the case of the WRSI index, data on crop type, start of growing season, length of growing season, soil type, potential evapotranspiration (PET) need to be provided (Senay 2004).

WRSI is currently the most widely used drought index in Africa but a continuing effort has to be undertaken to determine better indices, of course depending on data availability, in understanding the driving factors in yield volatility including rainfall, temperature and other factors such as soil types and irrigation. Ultimately, one could imagine that a multiple index approach where a weight of different indices are used, could work best.

# Exposure Module

The Exposure Module captures data on crop type and crop area by region. This type of information is typically collected from a country’s Ministry of Agriculture. The government data is typically available at some administrative level, as for instance the district level, and this data is distributed to the grid level using a crop mask that shows percentages of main crops grown in the cell. However, the collected data may not be available for many years and their reliability may be an issue, especially if there are have been shifts in cultivation, multi‐cropping is practiced, harvested area differs from sown area, etc. (FAO 2011).

# Loss Module

The Loss Module quantifies the crop damage caused by the drought in terms of yield reduction though it could be expressed in monetary loss terms as well if there is an indicative market price for the crop. Yield losses calculated at the grid level are accumulated to sub‐national and national levels over grids that lie within sub‐national and national boundaries (and may include agro‐climatic zones). The yield reduction (or monetary losses for that matter) by event and their annual rate of the simulated events for which can be used to generate the disaster profile at the national and sub‐ national levels by means of yield shortfall exceedance probability curves or alternately through yield shortfalls at specified return periods (shown in Figure 2).

<mark>One issue that’s worthwhile re‐emphasizing here is that food security in many of the SSA and SA countries is a recurrent phenomenon as can be seen in the disaster data collected by EM‐DAT</mark><sup>24</sup> <mark>. EM‐ DAT shows that for many of these countries drought events affecting large numbers of people occur on a regular basis. Therefore, for many of these countries when considering their future drought risks, the period of interest would be events that happen at low return periods, as low as once in 5 or 10 years. A 10‐year event for many of these countries with low resilience could be disastrous and the 50‐year event could be unthinkable but these may not that significant for another country with higher resilience. A case in point is the 2012 US drought which was the highest insured loss in the US in 30 years since data were collected in the 1980s but the country as a whole and the US crop insurance industry were able to absorb the losses. On the other hand as shown in Figure 2 the 1‐in‐ 10 year drought loss in Malawi causes a reduction of 3.5% percentage points in Malawi’s annual GDP and would require the Malawi government to request for emergency food aid. Also, as seen in the EM‐DAT database, these recurrent droughts affect large number of people and the associated costs</mark>

> 24 EM DAT is the international disaster database hosted by the Centre for Research in Epidemiology of Disasters (CRED), Belgium. EM‐DAT is available at http://www.emdat.be/.

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