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IDF Data and Climate Change

It is not appropriate to use IDF data based on historical information alone for long-term planning. ECCC recommends the use of a scaling methodology for short-duration rainfall IDF data. Read this article for information about integrating climate change into IDF data, including a practical example.

Module

Intensity-Duration-Frequency (IDF) Curves

Format

Article

Time to completion

5 minutes

Summary

Short-duration rainfall data, or Intensity-Duration-Frequency (IDF) data, are an important tool for decision-making about the risks of extreme precipitation. Because climate change is expected to increase extreme rainfall in Canada, IDF data based on historical observations alone are not appropriate for long-term decision-making. Environment and Climate Change Canada therefore recommends use of a scaling methodology, described in this article.

Climate change-scaled IDF data based on this methodology is available for all IDF stations on the ClimateData.ca Variable page. Click on a station to download full datasets or a Quick Start zipped file.

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 W231:25 “Developing and interpreting intensity-duration-frequency (IDF) information under a changing climate” 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 W231:25 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 way to update IDF curves for climate change.  However, careful consideration of uncertainties in estimates of future extreme rainfall is still required. Local temperature change projections depend on future emissions, climate model choices, and natural variability. And the historical IDF data that underlie any future IDF estimates may themselves be less than ideal estimates of past rainfall extremes.

For these reasons, the climate change-scaled IDF data provided on ClimateData.ca was developed using an ensemble of climate models. The datasets include multiple emissions scenarios, time periods, percentiles, and confidence intervals. Details on the scaling methodology and a worked example are provided below.

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

You can also access climate change-scaled IDF data based on this methodology on the Variable page.

1. For the location of interest, download historical IDF curve data from ClimateData.ca or develop based on guidance from ECCC and CSA W231:25. 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 1.07ΔT

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. You can also access climate change-scaled IDF data based on this methodology on the Variable page.

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:
    • 30-year average Historical Temperature that best represent the observational period used to develop historical IDF information:
      • For each RCP scenario-specific CSV file, use a spreadsheet program to determine a 30-year average annual historical temperature for each climate model. Center this 30-year time period in the middle of the observational period used to develop the original historical IDF curves.
        • 30-year average Future Temperature, centered on the final year of the proposed building lifetime:
          • For each RCP scenario-specific CSV file, use a spreadsheet program to determine the 30-year 2061-2090 average annual future temperature value for each climate model in the ensemble. This period is used because it brackets the year 2070, which is the estimated end-of-life for the proposed building, 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 1.07ΔT)  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.

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