possible impacts of climate change on heavy rainfall-related flooding risks in ontario, canada chad...
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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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 2 / 22
Study Area – Four River Basins in Ontario
Lake Huron
Lake Erie
Lake Ontario
0
Kilometres
200100
TorontoTorontoTorontoTorontoTorontoTorontoTorontoTorontoToronto
OTTAWAOTTAWAOTTAWAOTTAWAOTTAWAOTTAWAOTTAWAOTTAWAOTTAWA
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 3 / 22
Outline
Objectives Data used in the study Methodology Results Conclusions
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 4 / 22
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 5 / 22
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)
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 6 / 22
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 7 / 22
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 8 / 22
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 9 / 22
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)
24
1t
t
tkP
dayspreviousT 7
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 10 / 22
Evaluation structure of quantitative daily rainfall simulation results based on observations (Rideau River Basin, April–November 1958–2002)
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Per
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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.
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 11 / 22
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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0 15 30 45 60 75 90 105 120 135 150Observation (m3 s-1)
Sim
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-1)
Perfect line
Model fitting line
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 12 / 22
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 13 / 22
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 14 / 22
Temperature Dew Point Temperature
CGCM1 CGCM2-A2 CGCM2-B2 ECHAM5 GFDL-A2
Montreal
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Ottawa
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Windsor
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Obs His 2046–2065 2081–2100
Montreal
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ObsObs His 2046–2065 2081–2100
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)
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 15 / 22
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
Montreal
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Windsor
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Obs
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Obs His 2046–2065 2081–2100
0 2 4 6 8 10 12 14CGCM1 CGCM2-A2 CGCM2-B2 ECHAM5 GFDL-A2
Montreal
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ObsObs His 2046–2065 2081–2100
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 16 / 22
Part III—Future estimates
Future downscaled GCM scenarios
Estimate future synoptic weather types
Project future daily rainfall/streamflow and heavy rainfall-related flooding risks
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 17 / 22
Quantile-quantile plots of daily rainfall amount derived from downscaled GCM historical runs versus observations over the same period (April–November 1961–2000)
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 18 / 22
Quantile-quantile plots of daily streamflow volume derived from GCM historical runs versus observations over the same period (May–November 1961–2000)
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 19 / 22
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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Cha
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Seasonal Rainfall Total
>Trace ≥15 mm ≥25 mm Number of Days
The 1st bar: 2016–2035The 2nd bar: 2046–2065The 3rd bar: 2081–2100
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 20 / 22
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<5th Percentile ≥95th Percentile Number of Days
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
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 21 / 22
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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.
4th Int’l Symposium on Flood Defence, Toronto, May 8, 2008 22 / 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.
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