Developing geo-statistical indicators and adaptive ... · developing a set of geo-statistical...

Developing geo-statistical indicators

and adaptive pathways for disaster

risk reduction: cases of flood, drought, sand and dust storms,

and air pollution in Central Asian countries

Korea UniversityWoo-Kyun Lee,

Sea Jin Kim, Gang Sun Kim, Jiwon Kim, Eunbeen Park, Nahui Kim, Wona Lee, Soo Jeong Lee

Content 1. IntroductionA. Background

B. Objective of the research

C. Two scientific questions to answer

D. Research scope and area

E. Research area

2. MethodA. Definitions

B. Concept of the indicators

C. Method

D. Literature review

E. Preparing vulnerability index using geo-statistical indicators

F. Developing a framework for adaptive pathways for DRR

3. ResultA. Preparing vulnerability index with geo-statistical indicators

B. Vulnerability index using sensitivity and adaptive capacity indicators

C. Developing a framework for adaptive pathways for DRR

D. Linkage with the SDGs

4. Limitation and DiscussionA. Limitation

B. Discussion2

1. Introduction

3

1. Introduction

A. Background

• Conventionally, damage or losses from disaster were calculated

after a disaster (post-disaster assessment).

• However, ideally disasters should be prevented and disaster risk

reduced before a disaster occurs (pre-disaster assessment).

• This research introduces a framework for developing geo-statistical

indicators for assessing disaster risk and proposes adaptive

pathways for reducing disaster risk.

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1. Introduction

B. Objective of the research

• The main objective of the project is to establish a framework for

developing a set of geo-statistical indicators to assess disaster risk

(vulnerability) and prepare adaptive pathways for Disaster Risk

Reduction (DRR).

• These indicators aim to support policymakers and technical

officials in member states to prepare more effective policies and

actions to reduce disaster risk and prevent or mitigate human

suffering and economic and environmental damages.

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1. Introduction

C. Two scientific questions to answer

• “How can we identify risk before disasters using geo-statistical

indicators?”

• “How can we take adaptive pathways to reduce disaster risk before

the event?”

6

1. IntroductionD. Research Scope and Area

• Disasters:• Drought / Flood /

Sand and dust storm / Air pollution

E. Research Area

• Central Asian countries: Afghanistan

Kazakhstan

Kyrgyzstan

Tajikistan

Turkmenistan

Uzbekistan

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2. Method

8

2. Method

A. Definitions

• We assessed “disaster risk” using the concept of vulnerability, as

outlined by the Intergovernmental Panel on Climate Change.

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Term Definition from IPCC

Vulnerability “The degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes”

Exposure “The nature and degree to which a system is exposed to significant climatic variations”

Sensitivity “The degree to which a system is affected, either adversely or beneficially, by climate-related stimuli”

Adaptive capacity “The ability of a system to adjust to climate change to moderate potential damages, to take advantage of opportunities, or to cope with the consequences”

2. Method


• Geo-statistical indicators consists of geo-space and statistics.

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Geospatial

Statistical

Geo-statistical

Technology

GIS & RS


2. Method

Criteria for

Vulnerability Index

Exposure

(extreme climate) (non-controllable)

Sensitivity

(hard to control)

Adaptive capacity

(controllable)

Sector for

Adaptive Measures

Environmental

Socio-economic

Indicator for

Adaptive Measures

Geo(-spatial)

Statistical

Technology and Policy

for CC/DRR/SDGs

Infrastructure

Socio-Economic Policy

2. MethodC. Method

• To develop geo-statistical indicators for assessing vulnerability, we first selected indicators related to environmental sensitivity and socio-economic adaptive capacity through literature reviews.

• Then we prepared vulnerability indices and maps by computing the sensitivity and adaptive capacity indicators.

• Following this, we suggested adaptive pathways based on the statistical classification method.

• These methods are based on Geographic Information System (GIS) technology which prepares data into spatial forms and analyzes them statistically.

• Overlay

• Classification

• Raster calculation

• Interpolation

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2. Method


Index Indicator Description and Additional Information Usage Reference

Palmer Drought Severity Index

(PDSI)

temperature,

precipitation

An indicator to estimate relative dryness and that has been widely adopted in the

USA for long-term drought monitoring

FAO

USDM (USA)

Canada

Republic of Korea

Palmer (1965)

Keetch-Byram Drought Index

(KBDI)

precipitation,

temperature

An indicator of soil moisture deficit because it is directly related to drought stress

on cropsUSDA (USA)

Keetch and Byram

(1968)

Precipitation precipitation A simple indicator used all over the world

FAO

Canada

Syria

Percent of Normal

Precipitationprecipitation An indicator that uses simple calculation to identifying various impacts of droughts

Standardized Precipitation

Index (SPI-n)precipitation

A statistical indicator that is used to identify a precipitation shortage by comparing

the total precipitation received at a particular location during a period of n months

with the long-term rainfall distribution for the same period of time at that location

FAO

WMO

JRC (Europe)

Republic of Korea

Palestine

McKee et al (1993)

Standardised Precipitation-

Evapotranspiration Index (SPEI)

precipitation, potential

evapotranspiration

An indicator for determining the onset, duration and magnitude of drought

conditions based on climatic data to identify the impact of increased temperatures

on water demand and is an extension of SPI

Vincente-Serrano

et al. (2010)

Surface Water Supply Index

(SWSI)

reservoir storage,

streamflow, snowpack,

precipitation

An indicator calculated at the basin level used for water supply forecasting and is an

extension of PDSI since it adds additional information including water supply data

FAO

NRCS, USDM (USA)

Republic of Korea

Shafer and

Dezman (1982)

Drought (example)

2. Method


Palmer Drought Severity Index (PDSI)

An indicator to estimate relative dryness and that has been widely adopted in the USA for long-term drought monitoring

(Source: CEDA, http://catalogue.ceda.ac.uk/uuid/00cc4b46f53408de0b397beaf209ec0b)

(2000)

Drought

2. Method


Standardised Precipitation-Evapotranspiration Index (SPEI)

An indicator for determining the onset, duration and magnitude of drought conditions based on climatic data

(Source: SPEIbase v.2.5 [Dataset], 2017, http://catalogue.ceda.ac.uk/uuid/00cc4b46f53408de0b397beaf209ec0b)

(2000)

Drought

2. Method

E. Preparing Vulnerability Index Using Geo-Statistical Indicators

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Exposure, Sensitivity(Geo-spatial) Indicators

(Socio-economic) Adaptive Capacity Map

Vulnerability MapLevel 0Level 1Level 2Level 3

(Environmental) Sensitivity Map

Adaptive Capacity(Statistical) Indicators

c

c

Step 1 Step 2

2. Method

F. Developing a Framework for Adaptive Pathways for DRR

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High vulnerability

Medium vulnerability

Passive Pathway

Level 1

High vulnerability



Low vulnerability

Active Pathway

Level 2

Taking no action al all

Risky Pathway

Level 0

High vulnerability

Low vulnerability


Low vulnerability

Full Pathway

Level 3

3. Result

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3. Result

A. Preparing Vulnerability Index with Geo-Statistical Indicators

Index Indicator

Sensitivity

(geo-

spatial/env

ironmental)

Adaptive

Capacity

(statistical/

socio-

economic)

Vulnerability Source

Agricultural Drought Impact Index O

Agricultural land O ESA-CCI

Agriculture value OWorld

Bank

Palmer Drought Severity Index

(PDSI) O CEDA

Improved water source OWHO/UN

ICEF

Disaster prevention and preparedness

O OECD

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𝑨𝒈𝒓𝒊𝒄𝒖𝒍𝒕𝒖𝒓𝒂𝒍 𝑫𝒓𝒐𝒖𝒈𝒉𝒕 𝑽𝒖𝒍𝒏𝒆𝒓𝒂𝒃𝒊𝒍𝒕𝒚 𝑰𝒏𝒅𝒆𝒙= [ 𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑎𝑙 𝑙𝑎𝑛𝑑 × 𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒 𝑣𝑎𝑙𝑢𝑒× 1 − 𝑃𝐷𝑆𝐼 ] ÷ [ 𝐼𝑚𝑝𝑟𝑜𝑣𝑒𝑑 𝑤𝑎𝑡𝑒𝑟 𝑠𝑜𝑢𝑟𝑐𝑒× 𝐷𝑖𝑠𝑎𝑠𝑡𝑒𝑟 𝑝𝑟𝑒𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑝𝑟𝑒𝑝𝑎𝑟𝑒𝑑𝑛𝑒𝑠𝑠 ]

DescriptionThis index is developed to see the potential impact of drought on agriculture by its value, water source, government’s financial commitment to disaster prevention and preparedness, including the meaning of meteorological drought.

Drought

Agricultural land

3. Result

Drought

Source: ESA-CCI, World Bank, CEDA20

Sensitivity Map(geo-spatial / environmental)

A-1. Sensitivity Indicators

Agriculture value

PDSI

Improved water source

3. Result

Source: WHO/UNICEF, OECD21

Disaster preventionand preparedness

A-2. Adaptive Capacity Indicators

Adaptive Capacity Map(statistical / socio-economic)

Drought

3. Result

Source: ESA-CCI, World Bank, WHO/UNICEF, CEDA22

Vulnerability Map

B. Vulnerability Index Using Sensitivity and Adaptive Capacity Indicators

Sensitivity Map

Adaptive Capacity Map

𝑨𝒈𝒓𝒊𝒄𝒖𝒍𝒕𝒖𝒓𝒂𝒍 𝑫𝒓𝒐𝒖𝒈𝒉𝒕 𝑽𝒖𝒍𝒏𝒆𝒓𝒂𝒃𝒊𝒍𝒕𝒚 𝑰𝒏𝒅𝒆𝒙= [ 𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑎𝑙 𝑙𝑎𝑛𝑑 × 𝐴𝑔𝑟𝑖𝑐𝑢𝑙𝑡𝑢𝑟𝑒 𝑣𝑎𝑙𝑢𝑒× 1 − 𝑃𝐷𝑆𝐼 ] ÷ [ 𝐼𝑚𝑝𝑟𝑜𝑣𝑒𝑑 𝑤𝑎𝑡𝑒𝑟 𝑠𝑜𝑢𝑟𝑐𝑒× 𝐷𝑖𝑠𝑎𝑠𝑡𝑒𝑟 𝑝𝑟𝑒𝑣𝑒𝑛𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑝𝑟𝑒𝑝𝑎𝑟𝑒𝑑𝑛𝑒𝑠𝑠 ]

DescriptionThis index is developed to see the potential impact of drought on agriculture by its value, water source, government’s financial commitment to disaster prevention and preparedness, including the meaning of meteorological drought.

Drought

3. Result

C. Developing a Framework for Adaptive Pathways for DRR

ClassRisky Pathway Passive Pathway Active Pathway Full Pathway

Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%) Area (km2) Area (%)

LV 302,500 31.5 302,500 31.5 745,000 77.6 960,000 100

MV 442,500 46.1 657,500 68.5 215,000 22.4 0 0

HV 215,000 22.4 0 0 0 0 0 0

Sum 960,000 100 960,000 100 960,000 100 960,000 100

Agricultural Drought Impact Index

Passive Pathway

(HVMV)

Active Pathway

(HVMV, MVLV)

Risky Pathway

(no action)

Full Pathway

(HVLV, MVLV)

Drought

3. Result


Index Indicator

Sensitivity

(geo-

spatial/envi

ronmental)

Adaptive

Capacity

(statistical/s

ocio-

economic)

Vulnerabil

itySource

Control direction and Controllability

to reduce vulnerability

Agricultural Drought Impact

IndexO Control direction Controllability

Agricultural land O ESA-CCI ▼ X

Agriculture value OWorld

Bank▼ X

Palmer Drought Severity

Index (PDSI) O CEDA ▼ X

Improved water source OWHO/UNI

CEF▲ O

Disaster prevention and preparedness

O OECD ▲ O

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Drought

3. Result

25


Improved water source

Present

Vulnerability

Future

Increase in Adaptive Capacity

Drought

3. Result

26


Present

Vulnerability

Future


Disaster Prevention and Preparedness

Drought

3. Result

27


Present

Vulnerability

Future


Adaptive Capacity:

Improved water source + Disaster prevention and preparedness

DRR through improving AC:Spatially separating sensitivity and AC

Drought

3. Result

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Index

Drought

Agricultural Drought Vulnerability Index

Flood

Agricultural Flood Impact Index

Sand and Dust Storm

Agricultural SDS Vulnerability

Air Pollution

Agricultural Air Pollution Vulnerability Index

3. Result

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• Factor analysis on SDG 15 was conducted to estimate the

terrestrial ecosystem sustainability.

• There was not enough data to conduct factor analysis. Thus,

the missing values were substituted by neighboring values.

• The results show a trend of the SDG targets and a developed

Goal 15 index weighted with target indicators.

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Goal Indicator Variables Year Source EffectIncluded in

Analysis

15.1

15.1.1 Ratio of forest area to total land area (%) 1990-2015 KOSIS, UNSD (+) Yes

15.1.2Land Biodiversity Protection Area Ratio (%) 2000-2017 KOSIS, UNSD (+) Yes

Freshwater biodiversity protection area ratio (%) 2000-2017 KOSIS, UNSD (+) Yes

15.2 15.2.1

Forest area in protected area (ha) 2005-2010 KOSIS, UNSD (+) Yes

Above-ground biomass in forest per hectare (tonnes per hectare) 2000-2015 FAO (+) Yes

Above-ground biomass in forest (tonnes) 2000-2015 FAO (+) Yes

Forest area certified under an independently verified certification scheme (thousands of hectares) 2000-2017 FAO (+) Yes

Proportion of forest area with a long-term management plan (%) 2000-2010 FAO (+) Yes

Proportion of forest area within legally established protected areas (%) 2000-2015 FAO (+) Yes

Forest area net change rate (%) 2005-2015 FAO (-) No

15.3 15.3.1 Proportion of land that is degraded over total land area - - (-) No

15.415.4.1 Coverage by protected areas of important sites for mountain biodiversity - - (+) No

15.4.2 Mountain Green Cover Index 2017 KOSIS, UNSD (+) No

15.5 15.5.1 Red List Index 1993-2017 KOSIS, UNSD (-) Yes

15.6 15.6.1

Total reported number of Standard Material Transfer Agreements (SMTAs) transferring plant genetic resources for fo

od and agriculture to the country (number)2012 ABSCH No

Countries that are parties to the Nagoya Protocol (1 = YES; 0 = NO) 2012 UNCBD No

Countries that have legislative, administrative and policy framework or measures reported to the Access and Benefit-

Sharing Clearing-House (1 = YES; 0 = NO)2012 ABSCH No

Countries that are contracting Parties to the International Treaty on Plant Genetic Resources for Food and Agriculture

(PGRFA) (1 = YES; 0 = NO)2017 FAO No

Countries that have legislative, administrative and policy framework or measures reported through the Online Repor

ting System on Compliance of the International Treaty on Plant Genetic Resources for Food and Agriculture (PGRFA)

(1 = YES; 0 = NO)

2012-2017 UNCBD No

15.7 15.7.1 Proportion of traded wildlife that was poached or illicitly trafficked - - (-) No

15.8 15.8.1Proportion of countries adopting relevant national legislation and adequately resourcing the prevention or control of

invasive alien species- - (-) No

15.9 15.9.1Progress towards national targets established in accordance with Aichi Biodiversity Target 2 of the Strategic Plan for

Biodiversity 2011–2020- - V No

15.a 15.a.1 Total official development assistance for biodiversity (USD) 2007-2015 KOSIS, UNSD (+) Yes

15.b 15.b.1Total official development assistance for biodiversity, by donor countries (millions of constant 2016 United States

dollars)2007-2016- OECD- (+) Yes

15.c 15.c.1 Proportion of traded wildlife that was poached or illicitly trafficked - - (-) No

3. Result

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3. Result

32

3. Result

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Factor Eigenvalue Difference Proportion Cumulative

Factor 1 4.215 3.752 0.8429 0.8429

Factor 2 0.463 0.206 0.0927 0.9356

Factor 3 0.257 0.198 0.0515 0.9870

Factor 4 0.059 0.053 0.0118 0.9988

Factor 5 0.006 0.0011 1.0000

Sub Indicator Factor 1

Goal 15.1 0.889

Goal 15.2 0.937

Goal 15.5 -0.900

Goal 15.a 0.942

Goal 15.b 0.921

Sub-goal trend by factor 1 SDG Goal 15 Index

Increasing trend in South Korea

4. Limitation and Discussion

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4. Limitation and DiscussionA. Limitation

• Because of the lack of national data from the research countries, we had to depend on the satellite data and global statistical data.

• Most of the data was set to the year 2000.

• It is difficult to obtain adaptive capacity data of administrative units rather than national units.

• We have not yet grasped the extent of the impact on vulnerability by each indicator, for this reason, weights of each indicator were equally given.

• The research team has set the adaptive pathway quantitatively, but which and how much of each adaptive capacity have to be changed should be decided for the further study.

• Geo-statistical indicators should be verified on-site.

4. Limitation and Discussion

B. Discussion

• Next step for the further research would be:

• Developing indices by adding more adaptive capacity indicators

• Giving weights to the indicators to develop the indices

• Making Nationally Determined Pathway (NDP) for Central Asian countries

• Downscaling the results by using local dataset

• Conduct pilot study in Kazakhstan for application

Thank You

Prof. Woo-Kyun Lee

[email protected]

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