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Title: Understanding and predicting global leaf phenology using satellite observations of vegetation
Author: Caldararu, Silvia
ISNI:       0000 0004 2746 4200
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2013
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Leaf 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.
Supervisor: Palmer, Paul; Purves, R. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: phenology ; global vegetation models ; Bayesian methods