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aWhere Case Study: Weather Variability in Southern Africa

Inter-annual variability of rainfall has a significant negative impact on farmers and the overall population

Across southern Africa, an increase in rainfall variability compounded by lower total rainfall for most areas during December-February highlights the urgent need to target water management investments and data-driven advisory services to help farmers adapt to regional climate change.

Key Insights:

  • The Coefficient of Variation** (CV) for P/PET is increasing due to extreme daily and seasonal rainfall variability and points to required investments in watershed management. 
  • aWhere analysts highlight coefficient of variation (CV) to describe  the extent of weather variability in specific locations. The CV is shown as a percent and as a general rule, any location with a CV of greater than 20% is considered highly variable.
  • 82% of the population ( ~114M people) live in areas where the variance in P/PET for the main growing season is greater than 20%
    • 33 million live where P/PET CV > 40% and the average P/PET for this period is 0.71, too dry for maize.
  • This variability in rainfall poses a tremendous risk to farmers who over time lose confidence in investing in good agronomic practices. This in turn leads to lower productivity and risk of food insecurity.  
  • The map below shows the CV for the rainy season in this region from 2006-2019 for Precipitation over Potential Evapotranspiration (P/PET) as well as the population impacted by this variability.
**The coefficient of variation (CV) is the ratio of the standard deviation to the mean. The higher the coefficient of variation, the greater the level of dispersion around the mean. (INSEE)

Investigating Weekly Rainfall Patterns across Southern Africa

These charts demonstrate the rainfall variability across the region. The location in the bottom right in Madagascar experienced almost a complete failure of the rainy season with large rainfall events at the beginning and close of the rainy season but very little precipitation in the months between. These trends will impact farmers and crop production, and natural resource management.

  • Across the region, November 2019 through April 2020, there were weeks of very poor rainfall during the primary rainy season
  • The charts below compare the weekly rainfall totals (blue bars) to the long-term normal (orange line) for specific locations (latitude/longitude).


aWhere’s localized, daily weather data can help governments get ahead of weather events by looking at historical trends coupled with weather forecasts.  African farmers need in-time weather-based insights to make informed crop management decisions to increase productivity, profitability and resilience.  Fortunately, the tools and partnerships  now exist to address this knowledge gap – today.