Adapting Flood Risk Management Preparing for an Unknown Future in Ontario

Transcription

1 Adapting Flood Risk Management Preparing for an Unknown Future in Ontario Ryan Ness Senior Manager, Research and Development Toronto and Region Conservation Authority Fabio Tonto Project Manager, Research and Development Toronto and Region Conservation Authority

2 TRCA Jurisdiction: Size: 3500 km2 Population: 4 million

3 Topics 1. Predicting future rainfall IDF statistics for stormwater management design 2. Flood risk assessment in developing watersheds in a changing climate

4 Partners Essex Region Conservation Authority John Henderson and Richard Wyma McMaster University Dr. Paulin Coulibaly University of Waterloo Dr. Donald Burn

5 Project Rationale Ontario practitioners are requesting that IDF curves be updated to reflect climate change Methods for incorporating climate change into IDF are not standardized The effect of methodological choices on IDF outcomes is unknown

6 Study Objectives To understand the implications of using different methods for incorporating climate change into IDF curves To develop an approach to compare outcomes of different permutations of climate model outputs and IDF derivation methods To apply this approach to examine outcomes of alternate methods in Essex and Toronto regions

7 Study Sites

8 Approach Literature Review Historical Data Processing Distribution Function Evaluation Climate Projections and Downscaling Future IDF Curve Derivation Comparison

9 Results: Distribution Functions

10 5 Climate Models 1-2 Emissions Scenarios 2 Downscaling Methods 16 Permutations

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12 Key Findings Existing Conditions GEV emerged as the best-fit distribution function for both the Toronto and Essex study areas IDF statistics vary widely between stations in the same area Results reinforce pitfalls of reliance on point-based IDF curves for water management, particularly for short-duration and high-intensity events

13 Key Findings Future Conditions Analysis using raw climate model output confirmed to have significant error potential Outcomes between permutations vary widely for a single station often over 100% for short durations and larger return periods Bias correction and delta downscaling techniques do little to reduce variability in outcomes Only 16 of hundreds of possible methodological permutations have been considered

14 Uncertainty is Unavoidable From: Wilby and Desai,

15 What to do?

16 Flexible Solutions with Multiple Benefits

17 Humber Hydrology Impact Studies and Climate Change

18 Discussion Points 1. Hydrology modeling in the Humber watershed 2. Incorporating climate change

19 Hydrology Modeling Simulates rainfalls conversion to stormwater runoff in a watershed Design storm Continuous simulation Used to calculate development limits (floodline) Informs the development of stormwater criteria

20 Humber Study Scope of Work 1. Develop an calibrate a high resolution hydrologic model 2. Assess Impact of White Belt development on downstream regional flood flows 3. Develop stormwater management criteria to control peak flood flows to pre-development levels including Regional Storm

21 Potential Full White-Belt Buildout Humber Watershed Plan Land Use Scenarios Existing (2002) = 26% Urban Approved OP = 36% Urban Full Build-Out = 49% Urban

22 What about Climate Change?

23 Real-world Storms

24 Rainfall Intensity (mm/hr) Real World Storm Comparison yr AES vs August 19th AES 12hr, 100yr Head Office -Aug 19th, Time (minutes)

25 Design Storms Watershed Black Creek Table 6.7: Percentage Difference in Peak Flows between Baseline and Potential Ultimate Conditions Using AES 6-Hour and 12-Hour Design Storm Distributions Node AES 6-Hour Distribution Percent Difference for Specified Return Period (Years) R % 0.0% 0.0% 0.0% 0.0% 0.0% 02HC % 0.6% 0.0% 0.1% 0.0% 0.0% Cold Creek R % 8.2% 6.4% 5.4% 4.6% 3.8% East Humber R % -2.2% -1.2% -1.1% -2.1% -2.0% R % 0.2% 0.1% 0.2% 0.1% -0.1% R % 2.0% 2.9% 1.5% 1.4% 1.6% R % -7.1% -13.9% -10.5% -5.2% -13.0% King Creek R % 1.8% 1.9% 0.7% 0.2% 0.3% Lower Humber R % 0.0% 5.5% 6.1% 6.1% 5.4% R % 8.3% 5.4% 3.5% 4.4% 3.7% R % 8.6% 5.4% 3.3% 3.2% 3.3% R % 2.0% -1.9% 3.0% -4.8% -12.3% R % -1.8% -0.6% -0.2% 0.0% 0.0% R % 0.8% -3.1% 4.4% -1.2% -6.8% Lake Ontario 3.5% 2.8% 3.1% 1.3% 0.5% -0.7% Main Humber R % 7.9% 3.2% 2.3% 2.1% 1.5% Purpleville Creek R % -55.9% -60.3% -58.7% -61.5% -63.4% Rainbow Creek R % 41.8% -1.1% -23.3% -31.8% -37.8% R % 13.8% 7.9% -3.2% -11.2% -9.6% R % 44.9% 16.3% 15.7% 4.5% -1.0% R % 0.0% 0.0% 0.0% 0.0% 0.0% Upper Humber R % 8.1% 5.3% 4.8% 3.7% 2.9% West Humber R % -40.0% -31.6% -17.6% -8.1% 1.5% R % -10.4% -16.5% -14.6% -17.1% -19.8% R % -77.7% -77.9% -79.0% -79.5% -80.3% R % -78.4% -79.1% -80.2% -80.8% -81.2% R % -24.7% -25.2% -29.2% -30.0% -30.8% R % -35.9% -34.0% -37.9% -38.5% -39.6% R % -79.1% -81.6% -79.4% -82.5% -84.1% R % -83.7% -85.6% -86.5% -86.7% -88.1% R % -15.1% -31.5% -36.2% -39.1% -35.9% R % -0.2% -1.3% -10.1% -15.2% -18.3% R % -0.7% -12.4% -16.4% -20.2% -23.9%

26 Real-world Storms Watershed Black Creek Node Table 6.2: Simulated Peak Flows for Baseline Conditions (Official Plan Build-out) Using Local Historic Storm and Regulatory Events August 2005 Event July 2006 Event Peterborough 2004 Event Toronto August 2005 Event (Don River) Regional Storm R % 0.0% 0.0% 0.0% 0.0% 02HC % 0.0% 0.1% 0.0% 0.0% Cold Creek R % 0.2% 12.7% -3.1% -0.4% R % -4.0% -3.8% -5.6% 0.0% East Humber R % 0.0% -3.0% 0.3% 0.0% R % 1.6% 2.4% 3.6% -0.3% R % -3.6% 5.6% -1.8% 3.1% King Creek R % 0.5% 16.4% 2.7% 0.2% R % 12.8% 0.0% 0.9% 1.3% R % 9.2% 5.7% -1.1% 1.2% R % 8.6% 12.5% -1.2% 1.0% Lower Humber R % 2.7% -2.4% -5.2% 9.6% R % 2.5% -4.9% -4.9% 9.3% R % 2.8% -4.6% -5.4% 9.3% Lake Ontario -1.0% 2.6% -4.6% -4.9% 9.3% Main Humber R % 7.6% 9.8% 0.1% 0.8% Purpleville Creek R % -65.3% 390.9% -53.0% 11.7% R % 0.0% 157.1% -8.9% 19.7% Rainbow Creek R % 1.2% 16.0% -24.4% 7.2% R % 12.6% 18.1% -0.7% 12.8% R % 4.0% 3.2% 1.5% 5.0% Upper Humber R % 3.0% 2.3% 9.8% 0.3% R % -39.9% 39.9% -4.0% 1.5% R % -37.8% -8.6% -7.1% 3.2% R % -79.8% -47.7% 27.7% 9.5% R % -79.1% 0.3% 1.2% 6.2% R % -14.8% -42.5% -28.2% 3.0% West Humber R % -48.4% -24.2% -6.5% 7.6% R % -81.1% -45.5% -33.1% 12.6% R % -88.9% -69.0% -28.1% 10.7% R % -37.4% -26.6% -5.1% 7.3% R % 0.2% -4.5% -6.4% 9.2% R % -13.0% -7.8% -10.6% 9.0%

27 Conclusions Real world storms are not necessarily climate change May represent a climate change like stress How is this information incorporated into regulation Pick a real world storm to regulate to Used as a check Other?

28 Moving Forward Efforts should continue to develop regional IDF data, techniques and statistics for Ontario Study of additional methodological permutations can help further quantify uncertainty in future IDF, but: Conversations are needed about the uncertainty in existing conditions IDF curves, and in water-related engineering design Focus should be shifted away from the quest for precise future IDF statistics in water management design No regrets, flexible water management strategies are needed that address the greatest risks and provide multiple benefits

29 Thank You Ryan Ness Fabio Tonto

30 Climate Models Model Rationale Data Project Interval Historical Scenarios A2 RCP 4.5 RCP 8.5 CRCM3- CGCM3 Canadian context NARCCAP 3-hourly RCMs HRM3- HadCM3 Popular RCM NARCCAP 3-hourly CanRCM4- CanESM2 Canadian context CCCMA (for Cordex) 1-hourly GCMs HadGEM2-ES Popular GCM CMIP5 3-hourly MIRCO-ESM "Best" performing GCM in Sheffield et al. (2013) CMIP5 3-hourly Downscaling Delta and Bias Correcting Methods

31 Downscaling 1. Delta change approach 2. Adapted bias correction (based on GEV)