Possible Impacts of Climate Change on Heavy Rainfall-related Flooding Risks In Ontario, Canada

1. Possible Impacts of Climate Change on Heavy Rainfall-related Flooding Risks In Ontario, Canada Chad Shouquan Cheng, Qian Li, Guilong Li, and Heather Auld Meteorological Service of Canada Branch Environment Canada 4th International Symposium on Flood Defence Toronto, Ontario, Canada May 8, 2008

2. Rideau River Basin Humber River Basin Grand River Basin Upper Thames River Basin Study Area – Four River Basins in Ontario

3. Outline • Objectives • Data used in the study • Methodology • Results • Conclusions

4. Historical analysis: Synoptic weather typing Within-weather-type rainfall/streamflow simulation models Statistical downscaling: Hourly and daily climate change scenarios Future estimates: Synoptic weather types Future heavy rainfall and high-flow events Objectives – Three parts of the study

5. Surface weather Hourly and daily surface observations of data: many variables (1953–2002) Upper-air data: Six-hourly U.S. NCEP reanalysis data (1958–2002) Streamflow data: Daily streamflow volume at a selected station of each river basin (1961–2002) CGI flooding/sewer Monthly total insurance claims/costs backup cost data : (Apr.–Sep. 1992–2002) Climate change Five GCM models’ output from three Canadian scenarios: (CGCM1-IS92a, CGCM2-A2/B2), one U.S. (GFDL-A2), and one German (ECHAM5-A2) GCMs (1961–2000, 2016–35, 2046–65, 2081–2100) Data used in the study

6. Methodology—Synoptic weather typing • Synoptic weather typing: • Principal component analysis • Average linkage clustering procedure • Discriminant function analysis • Data: hourly observations of air temperature, dew point temperature, sea-level air pressure, total cloud cover, and south–north and west–east scalar wind velocities. • Identification of the weather types associated with the heavy rainfall events: • Statistical methods including χ2-test principles

7. Methodology—development of prediction models and downscaling transfer functions • Selection of regression methods • Multiple stepwise regression • Robust stepwise regression • Logistic regression • Multinomial logit regression • Nonlinear regression • Autocorrelation correction regression • Orthogonal regression • Selection of predictors

8. Predictors significantly contributed to rainfall events(combined all models)

9. Predictors used to develop streamflow simulation models • Antecedent precipitation index (API)*: • Pt—precipitation (mm) during day t • K—a decay constant = 0.84 • API2 • Antecedent temperature index (ATI)**: ATIi = 0.9ATIi-1 + 0.1 • Current-day, previous-day, and/or day-before-yesterday rainfall amount • Polynomial function of Julian day fitting into streamflow data * Bruce and Clark (1966); Richard and Heggen (2001) ** Hopkins and Hackett (1961)

10. Evaluation structure of quantitative daily rainfall simulation results based on observations (Rideau River Basin, April–November 1958–2002) Note: Diff indicates absolute difference of observed and forecasted in mm; Obs indicates observed rainfall in mm.

11. Perfect line Model fitting line Validation results: Daily streamflow observations versus model verification at Rideau River Basin (1970–2002) A cross-validation scheme was used for model validation 32-model: R2s: 0.95; RMSEs: 2.85–2.95 m3 s-1 (Overall mean and std: 6.12 and 13.57 m3 s-1)

12. Part II—Statistical downscaling (regression-based) • Spatial downscaling daily GCM scenarios to the selected stations • Temporal downscaling GCM scenarios from daily to hourly Cheng et al. (2008): Theoretical and Applied Climatology, 91: 129–147

13. Methodology—evaluation of simulation models and downscaling transfer functions • Validation of simulation models and downscaling transfer functions to avoid overfitting: • a cross-validation scheme • evaluating model R2s • Comparison between downscaled GCM historical runs and observations over the same period (1961–2000) • data distributions • diurnal and seasonal variations • extreme weather characteristics

14. Mean annual number of days with extreme eventsObservations (Obs) versus GCM historical runs (His) over the period 1961–2000 and future downscaled scenarios (2046–65, 2081–2100) Extreme events: 03:00 temperatures >20oC 03:00 dew point temperatures >18oC 15:00 temperatures >29oC 15:00 dew point temperatures >19oC Raw GCM outputs (four-city average)—the nearest grid point: The annual number of days with Tmax >29oC (1961–2000) CGCM1 CGCM2-A2 CGCM2-B2 5.5 1.1 1.0 Observation over the period 1961–2000 was 19.7 days per year.

15. Mean annual number of days with extreme eventsObservations (Obs) versus GCM historical runs (His) over the period 1961–2000 and future downscaled scenarios (2046–65, 2081–2100) Extreme events: Total Cloud Cover: ten-tenths Pressure (pooling 4 cities): the lowest 10th percentile for the period 1961–2000 03:00 15:00 1005.4 1005.1 Raw CGCM outputs (averaging 4 cities and 3 CGCMs) over 1961–2000: The annual number of days with ten-tenths cloud: 73 days Corresponding observation: 143 days. The corresponding number of days with sea-level pressure ≤1005.4 hPa derived from raw CGCM historical runs was about 25% higher than that observed.

16. Part III—Future estimates • Future downscaled GCM scenarios • Estimate future synoptic weather types • Project future daily rainfall/streamflow and heavy rainfall-related flooding risks

17. Quantile-quantile plots of daily rainfall amount derived from downscaled GCM historical runs versus observations over the same period (April–November1961–2000)

18. Quantile-quantile plots of daily streamflow volume derived from GCM historical runs versus observations over the same period (May–November1961–2000)

19. The 1st bar: 2016–2035 The 2nd bar: 2046–2065 The 3rd bar: 2081–2100 Percentage Change in frequency of future rainfall events from the current condition (Apr.–Nov. 1961–2002), averaged across the four selected river basins in Ontario and five GCM scenarios

20. Percentage Change in frequency of future high-/low-flow events from the current condition (May–Nov. 1961–2002), averaged across the four selected river basins in Ontario and five GCM scenarios The 1st bar: 2016–2035 The 2nd bar: 2046–2065 The 3rd bar: 2081–2100

21. Percentage changes in future monthly total number of insurance claims and costs from the current condition (Apr–Sep 1992–2002), averaged across the four selected river basins and five GCM scenarios The 1st bar: 2016–2035 The 2nd bar: 2046–2065 The 3rd bar: 2081–2100 • These estimates consider only possible changes in future rainfall, BUT not take into account other non-environmental factors such as: • Population growth • Economic changes • Changes in the location and value of assets • Aging properties and infrastructure • Land-use and urbanization • Any substantial changes in government policy, and etc.

22. Key Conclusions • Synoptic weather typing methodology could be considered as an appropriate tool to identify heavy rainfall and high-flow events; It could also be a suitable technique for climate change impact analyses. • The simulation models developed in the study are suitable in short-term predicting the occurrence of rainfall/streamflow events as well as daily amounts • The methodologies used in the study could be used to estimate long-term changes in frequency and magnitude of future relevant events.