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dc.contributor.advisorPalmer, Paul
dc.contributor.advisorPurves, R
dc.contributor.authorCaldararu, Silvia
dc.date.accessioned2013-08-02T14:31:08Z
dc.date.available2013-08-02T14:31:08Z
dc.date.issued01/07/2013
dc.identifier.urihttp://hdl.handle.net/1842/7627
dc.description.abstractLeaf phenology refers to the timing of leaf life cycle events and is essential to our understanding of the earth system as it impacts the terrestrial carbon and water cycles and indirectly global climate through changes in surface roughness and albedo. Traditionally, leaf phenology is described as a response to higher temperatures in spring and lower temperatures in autumn for temperate regions. With the advent of carbon ecosystem models however, we need a better representation of seasonal cycles, one that is able to explain phenology in different areas around the globe, including tropical regions, and has the capacity to predict phenology under future climates. We propose a global phenology model based on the hypothesis that phenology is a strategy through which plants reach optimal carbon assimilation. We fit this 14 parameter model to five years of space borne data of leaf area index using a Bayesian fitting algorithm and we use it to simulate leaf seasonal cycles across the globe. We explain the observed increase in leaf area over the Amazon basin during the dry season through an increase in available direct solar radiation. Seasonal cycles in dry tropical areas are explained by the variation in water availability, while phenology at higher latitudes is driven by changes in temperature and daylength. We explore the hypothesis that phenological traits can be explained at the biome (plant functional group) level and we show that some characteristics can only be explained at the species level due to local factors such as water and nutrient availability. We anticipate that our work can be incorporated into larger earth system models and used to predict future phenological patterns.en_US
dc.language.isoenen_US
dc.publisherThe University of Edinburghen_US
dc.relation.hasversionCaldararu, S., Palmer, P.I. and Purves, D.W. (2012). Inferring Amazon leaf demography from satellite observations of leaf area index. Biogeosciences, 9, 1389–1404.en_US
dc.subjectphenologyen_US
dc.subjectglobal vegetation modelsen_US
dc.subjectBayesian methodsen_US
dc.subjectphenology
dc.subjectglobal vegetation models
dc.subjectBayesian methods
dc.subjectGlobal Change Research Institute
dc.titleUnderstanding and predicting global leaf phenology using satellite observations of vegetationen_US
dc.typeThesis or Dissertationen_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US


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