Showing posts with label evidence based policy. Show all posts
Showing posts with label evidence based policy. Show all posts

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":


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 :


 
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

Book Review: The Rightful Place of Science: Disasters & Climate Change - Ontario Cities Flooding Perspective

Roger Pielke, Jr.'s 2014 book  The Rightful Place of Science: Disasters & Climate Change is a must-read for anyone interested in understanding and mitigating flood damages in Ontario cities. It reinforces several local themes presented in this blog as well, e.g., extreme rainfall is not increasing (due to climate change or anything else), flood damages are influenced by other factors and are not increasing as a result of more frequent of extreme rainfall. (see 2017 update at bottom of post, and 1970-2019 hurricane trend).

Pielke has been recently highlighted in the Wall Street Journal where he shares his "Unhappy Life as a Climate Heretic".

hurricane frequencyHurricanes

Pielke demonstrates that the frequency and severity or strength of hurricanes (tropical cyclones) has not increased over the past century. This is good news for Ontario where many river flood hazards are defined by Hurricane Hazel as the regional storm regulatory event in many Conservation Authority jurisdictions.

US hurricane damageHe also delves into damages resulting from these events, normalized to reflect the increase in the number of people and the amount of property in vulnerable areas. While losses have increased it is due to the increase in assets at risk - the normalized damages are in fact 'flat'.

In Ontario, the percentage of properties lie in river flood plains where hazards are governed by hurricane events is in the very low single digits. Nonetheless, decreasing hurricane frequency is a good thing.

Extreme Precipitation

extreme rain southern Ontario Toronto Burlington GTA GTHACiting IPCC, Pielke notes that there have been statistically significant decreases and increases in 'heavy precipitation' events, and that there are strong regional and subregional variations in the trends. It is important to note the definition of heavy precipitation is rainfall above the 95% percentile of daily rainfall .. so not really short-duration, high-volume, extreme rainfall that causes widespread urban flooding in Ontario.
extreme rainfall southern Ontario GTA IDF curves

As reported on this blog, Ontario has regional trends in annual maximum observed rainfall volumes. Charts at right present Environment and Climate Change Canada's Engineering Climate Datasets Version 2.3 trend data. For short durations, less than 6 hour duration, there are four times more statistically significant decreases in maximum rainfall than increases. So southern Ontario regional trends show decreasing rainfall severity. You could "tease out" a teeny, tiny Ontario-wide increase if you average the whole province together, but that would underestimate the increases up north and misstate the decreases down south.

flood damage trends unadjusted fro growthNormalized Catastrophic Losses in Canada

Intact has reported on damage trends in their 'insurance is evolving' webpage. They indicate that "Payouts from extreme weather have more than doubled every five to 10 years since the 1980s." and provide the chart at the right.

Canadian GDP growth reduces relative flood damagesSo while losses are increasing, how is GDP increasing in Canada? That is, are normalized losses increasing in Canada? Or are those trends like those identified by Peilke? The website tradingeconomics.com provides this Canadian GDP growth chart to the right. Since the early 1980's, GDP has increased about 500% (approximately $300B USD to over $1500B USD). That could explain a portion of the absolute increase in losses since the early 1980's identified by Intact.

* NEW * using Statistics Canada data for expenditure-based Gross Domestic Product and catastrophic loss data presented by Intact Centre for Climate Adaptation have been used to assess normalized losses as a fraction of GDP. While losses have increased significantly, but the normalized Canadian catastrophic losses are up and down. There is a low r-squared upward trend in normalized losses (i.e., not a strong trend). So using Pielke's 'detection vs. attribution' distinction, one could argue there is a detectable trend upward, however the attribution - what caused it - is unclear. As noted below, we have significant quantifiable upward trends in urbanization in Ontario cities over past decades, suggesting increased runoff stresses under a stationary climate, or extreme weather trends.
Adjusted catastrophic losses including flooding
 Pielke cites a 2014 IPCC report that notes the following:

"Economic growth, including greater concentrations of people and wealth in periled areas and rising insurance penetration, is the most important driver of increasing losses."

Ironic - some more losses are because of rising insurance penetration. Makes sense though. Here are Canadian cat losses normalized based on personal property net written premiums (according to Facts of the Property and Casualty Insurance Industry in Canada 2015 is published by Insurance Bureau of
Canada (IBC), 2015):
Adjusted losses including flooding by premium growth in Canada


Wow! Not much of a trend there to suggest that extreme weather is getting a lot worse - normalized losses are up and down with the maximum relative losses back in 1997 (note the 2014 and 2015 premiums are assumed to be 2% greater than previous years to extend this from 2013 to 2015 where cat loss information was available; similarly 1987-1989 premiums are assumed to increase at 2% to arrive at the reported 1990 value). The r-squared is very low too (0.012) meaning not a strong trend here over time. This muted trend is in stark contrast to the non-normalized losses typically reported by the insurance industry.

Intersection of Vulnerability and Extreme Events

A refreshing observation in Pielke's book is that it is the intersection of intrinsic vulnerabilities and exposure to extreme weather events that causes damages, like flood losses. More properties and belongings in the wrong place explain increased losses. So I made a fancy graphic to explain it:


I just found this infrastructure climate vulnerability Venn diagram in the PIEVC Engineering Protocol
For Infrastructure Vulnerability Assessment and Adaptation to a Changing Climate PRINCIPLES and GUIDELINES, so I guess we can't copyright it.



Note that PIEVC figure above describes infrastructure-climate interaction. Our figure substitutes infrastructure with development, meaning the property impacted by climate (weather). Where does infrastructure come into it? I suppose here:

The Media and Public Opinion

Pielke notes that the reporting on extreme weather has increased considerably, which feeds a public perception that extreme events are increasing in frequency or severity. For example in the New York Times the phrase 'extreme weather' has jumped in popularity in newspaper articles since the mid 1990's.  In this blog we have commented on the 'availability bias' that such frequent reporting can cause, skewing the public's perception on the true probability of events. A common statistical sin in such reports is to declare a record rainfall ... umm ... for a particular calendar day (July 8, 2013 in Mississauga, Ontario, or September 28, 2016 in Windsor/Tecumseh, Ontario). Once engineers start design infrastructure to operate differently on different calendar days of the year, such reporting of calendar day rainfall records will be worthwhile, saintly even. But sorry, that's nuts and not going to happen any time soon.

Key Take Away - Let's Get Back On Track, Put First Things First, and not be so ideologically driven to predetermined conclusions that we miss the obvious stuff.

I love this quote in the Pielke's book :

"There is such a furor of concern about the linkage between greenhouse forcing and floods that it causes society to lose focus on the things we already know on floods and how to mitigate and adapt to them."

Greater Toronto Hamilton Area Urban Growth Affecting Urban Flood Risk
This is from Flood risk and climate change: global and regional perspectives, Zbigniew W. Kundzewicz, Shinjiro Kanae, Sonia I. Seneviratne, John Handmer, Neville Nicholls, Pascal Peduzzi, Reinhard Mechler, Laurens M. Bouwer, Nigel Arnell, Katharine Mach, Robert Muir-Wood, G. Robert Brakenridge, Wolfgang Kron, Gerardo Benito, Yasushi Honda, Kiyoshi Takahashi, and Boris Sherstyukov, Hydrological Sciences Journal Vol. 59 , Iss. 1,2014.

Overland flow explains basement flood risks,
from "flood plain to floor drain".
That reference abstract also notes "Economic losses from floods have greatly increased, principally driven by the expanding exposure of assets at risk. It has not been possible to attribute rain-generated peak streamflow trends to anthropogenic climate change over the past several decades. Projected increases in the frequency and intensity of heavy rainfall, based on climate models, should contribute to increases in precipitation-generated local flooding (e.g. flash flooding and urban flooding)."

Along those lines, in this blog we have tried to expose some of those existing urban flood risks related to:

i) proximity to overland flow paths beyond valley systems (sometimes encumbered major drainage systems), and

ii) increased imperviousness cover in Ontario municipalities (e.g., mid 1960's to late 1990's).

BONUS - Yoda, Twerking, Extreme Weather Correlation

Maybe I didn't learn anything from Pielke's book, like the importance of data and statistical analysis to advance science and public policy. I did learn that the New York Times has a great online tool to analyze the frequency of extreme weather references in their publication. I have used that to help establish some undeniable correlations between extreme weather, twerking and Yoda, as shown in the graphic below.


New York Times Chronicle Tool - Correlation of Extreme Weather, Twerking and Yoda

Spurious correlation between temperature and disaster damages you have found?

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Before considering Hurricane Harvey, a category 4 hurricane, we can review 2017 trends from Pielke Jr. - there had been a large gap before Hurricane Harvey, globally hurricanes are less frequent, and in the US the losses have been decreasing as a proportion of GDP:

hurricane trends pre Hurricane Harvey #harvey

global hurricane trendsweather disaster loss trends

And some more recent hurricane frequency statistics from Pielke Jr.'s blog:

climate change extreme weather
Decreasing frequency of hurricanes making landfall suggests no climate change effects despite 2017 events.


climate change hurricane frequency and severity

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Dr. Pielke Jr. and atmospheric scientist Ryan Maue have recent reviewed hurricane (tropical cyclone) trends over the past 50 years - see article in Forbes. While the variability in hurricanes making land fall is quite large, making it hard to distinguish a clear trend the counts of events using the Saffir-Simpson (S/S) hurricane scale use by the U.S. National Oceanic and Atmospheric Administration is shown below:


A recent summary notes the consensus on a lack of anthropogenic influence trends, but predicted future increases (Pielke, 2019 - Forbes):

"NOAA [the National Oceanic and Atmospheric Administration] concludes ‘an anthropogenic
influence has not been formally detected for hurricane precipitation,’ but finds it likely that
increases will occur this century. Similarly, the WMO concluded, ‘no observational studies have
provided convincing evidence of a detectable anthropogenic influence specifically on hurricane-related precipitation,’ but also that an increase should be expected this century. The U.S. National
Climate Assessment concurred, explaining that there is agreement on predictions for a future
increase in hurricane-related rainfall, but ‘a limiting factor for confidence in the results is the lack
of a supporting detectable anthropogenic contribution in observed tropical cyclone data."

Despite this, damages are increasing. Klotzback et al. (2018) noted:

“While neither U.S. landfalling hurricane frequency nor intensity shows a significant trend since
1900, growth in coastal population and wealth have led to increasing hurricane-related damage
along the U.S. coastline.”

The following chart shows losses normalized, considering changes in inflation and wealth at the
national level as well as changes in population and housing units at the coastal county level in the
US:




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 2013
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
GO Train flood 1981
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.
Urban flooding
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.

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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.GO Train Worst Flood

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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



How We Communicate on Flooding - Social Media Connections - RIP Evidence Based Policy

Evidence-based policies require strong communication to support education and data sharing to build a better understanding of issues. Here are some connections between government agencies and insurance industry players who have a role in flood management :
Can you find Environment Canada in the twitter network above to find out who is following whom? Unfortunately Environment Canada (@environmentca), the federal agency that collects and analyzes extreme rainfall data that shows storm intensities are not increasing, is not prominent in communications.  They do not effectively communicate rain data trends like this:
Real data from Engineering Climate Data Set, Version 2.3.

Who is more prominent in terms of communication on flooding? The Ontario Ministry of the Environment and Climate Change is (@environmentont), but unfortunately they only provide the following infographics on weather and climate, which is not helpful in any way to understanding flood issues:

Connecting the dots on climate change infographics. Non data.  Nonsense.

The insurance industry is prominent in terms of communication on flooding and rainfall extremes, for example @insurancebureau, @iclrcanada, @avivacanada, or @the_cooperators. Unfortunately, Insurance Bureau of Canada's publications through the Institute for Catastrophic Loss Reduction substitute theoretical speculation on climate for actual data on historical extreme weather:

Mathematical conjecture, statistical smoke and mirrors are used by IBC to explain weather phemonena.  In this case, a theoretical one standard deviation shift in a bell curve is used as the basis for explaining Environment Canada rainfall trends.


 A full review of the insurance industry's promotion of incorrect data is in our previous post.

Unfortunately, in this age of Twitter, 140 characters are not enough to explain science, facts, evidence, details, engineering principles, analysis, etc. in any meaningful way and so society is often left uninformed and misled.

Evidence-based policies that must be built on substance and not infographics and mathematical conjecture.

The Ontario Government's is off track and has somehow linked flood damages to climate change, when in fact Environment Canada data shows no increase in storm intensities.

In a CBC report the Minister of the Environment and Climate Change says "Governments, too, are also getting hit. Murray said severe weather events have cost the government hundreds of millions."

Yes damages are increasing but storm frequency is not, so why mention weather events in the roll out of the government's climate change policy at all?  Flood damages are not caused by temperatures increases due to climate change. And extreme weather is different than long term climate.  Dear Minister, will you not respond to my letter?

Oh well, this post is longer than 140 characters so I have lost many of you by now and you have been distracted by some Google ad for drones .. no, wait, that was me.

RIP evidence-based policy in Ontario.




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