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EVALUATING COUNTRY-LEVEL POPULATION VULNERABILITIES
TO WATER ACCESS DUE TO CLIMATE RELATED HAZARDS USING
HIGH SPATIAL RESOLUTION METHODS
OVIK BANERJEEWATER INSTITUTE OF UNC CHAPEL HILL
WATER AND HEALTH CONFERENCEOCTOBER 30, 2012
Introduction Background Methods Results Conclusions
INTERSECTION BETWEEN WATER ACCESS AND CLIMATE CHANGE
Projected changes in precipitation Annual direction/volumes vary Increased variability and intensity
Other changes to hydrological cycle Altered seasonal flows (snow melt) Increased evaporation
Increase in water temperature Sea level rise Water quality (pathogens and blooms)
Dry conditions Heat waves more frequent and
longer Drier in mid-latitudes
Wet conditions Wetter in monsoon regions,
tropical Pacific and at high latitudes
Extremes increase more than annual averages
Introduction Background Methods Results Conclusions
WHAT HAS BEEN DONE BEFORE REGARDING CLIMATE CHANGE AND DRINKING WATER ACCESS?• Not exactly nothing, but very little
• Many global assessments of climate change and its projected effects, but very few if any assessing the vulnerabilities of countries themselves. Usually assign single numbers for vulnerability for an entire country (low resolution)
• Sullivan et al. Water Poverty Index• Most similar project, but has many more metrics, using data sets that
are not always available globally, little account of spatial resolution• Joint Monitoring Programme
• Measures increases in water availability, but does not take resilience into account
• Vision 2030 Report• Most comprehensive evaluation of the resilience of these technologies
but does not take into account spatial relationships
GOALS OF THIS PROJECT
Introduction Background Methods Results Conclusions
1) To develop a high resolution GIS-based population weighted methodology for assessing the exposure of individual country populations to various climate related hazards
2) To assess current vulnerability of country-level population drinking water access to hazards (flood, drought and cyclone) using available datasets
3) To rank countries based on vulnerability
4) To create a visual display to depict and compare these values easily on a country by country basis.
COMPONENTS
Introduction Background Methods Results Conclusions
1) Climate related hazards: A measure of the likelihood of the individual hazards was required in a geospatial form, i.e., a measure of the drought, cyclone, and flood likelihoods was needed at a spatial level.
2) Population: Global population information was needed, on a geospatial form as well instead of the traditional country by country break down. Spatially related population data was required to see the relationship between population and their exposure and relative vulnerability to hazardous events.
3) Technology coverage: Water delivery technology coverage was needed on a country by country level. Population data was important as well for technology coverage, because data had to be differentiated between urban and rural areas, so a demarcation was required .
4) Technology resilience: A scoring system of resilience of the individual technologies to the individual hazards associated with climate change was also needed.
5) Adaptive capacity: A measure of central adaptive capacity of the government of individual countries was also desired when calculating vulnerability to account for the ability of the country in question to react to a disaster that could occur.
Introduction Background Methods Results Conclusions
LIKELIHOOD OF EVENTS
• From the Center for Hazards and Risk Research• Based on historical data• 2.5 by 2.5 arc minute cells• Scale of 0-10. Standardized between values, not actually meaningful
numbers. 0 is no risk, 10 is highest risk
Why is likelihood used?• Other options were available: economic loss, mortality• Those factors would be double counted as they are a measure of the
metric we are using for adaptive capacity• Also, no better way to decide the likelihood of natural event coming
Introduction Background Methods Results Conclusions
Introduction Background Methods Results Conclusions
LIKELIHOOD OF EVENTS
Introduction Background Methods Results Conclusions
Global Cyclone Hazard Assessment (Adapted from CHRR)
Introduction Background Methods Results Conclusions
LIKELIHOOD OF EVENTS
Introduction Background Methods Results Conclusions
Global Drought Hazard Assessment (Adapted from CHRR)
Introduction Background Methods Results Conclusions
LIKELIHOOD OF EVENTS
Introduction Background Methods Results Conclusions
Global Drought Hazard Assessment (Adapted from CHRR)
Introduction Background Methods Results Conclusions
POPULATION• Worldwide 2.5 by 2.5 arc minute
break downs of population density. Used to determine urban/rural classification and total country population.
How is this used in final equation?• Initially, population density used
to classify grids into rural versus urban
Introduction Background Methods Results Conclusions
Introduction Background Methods Results Conclusions
POPULATION
Introduction Background Methods Results Conclusions
Population Density 2000 (CIESN and CIAT)
Introduction Background Methods Results Conclusions
10 10 10
100 100 100
1000 1000 1000
.01
.5
1
POPULATION WEIGHTED RISK EXPOSURE (HIGH)
Sample Country with likelihoods and population scores
Likelihood of hazard
3300
Total Country Population
0.946
Population of the cell (PopCell) Likelihood of the event of a specific hazard occurring (LEH) Population of the country (PopCountry)
Introduction Background Methods Results Conclusions
1000 1000 1000
100 100 100
10 10 10
.01
.5
1
POPULATION WEIGHTED RISK EXPOSURE (LOW)
Sample Country with likelihoods and population scores
Likelihood of hazard
3300
Total Country Population
0.063
Population of the cell (PopCell) Likelihood of the event of a specific hazard occurring (LEH) Population of the country (PopCountry)
Introduction Background Methods Results Conclusions
Likelihood of Event
PopulationCountry
PopulationCountry
Population Weighted Risk
Exposure
CHRR
2.5*2.5 Arc Minute Grid
CIESN/CIAT
2.5*2.5 Arc Minute Grid
Calculated from data
Country by country data
Technology CoverageTechnology Resilience Country
Resilience Score
JMP Data
Country by country data, Rural/Urban
Elliott et al. 2010
Scores same globally, specific to hazards
Adaptive Capacity
GAIN Index
Country by country data
Country Vulnerability
Score
Inputs
COLOR KEYOutputs Data Source Geospatial Scale
Introduction Background Methods Results Conclusions
Introduction Background Methods Results Conclusions
TECHNOLOGY COVERAGE• JMP Data- watsan coverage (percentage out of 100%), • Differentiates between urban and rural• Different technologies
2006 Improved Water Technology Coverage
Country %Total improved
Bangladesh 85
Cambodia 80
Cape Verde 86
Chad 60
China 98
Eritrea 74
Ethiopia 96
Guatemala 100
2006 Improved Water Technology Coverage
Country %Total improved % Piped
%Public standpost
%Protected well
%Protected spring
%Rainwater collection
Bangladesh 85 23 6 56 0 0
Cambodia 80 44 0 25 0 11
Cape Verde 86 41 2 0 43 0
Chad 60 17 27 15 1 0
China 98 94 1 2 1 0
Eritrea 74 42 27 5 0 0
Ethiopia 96 46 47 1 2 0
Guatemala 100 92 3 5 0 0
Introduction Background Methods Results Conclusions
Introduction Background Methods Results Conclusions
Flood Drought
Cyclone (Formerly Coastal Inundation)
Water
Supply
Protected Well (Tube Well) +++ ++ +++Protected Spring(Dug Well) + + +
Rainwater harvesting +++ + +
Community managed piped (piped and community standpost)
+ + +
Urban Piped and Public Standposts +++ +++ +++Unimproved No resilience No resilience No resilience
TECHNOLOGY RESILIENCE
Introduction Background Methods Results Conclusions
No resilience = 1 + = 0.7 ++ = 0.4 +++ = 0.1
Introduction Background Methods Results Conclusions
Likelihood of Event
PopulationCountry
PopulationCountry
Population Weighted Risk
Exposure
CHRR
2.5*2.5 Arc Minute Grid
CIESN/CIAT
2.5*2.5 Arc Minute Grid
Calculated from data
Country by country data
Technology CoverageTechnology Resilience Country
Resilience Score
JMP Data
Country by country data, Rural/Urban
Elliott et al. 2010
Scores same globally, specific to hazards
Adaptive Capacity
GAIN Index
Country by country data
Country Vulnerability
Score
Inputs
COLOR KEYOutputs Data Source Geospatial Scale
Introduction Background Methods Results Conclusions
Introduction Background Methods Results ConclusionsIntroduction Background Methods Results Conclusions
ADAPTIVE CAPACITY• Country level adaptability data
(GAIN) http://index.gain.org/• Readiness comprised of
economic, government, and social factors
• Different from other component of GAIN Index (vulnerability), which includes water, food, health, and infrastructure components.
• Water component consisted of metrics such as:– projected change in precipitation – percent population with access to
improved water supply,– projected change in temperature
Component (Weight) Measure (Weight)
Economic (40 %)
IEF Business freedom
IEF Trade freedom
IEF Fiscal freedom
IEF Government SpendingIEF Monetary freedom
IEF Investment freedomFinancial freedom
Governance (30 %)
WGI Voice & Accountability ‡
WGI Political Stability & Non-Violence ‡
WGI Control of Corruption ‡
Social (30 %)
Mobiles per 100 persons (5%)Labor freedom
Tertiary Education (10%)WGI Rule of Law (10%)
Introduction Background Methods Results Conclusions
Likelihood of Event
PopulationCountry
PopulationCountry
Population Weighted Risk
Exposure
CHRR
2.5*2.5 Arc Minute Grid
CIESN/CIAT
2.5*2.5 Arc Minute Grid
Calculated from data
Country by country data
Technology CoverageTechnology Resilience Country
Resilience Score
JMP Data
Country by country data, Rural/Urban
Elliott et al. 2010
Scores same globally, specific to hazards
Adaptive Capacity
GAIN Index
Country by country data
Country Vulnerability
Score
Inputs
COLOR KEYOutputs Data Source Geospatial Scale
Introduction Background Methods Results Conclusions
Introduction Background Methods Results Conclusions
POPULATION WEIGHTED RISK EXPOSURE
Introduction Background Methods Results Conclusions
POPULATION WEIGHTED RISK EXPOSURE
Introduction Background Methods Results Conclusions
POPULATION WEIGHTED RISK EXPOSURE
Introduction Background Methods Results Conclusions
POPULATION WEIGHTED RISK EXPOSURE
Introduction Background Methods Results Conclusions
RANKINGS
Country Rank ExposureSouth Korea 2Nepal 7Japan 10Singapore 25Mexico 31Afghanistan 35Myanmar 46USA 49Eritrea 61Morocco 77Australia 78Togo 112Estonia 167Iceland 170Qatar 172
Introduction Background Methods Results Conclusions
RESILIENCE
Introduction Background Methods Results Conclusions
RESILIENCE
Introduction Background Methods Results Conclusions
RESILIENCE
Introduction Background Methods Results Conclusions
RESILIENCE
Introduction Background Methods Results Conclusions
RANKINGS CHANGESCountry Rank Exposure Rank Resilience Change
South Korea 2 47 -45Nepal 7 13 6Japan 10 89 -79Singapore 25 125 -100Mexico 31 67 -36Afghanistan 35 3 32Myanmar 46 40 -6USA 49 115 -66Eritrea 61 24 37Morocco 77 77 0Australia 78 131 -53Togo 112 55 57Estonia 167 165 2Iceland 170 171 -1Qatar 172 173 -1
Introduction Background Methods Results Conclusions
VULNERABILITY
Introduction Background Methods Results Conclusions
VULNERABILITY
Introduction Background Methods Results Conclusions
VULNERABILITY
Introduction Background Methods Results Conclusions
COUNTRY LEVEL VULNERABILITY
Introduction Background Methods Results Conclusions
RANKINGS CHANGES
CountryRank Exposure
Rank Resilience
Rank Vulnerability
Change to Resilience
Change to Vulnerability
Total Change
South Korea 2 47 95 -45 -48 -93Nepal 7 13 10 6 -3 3Japan 10 89 124 -79 -35 -114Singapore 25 125 144 -100 -19 -119Mexico 31 67 84 -36 -17 -53Afghanistan 35 3 1 32 2 34Myanmar 46 40 13 6 27 33USA 49 115 143 -66 -38 -94Eritrea 61 24 4 37 20 57Morocco 77 77 78 0 -1 -1Australia 78 131 153 -53 -22 -75Togo 112 55 37 57 18 75Estonia 167 165 170 2 -5 -3Iceland 170 171 173 -1 -2 -3Qatar 172 173 171 -1 2 1
Introduction Background Methods Results Conclusions
RANKINGS CHANGES
CountryRank Exposure
Rank Resilience
Rank Vulnerability
Change to Resilience
Change to Vulnerability
Total Change
South Korea 2 47 95 -45 -48 -93Nepal 7 13 10 -6 3 3Japan 10 89 124 -79 -35 -114Singapore 25 125 144 -100 -19 -119Mexico 31 67 84 -36 -17 -53Afghanistan 35 3 1 32 2 34Myanmar 46 40 13 6 27 33USA 49 115 143 -66 -38 -94Eritrea 61 24 4 37 20 57Morocco 77 77 78 0 -1 -1Australia 78 131 153 -53 -22 -75Togo 112 55 37 57 18 75Estonia 167 165 170 2 -5 -3Iceland 170 171 173 -1 -2 -3Qatar 172 173 171 -1 2 1
Introduction Background Methods Results Conclusions
RANKINGS CHANGES
CountryRank Exposure
Rank Resilience
Rank Vulnerability
Change to Resilience
Change to Vulnerability
Total Change
South Korea 2 47 95 -45 -48 -93Nepal 7 13 10 6 -3 3Japan 10 89 124 -79 -35 -114Singapore 25 125 144 -100 -19 -119Mexico 31 67 84 -36 -17 -53Afghanistan 35 3 1 32 2 34Myanmar 46 40 13 6 27 33USA 49 115 143 -66 -38 -94Eritrea 61 24 4 37 20 57Morocco 77 77 78 0 -1 -1Australia 78 131 153 -53 -22 -75Togo 112 55 37 57 18 75Estonia 167 165 170 2 -5 -3Iceland 170 171 173 -1 -2 -3Qatar 172 173 171 -1 2 1
Introduction Background Methods Results Conclusions
RANKINGS CHANGES
CountryRank Exposure
Rank Resilience
Rank Vulnerability
Change to Resilience
Change to Vulnerability
Total Change
South Korea 2 47 95 -45 -48 -93Nepal 7 13 10 6 -3 3Japan 10 89 124 -79 -35 -114Singapore 25 125 144 -100 -19 -119Mexico 31 67 84 -36 -17 -53Afghanistan 35 3 1 32 2 34Myanmar 46 40 13 6 27 33USA 49 115 143 -66 -38 -94Eritrea 61 24 4 37 20 57Morocco 77 77 78 0 -1 -1Australia 78 131 153 -53 -22 -75Togo 112 55 37 57 18 75Estonia 167 165 170 2 -5 -3Iceland 170 171 173 -1 -2 -3Qatar 172 173 171 -1 2 1
Introduction Background Methods Results Conclusions
INITIAL CONCLUSIONS
• Geographic location is a primary driver of vulnerability (exposure to various hazards)
• Technology coverage/resilience and adaptive capacity both have the power to effect vulnerability
Introduction Background Methods Results Conclusions
LIMITATIONS
• The population numbers that were calculated using the CIESN and CIAT data did not always match up to data attributable to UN STATS.
• The map used for country boundaries did not always fully encapsulate populations.
• This had an effect on a very small number of countries
• Does not include the effects of one of the most prominent effects of climate change, sea level rise.
• Difficulty differentiating between drought and aridity.
Introduction Background Methods Results Conclusions
ASSUMPTIONS
• No available data for consideration of local adaptive capacity, so assumed it was consistent across the country
• Country-wide homogenous distribution of technology coverage
Introduction Background Methods Results Conclusions
APPLICATIONS
The greatest value in this work is in two parts:
1) Presenting the country level population weighted risk exposure, resilience, and vulnerability in a visual and geospatially relevant way
2) The actual scoring and ranking of countries at every level along the way
Introduction Background Methods Results Conclusions
FUTURE WORK
(1)Refining the parameters in each of the four major categories (climate, population, technology and adaptive capacity)
(2)Transitioning from using current estimates to using future projections
Climate(1)More representative
data for “current” hazard probability
(2)More comprehensive list of hazards (sea level rise)
(3)Hazard severity(4)Climate projections
Population(1)Urban/rural population
fractions(2)future population growth
and distribution(3)Higher resolution of the
population dataset
Introduction Background Methods Results Conclusions
QUESTIONS?
