The 13th AIM International Workshop
16-18, February 2008
NIES, Tsukuba, Japan

Impact assessment of vegetation
by climate change in Korea
18 February, 2008

Jae-Uk KIM* · Dong-Kun LEE* · Choon-Geol Moon**(*Seoul National University · **Hanyang University, Korea)

Contents
1. Summary
2. Backgrounds
3. Objectives
4. Methods
5. Materials
6. Results and Discussion
7. Conclusion

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Summary
Project Name

Development of an ecosystem model for change prediction
and management technique of vulnerable areas by climate change
Terms of Totality

Apr. 1st. 2007. ~ Mar. 31st. 2010. (3 years)

Term of this year

Apr. 1st. 2007. ~ Mar. 31st. 2008.

ParticipantSeoul National University, Kyung Hee University,Hanyang University

Objectives

To collect data and develop impact model of vulnerable fields(vegetation, alpine/subalpine, crop and arthropod) by climate change
To evaluate economic value in vulnerable fields

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Summary
Flowchart
Construction of DB
General Circulation Model

Development of assessment index
Vegetation

AlpineSubalpine

Water
resource

Development of prediction method
Agriculture Arthropod

Regional Climate Model
Scenarios of Society, Economy, Environment

Observed climate data
Soil map

Integrated
assessment

Assessment of adaptation ability
Adaptation
strategy

Water resource

Digital Elevation Model
Vegetation map
Landcover map
Administrative district map

WaterVegetation

Water demand

Agriculture

Need of water

Impact Model

Adaptation ofGlobal warming

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Backgrounds
Global mean temperature near the Earth's surface rose
0.74±0.18°C during the past century.
Climate models referenced by the IPCC project that
global surface temperatures are likely to increase by
1.1 to 6.4 °C between 1990 and 2100.
The effect of global warming is becoming more
apparent on various parts of the world including
dynamics in natural ecosystems.

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Backgrounds

2005

③①

① Camellia japonica L.

②2000

1996

③ Quercus

mongolica② Sasa quelpaertensis Nakai

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Objectives
To predict potential distribution of Pinus densiflora,
Quercus Spp., Alpine Plants and Evergreen BroadLeaved Plants
To assess a vulnerable area in climate change
To measure economic value of change in vegetation
due to climate change: focusing on pine and oak trees

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Methods IFuture
Climate

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Methods II
Contingent valuation : double boundedreferendum format
Virtual state : Conservation Fund for Pine andOak trees

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Methods II

Contents
Population

over 20 years old, nationwide

Sampling
size

400 persons (general public)

Sampling
methods

Proportionate Stratified Sampling

Samplingerror± 3.7% (p < 0.05)

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Methods IIContents
Interview
methods

Fax, E-Mail, TelephoneDatacollection
means

Structured Questionnaire

Interview
period30 November ∼ 18 December, 2007

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Methods II
Probability expressions for 4
types of responses

Standard logistic distribution
Log - linear model
Estimate of WTP : median

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Materials-Climate Models
HadCM3CSIRO-Mk2CGCM2

CCSR-NIES

General Circulation Model

NIES-RAMS

Regional Climate Model

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Materials-Climate Models

Temperature

Precipitation

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Materials-Plant communities
Dominant communities
Pinus densiflora Pinus densiflora (1)Quercus Spp.

Quercus acutissima, Quercus aliena, Quercus dentata, Quercus grosseserrata,
Quercus mongolica, Quercus serrata, Quercus variabilis (7)Alpine Plants

Abies holophylla, Abies koreana, Abies nephrolepis, Betula ermanii, Betula
platyphylla, Empetrum nigrum var. japonicum, Juniperus chinensis var.
sargentii, Juniperus rigida, Pinus koraiensis, Pinus pumila, Rhododendron
mucronulatum var. ciliatum, Taxus cuspidata, Thuja koraiensis, Thuja
orientalis L. (14)Evergreen
Broad-Leaved
PlantsCastanopsis cuspidata var. sieboldii, Castanopsis cuspidata var. thunbergii,
Camellia japonica L., Cinnamomum japonicum, Daphniphyllum
macropodum, Elaeagnus macrophylla, Ilex integra, Litsea japonica, Machilus
thunbergii, Quercus acuta, Quercus myrsinaefolia (11)15/42Materials-Environmental factors
Categories

Factors (16)

ClimateMean temperature (yearly, January, August, Spring,
Summer, Fall, Winter), Total precipitation (yearly, Spring,Summer, Fall, Winter)

Topography Elevation
Index

Warmth index, Coldness index

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Results-Current climate (1971~2000)

Temperature (10.1℃)

Precipitation (1,283mm)
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Results-Future climate (2050)

HADCM3 GCM

CSIRO-Mk2 GCM

CGCM2 GCM

CCSR/NIES GCM
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Results-Future climate (2050)

NIES/RAMS RCM (Temperature)

NIES/RAMS RCM (Precipitation)
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Results-Pinus densiflora
RangesArea

Mean

56.2 %

Elevation (m)1~1,492312

Meantemperature (℃)

1.5~14.8

10.5

Total
precipitation (㎜)971~1,741

1,252

Warmth index(month·℃)

34.3~120.3

89.3

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Results-Pinus densiflora
Simulated (1971~2000)
4 GCMsSimulated (1971~2000)
RCM

Pinus densiflora = 0.0015×DEM – 0.00252×Ptotal + 0.0175×Tdjf + 1.8593
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Results-Pinus densiflora
Predicted (2041~2050)
4 GCMs

Predicted (2041~2050)
RCM

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Results-Pinus densiflora
Ratio (%)

More
Reduced

17.5

Reduced32.2

No change

49.6

Expanded

0.6

More
Expanded

0.0

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Results-Quercus Spp.
RangesArea

Mean

30.4 %

Elevation (m)1~1,641509

Mean
temperature (℃)

2.0~15.9

9.0

Total
precipitation (㎜)974~1,810

1,298

Warmth index(month·℃)

36.8~130.7

79.4

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Results-Quercus Spp.
Simulated (1971~2000)
4 GCMsSimulated (1971~2000)
RCM

Quercus Spp. ; CI, DEM, Tmin
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Results-Quercus Spp.
Predicted (2041~2050)
4 GCMs

Predicted (2041~2050)
RCM

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Results-Quercus Spp.
Ratio (%)

More
Reduced

5.7

Reduced

6.9

No change

83.4

Expanded

1.3

More
Expanded

2.827/42

Results-Alpine Plants
Ranges

Area

Mean

0.3 %

Elevation (m)86~1,824

1,024

Mean
temperature (℃)

1.2~15.9

5.7

Total
precipitation (㎜)1,019~1,838

1,347

Warmth index(month·℃)

30.9~130.6

57.3

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Results-Alpine Plants
Simulated (1971~2000)
4 GCMsSimulated (1971~2000)
RCM

Alpine plants ; WI, DEM, Ttotal,Tmam
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Results-Alpine Plants
Predicted (2041~2050)
4 GCMs

Predicted (2041~2050)
RCM

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Results-Alpine Plants
Ratio (%)

MoreReduced

1.5

Reduced1.9

No change

96.0Expanded0.6

MoreExpanded-31/42

Results-Evergreen Broad-Leaved Plants
Ranges

Area

Mean

0.2 %

Elevation (m)

1~626

197

Mean
temperature (℃)

10.9~16.3

13.4

Total
precipitation (㎜)961~1,853

1,375

Warmth index(month·℃)

84.9~135.4

106.1

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Results-Evergreen Broad-Leaved Plants
Simulated (1971~2000)
4 GCMsSimulated (1971~2000)
RCM

Evergreen Broad-Leaved Plants = 0.6503×CI – 0.7949×Tmin
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Results-Evergreen Broad-Leaved Plants
Predicted (2041~2050)
4 GCMs

Predicted (2041~2050)
RCM

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Results-Evergreen Broad-Leaved Plants
Ratio (%)

More
Reduced

0.3

Reduced0.3

No change

89.7

Expanded

5.3

MoreExpanded

4.4

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Results-vulnerable area
Ratio (%)
More
reducedNochange

Moreexpanded

Grade 1

2.8

Grade 2

3.3

Grade 315.0

Grade 433.9

Grade 534.2

Grade 6

5.6

Grade 7

4.9

Grade 80.2

Grade 9

0.0

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Results-Economic value assessment
Willingness to pay to Conservation Fund for Pine and Oak trees: raw data
General public (% of willing payers)amount of 1stoffer

2nd response(to half of 1stamount)

1st response

2nd response(to double of 1stamount)

Sampling

292 persons

400 persons

108 persons

$1

4.3 %

59.6 %

58.8 %$3

19.5 %

28.1 %

31.3 %$8

11.9 %

26.3 %

20.0 %$ 10

19.5 %

28.1 %

12.5 %$ 14

11.9 %

27.6 %

18.8 %$ 18

16.7 %

15.8 %

11.1 %$ 51

10.9 %

3.5 %

-mean

14.0 %

27.0 %

31.5 %
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Results-Economic value assessment
Median Estimates of Willingness-to-Pay (WTP): statistical analysis

Grouped byContents

WTP

t-value

Sexmale$ 5.7/person

2.235

Age

20’s$ 4.5/person

2.512

Occupation

self employed$ 4.7/person

3.255$ 31,200~41,600 $ 6.0/person

1.995

Annualincomeoveralloverall$ 3.8/family

3.382

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Results-Economic value assessment
Economic value of vegetation change by climate change: median estimate for pine and oak trees- Economic value (B) = $ 3.8/family×15,887,128 families (as of 2005)= $ 60,371,086- Present value =(δ = discount rate )(1 + δ )× Β = (1 + 0.05)× Βδ

0.05= $ 1,267,792,806

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Results-Economic value assessment
Annual economic value and Present value
Value categories

Value criteria(share of the total)[subcategories(weight)]

Use value (0.380)
Non-use value(0.620)

Annualeconomic value

Present value

Direct use value (0.177)$ 10,685,682$ 224,399,327

Indirect use value (0.202)$ 12,194,959$ 256,094,147

Bequest value (0.211)$ 12,738,299$ 267,504,282

Option value (0.203)$ 12,255,330$ 257,361,940

Existence value (0.206)$ 12,436,444$ 261,165,318$ 60,371,086$ 1,267,792,806

Total economic value (1.000)

Direct use value : Timber, Mushrooms cultivation etc.
Indirect use value : Water storage, Soil erosion prevention, Wildlife protection etc.
Bequest value : Forest inheritance
Option value : Saving for the possibility of unspecified future use
Existence value : Forest existence
40/42Conclusion
Achievements
To challenges associated with predicting and assessing the futureclimate using climate models distribution of communities by climatechange in Korea
To attempt economic value assessment of Natural ecosystem byclimate change for the first time in Korea
Limitations and Considerations
To examine the potential distribution of communities by correlating theenvironmental factors without reflecting the natural succession
processes
Variability of multiple RCM output results under various climate change
scenarios were not sufficiently considered
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