The earth is changing from both human impacts and natural variation, and has consequences for freshwater resource availability, agriculture, and natural terrestrial ecosystems. However, many of these consequences are poorly understood, and poorly quantified, particularly at larger (continental and global) scales.
This project will aim to fill some of these important knowledge gaps by utilising easily accessible global datasets to ask how seasonal and annual dynamics between climate, the land surface, and vegetation are changing in both space and time. This work will then form the foundation for a deeper understanding of how sensitive different ecosystems of the world are to change, how resilient they are to natural variability, the limits of future agricultural expansion, and the likely changes to freshwater resource availability.
This project will use easily accessible databases of climate, vegetation, land use, and other land surface conditions like soil moisture. Seasonal and annual differences will provide diagnostic conditions of how sensitive these characterisitcs are to change in both space and time. This will encompass the large global gradients in climate and ecosystems, and provide a ‘big picture’ perspective on the sensitivity of terrestrial ecosystems to change. Global datasets on freshwater resources will then be added to assess how these resources are responding to global changes.
Training and Skills
CENTA students are required to complete 45 days training throughout their PhD including a 10 day placement. In the first year, students will be trained as a single cohort on environmental science, research methods and core skills. Throughout the PhD, training will progress from core skills sets to master classes specific to the student's projects and themes.
‘Big data’ is usually a generic reference to the growing amount of data available to ask scientific and analytical questions, and the challenges associated with analysing it. This project will develop ‘big data’ analytical skills with specific reference to global environmental datasets and problems. This includes how to combine and analyse multiple large spatial datasets, time series analysis on large datasets, all within high level programming environments (e.g. R). This will also develop an appreciation for the strengths and limitations of many important spatial data sources, especially remote sensing information, spatially interpolated measurements, and climate model outputs. Finally, the student will develop capacity in important statistical and numerical techniques for scientific research.
Partners and collaboration (including CASE)
This project will leverage widespread knowledge and expertise from a range of international researchers at Universities in Switzerland, Australia, USA, and the UK