Measuring the Sensitivity and Stability of Vegetation in Response to the Hydroclimate Across East Africa with an Empirical Dynamic Modeling Approach

Expanding access to remotely sensed Earth observations provides us with an opportunity to examine the underlying spatiotemporal coupling between vegetation, both natural and managed, and the hydroclimate. Applying approximately 20 years of satellite records, we demonstrate a method to quantify the sensitivity and stability of land-atmosphere interactions. Here we evaluate the predictability of vegetation via the Normalized Difference Vegetation Index (NDVI) across croplands, shrublands, grasslands, and woodlands of East Africa as it relates to fluctuations in precipitation, soil moisture, evapotranspiration, and land surfaced temperature. In this study, we detect the strength of state dependency among these variables at the dekadal (10-day) to monthly scale using a data-driven approach known as Empirical Dynamic Modeling (EDM). There is notable spatial variability in NDVI predictability, with equatorial areas generally expressing the poorest skill, which can be attributed to the inconsistent rainfall seasonality and high aridity. Woodlands exhibit strong predictability throughout the region while vegetation response to environmental drivers in grasslands is less reliable. Our results suggest water availability, uptake and storage are important factors influencing the NDVI cycle. For a one-month lead time, high predictive skill can be retrieved from the time series, though skill weakens by a four- to sixth-month lead, at which point the overall seasonality appears to play a dominant role. One contribution to highlight is the advancement in our understanding of the relationship between vegetation and land surface temperature, which is particularly valuable in drought-prone East Africa. In this presentation, we introduce an application of EDM for biogeosciences, assess how historical seasonal information of the hydroclimate and vegetation across various land use and land covers can inform future environmental patterns, and identify critical areas of inquiry with a changing climate and extending agricultural production.