About the Project
Current forest structure metrics (e.g. Leaf Area Index, sky view factor, mean canopy height, percentage cover) in hydrological models are insufficient to describe forests after instances of major disturbance, such as fire, beetle infestation, drought-driven forest mortality, logging or other management responses. Snow cover dynamics in forests are driven by energy and mass flux processes that are governed by forest structure; these processes in turn determine hydrologically and ecologically important factors such as water amount and availability and snow persistence or disappearance. However, snowpack properties and spatial variability are often not well represented in operational runoff forecasting models.
Recent improvements in coverage and availability of airborne lidar data make possible efficient and detailed characterization of pre- and post-disturbance forest canopy structure, as well as snow depth and water equivalent. These data provide an important and unrealized potential to implement forest structure metrics that specifically capture the processes governing snow dynamics into physically-based runoff models, and thereby allow improvements in model performance and reliability under forest and climate change trajectories.
In parallel with the increasing availability of lidar data, several recent research efforts have made important advances in high-resolution representation of canopy-regulated energy and mass fluxes and in reliably coarsening these results to spatial resolutions commensurate with operational runoff model implementations. These results offer a viable pathway to accurate forest/snow process representation, thereby contributing to hydrologic forecast robustness to watershed-scale forest disturbances.
Academic Enquiries
This project is supervised by Dr Nick Rutter. For informal queries, please contact nick.rutter@northumbria.ac.uk. For all other enquiries relating to eligibility or application process please use the email form below to contact Admissions.
Eligibility Requirements
- Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
- Appropriate IELTS score, if required.
- Applicants cannot apply if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.
Please note: to be classed as a Home student, candidates must meet one of the following criteria
- Be a UK National (meeting residency requirements), or
- have settled status, or
- have pre-settled status (meeting residency requirements), or
- have indefinite leave to remain or enter.
If a candidate does not meet the criteria above, they would be classed as an International student. Applicants will need to be in the UK and fully enrolled before stipend payments can commence.
How to Apply
For further details of how to apply and the application form, click the link below
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Applications must include a covering letter (up to 1000 words maximum) including why you are interested in this PhD, a summary of the relevant experience you can bring to this project and of your understanding of this subject area with relevant references (beyond the information already provided in the advert) and the advert reference (e.g. RDFC23/…).
Deadline for applications: 19 May 2023
Start date of course: 01 October 2023
Funding Notes
This opportunity is only open to students classed as ‘Home’. The studentship includes a full stipend at UKRI rates (for 2022/23 full-time study this is £17,668 per year) and full tuition fees. Studentships are also available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £10,600 per year and full tuition fees) in combination with work or personal responsibilities).
References
Sturm, M., M. A. Goldstein, and C. Parr (2017), Water and life from snow: A trillion dollar science question, Water Resources Research,53, 3534–3544,doi:10.1002/2017WR020840.
Hedrick, A. R., Marks, D., Havens, S., Robertson, M., Johnson, M., Sandusky, M., et al. (2018). Direct insertion of NASA Airborne Snow Observatory-derived snow depth time series into the iSnobal energy balance snow model. Water Resources Research, 54, 8045–8063.
Mazzotti, G., Currier, W. R., Deems, J.S., Pflug, J. M., Lundquist, J. D., &Jonas, T. (2019). Revisiting snow cover variability and canopy structure within forest stands: Insights from airborne lidar data. Water Resources Research, 55, 6198–6216.
Mazzotti, G., Essery, R., Webster, C., Malle, J., & Jonas, T. (2020). Process‐level evaluation of a hyper‐resolution forest snow model using distributed multisensor observations. Water Resources Research, 56, e2020WR027572.
