The development and propagation of liquid water content (LWC) in the snowpack of various snow types
This project was assigned to the student Thor Parmentier who started his Master's studies in Earth Sciences, Geophysics, in the Autumn semester 2022.
In Norway, slushflows pose a significant natural hazard. The number of slushflows is expected to increase in the next decades due to a warmer climate. Hazard prediction and early warning is therefore crucial to prevent casualties and damage to infrastructure.
Slushflows are rapid mass movements of water saturated snow. They release in low to moderate slopes (< 30°). Due to their high liquid water content, slushflows usually have a long runout, can transform into debris flows further downslope and can be highly destructive. A complex interaction between several factors is the key to triggering slushflows. Impermeable ground is a prerequisite. Porous snow structures are most prone to destabilization. Rate and duration of water supply, due to rain on snow and/or intense snowmelt, resulting from rapid air temperature increases, is crucial. However, studies so far have to a large degree been qualitative rather than quantitative.
A new operational early warning service for slushflow hazard on a regional scale has been established in Norway in the last ten years. Unlike early warning systems for floods, snow avalanches and landslides, no internationally standardised method existed for a four-level assessment of regional slushflow hazard. The Norwegian slushflow early warning is based on assessments of daily snow and hydrometeorological conditions for today and the next two days. The service has become a valuable tool for the prevention of impacts from natural hazards such as slushflows. Another benefit is that this approach can be implemented in other regions with slushflow hazard where the necessary input data are available.
To improve the accuracy, more knowledge on the behaviour of liquid water content in different snow types and snow depths is needed. The goal of this study is therefore to acquire quantitative data as input for the improvement of the slushflow early warning.
- How do the water supply amount and intensity influence the propagation and development of LWC in the snowpack?
- Accuracy and compliance between different LWC sensors
- Possible additional question: How well are the modelled grid values in Xgeo reflected in the local measurements?
Relevant locations: flat or very gently sloping areas (allowing some sites close to infrastructure) determined by different snow types. Traditional snow profile measurements and additional LWC measurement using LWC sensors: SLF Snow Fork (and hopefully DeTISS from Denoth, delivery date for that instrument is not yet confirmed). Can be combined with use of GIS to explore the representation of 1x1 km2 grids provided in Xgeo and in situ measurements.
- Own collection of data in field
- NVE has collected some LWC-data that could be used as well.
Proposed course plan during the master's degree (60 ECTS):
GEOV226 Field and Lavratory Course in Quaternary Geology
GEOV324 Polar Paleoclimate
GEOV302 Data Analysis in Earth Science
GEOV316 Practical Skills in Remote Sensing and Spatial Analysis
GEOV328 IceFinse – Arctic Climate Research and Fieldwork
Supplementary LWC-data can be provided by NVE
Field, lab and analysis
Collection of LWC data in the field. Field work is expected to amount to ~45 days in total.