Results 2014 from SP 4 FoU Snøskred: Work Package 3 Sørpeskred

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1 Results 2014 from SP 4 FoU Snøskred: Work Package 3 Sørpeskred Project nr: Title: Snow avalanche observations Total budget (knok) From Dept. of Oil and Energy (knok) Costs per (knok) Task 1: Evaluation of slushflow speeds Existing series of pictures of slushflow events are to be analyzed with regard to the front speed of these flows. Task 2: Setup of snowcover model for testing a wet-snow avalanche index The objective is to install SLF s one-dimensional snowcover model SNOWPACK at NGI and convert the data from the automatic weather station at Fonnbu so that it can drive SNOWPACK for simulations of the snowcover evolution at the station. The results can be compared to data from snow pits. Task 3: Establishing contacts for an international slushflow research project The objective is to establish initially informal contacts to research institutions in the Scandinavian countries, Russia and Alaska interested in slushflow research. These contacts should later form the platform for launching a dedicated international research project. Har prosjektet oppnådd de oppsatte mål: Ja: X Nei: X Begrunnelse for eventuelle avvik og beskrivelse av korrigerende tiltak: Task 2: After a failed attempt during the summer 2014, the model SNOWPACK is now running with data from Fonnbu. Due to shortage of manpower and channeling of funds into WP 4, detailed comparison of the simulated snow profiles to measurements from snow pits was not possible in Due to parental leave of C. Jaedicke WP 3 leader and specialist for snowpack processes we will probably not be able to carry out this work before spring Task 3: For lack of manpower, this task was not started in It has been made a priority for Dato Prosjektleder Dato Fagleder Dieter Issler (for Christian Jaedicke) Christian Jaedicke

2 Title: Project Manager: Project Members: Slushflows Christian Jaedicke / Dieter Issler (ad interim) Peter Gauer, Erik Hestnes, Dieter Issler TASK 1: EVALUATION OF SLUSHFLOW SPEEDS In Skarmodalen, Nordland county, three slushflows occurred on /16 due to a sudden, intense spill of warm weather. Mrs. L. Fontain, living in Skarmodalen, documented them by chance with the help of a digital camera and kindly provided NGI with the images for analysis (Figure 1). The photographer s vantage point could be determined to a precision of a few meters and some terrain and/or vegetation features identified both on the images and ortho-rectified aerial photographs. This made it possible to track the front position of one of the best-documented slushflows from image to image and obtain the evolution of the front speed along the path. The precision is moderate because the front is rather irregular and does not move steadily. However, the time integral of the front speed corresponds rather well with the distance covered by the flow between the first and last image of the sequence, meaning that the mean speed is reliable. Mostly, the front speed is in the range m/s on a track with rapidly varying inclination between 15 and 35. Interestingly, earlier numerical simulations of slushflows in Vannledningsdalen, Svalbard with the quasi-3d model RAMMS (Christen et al., 2010) gave slightly higher, but comparable speeds when the friction parameters were chosen to match typical, observed run-out distances (J onsson & Gauer, 2014). Vannledningsdalen is much less curvy, more channelized and more gently and constantly inclined than Skarmodalen. Figure 1. One of the images of the slushflow event in Skarmodalen on that were used for analyzing the front speed of the flow. (Photo: L. Fontain, Skarmodalen)

3 TASK 2: SETUP OF A SNOWCOVER MODEL FOR TESTING A WET-SNOW AVALANCHE INDEX SNOWPACK v.3.21 for Windows from the WSL Institute for Snow and Avalanche Research (SLF) in Davos, Switzerland (Bartelt et al., 2002; Lehning et al., 2002a,b) was installed at NGI in the summer together with the visualization program SNGUI v.8.4 and INIshell v (both are Java applications). The first attempt at running SNOWPACK with data from Fonnbu was unsuccessful due to small, but well concealed errors in the formatting of the meteorological and nivological input files, as it turned out in hindsight. The second attempt around the end of the reporting period succeeded in overcoming these problems. (This time, we used the Linux version of the code, but it appears unlikely that this was relevant to the success.) Applying a number of transformations to the data files produced by the data acquisition system at Fonnbu, SNOWPACK can now use the meteorological measurements as input data for simulating the snowcover in the immediate vicinity of the sensors. However, before one can extrapolate such simulations to the potential release areas of slushflows both above and below the station and calculate an index for wet-snow avalanches, the quality of the simulations needs to be validated. To this end, we will need to make detailed comparisons of the simulated snow stratigraphy with manually obtained snow pit data. Another point needing clarification is the extent to which SNOWPACK is able to simulate situations in which there is free water in the snowpack a prerequisite for the formation of slushflows, as opposed to the situation of wet-snow avalanches where the snowpack still is in the so-called pendular regime. These questions will be studied in detail in early TASK 3: ESTABLISHING CONTACTS FOR AN INTERNATIONAL SLUSHFLOW PROJECT There is at this point nothing to report on because this task was not started due to manpower shortage in the first half of 2014 and the strained funding situation because of significant avalanche damage to the measurement equipment at Ryggfonn (see report on WP 1) and costoverruns in WP 4. This task will therefore receive high priority in References Bartelt, P. & Lehning, M A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model. Cold Regions Science and Technology 35(3), Christen, M., Kowalski, J. & Bartelt, P RAMMS: Numerical simulation of dense snow avalanches in three-dimensional terrain. Cold Regions Science and Technology 63, Jónsson, Á. & Gauer, P Optimizing mitigation measures against slush flows by means of numerical modelling a case study Longyearbyen, Svalbard. In: M. Fujita et al. (eds.), INTERPRAEVENT2014 in the Pacific Rim Natural Disaster Mitigation to Establish Society with the Resilience. Extended Abstract in printed proceedings volume, pp Full-length article on DVD distributed with proceedings volume. Lehning, M., Bartelt, P., Brown, B., Fierz, C. & Satyawali, P. 2002a. A physical SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure. Cold Regions Science and Technology 35(3),

4 Lehning, M., Bartelt, P., Brown, B. & Fierz, C. 2002b. A physical SNOWPACK model for the Swiss avalanche warning: Part III: meteorological forcing, thin layer formation and evaluation. Cold Regions Science and Technology 35(3), Scherer, D., Groebke, L. & Parlow, E Photogrammetric analysis of a slush torrent in the Kevagge, Northern Sweden. Nordic Hydrology 31, PUBLICATIONS IN 2014 Jónsson, Á. & Gauer, P Optimizing mitigation measures against slush flows by means of numerical modelling a case study Longyearbyen, Svalbard. Extended Abstract in: M. Fujita et al. (eds.), INTERPRAEVENT2014 in the Pacific Rim Natural Disaster Mitigation to Establish Society with the Resilience. Extended Abstract in printed proceedings volume, pp Full-length article on DVD distributed with proceedings volume. PRESENTATIONS IN 2014 Jónsson, Á. & Gauer, P Optimizing mitigation measures against slush flows by means of numerical modelling a case study Longyearbyen, Svalbard. Poster P-56 at INTER- PRAEVENT2014 in the Pacific Rim Natural Disaster Mitigation to Establish Society with the Resilience. Nara, Japan, November 25 28, 2014.

occurred due to a sudden, intense spill of warm weather, which Mrs. L.

She kindly provided NGI with the images for analysis (see Figure 2)

5 DELIVERABLE D3.1 FRONT SPEED EVOLUTION OF A SLUSHFLOW IN SKARMODALEN, On , a slushflow occurred due to a sudden, intense spill of warm weather, which Mrs. L. Fontain, living in Skarmodalen, documented by chance with the help of a digital camera. She kindly provided NGI with the images for analysis (see Figure 2). Figure 2. Examples of the images series of the slushflow event in Skarmodalen on that were used for analyzing the front speed of the flow (top to bottom: t1, t s, t s). (Photo: L. Fontain, Skarmodalen).

6 Figure 3. Estimated front position (red lines) according to the photo series (numbers correspond to the image number). Based on the photo series, the approximate front positions of the slushflow could be estimated in the terrain and drawn as lines on a single picture taken in early fall (Figure 3). Knowing the time difference between each photo and the distance along the path, the mean velocity of each individual stretch can be estimated. Figure 4 shows the estimated front velocity along the track, derived from the photo series. The uncertainty is related to the precision of the timing of the images and to the location of the slushflow front. However, as the errors are not fully independent, the velocity estimates give a rather consistent picture overall. Mostly, the front speed is in the range m/s on a track whose inclination rapidly varies between 15 and 35. This speed range is clearly at variance with the results published by Scherer et al. (2000), who obtained much higher velocities (up to 70 m/s) from a video sequence of a comparable slushflow in Sweden. However, our consistency check on their analysis indicates that they must have erred because the time integral of the front velocity is much larger (approx. by a factor 1.5 2) than the actual distance (corresponding to the approximated profile given in their Fig. 1) covered by that slushflow during the observation intervals. References Scherer, D., Groebke, L. & Parlow, E Photogrammetric analysis of a slush torrent in the Kevagge, Northern Sweden. Nordic Hydrology 31,

7 Figure 4. Estimated front velocity of a slushflow in Skarmodalen,

8 DELIVERABLE D3.2 SETUP OF A SNOWCOVER MODEL FOR TESTING A WET-SNOW AVALANCHE INDEX As detailed in the short summary of activities in Work Package 3 during 2014, we chose the snowcover simulation model SNOWPACK (v.3.21) as the tool for determining the water content in the snowpack based on meteorological measurements at an automatic weather station. This code is developed and maintained by a group at the WSL Institute for Snow and Avalanche Research (SLF) in Davos, Switzerland (Bartelt et al., 2002; Lehning et al., 2002a,b). Besides the computational code snowpack itself, we used the Java applications INIshell v for setting up simulations and SNGUI v.8.4 for visualization of the results. SNOWPACK uses three input files, namely: an INI file providing the general set-up of the simulation, including the names of the remaining two input files and the location of the outputting, a file describing the initial stratigraphy of the snowpack, including the physical properties of the different layers. a file containing the meteorological data, i.e., the boundary conditions of air and ground temperature, precipitation, wind and radiation, and In our test case, no snowcover profile from a snow pit was available at the start of the simulation. However, the snow height was measured automatically. SNOWPACK then treats the initial snowpack as homogeneous new snow with the typical properties of snow that fell under the weather conditions at the start of the simulation. On the following pages, we list the INI file and the snowpack initial data file in full, but only the header and the first few data lines of the weather data file. Figure 5 and Figure 6 illustrate some of the results of the simulation graphically. In all plots, the lack of input data during a period of nearly one week in early winter is immediately visible. For short intervals without data, SNOWPACK offers several options for interpolating the missing data. In this case, however, the interval was too long for meaningful interpolation, and the simulation was resumed when data became available again. The weather station at Fonnbu measures snow depth, and this data is used by SNOWPACK. Hence, the snow height corresponds to the measurements, but the snow water equivalent may not. Several aspects of the simulation (and perhaps of the input data themselves) will need to be carefully checked in the follow-up work in For example, the temperature profiles show unrealistically deep and strongly varying temperatures at the bottom (Figure 5, middle panel). This temperature is expected to be rather stable and not too far below 0 C. An error here will strongly influence the temperature gradient in the snowpack, hence the transport rate of water vapor and the type and rate of metamorphism. These processes, in turn, influence settling of the snow layers and thus the water equivalent, given the measured snow height. Another point needing clarification is the extent to which SNOWPACK is able to simulate situations in which there is free water in the snowpack a prerequisite for the formation of slushflows, as opposed to the situation of wet-snow avalanches where the snowpack still is in the so-called pendular regime.

9 Listing 1. INI file setting up the simulation of the snow cover evolution at the research station Fonnbu during the winter 2012/2013 with the program SNOWPACK. [GENERAL] BUFF_CHUNK_SIZE = 370 BUFF_BEFORE = 1.5 [INPUT] COORDSYS = UTM COORDPARAM = 33N TIME_ZONE = 1 METEO = SMET METEOPATH = /home/di/desktop/snowpack STATION1 = Fonnbu_HS_trf01.txt ISWR_IS_NET = FALSE SNOWPATH = /home/di/desktop/snowpack SNOW = SMET [OUTPUT] COORDSYS = UTM COORDPARAM = 33N TIME_ZONE = 1 METEOPATH =./output EXPERIMENT = Test_01 SNOW = SMET SNOWPATH = /home/di/desktop/snowpack/output BACKUP_DAYS_BETWEEN = 1 FIRST_BACKUP = 1 PROF_WRITE = TRUE PROFILE_FORMAT = PRO PROF_START = 0.0 PROF_DAYS_BETWEEN = 1 HARDNESS_IN_NEWTON = FALSE CLASSIFY_PROFILE = FALSE TS_WRITE = TRUE TS_START = 0.0 TS_DAYS_BETWEEN = AVGSUM_TIME_SERIES = TRUE CUMSUM_MASS = TRUE PRECIP_RATES = TRUE OUT_CANOPY = FALSE OUT_HAZ = FALSE OUT_SOILEB = FALSE OUT_HEAT = TRUE OUT_T = TRUE OUT_LW = FALSE OUT_SW = FALSE OUT_MASS = FALSE OUT_METEO = FALSE OUT_STAB = FALSE [SNOWPACK] CALCULATION_STEP_LENGTH = 15 ROUGHNESS_LENGTH = HEIGHT_OF_METEO_VALUES = 5.0 HEIGHT_OF_WIND_VALUE = 5.0 ENFORCE_MEASURED_SNOW_HEIGHTS = TRUE SW_MODE = BOTH ATMOSPHERIC_STABILITY = NEUTRAL_MO CANOPY = FALSE MEAS_TSS = FALSE CHANGE_BC = FALSE

10 SNP_SOIL = FALSE [SNOWPACKADVANCED] ASSUME_RESPONSIBILITY = AGREE VARIANT = DEFAULT SNOW_EROSION = FALSE WIND_SCALING_FACTOR = 1.0 NUMBER_SLOPES = 1 PERP_TO_SLOPE = FALSE FORCE_RH_WATER = TRUE THRESH_RAIN = 1.2 THRESH_RAIN_RANGE = 0.0 THRESH_RH = 0.5 THRESH_DTEMP_AIR_SNOW = 3.0 HOAR_THRESH_RH = 0.97 HOAR_THRESH_VW = 3.5 HOAR_DENSITY_BURIED = HOAR_MIN_SIZE_BURIED = 2.0 HOAR_DENSITY_SURF = MIN_DEPTH_SUBSURF = 0.07 T_CRAZY_MIN = T_CRAZY_MAX = METAMORPHISM_MODEL = DEFAULT NEW_SNOW_GRAIN_SIZE = 0.3 STRENGTH_MODEL = DEFAULT VISCOSITY_MODEL = DEFAULT SALTATION_MODEL = SORENSEN WATERTRANSPORTMODEL_SNOW = BUCKET WATERTRANSPORTMODEL_SOIL = BUCKET SW_ABSORPTION_SCHEME = MULTI_BAND HARDNESS_PARAMETERIZATION = MONTI DETECT_GRASS = FALSE PLASTIC = FALSE JAM = FALSE WATER_LAYER = FALSE HEIGHT_NEW_ELEM = 0.02 MINIMUM_L_ELEMENT = COMBINE_ELEMENTS = TRUE ADVECTIVE_HEAT = FALSE [FILTERS] TA::filter1 = min_max TA::arg1 = ISWR::filter1 = min ISWR::arg1 = soft 0 RSWR::filter1 = min RSWR::arg1 = soft 0 ILWR::filter1 = min ILWR::arg1 = soft 0 [INTERPOLATIONS1D] WINDOW_SIZE =

11 Listing 2. Initial conditions for the simulation of the snow cover evolution at the research station Fonnbu during the winter 2012/2013 with the program SNOWPACK. SMET 1.1 ASCII [HEADER] station_id = Fonnbu_01 station_name = Fonnbu latitude = longitude = altitude = 943 epsg = nodata = -999 tz = 1 source = Norwegian Geotechnical Institute ProfileDate = T18:00 HS_Last = 0.0 SlopeAngle = 0.00 SlopeAzi = 0.00 nsoillayerdata = 0 nsnowlayerdata = 0 SoilAlbedo = 0.30 BareSoil_z0 = 0.01 CanopyHeight = 0.00 CanopyLeafAreaIndex = CanopyDirectThroughfall = 1.00 WindScalingFactor = 1.00 ErosionLevel = 181 TimeCountDeltaHS = 0.0 fields = timestamp Layer_Thick T Vol_Frac_I Vol_Frac_W Vol_Frac_V \ Vol_Frac_S Rho_S Conduc_S HeatCapac_S rg rb dd sp mk mass_hoar ne \ CDot metamo [DATA]

12 Listing 3. Header and first few data lines of the containing the weather data at the research station Fonnbu during the winter 2012/2013, as used in a preliminary simulation of the snow cover evolution with the program SNOWPACK. SMET 1.1 ASCII [HEADER] station_id = Fonnbu_01 station_name = Fonnbu latitude = longitude = altitude = 943 epsg = nodata = -999 tz = 1 fields = timestamp TA RH VW DW HS VW_MAX ISWR OSWR PSUM TSS TSG ILWR units_offset = units_multiplier = [DATA] T00: T01: T02: T03: T04: T05: T06: T07: T08: T09: T10: T11: T12: T13: T14: T15: T16: T17:

13 Figure 5. (Top) Simulated evolution of snowcover stratigraphy at the research station Fonnbu during the winter 2012/2013. The colors represent different snow grain types as given in the color bar below the plot. (The symbols correspond to the international classification scheme for snow on the ground.) (Middle and bottom) Snow temperature and latent heat exchange. Note that input data is missing during approximately one week in early winter.

14 Figure 6. Temporal evolution of snow density (top) and liquid water content in the snowcover at Fonnbu, as simulated by SNOWPACK for the winter 2012/2013 based on meteorological input data and automatically measured snow height.

Results 2015 from SP 4 FoU Snøskred: Work Package 3 Slushflows

Page: 1 Results 2015 from SP 4 FoU Snøskred: Work Package 3 Slushflows Project nr : 20140053-400 Title : Slushflows Total budget (knok) From Dept. Of Oil and Energy (knok) Costs per 2015-12-31 (knok) 300