Characterization of glacial water flow using lagrangian drifters

Water flow on, in and under glaciers still remains a poorly understood system. Water can flow over the surface of glaciers, as well as through channels inside and under the ice. The water thereby has strong influence both on ice melt and glacier dynamics, making glacial hydrology an important field of study.

Currently glacier hydrology is mainly studied by the use of chemical tracers as well as salt dilutions. In this project we go new ways and use modern underwater and sensor technology to study glacial water flow. We will use a submersible sensor platform, which can be deployed in glacial streams and once recovered, delivers a wealth of sensor data.

The first tests of the platform were succesfully conducted on Svalbard in summer 2018. During this project further field tests are planned and the thesis has the goal to characterize step-pool sequences of glacier channels. Step pool sequences are steps (sometimes even water falls) over which the water runs into a downstream pool. The drifters will float through the step-pool sequences and deliver pressure, acceleration, gyroskop and magnetometer data.

The student will analyze the sensor readings and extract distinct signals for step-pool sequences. The geometry will thereby be analyzed using photogrammetry from drone images. Later on the use of machine learning technologies for the automatic recognition of geometric features of the glacial stream will be explored.

Tasks

  • Field tests of drifters on glaciers
  • Drone image aqcuisition of glacier channels
  • DEM generation of glacier channels from drone imagery
  • Data analysis
  • Characterization of step-pool sequences
  • Testing of machine learning algorithms for automatic data extraction

Learning outcome

  • Hydrological/ Glaciological field experience
  • Photogrammetry skills
  • Data analysis skills
  • Programming experience
  • Knowledge of advanced underwater sensor platforms
  • Knowledge of glacial hydrology

Field work will be conducted in Finse and potentially Svalbard during summer time.

The here listed thesis is just a suggestion. Several other thesis possibilities exist within this project and the final thesis topic will be designed in consultation between supervisors and student. Thesis topics are at the interface between science and technology and include a mix of field work, data analysis and modeling. The topic is also suitable for students of the Computational Science: Geoscience programme option.
Contact Andreas Alexander for more details of the project or relevant literature.

Tags: Glaciology, hydrology, sensor technology, data analysis, machine learning, field work
Published Jan. 25, 2019 8:56 AM - Last modified May 14, 2019 1:59 PM

Scope (credits)

60