Africa RiskView END OF SEASON REPORT | (2017 SEASON)

This RiskView End-of-Season Report is a publication by the African Risk Capacity (ARC). The report discusses Africa RiskView’s estimates of rainfall, drought and population affected, comparing them to information from the ground and from external sources. It also provides the basis of a validation exercise of Africa RiskView, which is conducted in each country at the end of an insured season. This exercise aims at reviewing the performance of the model and ensuring that the country’s drought risk is accurately reproduced by Africa RiskView for drought monitoring and insurance coverage. The End-of-Season reports are also being continuously refined with a view to providing early warning to ARC member countries.

Highlights:

Rainfall: previous five years in the central parts and 90%-110% of the • Comparison of the cumulative rainfall received in 2017 to the median in the western and southern parts of Senegal long term mean (1983-2016) at the pixel level reveals that • The overall performance of the 2017 cropping season was most parts of Senegal received between 110% and 150% of modelled as better than average by Africa RiskView in most their long-term average, implying that above average rainfall parts of Senegal was received during the 2017 season Affected Populations: Drought: • The modelled estimates of Africa RiskView indicate that no • The final end-of-season WRSI for the 2017 season shows that households were affected by drought in the 2017 production 95% to 100% of crop water requirements were met for the season southern and central of Senegal—a pointer of ade- RISK POOL quate precipitation • Since a payout of over USD 16 million in 2014/15 due to the • Comparison of the end-of-season WRSI with the benchmark poor performance of the 2014 agricultural season in West Afri- (median WRSI for the previous five years) indicates that the ca, Senegal has not received a payout. Similarly, no payout is final WRSI in 2017 was more than 110% of the median of the expected this year.

Rainfall sistent rainfall of more than 20mm right from the start of the The rainy season in Senegal normally starts in dekad 14 (11 May) season in dekad 14 (Fig 9). and ends in dekad 31 (10 November). Spatial distribution of rain- Comparison of the cumulative rainfall received in 2017 to the long fall in 2017 indicates that the Southern re- term mean (1983-2016) at the pixel level reveals that most parts ceived cumulative rainfall of more than 800mm during the 2017 of Senegal received between 110% and 150% of their long-term rainfall season (Fig 1). Total cumulative rainfall in the central average, save for Saint Louis where rainfall was between 90% and regions of , Kaolack, Kaffrine and ranged from 110% of the long-term average and less than 90% of the long-term 600mm to 800mm. In the northern regions of Saint Louis, , average along the border with Mauritania (Fig 2). Overall, the Matam and Thies, the cumulative rainfall received ranged from performance of rainfall in 2017 as estimated by ARC2 rainfall 200mm to about 600mm. Whereas the southern most areas of estimates was above average in most regions of Senegal. This Matam and Louga received about 600mm of rainfall, the northern consequently produced positive outcomes in terms of agricultural areas of these regions received only about half the sum of rainfall production and productivity with above average harvests ex- realised in the southern parts. The lowest amounts of rainfall, as pected in most regions. expected, were realised in Saint Louis. Drought Index In terms of temporal distribution, the first significant amounts of Africa RiskView uses the Water Requirements Satisfaction Index rainfall of more than 20mm were received in dekad 18 in most (WRSI), an index of crop performance based on the availability of regions of the country (as shown in Figures 3 – 17). Only Kedou- water for the plants during the growing season, as an indicator for gou, located in the south-eastern tip of Senegal received con- drought. During the customisation of Africa RiskView by the In-

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Fig 1: Cumulative rainfall (mm) during the 2017 season, 11 May – Fig 2: Cumulative rainfall in 2017 (mm) compared to normal 10 Nov, ARC2, Senegal (1983-2016), Dek 14 – Dek 31, ARC2, Senegal

Country Technical Working Group (TWG) in Senegal for Pool 4, previous five years, which was selected by the TWG as the bench- groundnuts, which is a major source of income and an important mark WRSI, indicates that the final WRSI in 2017 was more than food security crop for many households was retained as the refer- 110% of the median of the previous five years in the central parts ence crop in the areas shown in the calculation mask (groundnut and 90%-110% of the median in the western and southern parts of growing areas) in Fig 18. The final end-of-season WRSI for the Senegal (see Fig 19). A few localised areas in Louga and Matam 2017 season shows that 95% to 100% of crop water requirements were modelled as having WRSI of less than 50% of the five-year were met for the southern and central regions of Senegal – imply- median. It can be concluded that performance of the 2017 crop- ing that precipitation was adequate (both absolute rainfall quanti- ping season was better than average in most parts of Senegal, ties received and the temporal distribution of rainfall) to meet based on the final WRSI values and compared to the median WRSI crop water requirements and crop development needs in these of the previous five years. regions. In the western regions of Thies, Fatick and , the Modelled Drought Impacts final WRSI ranged from mediocre to average based on Africa RiskView modelling. In Louga where groundnut is also In order to model drought conditions on the ground, the region- level WRSI at end of a season is compared to the benchmark, widely grown as shown by the calculation mask, the final WRSI which in the case of Senegal was determined as the median WRSI was poor to mediocre (only 50% to 80% of the crop water require- value of the previous five years (2012 to 2016 in this case). ments were met). During the customisation of Africa RiskView for Drought conditions are triggered if the end-of-season WRSI falls Pool 4, the TWG selected a WRSI aggregation method of 20mm, below the benchmark. Both the severity of the drought (the ex- 5mm, 5mm during the sowing window. This implies that at least tent of the deviation of the WRSI from the benchmark) and the 20mm of rainfall had to be received in one dekad and 5mm each vulnerability of the population in the affected area are used to determine the number of people affected. in the two subsequent dekads for Africa RiskView to model sowing As discussed in the previous section, the end of season WRSI was as successful. Although these conditions were largely met in most above or equal to the benchmark for most parts of Senegal. Only a of Senegal, a few pixels in Matam, Louga and Saint Louis regions few localised spots in the northern parts of the country (Matam did not meet these conditions during the planting window, thus and Louga) had WRSI of less than 50% of the benchmark. Moreo- no planting was deemed to have taken place in these selected ver, groundnut or crop production for that matter is undertaken locations (Fig 18). In Louga in particular, less than optimal rainfall on a very limited scale in these areas, as shown in Fig 19. As a was received in dekads 19 and 20 after the commencement of the consequence of the average to above average rainfall received in most of Senegal, little or no water deficits were experienced (Fig season in dekad 18 (see Fig 11), probably the reason for the medi- 20) and the modelled estimates of Africa RiskView indicate that no ocre to average end-of-season WRSI. households were affected by drought in the 2017 production Comparison of the end-of-season WRSI with the median for the season compared to the modelled estimate of 730,000 people

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Fig 3: Dekadal rainfall in mm compared to 1983- Fig 4: Dekadal rainfall in mm compared to 1983- Fig 5: Dekadal rainfall in mm compared to 1983- 2016 average (blue line), 11 May-10 Nov, 2017, 2016 average (blue line), 11 May-10 Nov, 2017, 2016 average (blue line), 11 May-10 Nov, 2017, Senegal , Senegal Diourbel, Senegal

Fig 6: Dekadal rainfall in mm compared to 1983- Fig 7: Dekadal rainfall in mm compared to 1983- Fig 8: Dekadal rainfall in mm compared to 1983- 2016 average (blue line), 11 May-10 Nov, 2017, 2016 average (blue line), 11 May-10 Nov, 2017, 2016 average (blue line), 11 May-10 Nov, 2017, Fatick, Senegal Kaffrine, Senegal Kaolack, Senegal

Fig 9: Dekadal rainfall in mm compared to 1983- Fig 10: Dekadal rainfall in mm compared to 1983 Fig 11: Dekadal rainfall in mm compared to 1983 2016 average (blue line), 11 May-10 Nov, 2017, -2016 average (blue line), 11 May-10 Nov, 2017, -2016 average (blue line), 11 May-10 Nov, 2017, Kedougou, Senegal , Senegal Louga, Senegal

Fig 12: Dekadal rainfall in mm compared to 1983 Fig 13: Dekadal rainfall in mm compared to 1983 Fig 14: Dekadal rainfall in mm compared to 1983 -2016 average (blue line), 11 May-10 Nov, 2017, -2016 average (blue line), 11 May-10 Nov, 2017, -2016 average (blue line), 11 May-10 Nov, 2017, Matam, Senegal Saint Louis, Senegal Sedhiou, Senegal

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Fig 15: Dekadal rainfall in mm compared to 1983- Fig 16: Dekadal rainfall in mm compared to 1983- Fig 17: Dekadal rainfall in mm compared to 1983- 2016 average (blue line), 11 May-10 Nov, 2017, 2016 average (blue line), 11 May-10 Nov, 2017, 2016 average (blue line), 11 May-10 Nov, 2017, Tambacounda, Senegal Thies, Senegal , Senegal

Fig 18: Final WRSI for the 2017 cropping season (11 May – 10 Nov, 2017 – Fig 19: Final WRSI for the 2017 cropping season compared to the bench- ARC2) mark (median WRSI of the previous five years)

affected by drought in 2016 and the average of 370,000 affected by drought annually (Fig 20). This is consistent with the projections by FEWS NET that food insecurity in Senegal is currently minimal (IPC Phase 1) and projections till May 2018 indicate that the food security will remain stable. Arc RISK POOL Senegal has been a member of the ARC Risk Pool since the first pool in 2014/15. In that year, the country benefited from a payout of over USD 16 million, due to the poor performance of the 2014 agricultural season in . Since then, the country has not qualified for a payout since the Africa RiskView estimate of people affected by drought has been lower than the attachment points (the point at which the insurance begins to pay out) during the Fig 20: Total crop water deficit in the 2017 season according to Africa subsequent risk pools. RiskView

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About ARC: The African Risk Capacity (ARC) is a special- The Africa RiskView software is the tech- The ARC Insurance Company Limited is the ised agency of the African Union designed nical engine of ARC. It uses satellite-based financial affiliate of the ARC Agency, which to improve the capacity of AU Member rainfall information to estimate the costs of pools risk across the continent through issu- States to manage natural disaster risk, responding to a drought, which triggers a ing insurance policies to participating coun- adapt to climate change and protect food corresponding insurance payout. tries. insecure populations.

Note on Africa RiskView’s Methodology:

Rainfall: Africa RiskView uses Drought: Africa RiskView uses Affected Populations: Based on Response Costs: In a fourth and various satellite rainfall da- the Water Requirements Satis- the WRSI calculations, Africa final step, Africa RiskView con- tasets to track the progression faction Index (WRSI) as an indi- RiskView estimates the number verts the numbers of affected of rainy seasons in Africa. Coun- cator for drought. The WRSI is of people potentially affected people into response costs. For tries intending to participate in an index developed by the Food by drought for each country countries participating in the the ARC Risk Pool are required and Agriculture Organisation of participating in the insurance insurance pool these national to customise the rainfall com- the United Nations (FAO), pool. As part of the in-country response costs are the underly- ponent by selecting the dataset which, based on satellite rain- customisation process, vulnera- ing basis of the insurance poli- which corresponds the best to fall estimates, calculates wheth- bility profiles are developed at cies. Payouts will be triggered the actual rainfall measured on er a particular crop is getting the sub-national level for each from the ARC Insurance Compa- the ground. the amount of water it needs at country, which define the po- ny Limited to countries where different stages of its develop- tential impact of a drought on the estimated response cost at ment. To maximise the accura- the population living in a spe- the end of the season exceeds a cy of Africa RiskView, countries cific area. pre-defined threshold specified intending to take out insurance in the insurance contracts. customise the software’s pa- rameters to reflect the realities on the ground.

Disclaimer: The data and information contained in this report have been developed for the purposes of, and using the methodology of, Africa RiskView and the African Risk Capacity Group. The data in this report is provided to the public for information purposes only, and neither the ARC Agency, its affil- iates nor each of their respective officers, directors, employees and agents make any representation or warranty regarding the fitness of the data and information for any particular purpose. In no event shall the ARC Agency, its affiliates nor each of their respective officers, directors, employees and agents be held liable with respect to any subject matter presented here. Payouts under insurance policies issued by ARC Insurance Company Limited are calculated using a stand-alone version of Africa RiskView, the results of which can differ from those presented here.

For more information visit our website: www.africanriskcapacity.org