Assessing vegetation dynamics in response to climate variability and change across sub-Saharan Africa

Le résumé fourni par la source

Understanding and predicting how anthropogenic climate change is likely to impact terrestrial ecosystems across sub-Saharan Africa is a key question for both ecology and for regional and global climate policy development.This predictive understanding hinges on a far better ability to detect, interpret, and attribute changes in vegetation cover and productivity, which is the basis for ecosystem response and resilience to anthropogenic climate change.Monitoring and modelling of vegetation dynamics in the context of climate change requires long-term datasets of key ecosystem indicators such as vegetation productivity and phenology.The use of remotely sensed vegetation indices to detect vegetation change related to climate has become an important application of remotely sensed imagery.The third generation Normalized Difference Vegetation Index (NDVI3g) time series from the Global Inventory Modeling and Mapping Studies (GIMMS) has a 34-year long history (1982-2015) and provides unprecedented opportunity to examine vegetation dynamics in response to changes in temperature, rainfall, and increases in atmospheric carbon dioxide (CO2).This thesis makes use of the NDVI3g time-series to examine the influence of climate on vegetation productivity and phenology in order to (i) assess recent shifts in vegetation across sub-Saharan Africa (SSA) and (ii) facilitate improved simulations of vegetation by Dynamic Global Vegetation Models (DGVMs).The NDVI3g information was integrated with climate data and large-scale climate fluctuations and oscillations in sea surface temperature and atmospheric pressure to test hypotheses on the role of both climate variability and change on vegetation activity.Seasonal and long-term patterns of change were compared with projections of a dynamic global vegetation model, the "adaptive Dynamic Global VegetationModel" (aDGVM) that was initially developed for application in sub-Saharan Africa.In the first component of the thesis results show that the vegetation of SSA is driven by rainfall and associated fluctuations and oscillations in sea surface temperature (SST) and atmospheric pressure, with the El Niño-Southern Oscillation (ENSO) being the most dominant driver of variability in both vegetation productivity and phenology over eastern and southern Africa.Vegetation tends to show a stronger positive response to rainfall in the 3 months preceding vegetation growth suggesting that time-lag effects are significant when assessing the influence of climate.In the second component, trend analyses provide evidence for a number of important spatial and temporal patterns of change in vegetation productivity and phenology over SSA, which are generally consistent with independently reported long-term trends.Significant added value was provided to previous studies through the use of productivity and phenology metrics, which facilitated an assessment of vegetation dynamics at both the seasonal and inter-annual scale.

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