	0.7%	0.8%	0.5%
1 hr	0.0%	0.4%	0.5%	0.6%	0.8%	0.8%	0.5%
2 hrs	-1.3%	-0.9%	-0.8%	-0.7%	-0.5%	-0.5%	-0.8%
6 hrs	-1.5%	-1.4%	-1.5%	-1.5%	-1.5%	-1.5%	-1.5%
12 hrs	-1.1%	-0.4%	-0.2%	0.0%	0.2%	0.3%	-0.2%
24 hrs	-0.4%	-0.3%	-0.3%	-0.3%	-0.2%	-0.2%	-0.3%
Avg.	-0.8%	-0.4%	-0.3%	-0.2%	-0.1%	-0.1%	-0.3%

Stations: Sarnia Airport, Chatham WPCP, Delhi CS, Port Colborne, Ridgetown RCS, St Catharine’s Airport, St. Thomas WPCP, Windsor Airport, Brantford MOE/Airport, Fergus Shand Dam, Guelph Turfgrass CS, London CS, Mount Forest (Aut), Stratford WWTP, Waterloo Wellington Airport, Bowmanville Mostert, Hamilton Airport, Hamilton RBG CS, Oshawa WPCP, Toronto City, Toronto International Airport (Pearson).                                                                                                                                                       

    Overall, these minor changes are neither statistically nor practically significant to design. The analysis considers 1026 station-years of data with an average record period of 49 years.     

TORONTO-AREA IDF TRENDS           

    Toronto-area IDF trends can be further explored considering the effect of the July 16 and August 17, 2024 storms.  ECCC’s v3.3 datasets (to 2021) for the long-term stations Toronto City (i.e., “Bloor Street” ID 6158355) and Toronto Intl. Airport (i.e., Pearson Airport, ID 6158731) indicate 100-year 24-hour rainfall volumes of 97.3 mm and 117.3 mm, respectively. Adding subsequent years maxima and the July 2024 rainfall of 83.6 mm at Toronto City^[23] and August 2024 rainfall of 128.3 mm^[24] at Pearson, the 100-year 24-hour rainfall increases to 98.4 mm and 122.2 mm. While the Pearson A. statistic has increased, it is virtually unchanged from the 1990 statistic (121.5 mm). The Toronto City statistic is 2.6% above the 1990 value (95.9 mm). TABLE 4 summarizes these values.  These minimal changes are consistent with municipal studies in southern Ontario that have also found limited changes in local design intensities^[25]. 

TABLE 4 – TORONTO CITY AND PEARSON AIRPORT 100-YEAR 24-HOUR RAINFALL STATISTICS

Record Period	Pearson A.	Ratio to 1990	Toronto City	Ratio to 1990
1990 (Pre v1)	121.5 mm	100.0 %	95.9 mm	100.0 %
ECCC v3.3 (to 2017/2021)	117.3 mm	96.5 %	97.3 mm	101.5 %
ECCC v3.3 extended to 2024 storms	122.2mm	100.6 %	98.4 mm	102.6 %
ECCC 1-30 Day IDF (1840-1910)	N/A	N/A	102.9 mm	107.3 %

      These findings suggest that previous, long-term Toronto rainfall intensities were higher than current values, but that the changes are insignificant relative to their underlying uncertainty. For example, the confidence intervals reported by ECCC for these 100-year statistics are typically large (Toronto City 95% confidence limits is +- 0.6 mm/hr, equivalent to +- 14.4 mm per day). This 28.8 mm confidence band is much wider than the changes in mean statistic values over time and suggests that designers should be as focused on core uncertainty in rainfall statistics as any changes in statistics (e.g., non-stationarity due to climate effects over time). 

   Toronto short-duration rainfall statistics above include 5-minute to 24-hour values and consider data as early as 1940 for Toronto City and 1950 for Pearson A. stations. ECCC’s 1–30-day IDF dataset extends significantly further back to 1840 and have been used in ECCC analyses (Pollock, 1974).  This Toronto data has been analyzed to determine a long-term 100-year 24-hour rainfall statistic, using the same Gumbel extreme value distribution used for the short-duration datasets. FIGURE 2 illustrates this long-term series. 

     The statistic for the early Toronto City record of 1840 to 1910 is 102.9 mm, which is 4.6% higher than the v3.3 short duration dataset with 2022-2024 added (i.e., 1940-2024).  This can be explained by high rainfall events in 1843, 1878, 1897 and 1905.  

     The changes in southern Ontario and Toronto-area statistics do not support the many strong statements by political figures and the media that have claimed increases in storm intensity.  Those statements are likely based on System 1 thinking and limited analysis, and can demonstrate an availability bias with a lack of awareness of many earlier extreme events noted previously. Long-term trends in southern Ontario daily maximum rainfall have mixed results based on ECCC data^[26].

 

FIGURE 2 - TORONTO CITY MAXIMUM 1-DAILY RAINFALL - COMBINED ECCC 1-30 DAY, v3.3 IDF DATASETS & 2022-2024 DAILY MAXIMA 

   

TRENDS IN ANTECEDENT MOISTURE CONDITIONS

     Runoff and extraneous flow in wastewater systems are complex and depends not only on peak intensities but also on soil and system moisture before a storm.  The Soil Conservation Service (SCS) Curve Number (CN) Method accounts for this by using the total rainfall of the preceding five days (the Antecedent Moisture Condition, or AMC). According to this approach: 

·         AMC I (dry): Less than 35 mm in the previous five days.

·         AMC II (normal): Between 35 and 53 mm.

·         AMC III (wet): Over 53 mm. 

     In Ontario, ‘return-period’ convective storms usually assume AMC II conditions, while historical storms like Hurricane Hazel use AMC III because of high preceding rainfall. 

    The ECCC 1-30 Day IDF annual series may also be used to assess trends in multi-day rainfall in southern Ontario. FIGURE 3 illustrates a 5-day maximum rainfall trend for Toronto from 1840 to 2002. It is noted that recent years’ data up to 2016 have been omitted due to incomplete/partial ECCC data^[27]. 

FIGURE 3 – TORONTO CITY 5-DAY MAXIMUM RAINFALL (ECCC 1-30 DAY IDF DATASET 1840-2002)

     The data shows a slight decrease in 5-day rainfall over 162-years, with some of the highest totals in the 1800’s (1841, 1843, 1878, 1894 and 1897). 

     Outside of Toronto, records for Woodstock, Welland, and Belleville indicate slight increases in multi-day totals.  For example, Welland’s 10-day rainfall has increased by 0.16 mm per year since 1872.  York Region flow monitoring shows that the 10-day antecedent rainfall can affect Rainfall Derived Inflow and Infiltration (RDII) in the wastewater system.  Over a 20-year planning horizon, a Welland-type increase of 3.2 mm in 10-day rainfall (i.e., from 100.3 mm today to 103.5 mm in 2045) could raise RDII volumes by about 3%^[28].  In regions with past and projected increases this factor could be considered in wastewater system planning, including as part of sensitivity analysis on system response. 

    In practice, the key take-away for hydrologic modelers and infrastructure designers for surface water systems is that absolute AMC values, rather than minor trends, matter most.  FIGURE 3 illustrates that the majority of years experienced AMC III (wet) conditions, not AMC II (normal) conditions applied in return-period storm hydrologic modelling.  Given the more prevalent ‘wet’ AMC conditions, higher CN values and more conservative runoff potential could be considered in return-period event simulations, such as for the 100-year storm. 

SPATIAL VARIABILITY OF EXTREME RAINFALL

 The July 16^th, 2024 storm in Markham demonstrated notable spatial variability.  TABLE 5 (adapted from AECOM’s York Region summary) illustrates how rainfall intensity return periods varied across 18 Markham gauges for various durations. 

TABLE 5 – MARKHAM RAIN GAUGE RETURN PERIOD VARIABILITY ON JULY 16, 2024

Site ID	Rainfall Intensity by Duration
	5 min	10 min	15 min	30 min	1 hr	2 hrs	6 hrs	12 hrs	24 hrs	Total Rain
R-MUN-MA-05	30	30	25	19.5	17.5	13.5	4.96	2.5	1.25	30 mm
R-MUN-MA-07	36	32.4	28.8	23.6	18.2	14	5.03	2.53	1.27	30.4 mm
R-MUN-MA-08	69	55.5	47	46.5	33.5	23.1	9.08	4.56	2.28	54.75 mm
R-MUN-MA-09	40.8	28.8	24.8	20.8	15	10.1	3.5	1.75	0.88	21 mm
R-MUN-MA-11	96	88.8	71.2	54.8	35.4	22.1	8.3	4.18	2.09	50.2 mm
R-MUN-MA-12	60	60	53.6	37.6	30.2	17.3	6.3	3.23	1.62	38.8 mm
R-MUN-MA-13	98.4	72	63.2	54.8	39.8	23	8.2	4.1	2.05	49.2 mm
R-MUN-MA-14	111	102	88	64.5	42.8	25.12	8.83	4.42	2.21	53 mm
R-MUN-MA-15	63	55.5	49	45	32.8	21.75	7.88	3.96	1.98	47.5 mm
R-MUN-MA-16	66	63	56	40	29	17.75	6.21	3.1	1.55	37.25 mm
R-MUN-MA-18	26.4	24	19.2	15.6	12.8	8.6	3.03	1.53	0.77	18.4 mm
R-MUN-MA-21	90	85.5	82	73	53.8	33.38	12.8	6.44	3.22	77.25 mm
R-MUN-MA-22	21.6	14.4	14.4	14	11.6	7.9	3.17	1.63	0.82	19.6 mm
R-TR-MA-06	105.6	90	80.8	70.8	44.6	25.4	8.97	4.5	2.26	54.2 mm
R-YR-MA-01	69.6	57.6	48	46.4	33.2	23.4	8.93	4.53	2.27	54.4 mm
R-YR-MA-03	64.8	56.4	52.8	44.4	33.4	22.1	8.6	4.37	2.18	52.4 mm
R-YR-MA-19	86.4	79.2	68	54.4	36.8	23.6	8.57	4.33	2.17	52 mm
R-YR-MA-20	100.8	81.6	70.4	59.6	40.6	23.4	8.37	4.3	2.15	51.6 mm
Return Period	Buttonville Airport IDF Intensities
2 Year Event	106.2	74.5	60.2	36.7	21.2	11.8	5.5	3.2	1.8
5 Year Event	136.4	95.6	77.8	51.2	31	16.6	7.2	4.1	2.3
10 Year Event	156.4	109.7	89.5	60.8	37.5	19.8	8.4	4.8	2.6
25 Year Event	181.7	127.4	104.3	72.9	45.6	23.7	9.8	5.6	3
50 Year Event	200.4	140.5	115.2	81.9	51.7	26.7	10.9	6.2	3.3
100 Year Event	219	153.6	126.1	90.8	57.7	29.6	12	6.7	3.6
Return Period	Number of Exceedances									% of all stats
2 Year Event	0	1	5	6	2	2	2	2	10	19%
5 Year Event	0	0	2	4	6	2	4	9	0	17%
10 Year Event	0	0	0	2	4	8	6	0	0	12%
25 Year Event	0	0	0	1	0	2	0	0	1	2.5%
50 Year Event	0	0	0	0	1	0	0	1	0	1.2%
100 Year Event	0	0	0	0	0	1	1	0	0	1.2%
Key:		more prevalent return period for rainfall duration

    

FIGURE 5 maps the spatial variability of total rainfall volumes and categorizes gauge intensities based on their highest return period.  

FIGURE 5 – MARKHAM RAIN GAUGE MAXIMUM INTENSITY RETURN PERIOD AND TOTAL EVENT VOLUME JULY 17, 2024 

     Although this event was described as “100-year”, only 1.2% of all gauge-duration statistics exceeded the 100-year threshold. Specifically, only the 2 and 6-hour intensities at R-MUN-MA-21 exceeded the Buttonville Airport’s 100-year IDF statistics.  Most prevalent return periods ranged from 2 to 10 years.  Shorter-duration intensities (less than 1 hour), which govern local storm sewer performance, generally matched 2 to 25-year return period intensities.  The response of the wastewater collection system to this and other events is described in a subsequent section, comparing peak flow return periods to average and maximum rainfall return periods. 

TEMPORAL VARIABILITY OF EXTREME RAINFALL FOR DIFFERENT STORM TYPES

     While the previous section illustrates spatial variability over an entire storm, this section focuses on temporal changes in intensity.  FIGURE 6 compares two events in Markham: a convective storm on August 17^th, 2024 and remnants of Hurricane Beryl on July 10^th, 2024.  The chart on the right shows highly variable accumulations across gauges for the convective event (coefficient of variation, or COV, of 0.41), while the chart on the left shows a more consistent accumulation for the hurricane remnant (COV of 0.11).  The temporal lags between the highest intensities, suggest movement of highest intensity cells across the city. 

FIGURE 6 – TEMPORAL VARIABILITY OF CONVECTIVE AND HURRICANE STORM TYPES IN MARKHAM IN 2024   

     Looking more broadly across York Region, the August convective storm generally displayed the highest COV, averaging 0.51 across 69 gauges, whereas the July hurricane remnant was more uniform (average COV of 0.18). A “spring” event on April 2, 2024 had intermediate variability (average COV of 0.30).  

     When IDF statistics are derived from Annual Maximum Series (AMS), the type of storm is not considered. That is, rainfall data resulting from convective storms (e.g., August 2005 and July 2013 Toronto events) is combined with rainfall data resulting from hurricane-type events (e.g., October 1954).  As these events have different origins and patterns, separate series and statistics could be considered.  Others have noted the importance of considering seasonal extreme rainfall statistics that may be explained by the variable storm types and seasonal factors.  For instance, Dickinson (1976) noted “The implications of this seasonal variability are significant for the estimation of flood peaks and their frequency of occurrence.”  Future research could assess the joint probability of factors like seasonal intensity variations and AMC conditions, particularly if rising AMC (wet) intervals align with higher storm intensities. 

AREAL REDUCTION FOR 2024 CONVECTIVE STORMS

     To characterize the areal extent of 2024 convective storms, rainfall event volumes were compiled from York Region, Peel Region and Toronto gauges.  Peel Region data includes data for gauges operated by the Region and the City of Mississauga. York Region data includes Region-, municipality- and Conservation Authority-operated gauge data.  FIGURES 7A, 7B and 7C illustrate total event volume surface for three major storms in June, July and August. 

FIGURE 7A – JUNE 19, 2024 GTA RAINFALL DISTRIBUTION

 FIGURE 7B – JULY 16, 2024 GTA RAINFALL DISTRIBUTION

 FIGURE 7C – AUGUST 17, 2024 GTA RAINFALL DISTRIBUTION

     Areal reduction curves were then derived for circular areas around each peak gauge: 96.8 mm at Markham R-MUN-MA-16 (June 19^th), 104.2 mm at Peel Region RG 07 (July 16^th), and 128 mm at Toronto RG-046^[29] (August 17^th).  FIGURE 8 shows how average rainfall drops relative to the peak gauge volume as the area expands.

     FIGURE 8 illustrates a wide variation in areal reduction around the peak gauge, consistent with the 100-year gauge counts noted earlier.  The June event (only two gauges over 100-year totals) had a steep drop-off in mean areal rain, whereas the more widespread August event maintained higher volumes over a larger area.  Mean rainfall for selected TRCA and CVC watersheds is also shown, illustrating how watershed size can affect average totals.  Despite that trend the mean Mimico Creek July rainfall was high relative to the maximum rainfall in that watershed, despite its moderate watershed size. 

FIGURE 8 – AREAL REDUCTION FOR THREE 2024 GTA EXTREME RAINFALL EVENTS COMPARED TO DESIGN GUIDANCE

 

    Design areal reduction factors have been added FIGURE 8 including the 1-to-24-hour ranges reported by the WMO and by NWS/NOAA in the US mid-west (Huff and Angel, 1992).  The WMO values are one source of factors for return period storms recommend by OMNR (2002).  While OMNR prescribes factors for historical storms (Hurricane Hazel and Timmins Storm) based on observed characteristics, on thunderstorms in small areas in notes data limitations: 

“When analyzing thunderstorm type rainfall in urban areas, storm distribution becomes an important factor. Variation in rainfall intensity over small areas, such as 5 km², can be significant. Unfortunately, due to lack of rainfall data, no guidelines are available on storm distribution within small urban areas.” 

    The WMO factors have been applied in the GTA such as the Don River Hydrology Update (AECOM, 2018) applying the 1-hour reduction factor to the 12-hour design storm.  The August event factor observed around the Toronto RG-046 gauge, and July event Mimico Creek factor would appear to exceed the Don Watershed study design values.  The Mimico Creek July factor aligns with the WMO 24-hour factor, suggesting that high conservative factors can be realized in real storms.  As the Huff and Angel factors are based on over 600 observed storms and can exceed the long duration WMO factors cited by OMNR, practitioners should consider the possibility of large area extreme storms. The 2024 storm observations confirm that high rainfall volumes may be sustained over very small study areas (e.g., local wastewater collection sewersheds or small storm drainage areas).  For example, the Carolyn Creek July storm factor is 0.95 over an equivalent circular area of 13 sq.km^[30]. Larger wastewater collection areas, e.g., that may be 250 sq.km in size^[31], may have limited reduction factors over long durations based on the upper bound of the WMO and Huff-Angel 24-hour curves. 

OBSERVED FLOW RATE RETURN PERIOD COMPARED TO AVERAGE AND PEAK RAINFALL RETURN PERIOD

     Peak flows in Markham’s wastewater system were compared to design storm outputs from the City’s calibrated InfoWorks model. Four monitoring sites^[32] are near rain gauge MA-016 (June 19, 2024 event), and two sites^[33] are near gauges MA-01 and MA-07 (August 17, 2024 event). These older Unionville and West Thornhill communities exhibited both high total volumes and periods of high intensity. 

    The return period of wastewater flow peaks at the four sites on June 19, 2024 was typically in the 5 to 10-year range, with one site recording less than a 2-year peak flow.  Even though the 2 and 24-hour intensities at MA-016 exceeded 100-year return periods, shorter durations (2 and 25-year return periods) actually governed the peak flows. So, while the storm event exhibited some 100-year characteristics, the critical characteristics that govern wastewater system response and performance risk were not nearly as unlikely. 

    Similarly, the return period of flows for the August 17, 2024 event were in the 2 to 10-year range.  While the MA-01 2 hour and MA-07 6-hour intensities both exceeded 100-year values, the shorter duration intensities (2 and 25-year return periods) were less severe and aligned with observed peak flows.  

   This discrepancy shows that long-duration metrics (i.e., total event volumes) do not always correlate with wastewater system flow peaks. In many cases, short-duration intensities are a more accurate indicator of potential infrastructure stress. Yet, storm mapping and news reports often highlight total volumes instead. 

CONCLUSIONS 

     Claims of changes in extreme storm severity and frequency are often inconsistent with observed and reported trends documented in ECCC’s Engineering Climate Datasets. For instance, v3.3 IDF files for southern Ontario do not show any overall increase in extreme rainfall intensities compared to pre-1990 values.  Even when considering data up to the 2024 extreme events, the Pearson Airport’s 24-hour, 100-year rainfall volume is only 0.6% higher than in 1990, and Toronto City’s 2.6% increase still remains 0.1% below the long-term value, including an additional 100 years of data from 1840 to 1940. 

    The probability of recording multiple extreme storms over multiple years in the GTA has increased with the growing number of municipal rainfall gauges.  With 220 gauges, observing six 100-year events in 12 years becomes statistically probably (over 99%) if only one gauge in the network is affected by a small-sized storm cell. However, observing four moderate-sized storms over that same period has a lower probability (around 22.1%) considering 11 affected gauges in each independent cluster.  This is consistent with ECCC’s reporting following the extreme May 16, 1974 storm, that a 4.2 inch or greater rainfall could be observed every two years in southern Ontario.  The areal extent of storms should therefore be considered an additional factor to assess the probability of events affecting large systems, which may be combined with the isolated peak volumes often used to characterize events and could be reflected in locally-derived areal-reduction factors for convective storms in urban areas. An assessment of 2024 extreme rainfall patterns suggests that areal reduction factors applied in practice can sometimes underestimate both these observed and literature reference values. 

    In several Markham catchments, wastewater system peak flows showed lower design return periods than event’s volume return periods (i.e., long-duration intensities).  These peaks aligned more closely with short-duration intensities, rather than 24-hour volumes. This suggests that the severity of storms should be more critically addressed for different systems considering more than the event volume (e.g., governing short duration intensities). 

    Extreme storms in the GTA in 2024 included both convective events, featuring high spatial and temporal variability, and remnants of tropical storms (hurricanes) with less variability.  These events have independent causes yet are combined in single annual series and IDF frequency analyses implying identical distributions, which is unlikely.  Modified IDF analysis excluding hurricane-type events from annual series should be evaluated.  This could assess how high daily hurricane-derived rainfall volumes affecting short duration design intensities when daily IDF volumes are used derive short duration intensities in traditional design hyetographs.    

     As desirable as simplifications are, design storms inherently simplify complex hydrologic processes, especially when dealing with varying antecedent moisture conditions or mixed storm populations. The lack of dedicated “urban hydrometric networks” historically justified relying on design storms that can be somewhat arbitrary (Marsalek and Watt). However, some jurisdictions, such as the City of Ottawa, have begun using frequency analyses of wastewater system RDII flows, reducing the need for design storms assumptions. This approach deserves continued consideration to overcome what could be an intractable challenge of defining the 100-year design storm. 

Bryan Karney, Ph.D, P.Eng., University of Toronto*
Christopher Zuccaro, B.A.S.c University of Toronto
Robert J. Muir, M.A.Sc., P.Eng., City of Markham
Zahra Parhizgari, M.Sc., PMP, P.Eng., City of Markham
Jack Zi, P.Eng., City of Markham

(originally presented at WEAO 2025 Technical Conference, London, Ontario, April, 2025, *corresponding author)

PRESENTATION

how-big-is-a-100-year-storm-a-simple-definition-with-a-surprisingly-complex-and-elusive-realityfrom Robert Muir

BIBLIOGRAPHY

Adams, B.J., Howard, C.D.D., (1986) Design Storm Pathology, Canadian Water Resources Journal, https://doi.org/10.4296/cwrj1103049

AECOM, (2018), Don River Hydrology Update, Toronto and Region Conservation Authority.

City of Ottawa (2008), Sanitary Sewer Extraneous Flow Analysis https://docs.google.com/document/d/0B9bXiDM6h5VianROT1EtV2c5UFU/edit?usp=sharing&ouid=115169455109461543967&resourcekey=0-sIt--IZF8kevusIkk76iMg&rtpof=true&sd=true

Dickinson, T. (1976), Season variability of rainfall extremes, Atmosphere, Volume 14, Number 4, https://doi.org/10.1080/00046973.1976.9648424

Huff, F.A., Angel, J.R. (1992) Rainfall Frequency Atlas of the Midwest, Bulletin 71, Midwestern Climate Centre (CAA, NWS, NOAA), Illinous State Water Survey. https://drive.google.com/file/d/1-jY16dLUu1Pi_9SDWd_kp8Sof3HMehSs/view?usp=sharing

Marsalek, J, W.E. Watt (1983), Environment Canada, National Water Research Institute, Design Storms for Urban Drainage Design. https://drive.google.com/file/d/1N4xc7qJAJv0ZhY24KDVOcSrLV0NbMn72/view?usp=sharing

Muir, R., (2018) Evidence Based Policy Gaps in Water Resources: Thinking Fast and Slow on Floods and Flow, Journal of Water Management Modeling, https://www.chijournal.org/C449

Ontario Ministry of Natural Resources, (2002), Technical Guide River & Stream Systems: Flooding Hazard Limit

Pollock, D.M, (1976), Environment Canada, Atmospheric Environment, The Rainstorm of May 16, 1974, In Southern Ontario. https://drive.google.com/file/d/1MRXf36wCUtA6ynzMl-3UMot4tNzVDM5T/view?usp=sharing

National Research Council of Canada (NRC) (2021) Guidelines on Undertaking a Comprehensive Analysis of Benefits, Costs and Uncertainties of Storm Drainage Infrastructure and Flood Control Infrastructure in a Changing Climate.

FOOTNOTES

---

[1] http://www.iheartradio.ca/cjad/news/was-climate-change-responsible-for-the-floods-1.2603124

[2] https://www.cbc.ca/news/canada/toronto/ottawa-announces-150m-for-flood-mitigation-across-gta-1.5071709#:~:text=The%20federal%20government%20on%20Tuesday,power%20outages%20following%20intense%20rainstorms

[3] https://www.cityfloodmap.com/2019/06/canadas-minister-of-environment-and.html

[4] https://financialpost.com/opinion/fast-flood-science-needs-slow-thinking

[5] https://www.cityfloodmap.com/2020/11/radio-canada-ombudsman-finds-standards.html

[6] https://www.cityfloodmap.com/2020/05/past-present-or-future-cbc-ombudsman.html

[7] https://www.cityfloodmap.com/2019/01/cbc-ombudsman-decision-finds-lack-of.html

[8] Keating Channel Flood Inquiry Report (Lorant, 1981) https://www.torontopubliclibrary.ca/detail.jsp?Entt=RDM209381&R=209381

[9] https://en.wikipedia.org/wiki/History_of_Toronto#:~:text=Toronto's%20population%20grew%20rapidly%20in,for%20a%20significantly%20higher%20population.

[10] https://www.toronto.ca/legdocs/mmis/2024/mm/bgrd/backgroundfile-247826.pdf

[11] City of Toronto Access Request Number FOI-2024-03265

[12] Toronto Open Data / Historic Rain Gauge Locations and Precipitation

https://open.toronto.ca/dataset/historic-rain-gauge-locations-and-precipitation/

[13] Toronto Open Data / Rain Gauge Locations and Water Collected (July 8, 2013 storm) https://open.toronto.ca/dataset/rain-gauge-locations-and-water-collected/

[14] Toronto Open Data / Rain Gauge Locations and Precipitation

https://open.toronto.ca/dataset/rain-gauge-locations-and-precipitation/

[15] York Regional and Municipal Inflow & Infiltration Assessment Reduction Assessment, Region of York, Rain Gauges Installation Report (AECOM, 2008)

[16] R. Muir, City of Markham, personal communication December, 2024

[17] M. Faye, Region of Peel, personal communication October, 2024

[18] Conservation Halton Open Data / Rainfall Rain Gauge 20140804 https://conservationhalton-camaps.opendata.arcgis.com/datasets/d17cc3ff48a940549a8f19c399e0939f_0/explore?location=43.449139%2C-79.868000%2C10.69

[19] Note that Toronto rainfall data reflects reported 3-hour total. Additional gauges may observe over 86.3 mm for longer durations.

[20] Insurance Bureau of Canada, Telling the Weather Story (2012), https://www.iclr.org/wp-content/uploads/PDFS/telling-the-weather-story.pdf

[21] https://stattrek.com/online-calculator/binomial.aspx

[22] August 4, 2009 Mississauga even had extreme rainfall for only one gauge, Station 6, per Cooksville Creek - August 2009 Flooding (EWRG, 2010) https://www.mississauga.ca/file/COM/Cooksville_Creek_Flooding_v03_Draft.pdf. August 7, 2018 Toronto extreme rainfall was recorded for only one Toronto gauge per City of Toronto Open Data https://open.toronto.ca/dataset/rain-gauge-locations-and-precipitation/  

[23] https://www.cbc.ca/news/canada/toronto/rainfall-flood-toronto-record-1.7266064

[24] August 17, 2024 per rainfallhttps://www.theweathernetwork.com/en/news/weather/severe/when-it-rains-it-pours-toronto-records-its-wettest-summer-on-record-ontario. Toronto City 2018-2021 rainfall added to series plus 2022 and 2023 trendline estimates.

[25] https://www.cityfloodmap.com/2018/03/extreme-rainfall-and-climate-change-in.html

[26] Welland, Belleville and daily maximum rainfall has increased by 2.7 mm, 2.2 mm and 6.9 mm per century based on data back to 1873, 1866 and 1870, respectively. Peterborough daily maximum has decreased by 5.9 mm per century based on data back to 1866.

[27] Incomplete/partial year data in the 1-30 Day dataset was replaced with short-duration IDF data for the previous 100-year statistical analysis.

[28] York Region RDII antecedent rainfall chart indicates approximate linear increase in RDII volume with antecedent rainfall totals grouped for total below 12.5 mm, 12.5-38.1mm and above 38.1 mm with a strong correlation between RDII volume storm volume for these groups of antecedent rainfall (i.e., reported R-squared values of 0.904, 0.8866 and 0.9622 respectively).

[29] Note – highest rainfall was 135 mm at Toronto RG-052, however RG-046 is situated more centrally within a higher rainfall area.

[30] Actual watershed area is 5.3 sq.km. Maximum rainfall in watershed was 102 mm while mean watershed rainfall was 97 mm.

[31] City of Toronto Ashbridge’s Bay Wastewater Treatment Plant approximate sewershed size is 25,000 ha (2021 Annual Report, www.toronto.ca)

[32] Monitor ID’s MA006a, MA006b_10, MA006c_2 and MA083_10

[33] Monitor ID’s MA044_10 and MA046_10