Toronto Extreme Rainfall Trends - 100-Year Daily Rainfall in Engineering Climate Datasets

Previous posts have reviewed trends in extreme rainfall across Canada, in various regions including southern Ontario, and in the Greater Toronto Area (GTA), including Toronto and Mississauga where long-term climate data is available for review.

Projections of future extreme rainfall increases are commonly made as part of climate change studies. A review of past trends in extreme rainfall was made in the 2021 National Research Council flooding cost benefit guidelines, as summarized in a previous post. The following chart was included in those guidelines and shows the trends in 100-year daily rainfall at two GTA climate stations in downtown Toronto and at Pearson International Airport in the adjacent municipality of Mississauga.

The chart shows the 100-year rainfall depth using data records up to 1990 and then adding more recent data up to 2017. The chart shows that the 100-year rainfall at Pearson Airport/Mississauga has been decreasing slightly when recent data is added after 1990. Meanwhile the Toronto rainfall has been increasing slightly (see dotted and dashed black lines on the chart above for the trends).

Several climate studies have projected that the 100-year daily rainfall would increase over coming decades as shown on the chart. The Toronto's Future Weather & Climate Driver Study by SENES projected a doubling of this rainfall statistic by 2040-2049, relative to a 2000-2009 baseline value (see the orange dashed line on the chart above, where the 2000-2009 value is shown at 2005 and the 2040-2049 value is shown at 2045).

Some additional data has been analyzed by Environment and Climate Change Canada for the Toronto climate station, now including data up to 2021. This allows the 100-year daily rainfall statistic to be updated with a few more years of data. The chart below shows the additional Toronto data point circled in yellow at 2021.

While the Toronto rainfall statistic up to 2017 was 97.5 mm, the value up to 2021 decreased slightly to 97.3 mm. The value up to 2017 reflected the prior July 8, 2013 extreme event, creating a jump after 2007 when the value was slightly lower at 94.7 mm. As more data is observed below the 2013 extreme, the statistic should continue to decrease as more data is added and analyzed.

The take-away? Observational data, including data up to 2021, does not support the projected significant increases in 100-year daily rain in climate studies. The Toronto data is available over the period of 1940 to 2021.

How far off are the projected increases in extreme rainfall? The Toronto Future Weather & Climate study projected a theoretical 31mm/decade increase over 40 years - that was for Pearson Airport climate station. Actual data at Pearson Airport shows an observed increase of only 3.1mm/decade.  This considers a value of 115.1 mm in the middle of the 1950-2003 period and a value of 125.5 mm for 2003-2017 - that later value is estimated to generate the current value of 117.3 mm by using a weighted average across all years from 1950 to 2017. For Toronto the actual increase is only 2.0 mm/decade.

On average the GTA (Toronto and Pearson/Mississauga) increase is about 2.5 mm/decade, or less than a tenth of almost 31mm/decade projected in the SENES climate/future weather study.

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Further reading in previous posts on extreme rainfall trends:

1) Rainfall intensity trends in Canada:

a) 226 long term climate stations in the Engineering Climate Dataset are used to show actual trends between rain intensity statistics up to 2007 and then up to 2017: https://www.cityfloodmap.com/2020/12/design-rainfall-trends-in-canada.html

b) more of the above plus observed annual maxima rainfall trends as reported in the 2021 National Research Council of Canada (NRC) "National Guidelines on Undertaking a Comprehensive Analysis of Benefits, Costs and Uncertainties of Storm Drainage and Flood Control Infrastructure in a Changing Climate": https://www.cityfloodmap.com/2022/02/nrc-national-guidelines-on-flood.html

2) Rainfall intensity trends in Southern Ontario:

a) ECCC's Engineering Climate Dataset Intensity Duration Frequency (IDF) trends for long-term southern Ontario climate stations, comparing statistics up to 1990 and current values (v3.3 datasets with some station data up to 2021): https://www.cityfloodmap.com/2023/05/southern-ontario-extreme-rainfall.html

3) Rainfall extreme reporting (?) in the media (including Toronto, Mississauga trend review):

a) Thinking Fast and Slow About Extreme Weather and Climate Change, inspired by the late Daniel Kahneman (RIP good sir), exploring the cognitive biases in extreme rainfall reporting in the media: https://www.cityfloodmap.com/2015/11/thinking-fast-and-slow-about-extreme.html

b) my paper with "Thinking Fast and Slow" themes published in the Journal of Water Management Modelling with the title "Evidence Based Policy Gaps in Water Resources: Thinking Fast and Slow on Floods and Flow": https://www.chijournal.org/C449

4) Local studies that observed no increases in design rainfall when updating IDF values:

Many studies: https://www.cityfloodmap.com/2018/03/extreme-rainfall-and-climate-change-in.html

While media and the insurance industry has repeated that climate change has been responsible for increased flood damages and insurance claims over past decades, the lack of increases in extreme rainfall means that other factors are at play. These include fundamental changes in hydrology in urbanized communities, e.g., increased watershed development and intensification. See previous posts for some examples of expanding urbanization in Ontario communities over previous decades: https://www.cityfloodmap.com/2016/08/land-use-change-drives-urban-flood-risk.html

When fact checkers look into media statements regarding extreme rainfall trends, including the CBC and Radio Canada Ombudsmen offices, data shows no overall increase in extreme rain across Canada. This post shares corrections made by the CBC over recent years: https://www.cityfloodmap.com/2019/06/cbc-correcting-claims-on-extreme.html

Evidence-based policy gaps in water resources - Thinking Fast and Slow on Floods and Flow

Twitter.

Wanted: Evidence-Based, Data-Driven Water Resources
Engineering Policy .. Braaaaaaaaains !

Fake News.

Click Bait.

Infographics.

Infotainment.

One might expect that the "dumbing-down" of media and our communications surrounding topics of great importance to society would not affect the engineering profession, and the important things that we do to serve the public and protect the environment. But you'd be wrong.

I first explored the how discussions and reporting on extreme weather and flooding in water resources engineering have fallen prey to the knee-jerk-reaction, quick-fix crowd back in late 2015 in this post called "Thinking Fast and Slow About Extreme Weather and Climate Change":

http://www.cityfloodmap.com/2015/11/thinking-fast-and-slow-about-extreme.html

My first inclination that facts were falling by the wayside came earlier in 2015 when I found that the Insurance Bureau of Canada and Institute for Catastrophic Loss Reduction's Telling the Weather Story cited arbitrary weather frequency shifts as real Environment and Climate Change Canada IDF data - that was laid out in this presentation.

Now my examination of how we frame and solve problems in the realm of flood risk management - including the identification and prioritization of causes of flooding - has been published in the Journal of Water Management Modeling. Its called Evidence Based Policy Gaps in Water Resources: Thinking Fast and Slow on Floods and Flow :

https://www.chijournal.org/C449

 

What's it all about? Well here's the paper's abstract:

"Water resources management and municipal engineering practices have matured in Canada over recent decades. Each year, more refined analytical tools are developed and used in urban flood management. We are now at a state where practitioners must use these tools within broad decision making frameworks to address system risks and the life cycle economics of prescribed solutions. Otherwise, evidence based policy gaps in the prioritization of risk factors and damages will widen and lead to misdirected mitigation efforts. For example, despite statistically significant decreases in regional short duration rainfall intensities in Southern Ontario, extensive resources are devoted to projecting IDF curves under climate change. Thinking fast, as defined by Daniel Kahneman, through listing recent extreme events to declare new weather reality risks based on heuristic availability biases, has replaced data driven policy and the statistical rigour of thinking slow problem solving. Under this skewed risk perspective, a high profile Ontario commuter train flood was mischaracterized as an unprecedented event despite a <5 y return period and a greater flood weeks before. Recent Ontario urban flood incidents have been attributed to unprecedented weather despite GIS analysis showing more critical hydrologic drivers. Constraints on effective water management are now less likely to be technical but rather scientific (inadequate representation of urban groundwater systems), institutional (arbitrary boundaries between city and watershed agency jurisdictions), economic (unaffordable green infrastructure solutions based on cost–benefit analysis and flat normalized loss trends), or operational. Evidence based policies and water management solutions are needed from a broad risk and economic framework that recognizes these barriers and uncertainties in the application of analytic tools."

If you've read the www.cityfloodmap.com blog you've seen these themes before. But nonetheless please give it a read and pass on your comments! Thanks so much.

Robert J. Muir, M.A.Sc., P.Eng.

Toronto

Thinking Fast and Slow About Extreme Weather and Climate Change

Thinking, Fast and Slow is a best-selling^[1] 2011 book by Nobel Memorial Prize in Economics winner Daniel Kahneman which summarizes research that he conducted over decades, often in collaboration with Amos Tversky.^[2][3] It covers all three phases of his career: his early days working on cognitive biases, his work on prospect theory, and his later work on happiness.

The book's central thesis is a dichotomy between two modes of thought: "System 1" is fast, instinctive and emotional; "System 2" is slower, more deliberative, and more logical.

The book delineates cognitive biases associated with each type of thinking, starting with Kahneman's own research on loss aversion. From framing choices to people's tendency to substitute an easy-to-answer question for one that is harder, the book highlights several decades of academic research to suggest that people place too much confidence in human judgment.

Source - Wikipedia

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Why talk about this book on this blog? Because it can explain, through the lens of Kahneman's research, biases in our thinking and understanding of extreme weather, flooding and climate change.

Kahneman's research helps explain to how the media, the public, and groups without scientific resources substitute an easy to answer question on meteorology for the harder ones on urban hydrology, infrastructure hydraulics, multi-objective decision making, extreme value statistics and risk assessment.  Here are some examples of the biases in thinking:

Heuristic biases

Anchoring or focalism is a cognitive bias that describes the common human tendency to rely too heavily on the first piece of information offered (the "anchor") when making decisions. In the context of extreme weather and climate change, most people have been exposed to well-documented temperature trend data for example from Al Gore in An Inconvenient Truth, and may use this "anchor" when making decisions about extreme rainfall trends (i.e., they assume historical rainfall trends are the same as temperature trends):

Exposure to temperature trend data anchors decision making on rainfall trends.  Exposure to trend data on annual rainfall (e.g., frequency of days with precipitation during a year) anchors decision making on frequency of short duration rainfall events that cause flooding - this is despite the fact that days with precipitation represents even minuscule 'trace' rainfall events (< 0.5 mm depth) while flood events typically require 100 times that threshold of rain (e.g., 50 mm depth).

Having weather personalities report that after extreme storms we had more than a month's rain in x hours, for example, anchors the public's perception about the frequency, or rarity, of the event when in fact a statistical evaluation of rainfall extremes would show that exceeding average summer monthly rainfall totals is not rare.

For example, after the July 8, 2013 storm in Toronto, where 126.0 mm of rainfall was recorded at Pearson Airport, the National Post reported "Before Monday, the highest rainfall ever experienced in Toronto for July 8 was 29.2 mm set in 2008 — a record that was more than tripled".  Tripling records sounds extreme when referred to a particular calendar day and anchors perception of rarity - but calendar days statistics are irrelevant given that summer convective storms are uncommon and 2/3 of July days are dry - furthermore daily totals of a similar magnitude were recorded twice before in 1980 and 1954 (119.9 mm and 137.4 mm respectively), and the previous July 8 record was exceeded by 200% in 7 other years between 1950 and 2013.  Headlines or course try to emphasize the rarity of events, not the commonplace.

In presenting Insurance Bureau of Canada and Institute for Catastrophic Loss Reduction's  "Telling the Weather Story" to the Empire Club in 2012 (YouTube) Dr. McBean first presents trends on temperature and discusses them for five minutes showing undeniable trends in warming, and warming rate - this anchors listeners.  He then switches to rainfall but shows no data, and instead only a theoretical bell curve frequency shift (see 13:10 in the video), but then concludes storm frequency is increasing as well. The listeners' cognitive bias due to anchoring on temperature will allow them to readily accept rainfall increases as facts as well, as opposed to recognizing rainfall increases as theoretical speculation as fully explored in this blog post and slide deck and in fact confirmed by Environment Canada and the CBC in response to inaccurate reporting.

The availability heuristic is a mental shortcut that occurs when people make judgments about the probability of events by how easy it is to think of examples.  In the context of extreme rainfall, 24-hour weather broadcasting, and 24-hour new channels give the public many example of flooding events that skew the perceived probability of occurrence. Hurricane Katrina and Hurricane Sandy are examples of extreme flooding that the public can recall in the context of flooding, but that have little relevance to urban flooding caused by convective thunderstorms. Likewise for Tsunamis.  Other types of flood events caused in large part by operational issues and inherent vulnerabilities are recalled and mistakenly associated with extreme rainfall as the sole cause (Union Station flooding June 1, 2012 was due to construction pump bypass capacity specifications, GO Train flooding July 8, 2013 due to rail line vulnerability (being below known moderate frequency flood levels)). 

Go Train Flood - Don River Floodplain - July 8, 2013

The availability heuristic leads to systematic biases, demonstrated in the judged frequency of repeated events.  It is irrelevant

Stranded GO Train in 1981 in same location as the
stranded GO Train in 2013 in the Don River valley.

that GO Train rail area flooding occurred on December 25, 1979, January 11, 1980, March 21, 1980, April 14, 1980, February 11, 1981 and May 11, 1981.  Under the availability heuristic people tend to heavily weigh their judgments toward more recent information, making new opinions biased toward that latest news. Nobody knows that the May 29, 2013 flood was worse (higher rainfall in East York, higher flow and flood levels at Todmorden gauge near the site) - because the train schedule missed the flood timing! Nobody remembers Ivan Lorant's flood inquiry report for Premier Bill Davis in the early 1980's.  Nobody asked the Toronto and Region Conservation Authority if this was a flood prone area and if this extent of flooding was unusual at the GO Train flood site. Nobody asked the Port Authority if the lack of Keating Channel dredging in the past few years contributed to flooding, just like it did in the early 1980's before the inquiry. 

Attribute substitution is a psychological process thought to underlie a number of cognitive biases and perceptual illusions. It occurs when an individual has to make a judgment that is computationally complex target attribute, and instead substitutes a more easily calculated heuristic attribute. This substitution is thought of as taking place in the automatic intuitive judgment system (System 1), rather than the more self-aware reflective system (System 2). Hence, when someone tries to answer a difficult question, they may actually answer a related but different question, without realizing that a substitution has taken place. This explains why individuals can be unaware of their own biases, and why biases persist even when the subject is made aware of them.

System 2 thinking about flooding must consider rain, runoff, flow and
flooding processes - a slow, effortful. complex and reliable approach.

As rain first causes runoff, which then creates flow, which then causes flooding, it is easy to mistakenly correlate increased flooding to increased rainfall.  The alternative is analyzing the complex problems of urban hydrology changes that influence runoff, the stormwater management mitigation measures that can lessen some impacts of some development at some scales, hydraulic interaction in the flow systems including riverine systems (Lisgar District Basement Water Infiltration Assessment is a wonderful example in Mississauga, or Basement Flooding Areas 4 and 5 in Toronto (see page 4 in the Executive Summary on Black Creek interaction)) with the overland, underground separated, combined and partially separated sewers, hydraulic impacts of operational constraints (bypass pumping during construction, inadequate dredging), hydraulic impacts of environmental protection measures (provincial F-5-5 compliance, federal Fisheries Act compliance, etc.), and hydraulic impacts of development intensification on overland flow routes and interactions with underground systems and private systems.  It is much easier to focus only on rainfall. And, conveniently, everyone has an opinion about the weather.

******

Many prominent organizations and individuals have established a cognitive bias based on temperature trends and have since applied Kahneman's fast and error-prone System 1 thinking approach to rainfall extremes.  The anchoring bias in media report emphasizes the rarity / frequency of events and ignores past events and other causes (operational or intrinsic vulnerability) of flooding - Environment Canada's extreme rainfall frequency and trend data is ignored. The availability bias of extreme flooding events reported through the media skews the public's perception on the true probability of events - it is very easy to find examples of flooded underpasses because these are designed to lower flood standards, but flood a lawyer's Ferrari in an underpass and it will be ingrained in the public's mind for a long time. Attribute substitution bias allows the public to simplify and explain flooding with rainfall (rain = flood) as opposed to thinking about the actual complex system (rain = baseline runoff + development runoff +- mitigation measures = flow +- capacity constraints +- operational factors = flooding).

Media support attribute substitution by ignoring even the most fundamental physical facts. For example, the GO Train flood on July 8, 2013 was cited as a 2013 Top Weather Story by CBC News as they associated the record at Pearson Airport with the flooding (record rain somewhere = flooding somewhere else).  They ignored the fact that Pearson is in the Etobicoke Creek Watershed, three watersheds away from the Don River Watershed where the GO Train flooded - this is basic hydrology: Mississauga rain = runoff in Etobicoke Creek, not in Don River). They ignored that no record rainfall occurred in the Don River Watershed as they were anchored to the Mississauga data 25 km away.

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System 2 Mode of Thought -  Data and Analysis - Slow, Methodical.
Source: Environment Canada Engineering Climate Datasets ver 2.3.

Evidence-based policies require us to check facts: 

“There will still be times when someone accuses us of having lost our way, of having chosen the wrong priorities, and I know that can be hard to hear. But in moments in great and important choice, when the stakes are high, and the consequences are long-lasting, we have to test our assumptions.” Premier Kathleen Wynne, AGM, June 6, 2015 

System 1 Mode of Thought - Infographics and Heuristics - Fast, Emotional.
Source: Environmental Commissioner of Ontario,
Connecting the Dots on Climate Data in Ontario.

Testing assumptions requires Kahneman's "System 2" thinking - slow, deliberate and logical, as opposed to fast, instinctive and emotional in order to overcome heuristic biases in our thinking. 

Please. More Data.

Fewer Infographics.

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"People are not accustomed to thinking hard, and are often content to trust a plausible judgment that comes to mind."


Daniel Kahneman, American Economic Review 93 (5) December 2003, p. 1450



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Robert Muir's presentation on infrastructure adaptation to the WEAO OWWA Joint Climate Change Committee explores in significant detail the trends in Southern Ontario rainfall extremes that affect flood risk and that drive mitigation priorities:

Infrastructure Resiliency and Adaptation for Climate Change and Today’s Extremes from Robert Muir

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Tada! "Thinking Fast and Slow" themes in the above post have been expanded and are now published in the Journal of Water Management Modelling with the title "Evidence Based Policy Gaps in Water Resources: Thinking Fast and Slow on Floods and Flow":

https://www.chijournal.org/C449