Vegetation response to precipitation variability in East Africa controlled by biogeographical factors. Issue 9 (20th September 2016) - Hawinkel, P.; Thiery, W.; Lhermitte, S.; Swinnen, E.; Verbist, B.; Van Orshoven, J.; Muys, B.
Abstract: Ecosystem sensitivity to climate variability varies across East Africa, and identifying the determinant factors of this sensitivity is crucial to assessing region‐wide vulnerability to climate change and variability. Such assessment critically relies on spatiotemporal data sets with inherent uncertainty, on new processing techniques to extract interannual variability at a priori unknown time scales and on adequate statistical models to test for biogeographical effects on vegetation‐precipitation relationships. In this study, interannual variability in long‐term records of normalized difference vegetation index and satellite‐based precipitation estimates was detected using ensemble empirical mode decomposition and standardized precipitation index with varying accumulation periods. Environmental effect modeling using additive models with spatially correlated effects showed that ecosystem sensitivity is primarily predicted by biogeographical factors such as annual precipitation distribution (reaching maximum sensitivity at 500 mm yr −1 ), vegetation type and structure, ocean‐climate coupling, and elevation. The threat of increasing climate variability and extremes impacting productivity and stability of ecosystems is most imminent in semiarid grassland and mixed cropland ecosystems. The influence of oceanic phenomena such as El Niño–Southern Oscillation and Indian Ocean Dipole is foremost reflected in precipitation variability, but prolonged episodes also pose risks for long‐term degradation of tree‐rich ecosystems in the East African Great Lakes region. Key Points: East African ecosystems respond to varying extents to interannual climate variability Mean annual precipitation, vegetation type, and ocean‐climate coupling control this relationship Additive modeling with spatiotemporal data products reveals the critical role of data uncertainty