Adaptation for water problems forclimate change
So Kazama
Department of Civil Engineering,Tohoku University

WATER. Key Issue on climate change• Climate change affects water (Hydrologic) cycle.
• Water influences wide field.
• Flood and drought problems are the biggest
impact for human life.
• Water environmental problems threat our life
for long time.

floodHigher flood risk

Model development
Application for climate change

floodFlood impact

Extreme rainfall increase (IPCC, 2007)
Heavy rainfall produces frequent flooding.
Rainfall with 100yrs return period increase 20%
from now in 2100. （JMA RCM results）

It is necessary to evaluate economic damage
in 2100 for the adaptation.
4

floodObjectives

Calculation of economic damage by flooding after climate change
in a whole Japan using extreme rainfall data.

Quantifying the adaptation cost using the increase ofdamage cost from current flood control.

17.7.2004, MLIT

5

floodModel Verification• Flood simulation
landusenAgri.&Forest

0.060

Traffic area

0.047

Others0.050

UrbanedWaterbody&Beach

0.050

WD 0.0m, 0.5m, 1.0m
WD 1.5m, 2.0m, 2.5m

0.020

6

Results （case : 100yrs RTN）

flood

Extreme rainfall

Max. Water depth

0m1m0700(mm/day)7

Estimation of damage cost1) Paddy field
D.C.＝rice production/area×rice price×inundated area
×damage rate to water depth
2) Crops field
D.C.=crops production/area×average crops price×inundated area
×damage rate to water depth3) Buildings ( 4)Golf links)
D.C.of houses=damaged floor area to water depth×price/m2
×damage rate to water depth
D.C.of house articles=house number to water depth×house article value/house×damage rate to water depth
D.C.of asset of office=worker number×（amortized asset value/person×coefficient to water depth＋
stock asset value/person×damage rate to water depth）
5) Public facilities
D.C.=general damaged asset value×1.694

8

floodEstimation of adaptation cost
Annual expected damage → Annual adaptation cost (B/C=2.3)

Billion USD
RTN

AnnualDamagePeriod extreme P. Cost

Interval Av. Interval
Damageprobability

Av. Annual
expecteddamage cost

50.20

380100.10

550

470

0.1

4730

0.03

7706600.067

44500.02910

840

0.013111000.01

1,120

1,020

0.010

10This amount is similar to annual expense of flood control in Japan.

Adaptation cost is 4.6bilion USD

9Annual Expected Damage Cost

Urban Areas have huge damage.
Rural areas have less damages.

100 million JPY/km2= 1 million USD/km2

Kazama et al., 2009. Sustainability ScienceslopeHigher slope failure risk

Model development
Application for climate change

slopeQuantity of slope hazard

Past hazard results

Effect of snow melt

Cumulo-frequency

There are a lot of slope hazards by
the extreme precipitation.

Return period of Daily extreme Precipitation

Table. Relation to Slope hazard and return
period on extreme precipitation
Initial slope hazard condition;
By precipitation(model sample of Tohoku region)
By extreme
Iwaki city, Fukushima prefecture
precipitation of 5year
1991.9.19
By snowmelt

Fig. Slope hazard map by literature

Fig. Hazards map in Iwaki

Probability model equationslope

1
P=1 + exp [− (β 0 + β h hydY h + β r reliefY r )]Where P is probability,β0 is intercept,βh:is coefficient of hydraulic gradient,
βr is coefficient of hydraulic gradient,,hydYh is hydraulic gradient,reliif Yris relief energy

Dangerous geology

Probability

Steep curve , rising point as small value

Colluvium

Neo
tertiary rock

Paleogenetertiary rockFig. Logistic curve of each geological features

Relief energy
Hydraulicgradient

GraniteslopeSediment hazard map

These are similar to actual
events.
These are considering rainfall
effects.

Probability(%)

Slope failure probability on 30 years return
period downpour.

Economic damage (billion yen)2500

0.0345x20001500y = 19.408e2
R = 0.562

Future-A2

Economic damage results
Climate change case

Future-A1B
2004

NearFuture-A2

1000

Future-B1

Present average

50010billion
USD

NearFuture-A1B
NearFuture-B10Ratio=1.50

2550

75

100

Ratio=2.0

125

150

Ratio=1.0 ( → 62times)

100mm/day Over times

Relationship between days of over 100mm/d
and damage costs
CONCLUSION
Huge water disasters increase rapidly caused by climate change.

Kawagoe et al., 2010. Hydrology and Earth System Science (submitted)

quality

Worse water quality

Model development
Application for climate changeDam basins
Average probability(%)

quality

Probability map

Year average
sediment yield
per unite area
(×103m3/km2/year)

Probability(%)
Probability

comparative verification

Specific sedimentyield

Average specificsediment yield(×103m3/km2/year)

Average specificsediment yield(×103m3/km2/year)

Probability(%)

The relationship between probability
and specific sediment yield was obtainedand verified about 59 dam areas in theJapanese Islands.

quality

Probability model reproduces sediment hazard
Spacific sediment yield(killo-m3/km2/year)

2.01.8y = 0.1051e0.0301xR2 = 0.6541

1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0010

2030

4050

6070

8090100

Probability (%)

Relationship between probability with return period of 5years
and specific sediment yield

An exponential equation shows the relationship.
→ sediment production model

quality

Sedimentation yield map
Average specific sediment yield(×103m3/km2/year)

High sedimentation yield areas will have lessreservoir capacity by increase of downpour.

quality

Water quality problems□ Influence of downpour
・Use of L‐Q formula・Extreme rainfall input to L‐Q formula for BOD and SS□ Influence of drought period
・Use of turbidity deposit function
・Input of drought period to a deposit function for BOD and SS

Show relationship between extreme period(return period ) and BOD and SS.

quality(Kiso)(Kiso)(Go, ohta)

Increase ratio(%)

Increase ratio(%)(Yoshino)

【BOD】

【SS】

Downpour affects WQ (RTN 50 years / 10years)

(Teshio)

quality(Shinano)(Abukuma)

(Ara)
(Ara)

Increase ratio(%)
【BOD】(Temryu)【SS】

Drought affects WQ change (RTN50uyears/RTN10years)
Kawagoe et al., 2008. Journal of Environmental Engineering

quality

Water quality of closed lakes

Ideal closed water body（Max depth 30m)
+Meteorological conditions in different climatezones (Temperature, Precipitation)
1ｋｍ

Simulation of water temperature change

Analysis of response temperature
Comparison of different climate zones
Same calculation conditions（ｍ）

Study Area
Surface Ares
Max. Depth
Max. DepthPosition

4000×4000 m
2.8×106 m230m
Centerquality

GCM UseResults of present climate condition Vertical density gradient in summer(2005)
Vertical density gradientSouth sphere

31 Jan. 2005

North sphere

31 July, 2005

Vertical density gradientquality

GCM UseInfluences of climate change for temperature in closed lakes
Change of density gradient in the future（2005 ‐ 2050）

Vertical density gradientOver 0.020kg/m3/m

Large impact areas （Gradient more than 0.020kg/m3/m）
North Sphere : North America, Eastern Europe, Northeastern Africa
Shida et al., 2008. Journal of Global Environmental Engineeringconclusions

Conclusions
1) The probability according to extreme precipitation
could show the spatio-temporal distribution of water
disaster hazard.
2) The rainfall pattern change affects water quality andresources management.
3) The high influence areas were specified through thedistribution map according to return period.
4) This algorithm will be applied using multi-GCM
models.