The QMEE CDT Project proposal database

Welcome to the QMEE CDT Project proposal database. This is a live list of projects proposals put forward by PIs across the CDT partner institutions

PIs/Supervisors will continue to add projects to this list over the next few months, so do keep checking back! You can search the projects using the box below: simply enter some text and press Search to do a text search across all the database fields. If you want to search more finely, the search tool also allows you to search on particular details of the project descriptions: you will see these finer search options appear if you click on the search box.

Click on the view button next to a project to get the full proposal description. If you want to download project details, either for all projects, or for a subset you have searched for, then click on the 'Download details' button.

To find a particular PI's email or look up other PI details, use the menu at the top of this page (PIs tab).

The impact of multiple environmental stresses of large scale mortality and recovery events in a global vegetation model.
Large-scale land clearing, droughts, fire and heatwaves are amongst the most important episodic plant stress determining the composition, structure and dynamics of vegetated ecosystems over a range of scales, with widespread implications for ecosystem services including global hydrologic and carbon cycles [1]. Predicting the impacts of such events on vegetation distributions is therefore one of the most important challenges in land surface modelling. However, representations of the effects of such disturbance events in vegetation models are still poorly constrained by observations [2], with the most recent IPCC report [3] highlighting an associated disagreement between models as a major cause of uncertainty in historical and future projections of the terrestrial carbon balance. Recent analysis of remotely sensed patterns of tree cover distributions and stress factors suggest that, while modelled impact of fire is far too strong, vegetation models fail to capture basic patterns of drought and heatwave impacts. Vegetation recovery from disturbance has also shown to proceed at too slow a pace[4,5]. Remote sensing products have therefore proven useful in helping assess the impact of environmental stressors on modelled vegetation. However, the complex interplay of climate controls, different stressors and land use are hard to tease apart from remote sensing alone. Trickier still is determining how vegetation mortality and recovery responses acclimate/evolve under changing climate and environmental conditions, and under changing CO2 concentrations - especially given the current length of the remote sensing record and the much longer post-disturbance recovery times of some ecosystems. This project will build on recent work fusing remotely-sensed vegetation cover, land use and fire information and meteorological data with simple vegetation distribution models to answer three key questions: (1) How much do different stressors affect vegetation dynamics? (2) How much of the impact of these stressors is a result of initial mortality, subsequent vegetation exclusion, or from the rate of recovery? And (3) how will these be affected under future environmental change? The student will use novel probabilistic programming techniques to aid the introduction of dynamic vegetation into this data-modelling framework, enabling them to test dynamic vegetation responses which have proven difficult to assess using traditional methods. The dynamic responses will include evolution and acclimation of mortality and recovery responses to land use, fire regimes, drought, heat waves, and even the alleviation of stress from CO2 fertilization. The project will aid the development of the dynamic vegetation enabled JULES land surface model [6] to explore changes in environmental stresses under future climate and land use change. Applying the results to future environmental change will provide the opportunity to use JULES within the UK's Earth System model ( allowing the student to gain expertise in climate modelling and advanced statistical analysis complemented by expertise in spatial data manipulation and management. 1 Bonan G. Science 2008;320 2 Veenendaal E et al. New Phytol 2018;218 3 Ciais P et al. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 2014 4 Zeppel M et al. New Phytol 2015;206 5 Kelley D et al. Geo. Model Dev. 2014;7 6 Clark D et al. Geo. Model Dev. 2011;4
Jon Lloyd
France Gerard, Centre for Ecology & Hydrology,
Development of mathematical theory, Computing, Quantitative data analysis
Probabilistic modelling is central to linking observations to land surface modelling and quantifying associated uncertainties. Advanced statistical methods will include Bayesian inference applied to different statistical models including generalised linear modelling and possibly probabilistic machine learning. Mortality and recovery trait acclimation/evolution will also likely involve calculus.
Representing vegetation responses to environmental stressors in terms of probability distributions is a new and innovative approach to building and developing vegetation model. Preserving uncertainty in dynamic vegetation responses will allow assessment of stress impacts not possible using traditional optimization methods, and will serve as a template for new methods of data-model integration.
Vegetation modelling normally assumes that fire is the main disturbance influencing vegetation. However, many recent studies have questioned this assumption. This leaves the key question: what disturbances do control vegetation distributions on a global scale? This project will identify key determinants of vegetation distribution, and explore how these are effected under transient conditions.
Controls on ecosystem composition, and how controls evolve is central to global change research. The assessment and possible reduction of uncertainty in vegetation responses into the future will not only aid assessment of the impacts of changes in climate but help assess changes in terrestrial carbon uptake - important for assessment of allowable carbon emission for meeting Paris climate targets.
Coupling vegetation responses directly to observations is a completely new way of optimizing vegetation models which would give more realistic uncertainties in land surface responses, constrained by observations. As observation records extend, this technique will serve to reduce uncertainties in future land responses, rather than increase as is the case over recent IPCC reports.
The project includes management of remotely sensed data, fused to vegetation modelling. It will use advanced statistical techniques to quantify the uncertainty of vegetation responses in order to assess risks of ecosystem shifts under future environmental change - including changes in human land use and practices. The project therefore covers 5 of NERCs 'Most Wanted' skills.
Climate and climate change, Geosciences / Planetary science, Ecosystem-scale processes and land use
The student will develop science communication skills and training in publication and project management. Training in modelling and probabilistic programming will be provided in-house by CEH, while training on using the JULES in the UM offered by the NCAS CMS will also be available. Spatial data manipulation and training in using a GIS or EO image analysis software may also be required.
Centre for Ecology and Hydrology
2019-05-20 17:16:41