PLEASE NOTE: Application deadline date 08 Jan 2024. Applications are no longer being accepted for this project
Project Overview
The frequency, intensity, and extent of extreme climatic events are expected to increase but their impact on forest ecosystem functioning, and adaptive capacity is less understood. This project aims to address this using historical field and satellite data and using machine learning algorithms to understand the vulnerability and resilience of European forests to extreme climatic events.
Project Description
Climate change is expected to lead to an increase in the frequency, duration, intensity, and spatial extent of extreme climatic events in the coming decades [1]. These extreme climatic events are expected to affect the functioning of terrestrial ecosystems. For example, climate extremes such as droughts and heatwaves have negatively impacted their function, which in turn reduces their capacity to provide essential ecosystem services [2]. However, the response of terrestrial ecosystems to extreme climatic events varies depending on factors such as an ecosystem’s diversity, the duration, frequency, and intensity of the extreme event, whether the extreme climatic event occurs in isolation or as compound events and so on. To develop strategies to minimise the impacts of future extreme climatic events, there is need to understand how ecosystems have responded to these events in the past and identify the traits/conditions that lead to increased vulnerability or resilience of ecosystems to these events. The aim of this project is to use historical in-situ (e.g., the Integrated Carbon Observation System - ICOS) data and long-term Earth Observation (EO) data to understand impacts extreme climatic events on forest ecosystems in Europe. The project will employ machine-learning algorithms and models to reconstruct disturbances in forests (e.g., rates of tree mortality) caused by extreme climatic events and model how various factors (e.g., duration, intensity and frequency of the extreme events, forest diversity, management practices, single vs. compound events etc.) influence how forests respond to these events.
The INSPIRE DTP programme provides comprehensive personal and professional development training alongside extensive opportunities for students to expand their multi-disciplinary outlook through interactions with a wide network of academic, research and industrial/policy partners. In addition, there will be opportunity to interact with European researchers within the ICOS network. The student will be registered at the University of Southampton and hosted at School of Geography and Environmental Science. Specific training will include:
- Earth Observation data retrieval, analysis, and interpretation
- Machine leaning and statistical regression techniques.
- Time series data analysis
- Climate data analysis.
[1] Flach, M., Brenning, A., Gans, F., Reichstein, M., Sippel, S. and Mahecha, M.D., 2021. Vegetation modulates the impact of climate extremes on gross primary production. Biogeosciences, 18(1), pp.39-53.
[2] Mahecha, M.D., Bastos, A., Bohn, F.J., Eisenhauer, N., Feilhauer, H., Hartmann, H., Hickler, T., Kalesse-Los, H., Migliavacca, M., Otto, F.E. and Peng, J., 2022. Biodiversity loss and climate extremes—study the feedbacks. Nature, 612(7938), pp.30-32.
