BACKGROUND
Cold regions (e.g. mountains, high latitude regions) are undergoing rapid environmental changes and the terrestrial water cycle is no exception. Changes in snow dynamics, surface water, soil, and groundwater are strongly interconnected and understanding these changes requires observations and approaches that can capture the spatial and temporal complexity of the water cycle. New satellite missions offer us exciting opportunities to observe these components of the Earth system and their interactions, deepening our understanding.
RESEARCH
We would like to explore how satellite observations, numerical modelling, and data-driven methods can be used to better understand and potentially predict the changes in the terrestrial water cycle. We have a particular interest in the recently launched Surface Water and Ocean Topography (SWOT) mission. SWOT’s high resolution observations open new possibilities for studying lakes, rivers and wetlands; and evaluating, constraining, and improving our understanding of hydrological processes. Our focus is high latitude and snow-dominated environments where snow accumulation and melt processes strongly influence the timing and magnitude of runoff and surface water dynamics as well as groundwater storage and recharge. These processes are closely connected to hydrological extremes such as floods and droughts.
MSc SCOPE AND METHODS
The thesis topic and research questions will be shaped together with the candidate(s) to fit your/their scientific background and interests, incorporating your own ideas. Here are some possible research directions:
- Investigating SWOT observations and comparing with in-situ discharge data and water level observations to study seasonal and interannual variations in water levels, extent, and storage with a focus on the opportunities and limitations in snow-dominated environments.
- Investigating connections between snow dynamics and surface water by combining SWOT observations with satellite data from MODIS and the Sentinels, meteorological data, and other relevant datasets.
- Exploring surface water and groundwater connections by combining GRACE, SWOT, groundwater observations or models, and hydrological modelling. A particular focus could be whether satellite observations can provide new information about changes in total water storage and exchanges between different components of the terrestrial water cycle.
- Machine learning for water cycle analysis which could include exploring whether data-driven approaches can be used to identify relationships, to predict changes, or to complement physics-based models.
The thesis does not need to be limited to either physics-based modelling or machine learning. One aim could be to explore how different types of observations and modelling approaches can be combined to answer given research questions about the terrestrial water cycle. Depending on the research question, this could involve satellite observations, hydrological or land-surface models, machine learning, data assimilation, or combinations of these approaches.
SUPERVISOR TEAM
From UiO: Yeliz Yilmaz (surface hydrology, snow, remote sensing, numerical modelling), Lena Tallaksen (hydrology, statistical modelling), Anja Sundal (groundwater, earth observation) and/or relevant external collaborators.
We are looking for motivated master’s candidates interested in the intersection of Earth observations, hydrology, hydroclimatology, numerical modelling, machine learning, and the terrestrial water cycle.
