{"id":946,"date":"2025-11-21T14:17:10","date_gmt":"2025-11-21T14:17:10","guid":{"rendered":"https:\/\/scholarshipidea.net\/?p=946"},"modified":"2025-11-21T14:17:11","modified_gmt":"2025-11-21T14:17:11","slug":"phd-in-habitat-dependence-of-tipping-points-in-upland-soils-and-implications-for-natural-flood-management-university-of-glasgow-uk","status":"publish","type":"post","link":"https:\/\/scholarshipidea.net\/?p=946","title":{"rendered":"PhD in Habitat dependence of tipping points in upland soils and implications for natural flood management, University of Glasgow, UK"},"content":{"rendered":"\n
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About the Project<\/h2>\n\n\n\n

Project Overview<\/strong>:<\/p>\n\n\n\n

Flooding and drought are intensifying under climate change. Natural Flood Management can reduce flood risk by restoring natural hydrological processes. This project addresses a key real-world challenge: how to optimise land and soil management in uplands using computer modelling to enhance infiltration, reduce runoff, and build resilience to extreme weather.<\/p>\n\n\n\n

Project Description<\/strong>:<\/p>\n\n\n\n

Soil structure is central to this hydrological partitioning, yet it is sensitive to extremes and management. Layered organo-mineral soils, covering ~20% of the UK, may respond to extremes with alternative stable states (Robinson et al. 2016), affecting local-scale flood risk and unknown consequences for intermediate scales. Management of these soils, especially mixing, may alter hydrological partitioning that the modelling will explore.<\/p>\n\n\n\n

This project will develop new dynamic models to investigate how upland soils respond to management and drought and how these changes influence infiltration\u2013runoff dynamics. The findings will directly inform the design and targeting of natural flood management interventions, supporting sustainable land management that works with natural processes to deliver multiple environmental and societal benefits.<\/p>\n\n\n\n

The core methodology for this work is multiscale mathematical modelling that is closely integrated with experimental observations\/data to enable new system function predictions in the changing climate. This will be achieved by combining analytical model reduction methods (asymptotic expansions) with numerical modelling methodologies and predictive machine learning (ML) and agentic AI processes to enable fast and accurate model prediction\/usage that would be compatible to be integrated into web-based surveillance tools.<\/p>\n\n\n\n

We are looking for:<\/strong><\/p>\n\n\n\n