Browsing by Author "Davis-Reddy, Claire"

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    Assessing vegetation dynamics in response to climate variability and change across sub-Saharan Africa
    (Stellenbosch : Stellenbosch University, 2018-03) Davis-Reddy, Claire; Midgley, Guy F.; Stellenbosch University. Faculty of Science. Dept. of Botany and Zoology.
    ENGLISH ABSTRACT: 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 Vegetation Model" (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. A clear latitudinal pattern of change was detected where significant increases in both productivity and the length of the growing season were observed over the northern hemisphere tropics (0-10°N) and sub-tropics of southern hemisphere (20-35°S) and significant decreases observed over the southern hemisphere tropics (0-20°S). The greening trends account for approximately 50% of the observed changes over SSA. Over West Africa and parts of central Africa the greening trends are linked to increases in rainfall and possibly atmospheric CO2 concentration as well as reforestation efforts in some countries. Over the south-western Cape, eastern coastline, and northeast extent of South Africa the greening trends are consistent with the observed patterns of bush encroachment and expansion of alien invasive species in these regions. The trends in many of these regions have been attributed at least partly to increased atmospheric CO2. Over southern Africa, simulations of vegetation productivity derived from the adaptive DGVM indicate that vegetation should have been increasing across southern Africa over the last 30 years. This finding combined with the lack of evidence of substantial changes in rainfall over the region suggests the role of land-use in limiting the increase in vegetation as observed in the aDGVM simulations. Lastly, the comparison between observed remotely sensed vegetation indices and historical simulated values of vegetation productivity from the aDGVM over SSA provided valuable insight into the utility of remotely sensed vegetation indices to assist in the validation, refinement and overall improvement of simulation of vegetation by DGVMs. While the model performs well over grassland and savanna regions of southern Africa it tends to underestimate grass productivity in East Africa and over the Sahel, overestimate tree cover in tropical humid forests of central and West Africa. The model also fails to capture the unique seasonal pattern of the Mediterranean-type vegetation of south-west cape of South Africa and the inter-annual pattern of variability in vegetation over SSA. These biases and limits of the model are likely to have implications for the performance of model in projecting future vegetation cover over these regions. This research contributes to the understanding of landscape-scale vegetation response patterns that will provide an important benchmark against which future vegetation change can be assessed. Importantly, it highlights that testing the performance of dynamic vegetation models in the context of regional climate models is a research imperative.
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    Large uncertainties in future biome changes in Africa call for flexible climate adaptation strategies
    (John Wiley & Sons Ltd, 2020) Martens, Carola; Hickler, Thomas; Davis-Reddy, Claire; Engelbrecht, Francois; Higgins, Steven I.; Von Maltitz, Graham P.; Midgley, Guy F.; Pfeiffer, Mirjam; Scheiter, Simon
    Anthropogenic climate change is expected to impact ecosystem structure, biodiversity and ecosystem services in Africa profoundly. We used the adaptive Dynamic Global Vegetation Model (aDGVM), which was originally developed and tested for Africa, to quantify sources of uncertainties in simulated African potential natural vegetation towards the end of the 21st century. We forced the aDGVM with regionally downscaled high-resolution climate scenarios based on an ensemble of six general circulation models (GCMs) under two representative concentration pathways (RCPs 4.5 and 8.5). Our study assessed the direct effects of climate change and elevated CO2 on vegetation change and its plant-physiological drivers. Total increase in carbon in aboveground biomass in Africa until the end of the century was between 18% to 43% (RCP4.5) and 37% to 61% (RCP8.5) and was associated with woody encroachment into grasslands and increased woody cover in savannas. When direct effects of CO2 on plants were omitted, woody encroachment was muted and carbon in aboveground vegetation changed between –8 to 11% (RCP 4.5) and –22 to –6% (RCP8.5). Simulated biome changes lacked consistent large-scale geographical patterns of change across scenarios. In Ethiopia and the Sahara/Sahel transition zone, the biome changes forecast by the aDGVM were consistent across GCMs and RCPs. Direct effects from elevated CO2 were associated with substantial increases in water use efficiency, primarily driven by photosynthesis enhancement, which may relieve soil moisture limitations to plant productivity. At the ecosystem level, interactions between fire and woody plant demography further promoted woody encroachment. We conclude that substantial future biome changes due to climate and CO2 changes are likely across Africa. Because of the large uncertainties in future projections, adaptation strategies must be highly flexible. Focused research on CO2 effects, and improved model representations of these effects will be necessary to reduce these uncertainties.