Moung Jin Lee - Asia Pacific Adaptation Network (APAN) file4/24 1.Background Ⅰ. Introduction...

Moung Jin Lee

Korea Adaptation Center for Climate Change

Korea Environment Institute

2/24

Introduction Ⅰ

Ⅱ

Ⅲ

Outline

Method theory & Study area

Analysis of relation between natural disasters and precipitation

Ⅳ Results of analysis

Ⅴ Conclusion and Discussion

3/24

1. Background Ⅰ. Introduction

Climate Change in Korea

▲ Temperature Change of 6 Major Cities in Korea

Precipitation Outlook of Korea

1.7℃ Increase

Temperature rise of 6 major cities(Seoul, Busan,

Incheon, Daejeon, Daegu, Gwangju) in Korea:

+1.7℃/100 yrs(Global average: 0.74℃)

Temperature rise prediction

2050s: + 2℃(SRES A1B, Global average: 1.8℃ )

2050s: + 3.2℃(RCP 8.5, Global average: 2.3℃)

Precipitation rise of 6 major cities in Korea:

Rainfall rise prediction

2050s: +15% (SRES A1B, Global average: 3.0%)

2050s: +15.6% (RCP 8.5, Global average: 3.2℃ )

Temperature

Precipitation

4/24

1. Background Ⅰ. Introduction

Natural Disasters and Climate Change

▲ Number of Large-Scale Natural Disasters by Year

since 1950

Occurs due to natural phenomena and recognition a risk

that is beyond human control.

Natural disaster is associated with extreme Climate

Change

Intense heavy rainfall events will become even more

frequent in the future

Natural Disasters

Green: Meteorological (Typhoon and storm etc)

Yellow: Climate (Heat wave and forest fire etc)

Blue: Hydrological (Flood and landslide etc)

Red: Geophysical (Earthquake and volcano etc)

(KMA, 2010)

1980s 1990s 2000s 2011 year

Hydrological disasters: Over 80% increasing

Geophysical disasters NOT increasing

Natural disasters increasing: influence of climate change

▲ Number of Heavy Rain Occurred by hourly over

300mm by Year since 1980s in Korea

Hourly over 300mm: about Doubled

5/24

2. Research Questions Ⅰ. Introduction

What is scientific and quantitative correlation natural

disaster and precipitation in Korea?

What are the driving factors affecting natural

disaster loss?

Which is method to predict the future of scientific

and quantitative for natural disaster and adapt

appropriate policies?

Which is method to predict the future of scientific

and quantitative for natural disaster and adapt

appropriate policies?

What natural disaster mitigation technology have

been adopted at the local level?

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4. Research Objectives Ⅰ. Introduction

Correlation Analysis of Precipitation and Natural Disasters

Literature Review, Natural Disasters Occurrence, Precipitation Arrangement

Predict Future Changes in Precipitation

Rainfall Probability, Analysis of a Future Climate Scenario

Detecting of Natural Disasters location

Aerial Photogrammetry, Optics and SAR Remote sensing

Geospatial Database Construction

Topography, Soil, Forest, Geology

Natural Disasters Vulnerability Analysis

Frequency Ratio, Logistic Regression, Neural Network

Climate Change Adaptation

With in Disaster Risk Management

7/24

1. Method theory Ⅱ. Method theory &

Study area

Existing researches on natural disaster and climate change individually conducted

Climate changes: 1)Rainfall probability & 2)Climate change scenario

1)Karl and Knight, 1998 and Sheng and Hashino, 2003

2)Ian A Nalder, 1998 and Craig D. Smith , 2009

GIS & Natural disaster 1)Probability Statistic & 2)Structural geology

1)Lee, S. et al., 2000-2009

2)Van Westen, C.J., 1996-2000

Natural disaster & Climate Changes : 1)Past climate events of landslide

1)Andrew Collison, 2000 and Matthias Jakob, 2009

8/24


Study area

Climate Change Mapping of Probability Statistic

)(exp

)(exp

1)( 00 xxxx

xf

Rainfall probability:

Gumbel Distribution Function

Spatial statistical downscaling of climate

change scenario:

Co-Kriging & Temperature lapse rate

)(

1

* )()()()(un

aaa umuZumuZ

|) (| × m)Elevation(+T_i = T

April)-(October(1.74) × Ri =R 1000

Elvation

September)-(May(0.46) × Ri =R 1000

Elvation

Frequency ratio

)(

)(

)(

)(

)(

)(

)(

)(

1

1

1

1

1

1

DLP

DLP

LP

LP

DP

DP

LDP

LDP

A

A

A

A

A

A

Logistic regression

Neural Network

- Accumulative distribution function and a probability density

function

- Using average and standard deviation

- The types of linear between non-observed spots and adjacent

observed spots

- Inferred and reflected as inversely proportional to distance and

observed values.

- The posterior probability of an occurrence given the presence and

absence of the predictor pattern are denoted.

- The dependent variable has only two groups, logistic multiple

regression may be preferred over discriminant analysis.

- Categorical data may also be used.

- The back-propagation training algorithm is trained using a set of

examples of associated input and output values.

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Study area

Mapping of probability statistic method to MATLB is implemented

Frequency ratio

Fuzzy Operator

Bayesian

Neural Network

10/24

2. Study area Ⅱ. Method theory &

Study area

Landslide occurred on July 14, 2006

- Rainfall over 400mm

Deokjeok-ri : 694 landslide occurred

Karisan-ri : 470 landslide occurred

Calculate to

Dekjeok-ri’s correlation

and weight

Applying to

Dekjeok-ri’s correlation

and weight to Karisan-ri

Dekjeok-ri: Longitude 128 11’ 00 128 18’ 00 and Latitude 38 2’ 30 38 60’ 30

Karisan-ri: Longitude 128 17’ 30 128 24’ 50 and Latitude 38 1’ 50 38 61’ 10

Cross-validation

11/24

3. Spatial database Ⅱ. Method theory &

Study area

Classification Factors Data Type Scale

Natural disaster Landslide Point 1:5,000

Remote sensing

IRS

SPOT 5

QuckBrid

ALOS PALSAR

Grid

5m x 5m

1m x 1m

1m x 1m

10m x 10m

Exposure Precipitation 3-day accumulate(449mm) Point -

Sensitivity

Topographic Map

Slope

Aspect

Curvature

Grid 1:5,000

Geological Map Geology Polygon 1:5,000

Soil Map

Topography

Soil drainage

Soil material

Soil thickness

Soil texture

Polygon 1:25,000

Adaptive capacity Forest Map

Timber diameter

Timber type

Timber density

Timber age

Polygon 1:25,000

IRS SPOT 5 QuckBrid ALOS PALSAR ALOS PALSAR

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2. Rainfall probability Ⅲ. Analysis of relation between natural disasters and precipitation

Calculation of rainfall probability of study area

Hourly weather data from KMA(1/1, 1973 and 12/31, 2006)

In study area maximum 3-day accumulate precipitation(449mm)

The future target years is 1, 3, 10, 50, and 100 years

Recurrence interval of rainfall probability (mm)

Future target year 3-Day

1year 163.5mm

3 year 199.5mm

10 year 290.0mm

50 year 400.9mm

100 year 447.7mm

447.7mm for the 100 target year, 3-day of consistence indicates 100 year frequency precipitation

of at least 447.7mm in 3-day.

13/24

3. Future climate change

scenarios Analysis of spatial statistical downscaling of climate change scenario

KMA-RCM Row Data Existing

3-day accumulation recurrence interval of parameters

Parameter Deokjeok-ri Karisan-ri

a b a b

1year 53.95 49.86 53.79 53.38

3year 58.51 51.38 53.78 58.40

10year 58.42 56.19 55.04 65.61

50year 57.18 60.86 52.46 68.84

100year 55.90 67.79 52.79 80.12

Ⅲ. Analysis of relation between natural disasters and precipitation

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1. Natural Disasters

Vulnerability Ⅳ. Results of analysis

Class Frequency ratio

Aspect

Flat 0.00

North 0.60

Northeast 0.39

East 1.77

Southeast 1.83

South 0.99

Southwest 1.03

West 0.91

Northwest 0.77

Curvature

Concave 1.10

Flat 0.67

Convex 1.21

Slope

(degree)

0~5° 0.14

6~10° 0.07

11~15° 0.22

16~20° 0.34

21~25° 0.78

26~30° 1.04

31~35° 1.86

36~40° 1.48

41~90° 1.81

Geology Banded gneiss 1.06

Granite 0.03

Timber

diameter

Non forest area 0.14

Very small diameter 1.03

Small diameter 1.19

Medium diameter 1.17

Timber

density


Loose 1.33

Moderate 0.84

Dense 0.56

Timber

type


Mixed broad-leaf tree 0.45

Pine 1.37

Needle and broad 1.17

Artificial pine 1.39

Rigida pine 0.47

Korea nut pine 1.12

Artificial Larch 0.66

Larch 0.00

Artificial mixed broad-leaf 0.00

Poplat 2.18

Class Frequency ratio

Timber

age


1st age 1.03

2nd age 1.28

3rd age 1.14

4th age 1.25

5th age 0.07

Soil drainage

No Data 0.00

Well drained 0.42

Somewhat poorly dray 1.54

Moderately well dray 0.76

Soil thickness

(cm)

No Data 0.00

20 0.80

50 0.54

100 1.41

150 0.30

Soil material

No data 0.00

Valley alluvium 0.18

Gneiss residuum 0.62

Fluvial alluvium 0.00

Colluvium 1.37

Alluvial colluvium 0.63

Soil

texture

No data 0.00

Sandy loam 1.09

Rocky loam 0.21

Loam 0.00

Silt loam 0.00

Rocky sandy loam 0.28

Very rocky loam 0.48

Overflow area 0.00

Topography

Nodata 0.00

Valley area 0.33

Valley and alluvial 0.00

Plains 0.00

Piedmont slope area 1.07

Lower hilly area an 0.43

Fluvial plains 0.00

Alluvial fan 0.26

Slope: 31~35°is 1.86

Aspect: Southeast is 1.83

Timber type: Artificial pine

is 1.39

Soil drainage:

Somewhat poorly dray is

1.54

Soil thickness: 100cm is

1.41

Correlation of natural disasters using frequency ratio

15/24

1. Natural Disasters


Correlation of natural disasters using neural network

Target year 1 2 3 4 5 6 7 8 9 10 Average S.D. N.W.*

Aspect 0.0655 0.0736 0.0599 0.0701 0.0674 0.0869 0.0560 0.0781 0.0882 0.0796 0.0725 0.0108 1.0000

Curvature 0.0773 0.0711 0.0802 0.0745 0.0710 0.0692 0.0741 0.0694 0.0688 0.0753 0.0731 0.0038 1.0077

Slope 0.0884 0.0824 0.0890 0.0873 0.0823 0.0818 0.0984 0.0810 0.0946 0.0885 0.0874 0.0058 1.2046

Geology 0.0829 0.0655 0.0699 0.0677 0.0741 0.0750 0.0720 0.1031 0.0819 0.0762 0.0768 0.0108 1.0593

Timber diameter 0.0820 0.0850 0.0737 0.0555 0.0682 0.0760 0.0899 0.0743 0.0763 0.0687 0.0750 0.0097 1.0335

Timber density 0.0746 0.1028 0.0846 0.0844 0.0902 0.0594 0.0697 0.0815 0.0559 0.0909 0.0794 0.0146 1.0947

Timber type 0.0545 0.0886 0.0804 0.0861 0.0846 0.0603 0.0668 0.0730 0.0826 0.0565 0.0733 0.0130 1.0112

Timber age 0.0807 0.0724 0.0738 0.0693 0.0784 0.0781 0.0571 0.0816 0.0616 0.0880 0.0741 0.0094 1.0216

Soil drainage 0.0783 0.0736 0.0928 0.0797 0.0868 0.0941 0.0826 0.0863 0.0849 0.0775 0.0837 0.0066 1.1535

Soil thickness 0.0735 0.0657 0.0744 0.0720 0.0730 0.0744 0.0809 0.0715 0.0713 0.0708 0.0728 0.0038 1.0030

Soil material 0.0885 0.0735 0.0879 0.0905 0.0802 0.0812 0.0871 0.0718 0.0694 0.0740 0.0804 0.0078 1.1086

Topography 0.0735 0.0777 0.0667 0.0774 0.0732 0.0770 0.0792 0.0571 0.0810 0.0660 0.0729 0.0075 1.0048

Slope(1.2046), Soil drainage(1.1535) is highest,

Soil thickness(1.0030), Aspect (1.0000) is lowest

16/24

1. Natural disasters


),(

),(*,

jib

TijiaePPH sji

PH is the probability of vulnerability

Ti the threshold (3-day: 449mm)

A and B are other parameters

Origin Deokjeokri & cross-validation to Karisanri

Application of threshold 3-day accumulative rainfall(449mm)

Future target year (1, 3, 10, 50, and 100 years)

Vulnerability model integration

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Without climate change Target 100 year

Future target year Low (1~57%) Medium (58~75%) High (76~87%) Very High(88~100%)

Without climate change 16.21 43.29 27.46 13.02

1 9.88 29.19 47.70 13.22

3 9.29 24.15 49.82 16.72

10 8.48 20.46 49.80 21.24

50 7.89 16.62 49.68 25.79

100 7.14 13.55 49.48 29.81

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Without climate change Target 100 year

Future target year Low (1~57%) Medium (58~75%) High (76~87%) Very High(88~100%)

Without climate change 31.60 59.89 21.47 5.45

1 10.28 80.89 21.59 5.49

3 8.26 78.12 23.80 8.08

10 6.59 74.38 26.61 10.67

50 5.36 68.18 31.34 13.37

100 4.58 62.29 34.48 16.90

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Very high Future target 100 year

Damaged Infrastructure 40%

Ecological zoning

map

1 grade 35%

2 grade 20%

3 grade 15%

20/24

1. Conclusion and Discussion Ⅴ. Conclusion and Discussion

Objective 1: What is scientific and quantitative

correlation natural disaster and precipitation in Korea?

- By Rainfall probability and climate change scenario, intensive

heavy rainfall will be increased

- 3-day accumulate precipitation affects natural disaster

occurrence more than the 1-day precipitation relation between

precipitation and natural disaster at 2000s

Objective 2.1: What are the driving factors affecting

natural disaster loss?

- Use of the frequency ratio, logistic regression, and neural

network determined the correlations among the natural

disaster-related factors and weights

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Ⅴ. Conclusion and Discussion

Objective 2.2: What are the driving factors affecting

natural disaster loss?

- The most important facter contributing to natural disasters

occurrence is slope

- Timber diameter: Very small diameter(1.03) is the lowest

importance.

Objective 3: What natural disaster mitigation technology

have been adopted at the local level?

- Analysis of natural disaster can be easily and conveniently

conducted by RS&GIS

- Spatial statistical analysis be possible to calculate weights

reflecting the types of natural disaster and the characteristics

of different areas.

- Correlation and weights are cross-validated to different areas

to prepare vulnerability.

1. Conclusion and Discussion

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Ⅴ. Conclusion and Discussion

Objective 4: Which is method to predict the future of

scientific and quantitative for natural disaster and

adapt appropriate policies?

- There are cause of extreme event for assessment and

management the vulnerability analysis to a disaster risk

factors.

- Temporal: Current event or Continuous future event

- There have same base on scientific and quantitative local risk

management

- The climate change adaptation is expanding disaster risk

management Characteristics Natural disaster risk management Climate change adaptation

Hazard

characteristics

Temporal Current event Continuous future event

Dynamics Stationary Non-stationary

Spatial scope Regional Global but heterogeneous

Uncertainty Low to medium Medium to very high

Systems of concern Social systems and built infrastructure All systems

System view Static Dynamic and adaptive

Targets for risk reduction Exposure to hazards and internal Vulnerability Magnitude of hazards and internal Vulnerability

1. Conclusion and Discussion

Thank you for Your Attention.

Lee, Moung Jin Ph.D.

Korea Adaptation Center for Climate Change

Korea Environment Institute

Moung Jin Lee - Asia Pacific Adaptation Network (APAN) file4/24 1.Background Ⅰ. Introduction...

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