Climate change is already demonstrated by changes in the frequency and severity of extreme weather events. The UK government is providing £3.87 billion through the International Climate Fund to help developing countries mitigate and adapt to climate change and within the UK aid programme, £110 million has been allocated to projects that aim to build local resilience.
This research focuses on livelihood impact-based analyses through exploitation of diverse, multi-source data and models. A key policy objective of research to date has been to enhance the capacity of developing countries to adapt to climate change. However, much of that work remains siloed or one dimensional and is not easily translated into knowledge and evidence that can be used by decision makers across all governance levels.
One of the keys to success for policy makers attempting to deal with climate change in developing economies is understanding the resilience of populations – the nature of their vulnerabilities and their ability to withstand shocks, for example to flooding or drought-induced crop failure, and the impact on the nutritional status of impoverished rural communities.
However, faced with complex research results, in multiple fields of study and little or nothing to help them understand the real impacts on real people, policy makers are operating with no genuine pragmatic guidance. They know they have a huge problem to deal with, but the science as presented does not allow them to make the bridge from data to action. What is needed is a platform to integrate the data from all these multiple, cross-disciplinary research initiatives together with tools and analytic applications and deliver to policy makers in government and other agencies a unified, distilled and user-oriented view. This will help them to make better and more timely decisions to prepare for and adapt to likely impact of shocks and long-term trends on local economies, and on the livelihoods of vulnerable people, their health and well-being.
The objective of this project is to integrate analysis of shock impacts across sectors (agriculture and health) and population dynamics, recognising that complex feedback systems are also highly context dependent and include social, political/institutional and economic factors.
This research will explore, integrate and publish available data sets including climate, crops, health and livelihoods information. It will exploit data mining and other statistical / machine learning approaches to develop techniques and algorithms with the objective of identifying the greatest vulnerabilities within and across defined populations. Exemplars will be taken from several DAC-listed countries in Africa where the Walker Institute and the Centre for Agri-Environmental Research, School of Agriculture, National Centre for Earth Observation and Evidence for Development (EfD) are already working together in partnership with national government, academic and NGO stakeholders.
Applicants should hold a minimum of a UK honours degree at 2.1 level, or equivalent, in a relevant discipline such as meteorology , physics, mathematics, geography or environmental science. A strong background in numerical/statistical techniques and the application of those techniques in big data and database programming is essential. Knowledge of data modelling and data integration would be advantageous. Prepared to travel in developing countries.
The project is interdisciplinary in nature, linking together hydrometeorological expertise with expertise on climate change adaptation, agriculture, livelihoods and disaster resilience.
To apply for this project please submit an application for a PhD in Ecology and Agri-Environmental Research at the Walker Institute, University of Reading at http://www.reading.ac.uk/graduateschool/prospectivestudents/gs-how-to-apply.aspx
1) Please quote the reference ‘GS19-WI-IDAPSX’ in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
2) When you are prompted to upload a research proposal, please omit this step.