Reconstructing Reconstructing the 1-in-100-yr Flood the 1-in-100-yr Flood

in in Northern ThailandNorthern Thailand

Renée Kidson

University of Cambridge

Synopsis:Synopsis:

• Hydraulic modelling and proxy dating identifies the 1-in-100 yr flood for the Mae Chaem river, Thailand

• Flood Frequency Models fitted to 50 yr gauging record: estimate 1-in-100 yr Q

• Compare with independent reconstruction

• Power-Law model produces closest estimate

Methods:Methods:• Hydraulic modelling

HEC-RAS; 8 km, 50 cross sectionsCalibration of Manning’s n:

• PSIs from several gauged high-flow events

• PSI dating proxies:C14 datingDendrochronologyOral historyWritten historical evidence AnecdotalClimatic

Methods (cont)Methods (cont)

• Flood Frequency Models:

Fitted to 48 yr gauged record (1953-2001)

Types:• Log- Normal (LN)• Gumbel EV 1• Log Pearson III (LP3)• Power-Law

Study SiteStudy Site

• Northern Thailand: South West (Indian) monsoon climate

• Tropical cyclone incursion from South China Sea

• Steep topography: orographic rainfall enhancement – prone to extreme events

Year

1960 1970 1980 1990 2000

Mon

th o

f the

Yea

r

4

5

6

7

8

9

10

11

12

4

5

6

7

8

9

10

11

12

1. Hydraulic Model Calibration

0 2000 4000 6000 8000 10000270

280

290

300

310

320

330

340

Main Channel Distance (m)

Ele

vatio

n (

m)

Legend

Sim Water Elev PF 1

Bed Elev

Obs Water Elev PF 1

2001 Flood:

Q (known) = 794 cms

n (inferred) = 0.072

PSIs: 2 palaeofloods

• Types:– Elevated fluvial sand deposits– Wood debris in gorge caves

0 2000 4000 6000 8000 10000270

280

290

300

310

320

330

340

Main Channel Distance (m)

Ele

vatio

n (

m)

Legend

Sim Water Elev PF 1

Bed Elev

Obs Water Elev PF 1

Palaeoflood 1:

n (assumed) = 0.072

Q (inferred) = 980 cms

Palaeoflood 1Palaeoflood 1

• Anecdotal evidence: Palaeoflood 1 = 1960 flood

• 1960 flood = 1030 cms

• Further Manning’s n calibration– n = 0.068

0 2000 4000 6000 8000 10000270

280

290

300

310

320

330

340

350

Main Channel Distance (m)

Ele

vatio

n (

m)

Legend

Sim Water Elev PF 1

Bed Elev

Obs Water Elev PF 1

Palaeoflood 2:

n (assumed) = 0.068

Q (inferred) = 2420 cms

2. Proxy Dating of

Palaeoflood 2

CC1414 Dating Dating

• Teak log in cave– Problematic for late Holocene specimens

• Heartwood & Sapwood dating• Dendrochronological count to cross-check• 161 tree rings• Heartwood: 1685-1726 • Sapwood: 1831-1880 • Palaeoflood 2 postdates 1880

• Written historical records:– Bombay Burmah Trading Corporation

purchased forest lease 1905

• Western artefacts in cave: Palaeoflood 2 postdates 1905

• Slackwater Deposit– Charcoal C14 date: 1889-1908

Other ProxiesOther Proxies

Climatic Proxy

• Contemporary Site Climate record:– Rainfall Anomaly correlation with

All India Rainfall Index

All India Rainfall Index 1876-2000

550600650700750800850900950

10001050

1876

1884

1892

1900

1908

1916

1924

1932

1940

1948

1956

1964

1972

1980

1988

1996

Year

Annual

Rai

nfa

ll [m

m]

1917 1961

Year [AD]

1880 1900 1920 1940 1960 1980 2000

Per

iod

[yrs

]

3

4

5

6789

20

30

40

50

60708090

10

100

3

4

5

6789

20

30

40

50

60708090

3. Flood Frequency Analysis

Return Period [yrs]

0.1 1 10 100

Peak

Ann

ual D

isch

arge

[m3s

-1]

100

1000

LNGumbel EV1LP3Power-LawAnnual SeriesData1-in-100-yr Flood Predictions

Model Predicted Q (m3s-1) Log-Normal 1219 Gumbel EV1 1039

Log-Pearson III 1011 Power-Law 2334

Reconstructed discharge: 2420 m3s-1

Power-Law ?• Malamud et al. 1996The 1993 Mississippi River Flood: a One Hundred or a One Thousand

year event?

• Theoretical basis: fractal statistics• Well understood for other phenomena e.g. earthquakes• Empirical evidence: essentiality of palaeoflood reconstructions• Alila and Mtiraoui (2002): ‘the selection of the most plausible distribution for flood frequency

analysis should be based on hydrological reasoning as opposed to the sole application of the traditional statistical goodness-of-fit tests’.

ImplicationsImplications

• Flood Risk Perception– Gumbel EV1 = official Thai distribution

• Global Warming scenarios:– recommend incorporation of Power-Law

estimates as an upper bound for FFA

ConclusionsConclusions1. Palaeoflood discharge estimate

Hydraulic modelling High confidence: calibration opportunities

(several gauged flood events)

2. Reliably dated (several proxies) as 1-in-100 yr flood

3. Compared with FFA models based on gauged record

4. Power-Law model produced closest estimate to reconstructed event