PhD Studentship : Foundation AI model for assessing human influence on hydroclimatic compound hazards in the UK

Growing evidence suggests that hydroclimatic compound events - where multiple hazards occur simultaneously or sequentially - are becoming more frequent and severe in some regions of the UK (Visser-Quinn et al., 2019). These events can lead to significant economic losses and are of particular concern because their combined impacts often exceed the effects of individual hazards (Chun et al., 2024; Ridder et al., 2020). For instance, prolonged droughts can harden soils and reduce infiltration, making it more vulnerable to flash flooding when heavy rainfall occurs. Successive heavy rainfall events can result in multiple floods, leaving little time for affected areas to recover. Heatwaves, by increasing evaporation, can be followed by sudden rainfall that intensity flash flood risks.

Human activities, such as reservoir construction, urban expansion, water abstraction, and wastewater discharge, are key drivers of changing hydroclimatic patterns (Han et al., 2022; Sarojini et al., 2016). However, our current understanding of how human activities influence hydroclimatic compound events remains limited. This is partly due to the lack of comprehensive human-related datasets representing diverse anthropogenic activities, as well as limitations of existing models in effectively integrating human data to quantify human influence.

Foundation AI models offer significant potential due to their strength in integrating multi-modal data (e.g., time series, geospatial data, text, and image - including both hydrological and human-related data) and capturing complex, non-linear relationships (Bommasani et al., 2021; Nguyen et al., 2023). By integrating large scale, multi-modal data and leveraging self-supervised and transfer learning, these models demonstrate satisfactory spatial-temporal simulation and predictions across domains, even with limited data.

Leveraging recent advances in foundation AI and availability of muti-modal data, this project aims to (overall workflow is in Figure 1):

- identify and classify hydroclimatic compound hazards in human-influenced catchments in the UK, including multivariate compound events, temporally compounding events and spatially compounding events

- enhance the temporal and spatial modelling of compound hazards by integrating human influence data (such as water abstraction, reservoir capacity, urbanization, population density) into a pre-developed foundational AI model

- quantify and attribute the extent to which human activities contribute to the occurrence and severity of hydroclimatic compound hazards

For further information on this project and details of how to apply to it please visit https://centa.ac.uk/studentship/2026-b16-foundation-ai-model-for-assessing-human-influence-on-hydroclimatic-compound-hazards-in-the-uk/

Further information on how to apply for a CENTA studentship can be found on the CENTA website: https://centa.ac.uk/apply/

Funding notes:
This project is offered through the CENTA3 DLA, funded by the Natural Environment Research Council (NERC). Funding covers: annual stipend, tuition fees (at home-fee level), Research Training Support Grant.

Academic requirements: at least a 2:1 at UK BSc level or a pass at UK MSc level or equivalent.

International students are eligible for studentships to a maximum of 30% of the cohort. Funding does not cover any additional costs relating to moving or residing in the UK. International applicants must fulfil the University of Birmingham’s international student entry requirements, including English language.  Further information: https://www.birmingham.ac.uk/postgraduate/pgt/requirements-pgt/international/index.aspx.

References:
Bommasani et al, 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.

Chun et al, 2024. Unravelling compound risks of hydrological extremes in a changing climate: Typology, methods and futures. arXiv preprint arXiv:2409.19003.

Han et al, 2022. Contribution of urbanisation to non-stationary river flow in the UK. Journal of Hydrology, 613.

Nguyen et al, 2023. Climax: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343.

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