Glaciers are sensitive indicators of climate change, and represent important water resources for populations around the world. Glacier mass change depends on a variety of factors, including air temperature and surface conditions. Snow microbiota (e.g., snow algae) can change the surface reflectance of snow and ice, increasing the amount of energy absorbed, increasing the amount of snow and ice melted in a given season, and thereby impacting water resources and climate. Difficulty accessing field sites makes studying these processes difficult, but the recent explosion of satellite images and proliferation of tools for rapid processing of remote sensing data give the potential for studying the impact of microbiota on large spatial scales.
Using archived Landsat and Sentinel-2 data, the successful candidate will work to develop tools for estimating algal abundance over long time periods for a glacier/region of their choosing, and investigate the relationship between local climate parameters, algal abundance, and glacier mass change. This work includes: processing large amounts of remote sensing data, processing/working with time series of climate data, statistical comparison of time series data.
Objectives of the Research:
*Use archived remote sensing data and tools such as Google Earth Engine to estimate snow algal abundance over time for a glacier/region of your choice, in order to better understand spatiotemporal patterns of snow algal blooms.
*Compare derived estimates of algal abundance to climate parameters such as temperature and precipitation, as well as records of glacier elevation and mass change.
Methods to be used:
Using Google Earth Engine, the candidate will produce time series of snow algal abundance using methods similar to those described by Takeuchi et al. (2006) or Ganey et al. (2017), for a glacier or region of their choosing, based on available records of glacier mass change and image availability. This record of algal abundance will be compared using statistical methods to existing climate data where available, as well as the records of glacier mass change.
Skills required of applicant:
*A background in Geosciences or related field.
*Prior experience with programming (any language).
*Interest in glaciology, climate, remote sensing, or some combination.
*Ability to work independently.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
*Prior experience with Google Earth Engine
*Experience in using statistical software packages such as R
*Experience in using and analysing remote sensing datasets
Takeuchi, N., Dial, R. J., Kohshima, S., Segawa, T., & Uetake, J. (2006). Spatial distribution and abundance of red snow algae on the Harding Icefield, Alaska derived from a satellite image. Geophysical Research Letters, 33(21), 1–6. https://doi.org/10.1029/2006GL027819
Ganey, G. Q., Loso, M. G., Burgess, A. B., & Dial, R. J. (2017). The role of microbes in snowmelt and radiative forcing on an Alaskan icefield. Nature Geoscience, 10(10), 754–759. https://doi.org/10.1038/ngeo3027
- To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
This is a self-funded MRes opportunity.
The Doctoral College at Ulster University
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