Africa RiskView END OF SEASON REPORT | (2019)

This Africa 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: • In general, the performance of the 2019 rainy season was benchmark WRSI values at the end of the 2019 agricultural sea- good with normal to above normal cumulative rainfall for most son. parts of the country. However, localised areas in south- Affected Populations: western Mali, notably Kayes, Yélimané, Bafoulabé, Keniéba, • Africa RiskView estimates that around 150,000 people were Nioro, Diéma and Kita experienced below normal cumulative affected by drought conditions in localised areas especially in rainfall at the beginning of the season. Bourem, Djenné, Diré, south-western Mali, this figure remains below the modelled Gao, Macina and Ménaka have experienced early cessation historical average of around 726,000 people. The highest pro- with very little rain for the month of October. portion of people affected by drought as modelled by Africa Drought: RiskView was in Diema, Kita, Macina, Nara and Nioro. • The end-of-season WRSI values were in line with the bench- RISK POOL mark selected by the country in most parts of Mali, except for • Due to the overall good performance of the season and de- Kayes, Yélimané, Djenné, Ménaka and Nioro where a signifi- spite localised drought impacts in south-western Mali, the trig- cant below benchmark WRSI values recorded. Tominian, Koro, ger point for a pay-out from ARC Ltd was not reached at the Douentza, Bandiagara, Mopti, San, Gourma-Rharous, , end of the 2019 agricultural season. , Barouéli and Bla recorded a not so significant below

Rainfall The rainy season in Mali starts from May and ends in November. and Goundam. In the southern parts of Mali, 200 mm to over 300 During the 2019 season, the cumulative rainfall totals varied mm of above long-term average rainfall was received during the significantly across the country from as low as 75 mm in Tom- 2019 season particularly in San, Kita, Bankass, , Kati, Niono, bouctou to as high as 1175 mm in Yanfolila. The northern part of Kolondiéba, Yorosso, , , Koulikoro, , Mali received less rain with less than 200 mm of rain recorded in Barouéli, Keniéba, Dioïla, Ségou, Bla, Kangaba and Yanfolila (Fig 2). Abeïbara, Goundam, Tessalit, Tin-Essako and Tombouctou. Most Regarding the temporal distribution of the rains, an analysis of parts of the country received a cumulative rainfall between 600 dekadal (10-day) rainfall estimates suggests that at national and and 1000 mm. The highest rainfall between 1000 mm and 1200 regional level, the season started on time in May, and progressed mm was recorded in Kati, Koutiala, Dioïla, Kolondiéba, Kadiolo, with normal to above normal rainfall until the end of the season. Bougouni, Sikasso, Kangaba, Keniéba and Yanfolila (Fig 1). Com- However, localised areas in south-western Mali notably Kayes, pared to the long-term average (1983-2018), satellite rainfall Yélimané, Bafoulabé, Keniéba, Nioro, Diéma and Kita experienced estimates suggest that normal to above normal rains were re- below normal cumulative rainfall at the beginning of the season. ceived through-out the country expect in the northern part of the The season ended earlier than normal in Bourem, Djenné, Diré, country–where below normal rains experienced in Tombouctou Gao, Macina and Ménaka, with below normal rainfall in October (Fig 11 - Fig 22).

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Fig 2: Cumulative rainfall (mm), compared to normal (MAY—OCT, Fig 1: Cumulative rainfall (mm), (MAY—OCT, 2019), (ARC2) 2019), (ARC 2)

Fig 4: Maize End-of-season WRSI compared to benchmark, Mali, Fig 3: Maize End-of-season WRSI, Mali, 2019 agricultural season 2019 agricultural season

Fig 6: Millet End-of-season WRSI compared to benchmark, Mali, Fig 5: Millet End-of-season WRSI, Mali, 2019 agricultural season. 2019 agricultural season

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Fig 7: SORGHUM End-of-season WRSI, Mali, 2019 agricultural sea- Fig 8: SORGHUM End-of-season WRSI compared to benchmark, son. Mali, 2019 agricultural season

Drought The in-country Technical Working Group (TWG) customised Africa According to The Famine Early Warning Systems Network (FEWS RiskView to model the impact of drought on maize, millet and sor- NET), the overall average harvest throughout the country favours ghum, the three important staple crops in Mali. The planting win- average to above average food availability during the 2019-2020 dow, during which farmers usually plant their crops, extends from food year except for some areas facing deficits due to inadequate late May to early July in southern Mali, early June to mid-July in rainfall in the western Sahel and insecurity in the north and centre the central parts of the country and from mid-June to late July in of the country. the north. The TWG had selected an average aggregation method for the WRSI, which means Africa RiskView uses the average end- of-season WRSI value resulting from all possible planting opportu- nities for each pixel.

According to Africa RiskView, the sowing criteria was satisfied in most parts of Mali, except in the northern tip of Kayes. The num- ber of potential sowing opportunities varies for one in the north to 5 in the south.

Compared to the benchmark selected by the TWG to model nor- mal conditions in the country, which was defined as the average of the last 5 years, the season performed normally in most parts of Mali. However, below benchmark WRSI values was observed in some parts of Mali, with Kayes, Yélimané, Djenné, Ménaka and Nioro recording a significant below benchmark WRSI values. Tominian, Koro, Douentza, Bandiagara, Mopti, San, Gourma- Fig 9: Percentage of population affected by drought, Mali, 2019 Rharous, Yorosso, Yanfolila, Barouéli and Bla recorded a not so agricultural season significant below benchmark WRSI values at the end of the 2019 agricultural season (Fig 4, Fig 6 and Fig 8).

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equivalent of around 500,000 drought affected people as modelled by Africa RiskView) was not reached.

The in-country Technical Working Group with support from the ARC Secretariat is currently reviewing the customisation of Africa RiskView in view of Mali’s participation in the 2019/20 ARC Risk Pool. The exercise aims at reviewing the drought index parameters used by the model, as well as updating input data such as the vul- nerability profile and poverty information used by Africa RiskView. Potential improvements to the model will help ensure that drought risks are accurately reproduced for drought monitoring and insur- ance coverage and that the modelling continues to evolve as new information is reported and gathered.

Fig 10: Estimated population affected by drought, Mali, 2001-2019

Affected Populations Based on the customisation of Africa RiskView, around 4 million people are vulnerable to drought in Mali. Of these, the model esti- mates that around 150,000 people are affected by drought condi- tions at the end of the 2019 season (Fig 10). These are located mostly in western Mali, namely in Kayes (57,922 people), Djenné (53,386), Nioro (19,470), Yélimané (14,079), Ménaka (4,295) and Tominian (592) (Fig 9). Compared to historical drought years mod- elled by Africa RiskView, it appears that the number of people affected in 2019 remains below the modelled historical average of around 726,000 people. Mali’s major droughts occurred in the 1980s and 1990s, and more recently in 2002, 2004, 2006 and 2011 (Fig 10).

The 2019 Cadre Harmonisé exercises concluded in November 2019 found that 648,330 people were severely food insecure (Phase 3 or worse) at the time of the analysis. This figure was projected to in- crease to around 1,117,001 people for the peak lean season. The high number of people facing acute food insecurity in Mali is main- ly attributed to civil insecurity and flooding in some parts which disrupted agricultural production. Trade flows have also been se- verely disrupted due to insecurity and displacement, thus increas- ing vulnerability of mainly rural populations. ARC Risk Pool Mali has been a member of the ARC Risk Pool since 2015/16. Dur- ing the current pool, the country did not receive a payout, given that the attachment level selected by the Government of Mali (the

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Fig 11: Temporal distribution of rainfall in Mali during the 2019 Fig 12: Temporal distribution of rainfall in Ansongo during the rain season 2019 rain season

Fig 13: Temporal distribution of rainfall in Bafoulabe during the Fig 14: Temporal distribution of rainfall in Diema during the 2019 2019 rain season rain season

Fig 15: Temporal distribution of rainfall in Dire during the 2019 Fig 16: Temporal distribution of rainfall in Gourma-Rharous rain season during the 2019 rain season

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Fig 17: Temporal distribution of rainfall in Kayes during the 2017 Fig 18: Temporal distribution of rainfall in Kenieba during the rain season 2017 rain season

Fig 19: Temporal distribution of rainfall in Kita during the 2019 Fig 20: Temporal distribution of rainfall in Menaka during the rain season 2019 rain season

Fig 21: Temporal distribution of rainfall in Niafunke during the Fig 22: Temporal distribution of rainfall in Yelimane during the 2019 rain season 2019 rain season

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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