Slushflows Christian Jaedicke Galina Ragulina Peter Gauer
Thoughts about future methods for slushflow warning Christian Jaedicke
What is a slushflow? rapid mass movements of watersaturated snow In other laguanges: sørpeskred, slasklavin, Sulzstrom, krapaflóð, avalansa umeda,
Slushflow occurrence Are "very" low frequency, high consequence events Common in Arctic climate, less in mid-latitudes Need to be considered in hazard zoning Easy to forecast on a regional and local scale.. Really? No?
Where do they occur? The most common terrain features are well known All places where water can pond or excess water is available
What is needed to release a slushflow? Water, lots of water More water in than water out Decided by Total water in Snow cover properties Ground properties
Considerations for forecasting All water input is important Rain Melt rate (radiation, turbulent fluxes) Drainage Amount of water needed to produce slush Cold content Snow stratigraphy
Available data today Precipitation Wind Meltwater (degree-day model) Snow wetness index Humidity Radiation fluxes Atmospheric stability
So, forecasting slushflows is easy! We have already a wet snow cover We expect lots of more water (rain, melt or both) That s the way we work today Many false alarms Many typical slushflow cases are not covered by this (rain on fresh snow in early season)
What do we need for future forecasting? Better models for total available water Including radiation and turbulent fluxes Snow cover structure Which modelled structures are critical? Index for water saturation of the snow cover Snowpack, Crocus need new modules for preferential flow and ponding
How does future slushflow warning look like? Index based Uses distributed snow cover modelling Now situation Snow cover structure Current water content Critical additional water needed Forecasted situation Available water next 24h Change of snow cover properties within 24h
Snow cover simulations at Fonnbu Galina Ragulina
Background Snowpack properties are shown to be of high importance for slushflow release A good snowpack model would allow to monitor snow-cover development without expensive manual snow-profiling There are two main physical snowpack models: SNOWPACK (by SLF) and Crocus (by MeteoFrance) In recent years SNOWPACK-model developed considerably in the direction of better simulating of waterflow in a snowpack: Richards-equation module has been implemented and there is an ongoing work on implementing Preferential flow -module
SNOWPACK-model requirements for input-data Required meteorological observations: air temperature (TA) relative humidity (RH) wind speed (VW) incoming short wave radiation (ISWR) or reflected short wave radiation (RSWR) incoming long wave radiation (ILWR) or surface temperature (TSS) precipitation (PSUM) or snow height (HS) ground temperature (TSG, if available) snow temperatures at various depths (TS1, TS2, etc. if available and only for comparisons) These parameters must be available at least at an hourly time step
How many stations in Norway meet the SNOWPACK-requirements for the input data? Went through over 3500 meteorological stations in the Norwegian official meteorological network Found 11 stations, which observe all the elements required as necessary input parameters for SNOWPACK model simulations Two of the stations Dovre-Lannem (560 m.a.s.l.) and Midstova (1162 m.a.s.l.) observe some snow temperature elements in addition Two Norwegian stations outside mainland Norway are also suitable for SNOWPACK model simulations, namely 99720 Hopen (6 m.a.s.l.) and 99950 Jan Mayen (10 m.a.s.l.)
How many stations in Norway meet the SNOWPACK-requirements for the input data? Station number Name Elevation 16271 HØVRINGEN II 940 16400 DOVRE-LANNEM 560 18700 OSLO - BLINDERN 94 23550 BEITOSTØLEN II 965 31620 MØSSTRAND II 977 33950 HAUKELISETER TESTFELT 990 53530 MIDTSTOVA 1162 54710 FILEFJELL - KYRKJESTØLANE 956 80610 MYKEN 17 84210 LOSISTUA 740 97251 KARASJOK - MARKANNJARGA 131
Meteorological observations at Fonnbu, Strynefjellet Meteorological research station Fonnbu observes all the required by SNOWPACK elements at a 10-min time step (recalculated for 1 hour time step) Snow-season observations from autumn 2009 were analysed 7 winter seasons (01.09.20xy 01.07.20x(y+1)) Most of the seasons the amount of error-records was between 2% and 5% per element Observations of wind are the most sensitive up to 45% missing observations Unlucky with the last season (2015-2016): problems with wind, precipitation and snow height observations 3 manual snow profiles conducted by the station in 2016: 8.03, 28.04 and 18.05
SNOWPACK-model set-up and challenges Complex physical model Requires advanced knowledge of processes inside and around snow cover (incl. atmosphere stability, energy fluxes from soil etc.) Permanently developing (not always with published explanations of the updates ) 1-dimentional (doesn t allow water-ponding)
Slush snow cover Fonnbu May 2010 1 picture / hour
What does a snow model provide for slushflow studies? Crystal size and type Snow temperature Density Water content Snow hardness When is the snowpack isothermal (temp)? How wet is the snow?
SNOWPACK-model set-up and challenges Because of the bad last observational season, difficult to calibrate the model Need more snow profiles! We got the snow height to be simulated acceptably Still not quite satisfactory simulations of energy fluxes inside the snowpack Better understanding of SNOWPACKmodel (physics in it) is required!
Slushflows What is the speed of a slushflow? Peter Gauer Norwegian Geotechnical Institute, Oslo, Norway
Slushflow example Kärkerieppe/Swedish Lapland in 1995 Observation slope: 8 to 10 deg vertical: 125 m horizontal: 800 m velocity: 10 25 m/s (approx. 350 m in 23 s) animated picture series Institute of Meteorology, Climatology and Remote Sensing Department of Geosciences, University of Basel http://www.unibas.ch/geo/mcr/projects/mosaic/kv/kv95.en.htm
Slushflow example Skarmodalen 2010-05-16 Photo Erik Hestnes (Photos courtesy of Louise Fontain)
Simulation with RAMMS S = μ a ρg h cos φ + ρ g ξ U2 μ a = μ D (1 r u ) parameters used: μ a = 0.05 g ξ 1 200
Simulation with RAMMS parameters used: a 0.05 g / 1/ 200 g / 1/100 Brahms de Chézy U c c r h i
Concluding Remarks Improvement of the rheological model multi-phase flows Turbulent closure for multi-phase flows A major problem is that there are basically no measurements available, like speed or pressures measurements, to validate models
Thank you Acknowledgment: The work was partially funded by: a NGI research grant from Oil and Energy Department (OED) through the Norwegian Water Resources and Energy Directorate (NVE)