A slow rainy season onset is a reliable harbinger of drought in most food insecure regions in Sub-Saharan Africa

Abstract

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the “Crisis” phase of AFI—requiring emergency food assistance—at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.

Trends of Rainfall Onset, Cessation, and Length of Growing Season in Northern Ghana: Comparing the Rain Gauge, Satellite, and Farmer’s Perceptions

“…Precise information on the onset and cessation of seasonal rain can reduce the risks and costs of re-sowing seeds due to the season's false onset [24]. The late onset of rains provides the first outlook of a rainy season and is a reliable early warning of food insecurity several months before harvesting [25]. A ten-day delay of onset of the rainy season makes drought conditions more likely.…”

An Agro-Pastoral Phenological Water Balance Framework for Monitoring and Predicting Growing Season Water Deficits and Drought Stress

“…In a world with increasingly extreme precipitation (Emori and Brown, 2005;Allan and Soden, 2008) and Indo-Pacific sea surface temperature volatility (Cai et al, 2013(Cai et al, , 2015, East African agricultural advances are struggling to cope with climate change (Davenport et al, 2018). As the combination of population growth, declining rainfall and climate volatility create increasing food stress (Funk et al, 2005(Funk et al, , 2015aFunk and Brown, 2009), improved integrated drought early warning systems (Funk et al, 2007;Thomas et al, 2019Thomas et al, , 2020Funk and Shukla, 2020;Shukla et al, 2021) and improved drought risk management practices and policies (Pulwarty and Sivakumar, 2014;Wilhite and Pulwarty, 2017) can help east Africa manage risk and boost productivity. The PWB framework, discussed here, will provide a relatively simple means of connecting satellite observations with climate, weather and land surface model simulations, helping to support integrated early warning systems.…”

Conflict and Climate Factors and the Risk of Child Acute Malnutrition Among Children Aged 24–59 Months: A Comparative Analysis of Kenya, Nigeria, and Uganda

“…NDVI describes the state of vegetation and has been shown to correlate well with deviations in crop yield, though performance is variable by region (Vrieling et al, 2008). A common practice is to use community-level aggregate NDVI as a proxy for local-scale crop health and agricultural production of food (Petersen, 2018, Teal et al, 2006, Shukla et al, 2021.…”

“…NDVI values range from −1 (no vegetation/ greenness) to 1 (high indication of greenness/vegetation). In general, more vegetation (higher NDVI values) are associated with more a better growing season and more agricultural productivity (Shukla et al, 2021). In addition, we include dummy variables that flag whether or not any conflict during the periods measured by the lagged variables coincided with the growing season.…”