PhD Student Vacancy Only DeepSoil Integrated Soil Hydrological Assessment with CPT–ML for Flood & Drought Risk Screening

Farming in Scotland faces increasing pressure from extreme weather events with floods and droughts threatening productivity soil health and water security. Yet most monitoring remains confined to the topsoils overlooking subsoil layers (02 m) that control infiltration storage and runoff generation. Conventional statistical approaches and pedotransfer estimates cannot capture the vertical heterogeneity of soil processes that regulate water movement. DeepSoil addresses this evidence gap by integrating Cone Penetration Testing (CPT) with machine learning (ML) and geostatistics to map soil infiltration and water storage functions. We operationalise two projectdefined CPTderived indices Infiltration capacity (I*) and Storage potential (S*) will quantify how water moves and is retained in the soil profile. Using CPT profiles calibrated with intact soil cores the project will create 10 m (field) and 25 m (catchment) resolution maps of soil hydraulic functioning across the soil profile enabling early identification of flood- and drought-prone zones. Soil degradation already costs Scottish agriculture an estimated 2575 million annually through compaction alone and each 1 % increase in runoff can raise flood losses by 5776 k per affected property underscoring the urgency of improved hydrological risk screening.

Aims and Objectives:
The project aims to develop an integrated CPTMLgeostatistical framework for deriving and mapping the I* and S* indices to assess soil resilience under climatic and land-use pressures. Its objectives are to: (i) calibrate CPT data against soil cores and hydraulic tests; (ii) upscale point measurements using ML and kriging to produce uncertainty-aware maps; and (iii) combine static capacity with dynamic environmental data (rainfall soil moisture PET) to identify flood and drought hotspots. Outputs will include validated maps uncertainty layers and dashboards to inform sustainable land and water management.

Methods and Approach:
Representative sample locations will be statistically determined to obtain with CPT soundings and co-located soil cores across two long-term experimental platforms: the Centre for Sustainable Cropping (CSC) in Balruddery Farm offering over a decade of soil health data under regenerative and conventional management and the Glensaugh Climate-Positive Farming Initiative (CPFI) representing hill farming and upland soil contexts. CPT variables (qₚ fₛ u₂) will be calibrated to measured hydraulic properties producing local I* and S* values. ML models (e.g. Random Forest) will predict I* from covariates such as terrain indices geology land cover and Sentinel indices while S* will be interpolated using kriging with uncertainty propagation. These static maps will be fused with CHESS-SCAPE rainfall COSMOS-UK and river flow from SEPA gauges to generate dynamic flood/drought indicators validated against observed events. Technical considerations include corrections for peat and stony tills CPT normalisation and explicit treatment of measurement and model uncertainty.

This 4yr PhD project is a competition jointly funded by The James Hutton Institute and Abertay University. This opportunity is open to UK students and will provide funding to cover a stipend and UK level tuition. International students may apply but must fund the difference in fee levels between UK level tuition and international tuition fees. Students must meet the eligibility criteria as outlined in the UKRI guidance on UK and international candidates. Applicants will have a first-class honours degree in a relevant subject or a 2.1 honours degree plus Masters (or equivalent).

We will not consider the use of 3rd party recruitment agencies for the sourcing of candidates for this position.

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