security which could be then used in the design of sovereign level food security insurance schemes, as is done for instance in ARC’s technical model Africa RiskView<sup>20</sup> used in the design of the insurance pooling.

Catastrophe property models developed in the late 1980s generate probabilistic distributions of property losses for natural perils such as earthquakes and hurricanes (Grossi and Kunreuther 2005). These models which now cover other perils and a wider base of countries have found wide usage in the (re)insurance and capital markets. The catastrophe risk modeling framework is comprised of Hazard, Vulnerability, Exposure and Loss Modules which in the context of sovereign level catastrophe crop models for drought would have the components as described next.

Hazard Module The hazard module contains a set of gridded values of a drought index that best correlates with crop yield reductions. A common drought index that has found wide usage is the water requirement safety index, WRSI (Senay 2004), which is used in FEWS NET, GIEWS and ARV. There are other drought indices used in other crop models, such as Aquacorp from FAO, EPIC from USDA, CERES and CROPGRP from DSSAT but these models tend to be more complicated and require a large amount of additional data. In the context of developing countries, data required for these models may not always be available and assumptions need to be made in using the more complex crop models thus making them less desirable for operational purposes. In some instances, the Standard Precipitation Index, SPI, which is a simple index based on rainfall data only<sup>21</sup> has been used for drought exposed crop areas with good results but as shown in a recent paper (Legesse and Suryabhagvan 2014) WRSI was a better indicator of agricultural drought than SPI and NDVI<sup>22</sup> (a RS measure of vegetation) in Ethiopia.

Gridded WRSI is generated using gridded estimates of satellite‐based rainfall data, soil runoff, soil water holding capacity, potential evapotranspiration (PET) and crop‐specific characteristics of cultivated crops (cultivars) such as sowing season, length of growing season, crop growing stages specified by their respective crop coefficients (Kc), crop water requirements and percentage length of growing season. The end of the growing season WRSI is used as the drought index.

The historical gridded RS rainfall data available for 30 years serve as the basis for generating gridded hazard data that could occur in the future. One way future seasonal rainfall data could be generated is to use a bootstrapping technique to combine random sequences of historical dekads (10‐day) rainfalls to generate rainfall for the entire growing season (Jayanthi and Husak 2013). A different approach is taken by ARC (Syroka J., personal communication 2014) whereby the 30 years of historical rainfall data were analyzed using principal component analysis, PCA, which is commonly used in analyzing spatio‐temporal data, to reduce the dimensionality of the data set. The reduced dimensionality is then used to simulate future seasons for a specified number of years and as each season is equally likely the annual rate of occurrence of a simulated season is the reciprocal of the number of years of simulation. A potential enhancement would be to use rainfall and temperature

> 20 As described in the section on introduction and methodology of the ARV available at <u>http://www.africanriskcapacity.org/africa‐risk‐view/introduction.</u>

> 21 SPI is discussed in detail at http://www.wrcc.dri.edu/spi/explanation.html.

> 22 NDVI is discussed in detail at http://earthobservatory.nasa.gov/Features/MeasuringVegetation/.

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