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 BranchEnvironment Canada

4th International Symposium on Flood DefenceToronto, Ontario, CanadaMay 8, 2008

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Study Area – Four River Basins in Ontario

Lake Huron

Lake Erie

Lake Ontario

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KitchenerKitchenerKitchenerKitchenerKitchenerKitchenerKitchenerKitchenerKitchener

LondonLondonLondonLondonLondonLondonLondonLondonLondonUpper Thames

River Basin

Grand River Basin

Humber River Basin

Rideau River Basin

Streamflow volume (m3 s-1) for the selected river basins (Apr.–Nov. 1961–2002) River Basin Thames Grand Humber Rideau Overall mean (Std Dev) 2.61 (5.06) 8.94 (16.06) 0.77 (1.38) 6.12 (13.57) Mean annual maximum 37 119 11 80 Extreme maximum 116 320 25 142

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Outline

Objectives Data used in the study Methodology Results Conclusions

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Objectives – Three parts of the study

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

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Data used in the study

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/sewerMonthly total insurance claims/costsbackup 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)

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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 heavyrainfall events:

Statistical methods including χ2-test principles

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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

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Predictors significantly contributed to rainfall events(combined all models)

Principal Component Variables

Temperature at surface, 925, 850h, 700 and 500Pa Surface wind speed Zonal and meridional wind at 925,850, 700 and 500hPa Dew point depression at 925, 850,700hPa Sea level pressure Sea-level pressure change in past 6 h

Dummy Variables

Total cloud cover Lifted index K index Precipitable water Surface dew point depression Total totals index Surface wind direction index

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Predictors used to develop streamflow simulation models

Antecedent precipitation index (API)*: Pt—precipitation (mm) during day t

K—a decay constant = 0.84API2

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)

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Evaluation structure of quantitative daily rainfall simulation results based on observations (Rideau River Basin, April–November 1958–2002)

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Excellent Good Fair Poor

Rainfall <12.5 12.5–32.5 ≥32.5 Number = 3574 189 246

Correct level Observed rainfall < 5 mm Observed rainfall ≥ 5 mm

Excellent Diff ≤ 1.5 mm Diff ≤ 30% of Obs

Good 1.5 mm < Diff ≤ 3.0 mm 30% of Obs < Diff ≤ 60% of Obs

Fair 3.0 mm < Diff ≤ 4.0 mm 60% of Obs < Diff ≤ 80% of Obs

Poor Diff > 4.0 mm Diff > 80% of ObsNote: Diff indicates absolute difference of observed and forecasted in mm; Obs indicates observed rainfall in mm.

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Daily streamflow observations versus model verification at Rideau River Basin (1970–2002)

A cross-validation scheme was used for model validation32-model: R2s: 0.95; RMSEs: 2.85–2.95 m3 s-1

(Overall mean and std: 6.12 and 13.57 m3 s-1)

Validation results:

R2 = 0.95RMSE = 2.98

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Model fitting line

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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

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Methodology—evaluation of simulation models and downscaling transfer functions

Validation of simulation models and downscaling transfer functions to avoid overfitting:

a cross-validation schemeevaluating 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

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Temperature Dew Point Temperature

CGCM1 CGCM2-A2 CGCM2-B2 ECHAM5 GFDL-A2

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Extreme events:

03:00 temperatures >20oC03:00 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.

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)

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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-tenthsPressure (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.

Sea-level Pressure Total Cloud Cover

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Part III—Future estimates

Future downscaled GCM scenarios

Estimate future synoptic weather types

Project future daily rainfall/streamflow and heavy rainfall-related flooding risks

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Quantile-quantile plots of daily rainfall amount derived from downscaled GCM historical runs versus observations over the same period (April–November 1961–2000)

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Quantile-quantile plots of daily streamflow volume derived from GCM historical runs versus observations over the same period (May–November 1961–2000)

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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

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>Trace ≥15 mm ≥25 mm Number of Days

The 1st bar: 2016–2035The 2nd bar: 2046–2065The 3rd bar: 2081–2100

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Seasonal MeanStreamflow

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: 2016–2035The 2nd bar: 2046–2065The 3rd bar: 2081–2100

Thames Grand Humber Rideau5th percentile 0.246 1.930 0.162 0.05095th percentile 6.73 18.90 2.91 11.60Overall mean 2.61 8.94 0.77 6.12

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Number of Claims Incurred Insurance Cost

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: 2016–2035The 2nd bar: 2046–2065The 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.

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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.