Authors: Pui, A., Lall, A., Sharma, A.

Acknowledgments: Australian Research Council

Positive Phase 1

Negative Phase

Positive Phase 2

The IPO is a coherent pattern of sea surface temperature (SST) variability over the Pacific Ocean occurring on inter-decadal timescalesPower, S.B., T. Casey, C Folland, A Colman, and V Mehta, 1999: Inter-decadal modulation of the impact of ENSO on Australia. Climate Dynamics, 15, 319-324

Recent studies have shown that Flood Risk is not stationary and is conditioned to the IPO phase

Kiem, Anthony S. et al., "Multi-decadal variability of flood risk." Geophysical Research Letters, 2003: 1-4.

Negative Phase : Increased Flood Risk

Positive Phase : Decreased Flood Risk

1 in 6 year flood (IPO –ve)

1 in 100 year flood (IPO+ve)

Flood risk is influenced by the IPO.

Is this caused by changes in design rainfall or antecedent conditions?

1. Do antecedent wetness conditions influence the design flood estimate?

2. Does design rainfall vary between opposing IPO phases?

3. Do antecedent conditions vary between opposing IPO phases?

Pi

Catchment Antecedent Conditions are approximated by ANTECEDENT PRECIPITATION INDEX (API):

Where :• P = annual maximum 24 hour rainfall amount• i = day on which the annual maximum event occurs• K = API exponential decay factor ( 0.92) • n is the specified time lag (10)

K Pi-1

K2Pi-2

K3 Pi-3

KnPi-n

Cordery I . Antecedent wetness for design flood estimation. Civil Eng Trans I E Aust 1970; 12:181–5

High P is defined as above 50th percentile annual rainfall maxima

>

<

High rainfall corresponding to high flows is most likely when catchment in a wetter state

Test Statistic:

H0 : No. Stations Iratio > 1 <= No. Stations Iratio < 1

HA : No. Stations Iratio > 1 > No. Stations Iratio < 1

Proportion: Australia wide Iratio > 1 = 0.64

East Australia Iratio > 1 = 0.61

Not significant as per field significance test (0.95%)

Iratio > 1

Iratio < 1

Test Statistic:

H0 : No. Stations APIratio > 1 <= No. Stations APIratio < 1

HA : No. Stations APIratio > 1 > No. Stations APIratio < 1

APIratio > 1

APIratio < 1

Proportion: Australia wide APIratio > 1 = 0.78

East Australia APIratio > 1 = 0.86

Significant as per field significance test (0.95%)

We have shown:

1. antecedent wetness conditions influence the design flood estimate

2. Variation in design rainfall between opposing IPO phases is not statistically significant

3. However, antecedent conditions vary significantly between opposing IPO phases?

What does this mean for current approaches to Design Flood Estimation?

Years

Years

Rain

fall

AEP(%)

Duration

Inte

nsity

Stre

am F

low

Stre

am F

low

Rain

fall

Years

Stre

am F

low

Annual Maximum P IFD Relationship Flood Frequency Curve

Continuous P Annual Maximum Q Flood Frequency Curve

AEP(%)

Stre

am F

low

Flood Frequency Curve

AEP(%)

Rain

fall

HyetographRa

infa

llRa

infa

ll

Stre

am F

low

Stre

am F

low

AEP(%)

AEP(%)

• Future approaches for flood estimation need to account for the non-stationary character of antecedent moisture.

• This seriously compromises the assumption that design rainfall leads to design floods (‘AEP neutrality’).

• Also beware of rainfall-runoff models calibrated to data from a single IPO state.

Alexander Pui

School of Civil & Environmental Engineering, UNSW

Email: a.pui@student.unsw.edu.au