1. Africa RiskView African Risk Capacity (ARC) Project
A Project of the African Union

2. Presentation Outline Role of Africa RiskView Methodology Overview
Rainfall
Drought Indexing
Vulnerability Profiling
Operational Response Costs

3. Africa RiskView: Technical Engine of ARC
Africa RiskView (ARV) is a software tool that allows countries to:
Analyze and monitor their drought-related food security risk
Define their participation in ARC using transparent criteria
Monitor potential ARC payouts
By bringing together existing information on vulnerable populations with drought and crop early warning products, ARV defines a standard setting methodology that allows countries to identify and quantify drought risk and to transfer a portion of this drought risk to ARC
All model settings in ARV can be customized for each country and to reflect national risk transfer decisions

4. Africa RiskView Methodology

5. Africa RiskView Approach
Africa RiskView estimates the impact of observed weather data on vulnerable populations. To do this requires an understanding of how weather hazards interact with people vulnerable to food insecurity in order to convert information about the magnitude and spatial extent of rainfall shocks into estimates of the number of people affected and the cost of a possible response.
For modelling the relationships between these variables, Africa RiskView uses a rainfall-based drought index (WRSI) combined with a scaling factor, to estimate drought-related agricultural livelihood shocks, together with information on a population’s vulnerability to such shocks to estimate the number of people affected; costs are then estimated from response interventions planned to assist them.
WRSI and Scaling Factor
Vulnerability Profiling
Costs Estimates

6. Box 1: Rainfall

7. Rainfall in ARV
Cumulative rainfall data for the 3rd dekad of January 2012 of “Country J”
Rainfall estimates (RFE) in ARV are satellite based because:
Ground data scarce
Ground data not consistently available in real-time
Advantage of satellites:
Pan-African, reliable coverage
No human interference
ARV includes more than one RFE source:
RFE1 ( ) and RFE2 (2001-present) from NOAA
ARC2 (1983-present) from NOAA
Users can upload their own datasets for analysis
Resolution is 10 x 10 km across all Africa
Dekadal (10 day) time step:
RFE2 and ARC2 download into ARV automatically from the internet
Cumulative rainfall data for the 3rd dekad of January 2012 compared to normal of “Country J”
NOAA – National Oceanic and Atmospheric Administration

8. Box 2: Drought Index

9. Water Requirement Satisfaction Index (WRSI)
WRSI value for the “Country J” Main Season 2011
Advantages of WRSI:
FAO’s water balance model
Simple and transparent, well accepted
Used to empirically estimate yield or for monitoring the status of crops and rangeland
Better than cumulative rainfall; needs less processing than NDVI
Can be used as an early warning indicator
Crop specific
WRSI Interpretation for Cropping Areas: 0 = “no water” or “no planting”
50 or less = “failure” 100 = “no water stress”
WRSI value for “Country J” Main Season 2012 compared to normal
Most appropriate for a weather insurance scheme, as it only accounts for rainfall, and not external shocks to land. Those participating in ARC are ensured the most fair system possible.
NDVI – normalized difference vegetation index

10. All settings can be changed and customized
Water Requirement Satisfaction Index (WRSI)
WRSI is the primary drought index used in ARV to convert rainfall into a meaningful indicator for crops and pasture.
Main variable input is rainfall, but information on PET, soil water holding capacity, cycle lengths, cropping calendars etc. is required
ARV uses reference crops for seasons and regions
Input data pre-loaded based on FEWSNET’s settings
WRSI calculated on 10 x 10 km pixel grid like RFE
WRSI value for the “Country J” Main Season 2011
WRSI value for “Country J” Main Season 2012 compared to normal
FEWSNET – Famine and early warning system network (USAID)
All settings can be changed and customized

11. All triggers can be changed and customized!
Drought Definition in Africa RiskView
Drought is defined at the administrative level, or other spatial aggregation shape, such as a livelihood zone, by averaging the WRSI values of pixels that fall within that polygon.
For the season ahead, for each area (polygon) considered in a country:
Normal Conditions (WRSI Benchmark) in the area are defined as:
Median WRSI value for that area over the previous 5 years
Mild Drought in the area during the season ahead is defined as:
A WRSI value that is between 90% and 80% of the benchmark
Medium Drought in the area during the season ahead is defined as:
A WRSI value that is between 80% and 70% of the benchmark
Severe Drought in the area during the season ahead is defined as:
A WRSI value that is at and below 70% of the benchmark
Median chosen over mean because median provides a more robust set of calculations against extreme shocks (less false negatives) without failing to account for extreme shocks
5 years chosen since it modeled the best results, but this can and should be changed with the input of national experts
All triggers can be changed and customized!

12. Box 3: Modelled Impact

13. Vulnerability Profiling
“Vulnerability profiling” is the process by which households are categorized by their degree of vulnerability to different levels of drought, in each area (vulnerability polygon) for which data on households are available and representative
Vulnerability to drought is defined in two dimensions:
Exposure to drought – Represents the impact that a certain level of drought would have on a household’s livelihoods. It is measured by the share of household income coming from livestock and agricultural related activities.
Resilience – Represents the ability of a household to cope with a livelihood shock. It is measured by the poverty status of the household with respect to the national poverty line.

14. Impact of Drought on Income
Drought-related agricultural income losses for households in a polygon are related to deviations of the polygon’s WRSI below the benchmark, and are computed with a constant “Scaling Factor” To get from a WRSI deviation to an income loss, two conversions are necessary:
WRSI DEVIATION
YIELD LOSS
INCOME LOSS
The Scaling Factor in ARV includes both conversion factors
By default, ARV adopts a Scaling Factor of 1.5, but this can be customized
1 to 1.5 ratio from WRSI deviation to yield loss, 1 to 1 ratio from yield loss to income loss

15. Vulnerability Profile – Impact of Drought on Income
Drought-related agricultural income losses for households in a polygon are related to deviations of the polygon’s WRSI below its benchmark via a constant Scaling Factor
Example, Scaling Factor = 1.5:
Mild Drought
WRSI ≥ 10% below normal
Agricultural-Related Income ≥ 15% below normal
Medium Drought
WRSI ≥ 20% below normal
Agricultural-Related Income ≥ 30% below normal
Severe Drought
WRSI ≥ 30% below normal
Agricultural-Related Income ≥ 45% below normal
10% x 1.5 = 15%

16. Vulnerability Profile – Exposure to Drought
Each exposure category is defined by a given loss of livelihood (household income).
The more severe the drought, the higher is the impact on a household’s livestock and agricultural related income.
The more a household relies on livestock and agricultural activities for its income, the greater its exposure to a drought of any magnitude.
In ARV, a household that loses more than 12%1 of their total household income (livelihood) as a result of a drought is considered to be highly exposed to that drought category. This minimum livelihood loss threshold can be set for each area.
For each vulnerability polygon, using household survey data, it is therefore possible to determine the percentage of the population falling into each level of exposure for any type of drought.
Exposure
Categories
Drought Severity
Mild
Medium
Severe
Not Highly Exposed to Drought
74%
36%
10%
Highly Exposed to Drought
26%
64%
90%
Total
100%
The amount of livelihood loss for a given household can be calculated for a given percentage of total household income derived from agriculture and livestock.
Source of livelihood data: WFP CFSVA, assuming 12% minimum livelihood loss threshold
1 This number can be customized by changing the scaling factor and the exposure category levels

17. Vulnerability Profile – Exposure to Drought, Example Household
Remember the impact of drought on income:
Mild Drought: ≥ 15% below normal
income levels
Medium Drought: ≥ 30% below normal income levels
Severe Drought: ≥ 45% below normal income levels
15% x 50% = 7.5%
30% x 50% = 15%
45% x 50% = 22.5%
Household’s Income Loss by Drought Type Compared to Livelihood Loss Threshold
This is repeated for all households in the household survey dataset to calculate the percentage of the population of the polygon exposed to each category of drought
Mild Drought
Medium Drought
Severe Drought
7.5% < 12%
15% > 12%
22.5% > 12%
This household is only “at risk” to medium and severe droughts, as they lead to income losses >12%
*Assuming a Scaling Factor of 1.5 and a minimum Income Loss Threshold of 12%

18. % people above the poverty line
Vulnerability Profile – Resilience: Ability to Cope With a Drought
Resilience
Whenever available, the national poverty line is preferred as more representative of the country context, otherwise the international standard of US $1.25 a day is used.
Resilience
Categories
Drought Severity
Mild
Medium
Severe
Low
% people living below the poverty line
High
% people above the poverty line
It is then possible to determine the percentage of population falling into each category for any type of drought for each vulnerability polygon as seen in following example:
Because resilience is independent of drought severity, we have the same percentage of households below the national poverty line (low resilience) and above the national poverty line (high resilience) for each drought category.
Resilience
Categories
Drought Severity
Mild
Medium
Severe
Low
56%
High
44%
Total
100%
Data source: The income distribution function is derived using the Gini coefficient and the GDP per capita (PPP US$) provided by the UNDP Human Development Report 2011

19. HWI < WIT: Low Resilience
Vulnerability Profile – Resilience, Example
Resilience
Households in polygon ranked by Wealth Index
Income Level
Richest
National Poverty Line
HWI < WIT: Low Resilience
Poorest
If a Household's Wealth Index (HWI) is below the Wealth Index Threshold (WIT) that corresponds to the national poverty line, the household is classified as having Low Resilience to drought

20. Vulnerability Profile – Exposure and Resilience
For each drought category, the actual vulnerability profile of a polygon is determined by the share of “highly exposed population” that also has a “low resilience.” The example below is for mild drought.
Below the National Poverty Line
(Low Resilience)
Highly Exposed to Mild Drought
At-Risk to Mild Drought

21. Vulnerability Profile – Exposure and Resilience
In the sample polygon, 56% of households are below the national poverty line (low resilience). 26% of all households are highly exposed to drought (high exposure). 13% of all households in this polygon have both low resilience and high exposure, and are therefore at-risk to mild drought.
Resilience
Categories
Drought Severity
Mild
Medium
Severe
Low
56%
High
44%
Total
100%
Exposure
Categories
Drought Severity
Mild
Medium
Severe
Not Highly Exposed to Drought
74%
36%
10%
Highly Exposed to Drought
26%
64%
90%
- We can show a spreadsheet to those interested in how the 13% was derived from the two tables above; it’s not possible to determine simply by looking at the charts.
Exposure to MILD Drought
Resilience to MILD Drought
Low
High
Total
Not Highly Exposed to Drought
43%
31%
74%
Highly Exposed to Drought
13%
26%
56%
44%
100%

22. Vulnerability Profile Example
Example: “Country J”
- It could be the case that in Country J, the area on the right is more affluent in general, hence lower WRSI but less people not at-risk to drought. That is one reason why we use both exposure and resilience in our vulnerability profiling.
% Population Not At-Risk
% Population At-Risk to Mild Drought
% Population At-Risk to Medium Drought
% Population At-Risk to Severe Drought

23. Number of People Affected in ARV
ARV estimates the population affected by drought in a polygon by overlaying the WRSI deviation for the season on the vulnerability profile
For each area (polygon) considered in a country for a season, the estimated population affected by drought, N, is determined by:
If WRSI > Mild Drought Trigger
N = 0, No People Affected
If Mild Drought Trigger > WRSI > Medium Drought Trigger
Population At-Risk Mild Drought > N > Population At-Risk Medium Drought
If Medium Drought Trigger > WRSI > Severe Drought Trigger
Population At-Risk Medium Drought > N > Population At-Risk Severe Drought
If WRSI < Severe Drought Trigger
N = Population At-Risk Severe Drought
These estimates can be aggregated over all polygons considered in a country to estimate national population affected
- We have our vulnerability profile and we have our WRSI deviation figures, so we can make a graph with these data points, and linearly interpolate all N values in between.

24. Number of People Affected in ARV
Example: Population Affected for Country J
- We have our vulnerability profile and we have our WRSI deviation figures, so we can make a graph with these data points, and linearly interpolate all N values in between.

25. Number of People Affected
ARV estimates historical populations affected in a polygon assuming today’s population and vulnerability profile:
Example: “Country J” Estimated Population Affected by Drought, Main Season, 1996/7 – 2011/12
- This graph uses current population data combined with historical drought information to model what would happen if a drought in the past happened today. Important for insurance modeling.

26. Box 4: Modelled Costs

27. Required Response Costs
Lastly, ARV estimates response costs by multiplying the population affected estimates by a response cost per person
Example: “Country J” Estimated Response Costs, Main Season, 1996/7 – 2011/12
Total Response Cost by Polygon = Number of People Affected x Cost per person
Total Response Cost by Country = Sum of all Polygon Response Costs
The current default setting is $50 per person in bimodal seasons and $100 per person in unimodal seasons.
Cost per Person is a variable parameter by polygon that will depend on:
Most appropriate type of intervention
Cash and vouchers, food aid, scale-up of existing safety net programme, etc
Contingency planning
Location of the beneficiaries
Response cost numbers can and should be adjusted to reflect budgeted contingency plans

28. Estimated Response Costs As-Of D4
In-Season Monitoring
Estimated Response Costs As-Of D4
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

29. Estimated Response Costs As-Of D7
In-Season Monitoring
Estimated Response Costs As-Of D7
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

30. Estimated Response Costs As-Of D9
In-Season Monitoring
Estimated Response Costs As-Of D9
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

31. Estimated Response Costs As-Of D13
In-Season Monitoring
Estimated Response Costs As-Of D13
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

32. Estimated Response Costs As-Of D16
In-Season Monitoring
Estimated Response Costs As-Of D16
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

33. Estimated Response Costs As-Of D19
In-Season Monitoring
Estimated Response Costs As-Of D19
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

34. Estimated Response Costs As-Of D22
In-Season Monitoring
Estimated Response Costs As-Of D22
Country J – Main Season
ARV updates every 10 days as rainfall is reported during a season

35. Summary

36. Customizable Parameters
Model Component:
Customizable Parameters:
Users can select between RFE2 and ARC2
Can upload their own gridded datasets as long as they confirm to strict quality criteria for risk transfer
Rainfall
All WRSI input settings are changeable by season:
Environmental: PET, Water Holding Capacity etc.
Crop: Crop Type, Ky, Cropping Calendar, etc.
Other water balance outputs from WRSI calculation can be used
All drought triggers and benchmarks
Drought Index
ARC2 = African Rainfall Climatology v2
Vulnerability polygon layer
Scaling factor
Population data
New household survey data can be used to refine profiles
Profiling approach can be modified to reflect in-country processes
Vulnerability Profiles
Cost per person
Existing response mechanisms can be taken into account at the polygon level
Response Costs

37. ARC Risk Transfer Parameters
Once the underlying ARV model is set, countries can select ARC risk transfer parameters, specifying the terms of their participation in a risk pool, such as:
Deductible, the low severity/high frequency drought risk, as modelled by the ARV risk model, countries wish to retain and not transfer to ARC;
Ceding Percentage, the percentage of their total modelled risk, beyond the deductible, that countries choose to transfer to ARC;
Limit, the maximum payout countries would receive from ARC in a catastrophic drought scenario.
Example: “Country J “Estimated Response Costs, Main Season, 1996/7 – 2011/12
Example: Portion of Modelled Risk transferred to ARC shown in red

38. Preparation for ARC Participation
Every aspect of Africa RiskView can be changed and customized by users
As part of the ARC participation process each country will review, refine and edit Africa RiskView settings based on their own national disaster risk management plans, early warning processes and risk management tools
In order to transfer risk to ARC, countries will also need to:
Specify the seasons they would like to insure and define the ARC risk transfer parameters for each season;
Pay the corresponding premium to ARC;
Define contingency plans for potential ARC payouts for those seasons