1) We developed a method to estimate comprehensive flood loss from numerically simulated river and inundation data using a global river model. 2) We applied this method to estimate direct and indirect economic costs from the 2011 Thailand floods using damage functions, capital stock data, and numbers of affected households and companies. 3) Our damage estimates were similar to independent estimates by the World Bank, and computational general equilibrium modeling estimated a GDP loss of 1.75% from the floods, similar to the World Bank's estimate of 1.1%. The model also found industrial activity stoppage had the largest impact on GDP loss.

1. A CGE analysis of economic costs of flood considering indirect
loss: A case study of 2011 Thailand flooding disaster
Shogo Nakata (1), Yukiko Hirabayashi (1)*, Shiniciro Fujimori(2) and Satoshi Watanabe(1)
(1)Institute of Engineering Innovation, The University of Tokyo, Japan
*contact : hyukiko@sogo.t.u-tokyo.ac.jp
(2)National Institute for Environmental Studies, Japan
BACKGROUND AND OBJECTIVES RESULTS
REFERENCES
Koirala S, Yeh PJ-F, Hirabayashi Y, Kanae S, Oki T (2014) Global-scale land surface hydrologic modeling with
the representation of water table dynamics. Journal of Geophysical Research-Atmospheres 119:75-89.
Yamazaki D, Kanae S, Kim H, Oki T (2011) A physically based description of floodplain inundation dynamics in
a global river routing model. Water Resources Research 47.
Fujimori S, Mosui T, Matsuoka Y (2014) Development of a global computable general equilibrium model
coupled with detailed energy end-use technology. Applied Energy 128:296-306.
• Globally, economic losses from flooding exceeded $ 19 billion in 2012
(Munic Re, 2013).
• If global warming proceeds, flood risk will increase globally in
particular Asia and Africa (Hirabayashi et al., 2013).
• Previous studies have been focused mainly on direct tangible
losses, however, indirect loss could cause considerable effects on
local eonomy as well.
•A retrospective river and inundation simulation
was conducted by CaMa-Flood (Yamazaki et al., 2011)
•The CaMa-Flood was obtained from a land
surface model, MATSIRO-GW (Koirala et al., 2014),
forced with a corrected reanalysis data (Iizumi et al.,
in prep.).
•Satellite-derived river width (Yamazaki et al., 2015)
was used for the simulation, while a parameter
of river depth was calibrated to obtain best-fit of
observed flood peak for the period between
1990 and 2000.
•Calculated daily inundation depth from CaMa-
Flood at a 0.1-deg horizontal resolution was
diagnostically downscaled onto DEM at 18 arc-
second (about 500m).
Direct asset loss
Modeled flood loss
Objective
①Estimating comprehensive flood risk including indirect loss.
②Estimating economical spreading effect of flood loss
METHOD
Damage RatioCapital stock ( 資産 )
Modeled annual maximum
inundation depth
Global River-Floodplain Model
CaMa-Flood
[Yamazaki et al., 2012]
Climate forcing(GFDJ55)
Land Surface Model
MATSIRO-GW
[Koirala et al., 2014]
Computable General Equilibrium model
CGE
[Fujimori et al., 2010]
Satellite-derived
inundation area
(MODIS)
validation
runoff
Damage depth function
Inundation Depth
Inundation Period
Direct Loss
Loss of direct
asset
Loss of
farmland
Indirect Loss
Emergency aid
expense
Recovery Cost Industrial
activity stop
Objective① Comprehensive Flood Loss
GDP
Observed
GDP
validation
Objective② Economical spreading effect
•Direct loss was estimated from modeled flood
exposure (potentially affected capital stock) and
damage ratio defined from damage-depth functions
for 3 land use types (urban, forest and cropland).
•Capital stock was obtained from re-gridded GPP
(Gross Provincial Product) multiplied by a
coefficient, 5.5, obtained from reported GPP in 2010
and estimated capital stock in 2010 by World Bank.
•Re-gridded GPP was obtained from a correlation
of percentage of urban grid and GPP for 77
province, as capital stock of urban grid was 2482
times of other grid.
GPP × 5.5
100.5E 100.75E
14.55N
13.95N
Million THB
Original GPP
GDP per capita multiply by
population density at 9km.
Re-grided GPP
Re-distribution of GPP
at 500 m.
Industrial area
Urban area
100.5E 100.75E
14.55N
13.95N
Million THB
Indirect asset loss
•Loss of industrial activity stop, ILi, was estimated as a number of days of activity stop multiplied
by total value-added amount as ILi = p x d, where p is value-added amount per day and d is the
number of activity stop (1.75 x days of inundation depth > 1m). This ratio was decided from
reported inundation period and stoppage of operation at 7 industrial estates.
•Emergency expense was calculated for households and business. The former includes working
cost of cleaning and additional family expense of fallback activities. Daily cleaning cost per
household (1935 THB, on 2010 PPP), total cleaning day at inundation level, additional family
expense and emergency expense per company were obtained from Japanese flood report (MLIT,
2005). Number of household and company of each province was obtained from Alpha Research
Company Ltd. (2008) and uniformly distributed.
We first developed a method to estimate comprehensive flood loss from numerically
simulated variables from river and inundation model. We then conducted CGE
analysis of 2011 Thailand flood to show economic costs of flood.
Flood simulation
CGE analysis
• A Computable General Equilibrium Model,
AIM/CGE (Fujimori et al., 2014) calculates economic
equilibrium.
• Model input was obtained from damage estimation
by World Bank.
Direct asset loss
(Industry, capital, household)
Loss of farmland
Emergency aid expense
( 救急費用 )
Industrial activity stop
( 産業活動停止 )
Recovery cost
( 修理費 )
Reduce capital stock
( 資本ストックを減じる )
All related variables×loss rate
Additional expenditure to
household ( 家計の追加的支出 )
Reduce capital operation rate
( 資本稼働率を減じる )
Increase capital stock
( 資本の形成 )
directindirect
Direct asset loss 845 bil.THB (The world Bank ： 630 bil. THB)
Industrial activity stop 223 bil.THB (The World Bank ： 528 bil. THB(industry +
agriculture))
Emergency expense 58 bil.THB (The World Bank ： 38 bil. THB (household sector))
Modeled flood loss showed similar in magnitude comparing to
independent estimation by the World Bank.
Emergency expenseDirect asset loss Industrial activity stop
[Million THB]
CGE analysis of flood loss
Direct asset loss
excep. farmland
Capital Stock
(bil. THB)
Damage
(bil. THB)
Percentage
(%)
Agriculture, Forestry
and Fisheries
1,676 5.67 0.34
Industry 211,770 513 2.36
Service 19,185 633 0.13
Transportation 6,224 248 1.02
Industrial activity
stop
Production
(mil. THB)
Damage
(bil. THB)
Percentage
(%)
Agriculture, Forestry
and Fisheries
1,327,575 34,715 2.61
Industry 13,275,427 493,258 3.72
Service 5,566,942 244,822 4.4
Transportation 1,468,494 9,496 0.65
• Loss of farmland: 8.57 %
• Recovery cost: 3.67%
• Aid from abroad: 10 million USD
GDPlossrate(%)
• Calculated GDP loss by CGE model (GDP of 2011 Thailand
flood minus GDP without flood) was -1.75%. This value is
similar to the estimation of the World Bank (-1.1%).
• Although loss of asset is the largest, effect of GDP loss from
Industry activity stop is the largest.
• The negative effect of flood loss lasts longer than 20-years
after the event.
GDPlossbyflooddamage
(millionUSD)
ACKNOWLEGEMENT
This study was supported by the Funding Program for the Global Environmental Research
Fund (S-14) by the Ministry of the Environment, Japan