Observing and modeling changes in snow properties and snowmelt runoff

The accelerating climate change is influencing the boreal snow cover. Snow is exposed to new warmer conditions that alter its bulk properties and microstructure. Detailed analysis of the boreal taiga snowpack properties with novel techniques , and linking observed snow properties to process based snow modelling tools is needed to simulate and predict the changes in snowpack properties.

We are looking for a talented and motivated candidate to advance in-situ snow observations and numerical modelling in the Digital Waters (DIWA) flagship project. The PhD project aims at understanding the ongoing changes in the characteristics of arctic and boreal snowpacks based on existing long-term snow observations and model estimates. Main objective of the project is to use detailed data on snow structural properties (e.g. spatial distribution, stratification, evolution of snow microstructure) in conjunction with physics-based snow and hydrology models. In addition, the project aims to improve field observation approaches with novel monitoring tools .
The doctoral researcher will carry out research in the Finnish Meteorological Institute (FMI), together with the multi-disciplinary group of DIWA flagship. The PhD thesis consists of following topics: 1) Analysis of long-term snow structural measurements and their differences between e.g. peatlands, lakes and forests in a boreal taiga snow, and 2) use physically-based snow models to resolve the snow properties in high resolution and explore future conditions with climate scenario modeling, 3) couple the physics-based snow model with a hydrological rainfall-runoff model for improved understanding of snowmelt runoff generation 4) explore the application of emerging machine learning tools to address issues with computationally expensive high resolution model runs. The doctoral researcher will have access to existing field data, and snow and hydrology models. They are expected to process the data and utilize them to further develop numerical models for simulating physical snow and hydrological processes.
Work is supervised by Associate Professor Pertti Ala-aho together with Dr. Ioanna Merkouriadi and N.N at FMI. Work is done in close collaboration with a team in FMI and other collaborators and will use snow data from the well-established Sodankylä, Pallas and Oulanka research stations in the boreal-subarctic region.

The position requires an active approach, diligence and cooperation skills, willingness to work as a part of a team and passion to learn. We also expect a Master’s degree in, for example, hydrology, meteorology, water and environmental engineering, physical geography or a related field of environmental sciences. Due to the nature of the described PhD project, the candidate would benefit also from having e.g. the following skills: (1) Good understanding of snow physics and hydrological processes; (2) Good and demonstrated data analysis and numerical skills, and knowledge from programming languages such as R, Python, Matlab, Fortran. (3) Experience in computational modelling especially for snow applications (4) familiarity with machine learning tools.

Finnish Meteorological institute

Water, Energy and Environmental Engineering Research Unit, Faculty of Technology

Pertti Ala-aho (UOULU)

Juha Lemmetyinen (FMI)

Ioanna Merkouriadi (FMI)

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