High spatial and temporal resolution observation using KAZNET for early warning and de-risking in the Horn of Africa

Drought is the leading cause of livestock mortality and climate induced poverty traps in the drylands of Africa. Despite the progress made in the development of indices to quantify drought impacts, challenges remain due to the nature of climatic shocks and variation in the onset, severity, progression and critical stages in drought manifestation. Socio-economic drought is latent and requires a combination of high spatial and temporal resolution data to observe the onset, progression and important points of change during the shock. Lack of data impedes full utilization of drought monitoring indices in developing countries. To understand the impact of drought on households, key indicators should be monitored at the expected frequency of change. For instance, acute measures of child undernutrition have been shown to respond to sudden changes in food intake or illness which could be due to extreme weather events. Early warning systems are designed to monitor drought drivers and predict anomalies, enabling the early detection of rainfall deficit that facilitate timely interventions to avert potential loss and damage. Ground-truthing is a crucial element of an effective early warning system, as it facilitates verification to ensure that the established early warning triggers are responsive to monitored shocks. This process necessitates complementary datasets that provide objective measurements of shock transmission. Index-based insurance products have gained traction and are being scaled due to their capacity to address challenges such as moral hazards and adverse selection associated with traditional insurance. However, these approaches continue to encounter significant challenges, particularly basis risk, which has been described as the Achilles’ heel of index insurance. While there is anecdotal consensus regarding the transmission of drought effects to households, empirical studies documenting these mechanisms are markedly deficient. This could be potentially due to lack of mechanisms to gather information at granular level, with the appropriate temporal resolution to support the formulation of effective interventions. This report documents a high frequency data collection approach and the potential use of the high frequency data for early warning and early action and improving the design of the index insurance products. Household welfare and livestock market data gathered using the approach was summarized and plotted to show trends, while rangeland data in form of nadiral- (ground looking) images were analyzed using a hue-based color transformation to derive Fractional Green Canopy Cover (FGCC) providing a quantitative measure of the proportion of green vegetation in the images. Additionally, the socioeconomic data, descriptive statistics and trends show the temporal changes that occur in households and markets. These analyses demonstrate the value of the data, and its ability to provide the required granularity for disentangling some of the complex shocks faced presently. These data fill a void in our ability to detect some subtle changes in key indicators of importance in response to external stimuli.