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.

Does Higher Temperature Increase Rain Intensity? Not Always, Observations Show Decreasing Rain Intensity. Southern Ontario Twice As Many Statistically Significant Decreases In Annual Maximum Rainfall.

One degree temperature rise increases water vapour holding capacity
by 7%, but does it increase rainfall intensity?

High school science teachers and media have been saying that temperature increases associated with climate change cause a direct increase in water vapour and therefore, by association, more extreme rainfall.  This has been reported for years, like here in the Guardian where they say "A warmer atmosphere can hold more moisture, and globally water vapour increases by 7% for every degree centigrade of warming."

The Clausius-Clapeyron (C-C)
equation describes the water-holding capacity of the atmosphere as a function of temperature.

Geophysical Research Letters research looks at historical data to see if this theory linking temperature and rain intensity can be verified and what other explanatory variables are available. Researches from Lamont-Doherty Earth Observatory, Columbia University, MIT, and Institute Centre for Water Advanced Technology and Environmental Research (iWater), Masdar Institute of Science and Technology, and Department of Chemical and Environmental Engineering, Masdar Institute of Science and Technology analyzed how extreme rainfall intensities in the USA depend on temperature (T), dew point temperature (Td), and convective available potential energy (CAPE). The analysis considers geographic sub-region, season, and averaging duration.

What did researchers find in the data?:

"When using data for the entire year, rainfall intensity has a quasi Clausius-Clapeyron (CC) dependence on T, with super-CC slope in a limited temperature range and a maximum around 25°C

So Clausius-Clapeyron is only quasi-valid, meaning there is not a strong relationship between rain intensity and temperature. And rain intensities peak at 25 degrees Celcius ... they do not keep going up with temperature increases. The Guardian missed these details. Who else made the temperature-water vapour-rainfall relationship claim:

The magazine Science article How Much More Rain Will Global Warming Bring? touches on the 1 degree - 7 % atmospheric vapour relationship back in 2007. Bloggers around the world repeat this, and even David Suzuki is saying it. But lets look at more the the research findings based on actual data in Geophysical Research Letters. These charts show how rain intensities do not increase at the CC rate above 22 degrees:

The fourth column of charts shows temperature T on the x-axis. On the y-axis is slope of the relationship between rain intensity and temperature. The dashed red line is the predicted CC rate, meaning above 22 degrees rain increases less that predicted by CC. So no, this theory does not hold water (pun intended). In fact for some of the highest temperatures for some quantiles in the North Central and South, slope is negative, meaning that increased temperature DECREASES rainfall intensity (black lines go below zero).

Looking at the third column of charts with LnP on the y-axis, we see that for several quantiles of precipitation in both winter and summer, LnP does not reach the predicted rate at all (coloured lines below the predicted rates shown in the black dashed lines). In plain english this means the predicted increase in rain intensity with temperature is never met for small storms, e.g., the ones responsible for erosion, etc. So the theory is flawed for small storms.

In the summer, i.e., black lines in third column, precipitation as LnP flattens out or sometimes decreases at the highest temperatures, mostly in the South and Central of the US - for the lower 2 to 3 quantiles the CC rate is not met or just met. In the North, rain intensity for the lower quantiles of precipitation flattens out and decreases above 25 degree Celcius.

The take-away is that simple relationships make great theories. Real systems are more complicated than the Clausius-Clapeyron (CC) would suggest.

Lets look at something simpler in Ontario, Canada. Temperatures have increased. At right are temperature trends plotted by Statistics Canada. There is an increase from the late 1940's to 2008. Pretty clear.

Below are maximum annual observed rainfall trends for Toronto's long term climate station from Environment and Climate Change Canada's Engineering Climate Dataset Version 2.3, from the 1940's to 2007. It shows decreasing annual maximum rainfall for all rainfall durations from 5 minutes to 24 hours. Obviously the real world data shows us that despite increasing temperature, there is no corresponding increase in maximum observed rainfall.

The hypothesis that rising temperatures result in higher water vapour and then also more extreme rainfall is rejected based on the observations in southern Ontario. While temperatures are up in Ontario, there are twice as many statistically significant decreasing annual maximum rainfall trends as increasing ones as summarized from the Engineering Climate Dataset (version 2.3):

More statistically significant DECREASES in rainfall intensity are observed than increases.

For short duration rainfall, the convective storms that cause flash flooding in urban areas, we can look at the duration of 2 hours or less - there is just one statistically significant increase in annual maximum rainfall, and 6 examples of statistically significant decreasing rainfall maximum.

Evidence-based policies for flood mitigation and other stormwater or water resources management activities first require accurate characterization of factors affecting runoff and flow conveyance in municipal and natural drainage systems. By hypothesizing that rainfall intensities are increasing as a result of higher temperatures, flood damage mitigation could be achieved by combating green-house gas emissions to stall temperature increases. Data shows that extreme rainfall is not increasing with temperatures, and therefore an increase in flood damages is due to other factors (e.g., hydrology, hydraulics) - as a result effective flood damage mitigation must focus on key drivers and not temperature or rainfall trends.

We cannot explain severe weather, extreme rainfall, tornados and hail in Ontario with simple relationships that have been shown to contradict observation data.

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Canadian data analyzed by researchers at the University of Western also concluded that the Clausius-Clapeyron (C-C) equation did not match real temperature and rain data as observed in climate stations including Vancouver, Brandon, London and Moncton. As shown on the following graphs the real data relationships (coloured lines) do not follow the theoretical C-C scaling lines (dashed lines).

For Vancouver, precipitation decreases at higher temperatures (downward sloping solid lines).

For Brandon, London and Moncton, the slope of the precipitation-temperature trend line is less than the theoretical dashed line for most positive temperatures. In Moncton the trend is flat, meaning higher temperatures above 5 degrees C do not increase precipitation.

Key conclusions of the Western analysis were:

"Summary
- The sub-daily daily maximum precipitation shows weak linear correlation to the daily temperature for most stations and durations. Only lower durations for Moncton, London and Brandon show correlations roughly identical to the theoretical C-C 7% per 0C rate.
- For Vancouver station none of the sub-daily durations present linear correlation to temperature. For temperatures higher than 10 ºC negative slopes are observed.

Conclusion
- The Clausius-Clapeyron scaling rate clearly does not apply for any of the stations consider in this study, and should not be arbitrarily applied to derive IDF curves for future."

The analysis was presented at the ICLR Friday Forum in March 2017.

Researches also concluded that the use of Western's IDF_CC tool projections of future IDF would be preferred to any reliance on the C-C equation and its theoretical 1 degree = 7% scaling factor.