With a growing population and climate-related disasters projected to become more severe, the need to anticipate and better prepare for food crises has never been higher. Status quo early warning systems perform well, by and large, but complex weather phenomena are a major source of uncertainty. In this dissertation, I explore whether statistical diagnostics associated with tipping point theory can be used to improve detection of major crises.
First, the accuracy of early warning systems is evaluated – with a focus on the Greater Horn of Africa. I discuss geographical disparities in early warning skill, and explore the various sources of uncertainty that impact food security projection accuracy. I quantify the contribution of climate forecast skill and conflict events in overall early warning skill to use as a baseline upon which to improve.
Second, the potential applications of tipping point theory are discussed in relation to the use of remotely-sensed environmental variables for food security early warning. I evaluate the different statistical diagnostics that can be applied to identify food security tipping points and the data requirements in terms of spatial and temporal resolutions, data availability, latency and geographic coverage in order to be maximally useful for early warning analysis.
Finally, I test whether statistical diagnostics of tipping points can be used for detection of food security transitions. Through this work, I demonstrate the utility of combining soil moisture measurements from the SMAP mission with food price data for identifying transitions, with a lead time of approximately 3 months. The model presented here was used to detect all major food security transitions (both towards and away from a crisis) in the observational record of soil moisture. Importantly, the diagnostics are also correlated with the magnitude of change in food security conditions which is critical information for food security planning.
Overall, the dissertation advances the science and capabilities of food security early warning, which will lead to an improvement in saving lives and resources.