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

Future Extreme Rainfall IDF Values in Canada Include Decreasing Intensities for Some Emissions Scenarios and Regions

 A research paper Assessment of non-stationary IDF curves under a changing climate: Case study of different climatic zones in Canada in the August 2021 Journal of Hydrology: Regional Studies by Silva et al. projected future IDF curves in several regions of Canada under various emissions scenarios (link: https://www.sciencedirect.com/science/article/pii/S2214581821000999). A previous post discussed historical annual maximum rainfall at climate stations with long-term records and noted stationary values for several stations and rainfall durations (i.e., no change in annual extreme rainfall observations) - see post: https://www.cityfloodmap.com/2021/12/has-extreme-rainfall-become-more-severe.html.

The paper presents changes in typical design intensities for:

i) 120-minute (2-hour) 5-year return period values (e.g., along with intensities for shorter durations, these data are used to design storm sewers in many jurisdictions), and

ii) 1440-minute (24-hour) 100-year return period values (e.g., used to design stormwater detention facilities, or used to derive design hyetographs for floodplain mapping in some watersheds and for RDII analysis in some wastewater collection networks prone to wet weather impacts, etc.).

Future projections are made for the period 2020-2100. The main paper presents percentage changes for the RCP8.5 emissions scenario as follows:

The charts on the above left show changes for stationary distributions, and Hamilton (HAM) and London (LON) columns are highlighted. Those stations were shown to have stationary annual maximum extreme rainfall in the paper, as shared in the previous post. The London and Hamilton 100-year rainfall intensities increase from 4 to 13 percent under the RCP 8.5 emissions scenario (stationary table on the bottom left). 

Under the RCP8.5 scenario the return period of today's 100-year 24-hour becomes smaller, meaning that intensity can occur more frequently than the 1% chance per year today. Today's 5-year 2-hour intensities become more frequent as well. The paper shows these projected intensities: 

So a 100-year return period 24-hour (1440 minute) intensity becomes a 73.5 year return period intensity or a 18.5 year return period intensity in Hamilton and London, Ontario, respectively.

Supplemental material shows that for other emissions scenarios intensities do not increase
at these Great Lakes climate stations for the rare 100-year intensities. See below:

For a RCP2.6 scenario, the 100-year intensities in the table (again on the bottom left for the stationary model), decrease by 3 percent or are unchanged. With those decreases the return period of today's design intensities become longer, meaning today's intensities are less frequent. That means reduced risks for extreme rainfall compared to today.

The RCP4.5 scenario projected 100-year intensities are essentially unchanged in Hamilton and up slightly in London: 

So what are future rainfall intensities in Southern Ontario? That depends on the emissions scenario you select.

Recently the Pacific Climate Impacts Consortium questioned if RCP8.5 should be considered as 'business as usual', that is, is it the most likely future scenario? See a detailed discussion in their Science Brief: https://www.pacificclimate.org/sites/default/files/publications/Science_Brief_39-June_2021-final.pdf, an excerpt which is below.

The Science Brief concludes that:

"Given that RCP8.5 is not the most "likely" outcome of emissions following business-as-usual or stated policy intensions, its reasonable to refer to it as a high emissions scenario instead of business-as-usual."

A previous post also noted that others have also questioned the validity of RCP8.5:

i) Roger Pielke Jr. as reported in Forbes (https://www.forbes.com/sites/rogerpielke/2019/09/26/its-time-to-get-real-about-the-extreme-scenario-used-to-generate-climate-porn/?sh=23797f704af0) and

ii) Roger Pielke Jr. and Justin Ritchie as reported in Issues in Science and Technology (https://issues.org/climate-change-scenarios-lost-touch-reality-pielke-ritchie/). 

Therefore, the paper's projections for future intensities under RCP8.5 could be considered in stress tests of infrastructure or hydrologic systems. Such tests can identify low-regret design modifications that can be incorporated initially, or to identify future adaptive management if modifications today would be too costly and yield uncertain and limited benefits (i.e., based on the limited likelihood of such severe intensities occurring with less likely scenarios). Assessment of most-likely conditions should consider projected future intensities based on emissions under stated policy intentions, such as presented in the paper's supplemental material (e.g., RCP4.6).

Both the US Federal Highways Administration (FHWA) and the American Society of Civil Engineers (ASCE) have developed risk-based approaches to designing infrastructure for future climate conditions. 

The Canadian government describes a method for projecting future rainfall intensities based on temperature-scaling, considering RCP4.5 and RCP8.5 (see previous post  https://www.cityfloodmap.com/2021/12/adjusting-idf-curves-to-account-for.html). The approach notes one should:

• Apply risk-based decision-making to choose the future extreme rainfall value that is most appropriate for asset risk thresholds.  For example, if rainfall consequences to infrastructure are severe, consider applying upper end of projected future RCP 8.5 1-hour 1-in-100-year rainfall intensities to infrastructure design.

A robust risk-based approach could also consider other scenarios such as RCP2.6 as well.

It is noted that the ASCE identifies levels of analysis in its Manual of Practice 140 entitled Climate-Resilient Infrastructure: Adaptive Design and Risk Management. In Chapter 7 Adaptive Design and Risk Management the manual provides recommended levels of climate analysis based design life and risk category. The following table excerpt shows the risk categories for buildings and other structure:

These table excerpts show the levels of analysis based on risk category and design life (top table), and the climate analysis characteristics.

So for buildings and structure with design life up to 75 years, where the risk category is low (Risk Category I, meaning low risk to human life), Level I climate analysis using "extremes based on historical observations is appropriate". For a longer design life beyond 75 years, the Risk Category I buildings and structures (low risk) should also include climate projections per climate analysis Level II. In my opinion, a deterministic future projection could be considered for such analysis based on a likely scenario (e.g., RCP 4.5).

Where there is a 'substantial risk to human life' or hazardous or toxic materials involved under Risk Category III, Level III analysis is recommended by ASCE for moderate design life (30-75 years). Such analysis would account for uncertainty as well, for example, and could consider confidence bands reflecting uncertainty on a future climate projection (e.g., bands surrounding RCP 4.5 projections).

For the highest Risk Category IV that applies to essential buildings and structures that are deemed 'essential facilities' and whose failure could pose 'a substantial hazard to the community', or that involve hazardous/toxic materials, more extensive climate analysis is required (i.e., Level IV). ASCE recommends "rigorous analysis of risk" for moderate design life and longer. Such an analysis could consider a range of scenarios, e.g., from RCP 2.6 to RCP 8.5, and the consequences of exposure to flood hazards. The likelihood of each scenario would have to be estimated to support rigorous risk-based analysis.

Has Extreme Rainfall Become More Severe in Canada? - Research Shows Rain Intensities Mostly Unchanged (Stationary)

A research paper Assessment of non-stationary IDF curves under a changing climate: Case study of different climatic zones in Canada in the August 2021 Journal of Hydrology: Regional Studies by Silva et al. evaluated trends in historical annual maximum rainfall across Canada for a range of durations at climate stations with long-term records (over 50 years) (link: https://www.sciencedirect.com/science/article/pii/S2214581821000999). Projected future IDF curves are discussed in a future post.

Regarding observed rainfall maxima, the paper comments on the lack of consistent trends observed:

"There is no clear spatial pattern of trend in precipitation among the considered regions of Canada. Only the Moncton station shows a significant non-stationary behaviour in GEV modelling over most of the durations. No change pattern (i.e., trend detection) is confirmed for all durations at two sites under the influence of Great Lakes (London and Hamilton)."

The following Table 2 excerpt from the paper shows where annual maximum precipitation is stationary (not changing, as noted with the symbol "I") and non-stationary (changing) over the observation period.

Table 2. The best GEV model for each station and different durations in the historical period*.

Duration (minutes)	Selected Station
	Calgary	Hamilton	London	Moncton	Vancouver	Winnipeg
5	I	I	I	I	I	I
10	VI (0.030)	I	I	I	I	II
15	I	I	I	VI (0.043)	I	I
30	I	I	I	V (0.030)	I	I
60	I	I	I	II (0.003)	I	I
120	I	I	I	VII (0.047)	I	I
360	I	I	I	II (0.041)	VII (0.011)	I
720	II (0.036)	I	I	II (0.016)	I	I
1440	I	I	I	II (0.002)	I	VII (0.025)

The best GEV model is shown using I to IX, according to the list of models in Appendix A. I corresponds to the stationary model, while values from II to IX correspond to the non-stationary models.

In central Canada, 26 of 27 rainfall series of maximum rainfall over durations of 5 minutes to 24 hours for Hamilton, London and Winnipeg stations were stationary, i.e., unchanged. The Great Lakes region represented by London and Hamilton, where no change in annual extreme rainfall was observed, also includes several large urban centres. 

A previous post evaluated Environment and Climate Change Canada annual maximum rainfall trends for 676 climate stations across Canada: https://www.cityfloodmap.com/2021/10/annual-maximum-rainfall-trends-in.html

There were few statistically significant trends, up or down, in the most recent v3.20 datasets, as noted in the following chart:

This summary table below shows that earlier datasets had similar trends with the majority of trends (i.e., over 90% of stations with calculations available showed no significant trend):

Trend in Maximum Rain    v3.20       v3.10       v3.00         v2.30
Significant Increase              4.16%     4.28%       4.18%        4.09%
Significant Decrease             2.25%     2.24%       2.33%        2.30%
No Significant Trend          85.73%    85.80%     85.55%      86.37%
No Calculation                      7.86%      7.68%       7.94%        7.24%

Looking at particular regions such as Southern Ontario and Manitoba where stationary annual maximum rainfall was observed in the paper above, one can see the variability in trends and significant across different stations and durations.

Southern Ontario, including the Hamilton and London stations has more decreasing trends than increasing ones:

In Manitoba, the trends vary by station with some recording increases over all durations with other recording decreases. Winnipeg has a combination of decreases, no change, and an increase for the 9 durations evaluated as shown below:

These trends are as reported in Environment Canada's v3.10 Engineering Climate datasets and will be published for all regions in the upcoming National Research Council of Canada cost benefit guideline for flood control infrastructure in a changing climate.

The tables above suggest that evaluating single stations may not provide a complete picture of overall changes in a region. For example, the paper highlights the non-stationarity in Moncton annual maximum rainfall. Other long term stations in New Brunswick, such as Fredericton have some trends that are opposite to those observed in Moncton (i.e., the Fredericton station has more decreasing trends than increasing ones and a statistically significant decrease), and others do not exhibit as strong increasing trends (e.g., the Charlo Auto station does not have the same number of statistically significant increases as Moncton and its 1-hour rainfall has not increased). Therefore Moncton is not representative of other long-term New Brunswick station trends.

The following charts show the v3.20 annual maximum series trends for the six stations studied in the paper, i.e., Calgary, Hamilton, London, Moncton, Vancouver and Winnipeg.

Only Moncton 6, 12 and 24-hour series have statistically significant increases. As noted above, Fredericton has observed decreasing trends as well, including a statistically significant decrease in 5-minute annual maximum rainfall, and no statistically significant increases:

Ultimately, annual maximum rainfall series are used to derive design rainfall intensities (IDF curves) by fitting a probability distribution to observed annual maxima. A previous post demonstrated how IDF values changed following the addition of recent observations (link:  https://www.cityfloodmap.com/2020/07/how-have-rainfall-intensities-changed.html):

On average, extreme intensities (red dots represent 100-year intensities) have decreased slightly for all durations. Less extreme intensities (green dots represent 2-year intensities) have increased slightly. Regions have different trends, sometimes with short duration intensities increasing and long-duration intensities decreasing, and vise versa as shown in another post: https://www.cityfloodmap.com/2020/07/can-we-use-daily-rainfall-models-to.html

***

The paper also presents future rainfall intensity projections that will be reviewed in an upcoming post. A review of projected results, included in supplemental material but not the main document shows that under some emissions scenarios (i.e., representative concentration pathways), rainfall intensities are projected to decrease in Ontario. The main document only presents projections for a high emissions scenario (RCP8.5) that has been questioned in terms of its likelihood by the Pacific Climate Impacts Consortium (see Science Brief: https://www.pacificclimate.org/sites/default/files/publications/Science_Brief_39-June_2021-final.pdf)

The Science Brief notes "Given that RCP8.5 is not the most "likely" outcome of emissions following business-as-usual or stated policy intensions, its reasonable to refer to it as a high emissions scenario instead of business-as-usual.

Others have also questioned the validity of RCP8.5 as Roger Pielke Jr. reported in Forbes (https://www.forbes.com/sites/rogerpielke/2019/09/26/its-time-to-get-real-about-the-extreme-scenario-used-to-generate-climate-porn/?sh=23797f704af0) and with Justin Ritchie in Issues in Science and Technology (https://issues.org/climate-change-scenarios-lost-touch-reality-pielke-ritchie/).

Why is understanding emissions scenario relevant to rainfall design intensities? Because temperature-based adjustments are recommended to project future rainfall design intensities (see Climatedata.ca and CSA IDF Guide approach in an upcoming post), and temperature changes depend on the emissions scenario considered. High emissions scenarios can be considered in future projections "if rainfall consequences to infrastructure are severe" as part of risk-based decisions making, according to Climatedata.ca. The ASCE's MOP 140 Climate-Resilient Infrastructure: Adaptive Design and Risk Management, in particular Chapter 7 Adaptive Design and Risk Management, also provides recommended levels of climate analysis as a function of design life and risk category, and the characteristics of various levels of climate analysis.

Adjusting IDF Curves to Account for Climate Change

The following page "IDF Curves and Climate Change" is presented at climatedata.ca and describes a methodology for adjusting rainfall design intensities for future conditions. The need to account for future conditions in long-term planning is noted in Environment and Climate Change Canada's Engineering Climate Datasets IDF Files:

Past changes in IDF values have been reviewed in a previous post: https://www.cityfloodmap.com/2020/07/how-have-rainfall-intensities-changed.html

Note that the ECCC guidance relies on RCP4.5 and RCP8.5 scenarios based on historical and future annual average temperatures for IDF stations from the Climate Atlas. Recent research by Roger Pielke Jr, Matthew G. Burgess and Justin Ritchie suggests those RCPs are not the most plausible scenarios (see: "Plausible 2005-2050 emissions scenarios project between 2 and 3 degrees C of warming by 2100" in Environmental Research Letters - https://iopscience.iop.org/article/10.1088/1748-9326/ac4ebf). Accordingly practitioners involved in robust risk assessments should consider how scenarios that are less plausible are considered in analysis.

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Learning Zone

Topic 6: Intensity-Duration-Frequency (IDF) Curves

IDF Curves and Climate Change

It is not appropriate to use IDF curves based on historical information alone for long-term planning. To account for climate change impacts to extreme rainfall, ECCC recommends use of a scaling methodology to adjust IDF curves. Read this article for additional information about integrating climate change into IDF curves, including a practical example.

TIME TO COMPLETION

5 min

Summary

IDF curves are an important tool for decision-making about risks of extreme precipitation, but climate change is expected to increase extreme rainfall in Canada. Because of this, IDF curves based on historical observations alone are not appropriate for long-term decision-making. To account for climate change impacts to extreme rainfall and IDF curves, Environment and Climate Change Canada recommends use of a scaling methodology.

Climate change has intensified extreme rainfall events in North America and is projected to do so even further in future. However, projecting future rainfall metrics shown on IDF figures remains challenging due to sparse extreme rainfall observations, challenges in modelling local extreme rain events, and climate variability.

Due to the challenges described in the Primer on Climate Change and Extreme Precipitation, both climate models and statistical tools have limitations in their ability to project future short duration rainfall events. This is described in more detail in CSA PLUS 4013:2019: Technical Guide: Development, Interpretation And Use Of Rainfall Intensity-Duration-Frequency (IDF) Information along with corresponding guidance for practitioners.

However, a relationship between warming temperatures and precipitation extremes provides an alternate means for adjusting historical IDF curves. This ‘temperature scaling’ method is being increasingly used to estimate future Canadian rainfall extremes.  It is described in CSA PLUS 4013 and is being used to develop future rainfall estimates for the National Building Code of Canada and Canadian Highway Bridge Design Code.

Temperature scaling provides a simple and robust way to update IDF curves for climate change.  However, careful consideration of uncertainties in estimates of future extreme rainfall is still required.  Scaling factors may vary between locations and rainfall event types.  Local temperature change projections depend on future emissions, climate model choices, and natural variability.   And the historical IDF curves that underlie any future IDF curves estimates may themselves be less than ideal estimates of past rainfall extremes.

ECCC suggests the following method for estimating future changes to extreme rainfall magnitudes described on IDF curves:

    1. For the location of interest, download historical IDF curve data from ClimateData.ca or develop based on guidance from ECCC and CSA PLUS 4013:2019.  Use this data to determine historical estimated rainfall intensity (RC) for the storm duration and return period of interest.

    2. Determine an appropriate future timeframe and emissions scenario, and using ClimateData.ca, the Climate Atlas of Canada, or Climate Resilient Buildings and Core Public Infrastructure data, find the long-term (30-year mean) annual mean temperature change (ΔT) for your location, timeframe and emission scenario.  Develop this information by using results from an ensemble of climate models.

    3. Determine future estimated rainfall intensity value (RP) according to the equation:

RP = RC x 1.07^ΔT

Integrating Climate Change into IDFs: A Worked Example

This exercise provides an illustrative step-by-step example of how to apply the temperature scaling approach to shift IDF values and estimate future extreme rainfall conditions.

Example goal: Estimate change in intensity of 1-in-100-year rainfall, for a 1-hour storm duration, for design of new a Canadian infrastructure project that is expected to last 50 years.

1. Estimate observed 1-in-100-year rainfall intensity for a 1-hour storm event.
2. Estimate change in temperature, 50 years in the future.
3. Estimate change in extreme rainfall 50 years in the future.

Step 1: Obtain a historical estimate of 1-in-100-year rainfall for 1-hour storms:

Download available ECCC IDF historical baseline information from ClimateData.ca -> Navigate to the ClimateData.ca IDF map, and identify the closest available data to your location, with a long record of rainfall observations.

Identify historical 1-in-100-year 1-hour storm intensity for your location.

Step 2: Estimate change in temperature for your location, for 50 years in the future:

Download historical and future annual average temperatures for the IDF station location from the Climate Atlas (or another suitable resource) for an ensemble of climate models and for RCP4.5 and RCP8.5.  If using the Climate Atlas: obtain a location-relevant climate summary report, and for Variable=‘Mean Temperature’ and Type of display=‘Time Series’, from the ‘Climate model data’ link obtain two CSV format spreadsheets, each containing multi-model RCP 4.5 and RCP 8.5 projection information.

• For each climate model simulation and RCP scenario within the downloaded ensemble, calculate:
• 31-year average Historical Temperature that best represents the observational period used to develop historical IDF information:
• For each RCP scenario-specific CSV file, use a spreadsheet program to determine a 31-year average annual historical temperature for each climate model. Center this 31-year time period in the middle of the observational period used to develop the original historical IDF curves.
• 31-year average Future Temperature, centered on the final year of the proposed asset lifetime:
• For each RCP scenario-specific CSV file, use a spreadsheet program to determine the 31-year 2065-2085 average annual future temperature value for each climate model.   This period is used because it brackets the year 2070, which is the estimated end-of-life for the proposed asset, given the 2020 construction year, and a 50 year design lifetime.
• Change in temperatures over the asset lifetime:
• For each climate model and RCP scenario, use Microsoft Excel or a similar program to calculate the change in temperatures:

Temperature Change = Future Temperature – Historical Temperature

Step 3: Estimate change in extreme rainfall for your location, for 50 years in the future:

• Given historical rainfall intensity and the range of climate model and RCP scenario-specific temperature change values, use temperature scaling to calculate range of estimated future 1-in-100-year 1-hour intensities for your location.  For each climate model and RCP scenario, use a spreadsheet program to calculate future 1 hour 1-in-100-year rainfall intensities RP using the equation  (RP=RC x 1.07^ΔT)  and setting RC to the historical rainfall intensity from the historical IDF curve, and ΔT equal to each climate model’s temperature change value, for each RCP scenario.
• Aggregate future rainfall intensities into summary statistics for use in risk-based decision-making.  For each RCP scenario, calculate summary estimates of future rainfall intensity (for example, 10th, 50th and 90th percentiles).
• Apply risk-based decision-making to choose the future extreme rainfall value that is most appropriate for asset risk thresholds.  For example, if rainfall consequences to infrastructure are severe, consider applying upper end of projected future RCP 8.5 1-hour 1-in-100-year rainfall intensities to infrastructure design.

Now that you have read IDF Curves and Climate Change, you may wish to review IDF Curves 101 and Best Practices on Using IDF Curves.

Design Rainfall Trends in Canada - Extremes not Trending with Means - Time to Check Theories on Shifting Rainfall IDF Statistics

Previous posts have reviewed changes in design rainfall intensities, that is, the statistics used to define IDF (intensity-duration-frequency) curves (https://www.cityfloodmap.com/2020/07/how-have-rainfall-intensities-changed.html).  These rainfall characteristics are the most commonly applied data in urban storm drainage system design, and are often directly related to the peak runoff rates accommodated in flow conveyance infrastructure systems.  These data are also used to derive hyetographs, temporal rainfall "storm" patterns or event time series, used in hydrologic analysis of urban collection systems (i.e., storm, partially-separated sanitary or combined), as well as urban and rural catchments. 

The most recent update of Canadian climate station data includes "IDF Files" in the Engineering Climate Datasets.  Probability distributions are fit to the observed annual maximum intensities to estimate design intensities at various 'return periods', i.e., event probabilities.  Low return periods characterize frequent events, such as 2-year storms that have a 1/2 or 50% annual exceedance probability.  Meanwhile high return period characterize rare events, such as 100-year storms that only have a 1/100 or 1% probability.

The most recent update to Environment and Climate Change Canada's IDF Files adds 8.7 years, on average, to climate station records that exist in both the earlier V2.00 and the newest V3.10 sets - here is a link to those IDF Files: https://climate.weather.gc.ca/prods_servs/engineering_e.html.  As the V2.00 data had a 29.1 year average record length, the longer V3.10 records with an average of 37.8 years of data are 23% longer.  This improves the reliability of the derived statistics, allowing higher return period event intensities to be estimated.  A rule of thumb is that return periods that are double the record length can be reliably estimated - so 38 years of data can be used to estimate 76 year return period intensities with good confidence.

The following chart illustrates the change in median design rainfall intensity from the earlier V2.00 to the most recently updated V3.10 datasets. 

Design Rainfall IDF Trends Canada - Environment and Climate Change Canada IDF Files / Engineering Climate Datasets

Small frequent rain intensities, the 2 year rates observed in an average year, and represented by the green markets, have increased by almost up to 1% as a result of the longer records being added.  The less frequent 5 year and 10 year intensities, represented by purple and blue markers respectively, are generally unchanged overall.   The rare 50 and 100 year intensities, represented by the orange and red markers, have decreased the most, although the absolute change is limited.

The following table shows the % change in intensities from V2.00 to V3.10. 

Rainfall intensity changes in Canada at long-term stations (median IDF curve intensity trends) .. note '20 Year' should be '25 Year'

The percentage changes are small when new data is added. When one compares the newest data added to the earlier V2.00 data a larger percentage change is apparent.  The following graphic illustrates this where the initial series, similar to the V2.00 data, is represented by the blue markers and the whole current series is represented by the orange markers.  While the average of the whole data series, represented by the orange dashed line, is only slightly above the initial series average (blue dashed line), the new data average is significantly higher (green dashed line). 

Shifts in rainfall statistics with new longer periods of record (observations)

When the percentage changes in the above table are factored to account for the the changes in the new data relative to the initial series, the percentages increase by 4.3 times more, as shown in the table below. 

Rainfall intensity shifts in Canada - new observation comparison with earlier Environment and Climate Change Canada Engineering Climate Datasets

While individual stations will vary, the above table shows that on average the rare 50 to 100 year rainfall intensities in the almost 9 years of new data, added between the V2.00 and V3.10 datasets, are 2.4% to 2.2% lower than the earlier V2.00 values.  These changes are over 4 times greater than the shift in V2.00 to V3.10 values. In contrast the 2 year intensities are 2.6% higher in the new data relative to the V2.00 data.   Again this is over 4 times the V2.00 to V3.10 shift in the previous table.

As the confidence interval for rainfall statistics is wide relative to these changes, they may not represent statistically significant changes in rainfall intensity.  Some change may be significant however.  For example, the Toronto City confidence limits are shown below.

The 95% confidence interval for the 5 minute 100 year intensity of 42.9 mm/hr is plus or minus 16% of the expected value of 261 mm/hr.  A decrease of 6.8% in that statistic (the decrease in 5 minute 100 year intensities in new vs. V2.00 data) could be statistically significant.

To evaluate significance of trends, the Engineering Climate Datasets have included trends on annual maximum series observations since the V2.30 datasets.  These are explored for the entire V2.30, V3.00 and V3.10 datasets in this post: https://www.cityfloodmap.com/2020/05/annual-maximum-rainfall-trends-in.html

Above and below are summaries of these trends.

Trend in Maximum Rain     v3.10        v3.00         v2.30

Significant Increase                 4.28%       4.18%        4.09%

Significant Decrease                2.24%      2.33%        2.30%

No Significant Trend             85.80%     85.55%      86.37%

No Calculation                         7.68%      7.94%        7.24%

Trends at long-term stations in Canada are shown in this post: https://www.cityfloodmap.com/2020/06/yes-were-getting-more-extreme-rainfall.html

An example of these trends is shown below for Alberta.

These annual maximum series (called AMS) trends are taken from summaries in Environment and Climate Change Canada's IDF Files, 'tucked away' text files called "idf_v-3.10_2020_03_27_trends.txt", and that look like this:

The AMS trends described in the text file above are also shown graphically on charts for each station and can be used to check if AMS trends may be significant.  We can look at an example to see if a large change in IDF values has an underlying significant change in AMS used to derive IDF values.

A summary of changes over time at the Toronto City climate station for 5 minute durations is shown below. The Toronto 5 minute 100 year design intensities have decreased from 268.5 mm/hr in the V2.00 period to 2007 to 261.0 mm/hr in the V3.10 (and V3.00) period up 2017.   That is a decrease of 2.8% since 2007.  

This appears to be a large change. But is the underlying AMS observed data change significant?  The AMS trend chart for Toronto City is shown below and shows that the 5 minute trend in the top right chart is decreasing but the trend is not significant.  Significant trends are illustrated with a blue trend line.

The 12 hour trends in the middle chart on the bottom is a significant trend in the AMS of observed rainfall though.  How have the IDF values over a 12 hour duration changed at the Toronto City Station? Does this significant trend in AMS result in a large change in derived IDF values for that duration?  The following table shows trends in IDF values for 12 hour durations from recent Engineering Climate Datasets.

Surprise !

Despite the 2 year frequent storm intensity decreasing for 1990 to 2017, the IDF value from 2007 to 2017 does not change - it does not decrease in step with the AMS trend that is significantly decreasing.

Why?

This has to do with the effect of the extreme 2013 event on the Gumbel probability distribution used to derive IDF intensities from the AMS series.  The standard deviation of the 12 hour AMS was only 13.8 mm up to 2007 and this increased to 14.8 mm up to 2017.  This extends the tails of the distribution, resulting in the increased 100 year 12 hour design intensity from 7.2 to 7.5 mm/hr.  

What about other durations and the decreasing 5 minute intensities in the earlier table? What is the effect of 2013 on other durations?

The extreme 2013 event increases the 100 year 24 hour design intensity, but the frequent 2 year 24 hour intensity has decreased since 1990, and has stayed steady since 2003, as shown below.

As for the short-duration design intensities that govern urban drainage / conveyance system design and performance, those are decreasing overall since 1990, despite some increases from 2007 to 2017 in the 1 hour data. 

Most infrastructure in the Toronto area was built by 1990, considering that almost 90% of buildings were already in place by 1991.  Therefore most infrastructure was designed considering rainfall data that was available before 1990.  The performance of that infrastructure during short duration rainfall events would be affected by the design assumptions and the likely limited rainfall data available at the time.  It would also be affected by land use changes that have been significant over pervious decades as explored in previous posts like this https://www.cityfloodmap.com/2020/10/town-of-oakville-class-action-lawsuit.html or this https://www.cityfloodmap.com/2016/08/land-use-change-drives-urban-flood-risk.html.

In examining the Toronto IDF trends it is worth thinking about a common assumption that changes in mean values are reflected in changes in extremes.  The 12 hour and 24 hour data show that decreases in 2 year values can be accompanied by increases in 100 year values - a change in the opposite direction.  The data across Canada in the very first table above show a reversed trend - while 2 year intensities have increased overall and for all durations, the 20, 50 and 100 year intensities have all decreased overall and for all durations with one single exception (15 minute 100 year intensities increased).  A presentation on the Institute for Catastrophic Loss Reduction's report Telling the Weather Story for the Insurance Bureau of Canada (IBC) is on the IBC YouTube channel (https://www.youtube.com/watch?v=aRppaOquP5E), and makes the assumption that changes to averages also affect the extremes.  This graphic is used to explain how a shift in probability distribution (1 standard deviation in this illustration) is supposed to effect the extreme probabilities (see 13:10 into the presentation):

The speaker indicates "if we shift the means, we necessarily shift the frequency of occurrence of those extreme events".  That theory, or "necessity", is not showing up in the IDF shifts in Canadian data however.  In fact the shift in means, represented by the 2 year rainfall intensities, are going up overall, but this is accompanied by a decrease in the extremes (20, 50 and 100 year rainfall intensities) overall.  The shifts in means and extreme are not in the same direction.  And we see examples of the opposite occurring too - Toronto 2 year intensities decreasing with 100 year intensities increasing for some durations.

Its always good to check theories with data.

Warming in Canada is assumed to bring more intense rainfall in theory, but data has not shown an increase as illustrated above with the Engineering Climate Datasets.  Shifts in means or averages "necessarily" shift extremes too, again in theory, but not in Canadian rainfall observations, again as shown above.  Shifts in daily rainfall, often simulated in climate models, are assumed to predict changes in short duration extremes affecting urban flooding as well - but Canadian data shows that in regions across the country the shifts in daily rainfall can be opposite to the shifts in short duration rainfall (see this post that drills down into IDF shifts in several regions of the country and there 24 hour intensities and short duration ones are going in opposite directions: https://www.cityfloodmap.com/2020/07/can-we-use-daily-rainfall-models-to.html).

Theories need to be tested with experiments, i.e., real observational data, of course.  Richard Feynman perhaps said it best: "It doesn't matter how beautiful your theory is, it doesn't matter how smart you are. If it doesn't agree with experiment, it's wrong."