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 University Woo-Kyun Lee, Sea Jin Kim, Gang Sun Kim, Jiwon Kim, Eunbeen Park, Nahui Kim, Wona Lee, Soo Jeong Lee
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Developing geo-statistical indicators and adaptive ... · developing a set of geo-statistical indicators to assess disaster risk (vulnerability) and prepare adaptive pathways for
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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
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.
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 (%)
• 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