Gauer, Peter

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Norwegian Geotechnical Institute (NGI) Digital Archive

International Snow Science Workshop (2002: Penticton, B.C.) The use of a numerical snow-drift model as adecision making tool in the planning of avalanche protection measures Peter Gauer* Norwegian Geotechnical Institute, Os10, Norway Abstract Blowing and' drifting snow is not only a major factor in the assessment of the acute avalanche danger, its contribution to the snow distribution also influences land-use planning. In many parts of alpine terrain end an ge red areas are secured by expensive avalanche protection measures. The effidency of these measures can be affected by the redistribution of snow due to wind. Numerical snow-drift simulation could providc useful information at the planning state of protection measure to increase their effidency and so to reduce costs. Keywords snow-drift, numerical modeling, meso-scale weather model, nesting, planning tool 1 Intruduction In alpine terrain, snow and wind go together. Ev-eryone concerned with avalanche warning knows the influence of recent snow-drift on the acute avalanche danger. Hence, it is no wonder that some effort is made to incorporate snow-drift models as part of avalanche forecast systems, at least on basis of para-metrization, e.g., (Doorschot, 2002; Durand et al., 2001; Lehning et al., 2000). On the other hand, snow-drift is also a concern for land-use planning. For example, buildings or roads can be affected by drifting snow, but also the effectiveness of permanent avalanche defense struc-tures can be reduced due to snow-drift. There are some examples in which numerical snow-drift models are used in those kind of tasks, e.g., (Jaedicke, 2001; Kawakami et al., 1997; Sundsb~, 1997), who tried to simulate the snow distribution around buildings. Snow-drift models for each of the above men-tioned tasks may differ conceptually from each other. Models used in the avalanche warning should in-dicate the occurrence arid give an estimate of the amount of snow redistribution. The models should be cable to work at least on a regional scale, and for operational use the allowable execution time of the model is a limiting factor. ·Corrcsponding author's address: Pcter Gauer Norwegian Geotcchnical Institute P.O. Box 3930 Ullevaal Sta-dion, N-0806 0510, Norway tel: ++47 22 02 31 29; fax: ++47 22 23 04 48; email: pg@ngi.no The requirements for models used in land-use planning are different. In this case, one wants to know the deposition pattern as exact as possible. For that, it is necessary: • to resolve a 3-dimensional wind-field; • to be able to include buildings and/or different kind of obstacles; • to have a high spatial resolution; • to adapt the changing surface topography; • to describe snow-drift in a reasonable manner; • etc. Although the time restriction for the execution time is not as strict as for a forecast model it is still a factor which has to be considered. The following shows an example of snow-drift simulation for the purpose of land-use planning and how one of the major problems in those kind of sim-ulation might be overcome, i.e., the setting of the boundary conditions for the wind-field simulation in rat her complex topographies. . 2 Model A full description of the used blowing and drifting snow model can be found in (Gauer, 1999, 2001). Here, only a summary of the main features is given. The model is implemented in the commercial flow solver CFX4 from AEA (1995, present version 4.4). 604 This is a multi-purpose flow-solver based on a finite-volume approach. The snow-drift model is incorpo-rate in this code by me ans of user defined Fortran routines. The main features of snow-drift model are: • a fully 3-dimensional non-steady-state model-ing; • Eulerian-Eulerian approach; • modeling of the two main transport modes: - saltation (founded on a height-averaged dynamical description); (slight modifica-tions of the saltation layer parametriza-tion was done since (Gauer, 1999, 2001), e.g., the influence of the slope angle is now better accounted for); - suspension; • dynamical modeling of deposition and erosion, distinguishing between aerodynamic entrain-ment and ejects due to particle impacts; • back-reaction of the particles in the saltation on the flow (two-way coupling); • periodical grid adaptation to the new snow depth. • possibility of externally-forced boundary con-ditions. 3 Case study The purpose of the snow-drift simulation was to pro-vide a case study of the effect of drifting snow for dif-ferent scenarios of avalanche protection measures for a kindergarten. The following scenarios were consid-ered: • catching dam south-east of the kindergarten; • combination of snow fences and catching dam. • combination of snow fences and avalanche de-fense structures; Figure 1 shows a comparison between the differ-ent scenarios. All snow depths are normalized fol-lowing the equation: dSD SD -SDo SDe = SDe (1) Figure 1: Snow-drift simulation around two build-ings and three variants for avalanche protection mea-sures; 10 m contour lines in black, west end side is approximately at sea level; 0.125 m isolines of the normalized snow depth in withe. 605 Wind Transported Snow t=:c=;r;:=;:t:=:i::;:::::t==;::t;~:;:t:;::::::t:-: 1411")0 l.a: catching dam south-east of the kindergart~n _Im] ~ ~ ~ '1 ., 1""700 .[ITt) .,,--r--\---t--\-- 1411100 . 14""00 I wi d dire tion at nflow l.c: avalanche defense structures and fences N 1 ::1 .~ ." ~ ::t ~ Da f D 110 ~ : N f ~~I ~ :: IJ ~ 0 ~~~l 11 110 ~ .; ~: . -0 2S ~. :', N t ::1 .~ ." ,~ ~. c~U i ,all c International Snow Science Workshop (2002: Penticton, B.C.) where SD is the simulated snow depth, SDo is the simulated snow depth for the status quo, and SDe in the initial erodible snow depth. This normaliza-tion means: zero values indicate no difference, nega-tive values indicate areas of depleted snow depth and positive values show areas of enhanced snow depth compared to the status quo, Le., a simulation only including the two buildings. The simulation considers only the redistribution of snow, no additional precipitation is included. At the south-east boundary, the reference wind speed at 10 m is set to 12.5 m s-1 and the critical shear stress, Tc, for erosipn to start is assumed to be 0.15 Pa. As might be expected, the influence of the sole dam is rat her smalI, which can be seen in Fig. 1.a. Due to its larger cross section and in combination with a jet effect, the formation of the typical horse-shoe pattern is more promote. The effect of addi-tional snow fences is shown in Fig. 1.b. Most of the snow is already caught on the plateau up-slope. Similar in Fig. 1.e, the eombination of snow fenees and avalanche defense structures keeps most of the snow uphill. However, it ean also be seen that the influence of these structures is still noticeable further down. 4 Difficulties One of the major difficulties in this kind of sim-ulation is it to provide reasonable boundary con-ditions for the wind field. In the example above, rather crude estimates were used in setting sym-metry planes at the north-east and south-west side of the considered domain and a logarithmie wind-profile at the inflow. No wind measurements were available for the area interest. That is a eommon case and one ean be lucky to have information about the wind eonditions at least at one point nearby. This causes also difficulties to verify simulations. One way in dealing with know boundary eondi-tions is to bring them as far away as possible from the area of interest. Thus their influences become weaker-hopefully. The disadvantage of this proce-dure is tImt a larger domain has to be consider in the snow-drift simulation, which increases the com-putational effort. Figure 2: Modeled surface wind-field; rectangles mark the areas which are used in the different nest-ing stepsj notice the different color co ding for the wind speed 606 I 'Im] ~~~~~~~~~~~~~~ ITITrmTTTTTTTmrTmTTTTTTmmTT11TrrmT1TTlTTTTTTITITIllTTTll .70000 540000 ~ 535000 I 530000 .'5000 ~10000 1--I-+-+--+---+-4-i--I-+--+--+--i--3 50'000 N I :::"~ ... .. ' tot ' L---'---'--'------'---::"-----'_'---'---'---'---'---'---'" 5OO1l1» _ 2.a: 60 X 60 km2 with 1 km horizontal resolution using ARPS; 100 m contour Iines x [m] 0 0 '" 0 0 0 0 0 0 0 0 0 0 0 H ~ ~ ~ 0 ~ 0 S': 0 C> g ~ '" ~ 0 ~ ~ 2 ;\ 0 g ::l .. " .) .., '" .., .., r· I. I' I' I' I. <- I' " " " " " ~415CO 541000 54~500 54:>000 533500 5)9000 538500 '< 533000 I 537500 537000 535500 5~5000 100 m contour lines , [1r I I" 0 0 a § 0 ~ '" ~ ~ g 0 <> ~ g ~ :;0 ~ ~ i?, '" '" '" ., '" n '" f' " ... ,. I' " " " I' 5391CO ~39000 S3A900 5JeeOC 53e:7QO 53~600 53e~OO 533400 5JBJOQ 538200 S,l.l!100 53·:000 537900 f-:,;'-t.-+-+-+--t--i--+-1i-+-3 537800 I N 1 I::! ..•• . ~·n 'Ir" : .. ~. :; "} 2.c: ~ 1 x 1 km2 with 10 m horizontal resolution using CFX; 10 m contour Iines N .,:.. .. \ ~ j j . l ~ i • i ! .. One way to overcome this problem is to use a meso-scale weather model and to use a common tech-nie from the numerieal weather forecast: nesting. That means in this case, first, the wind field is cal-culated for a larger area with low spatial resolution and than these results are used as initial and bound-ary conditions for a smaller area with a higher reso-lution. In this way it is possible to place the initial boundaries far away from the area of interest and be still able to resolve the area of interest with the de-manded high spatial resolution and reasonable com-putational effort. Figure 2 shows an example were this technie was used to get the boundary conditions for a snow-drift simulation around a mountain top. The first two steps were carried out using the meso-scale weather model ARPS from the Center of Analysis and Pre-dietion of Storms (Xue et al., 1995). The final wind field was than calculate with CFX. At that point, CFX is used because off its capability to include ob-stacles and to deal with a time-dependent topogra-phy. 5 Summary These examples show how numerical snow-drift sim-ulation can further develop into a tool for land-use planning. However, there is still work to be done. One should remember, using a nesting technic for the wind field simulation only transfers a problem and meso-scale models have still some problems in mountainous terrain. There is also the question: What are reasonable scenarios for those kind snow-drift simulations. This question is not limited to the meteorological conditions for which simulations should be done. There is also the part of the snow and its mechanical properties, e.g., the erodibility, which effect snow-drift rates. Thus, there are still topics for development and research left. And one should keep in mind: numerical models are only models with a need of verification. Hence, there is also room left for experienced judgement. Acknowledgement The author is grateful to Ed Adams and Bob Brown, who gave hirn the opportunity to stay at the Western Transportation Institute, Bozeman, where he could gain his first experiences with ARPS. Some of the simulations were made using the Ad-vanced Regional Prediction (ARPS) developed by the Center for Analysis and Prediction of Storms Wind Transported Snow (CAPS), University of Oklahoma. CAPS is sup-ported by the National Science Foundation and the Federal A viation Administration through combined grant ATM92-20009. References AEA, Technology. 1995. CFX-4.1: Flow solver User guide. Doorschot, Judith. 2002. Mass Transport 01 Drilting Snow in High Alpine Enviroments. PhD thesis, ETH Zurich, Zurich, Switzerland. Durand, Yves, G. Guyomarch, and L. Merindol. 2001. Numerical experiments of wind transport over a mountainous instrumented site: 1. regional scale. Annals 01 Glaciology, 32, 187-194. Gauer, Peter. 1999. Blowing and drifting snow in alpine terrain: A physically-based numerical model and related field measurements. Mitt. Eid-genöss. Inst. Schnee- Lawinenforsch. 58. Flüelastr 11. CH-7260 Davos, Eidgenöss. Inst. Schnee- Law-inenforsch., Davos. Gauer, Peter. 2001. Numerical modeling of blowing and drifting snow in alpine terrain. Journal 01 Glaciology, 47(156), 97-110. Jaedicke, Christian. 2001. Drifting snow and snow accumulation in complex arctic terrain. PhD thesis, Geophysical Institute, University Bergen, Bergen, Norway. 607 Kawakami, S., T. Uematsu, T. Kobayashi, and Y. Kaneda. 1997. Numerical study of a snow wind scoop. In Izumi, Nakamura, and Sack, editors, Snow Engineering: Recent Advances. Rotterdam, Balkema. Lehning, M., J. Doorschodt, and P. Bartelt. 2000. A snow drift index based on snowpack model cal-culations. Annals 0/ Glaciology, 31, 382-386. Sundsbo, Per-Arne. 1997. Numerical Modelling and Simulation 0/ Snow Drift. PhD thesis, Norwegian University of Science and Technology, Trondheim, Norway. Xue, M., K.K Droegemeier, V. Wong, A. Shapiro, and K Brewster. 1995. ARPS Version 4.0 User's Guide. Center of Analysis and Prediction of Storms, University of Oklahoma. 

The use of a numerical snow-drift model as a decision making tool in the planning of avalanche protection measures

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The use of a numerical snow-drift model as a decision making tool in the planning of avalanche protection measures

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