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?”
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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
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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
B. Concept of the indicators
• Geo-statistical indicators consists of geo-space and statistics.
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Geospatial
Statistical
Geo-statistical
Technology
GIS & RS
B. Concept of the indicators
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
D. Literature review
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
D. Literature review
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
D. Literature review
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
Medium vulnerability
Medium vulnerability
Low vulnerability
Active Pathway
Level 2
Taking no action al all
Risky Pathway
Level 0
High vulnerability
Low vulnerability
Medium 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
C. Developing a Framework for Adaptive Pathways for DRR
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
C. Developing a Framework for Adaptive Pathways for DRR
Improved water source
Present
Vulnerability
Future
Increase in Adaptive Capacity
Drought
3. Result
26
C. Developing a Framework for Adaptive Pathways for DRR
Present
Vulnerability
Future
Increase in Adaptive Capacity
Disaster Prevention and Preparedness
Drought
3. Result
27
C. Developing a Framework for Adaptive Pathways for DRR
Present
Vulnerability
Future
Increase in Adaptive Capacity
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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D. Linkage with the SDGs
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
29
D. Linkage with the SDGs
• 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
33
D. Linkage with the SDGs
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
34
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