Michael Lehning

A physical SNOWPACK model for the Swiss avalanche warning Part I: numerical model

Abstract The numerical formulation of a one-dimensional physical snowpack model is presented. The model is operationally employed on a day-to-day basis by avalanche warners to predict snowpack settlement, layering, surface energy exchange and mass balance. Meteorological data obtained from automatic weather stations positioned near avalanche starting zones is used as model input. In this paper, the one-dimensional equations governing the heat transfer, water transport, vapour diffusion and mechanical deformation of a phase changing snowpack are stated. New snow, wind drift and snow ablation are treated as special mass boundary conditions. Snow is modelled as a three-component (ice, water, air) porous material capable of undergoing large irreversible viscous deformations. Phase changes between the components are simulated. Snow layers are defined not only in terms of height and density, but also microstructure. That is, by the size, shape and bonding of the grains composing the ice lattice. The governing differential equations are solved using a fully implicit Lagrangian Gauss–Seidel finite-element method. Example calculations from the catastrophic avalanche winter 1999 are presented to document model performance. The overall mass balance evaluation shows that the model accurately predicts the build-up and ablation of the seasonal alpine snowcover.

A physical SNOWPACK model for the Swiss avalanche warning Part III: meteorological forcing, thin layer formation and evaluation

Abstract The development of the seasonal snow cover is entirely driven by atmospheric forcing. SNOWPACK uses measured snow depths to determine snow precipitation rates via the calculated settling rates. This requires a rigid data control algorithm. A new statistical model is used to estimate fresh snow density as a function of the measured atmospheric conditions. A statistical model is also derived for the snow albedo, which is necessary to determine the absorbed radiation. The surface sensible and latent heat flux parameterizations are derived from Monin–Obukhov similarity and include a formulation for wind pumping. The formulations will also adapt to drifting snow conditions. The new suggestion is consistent with the observation of different roughness lengths for scalars and momentum over snow. An accurate formulation, especially for the latent heat exchange, is crucial because latent heat exchange determines the formation of surface hoar, a very important weak layer. We also account for the effect of wind pumping on the thermal conductivity in the uppermost snow layers. The surface energy and mass exchange formulations are evaluated by looking at the formation of the important thin layers surface hoar and melt–freeze crusts in SNOWPACK. Those layers are well simulated. In addition, the complete snow profile development is modeled successfully for the parameters grain type, temperature, density, grain size and liquid water content. An overall score between 0 and 1 is used to describe the profile agreement with observations and an average score of over 0.8 is reached.

A physical SNOWPACK model for the Swiss avalanche warning Part II. Snow microstructure

The snow cover model SNOWPACK includes a detailed model of snow microstructure and metamorphism. In SNOWPACK, the complex texture of snow is described using the four primary microstructure parameters: grain size, bond size, dendricity and sphericity. For each parameter, rate equations are developed that predict the development in time as a function of the environmental conditions. The rate equations are based on theoretical considerations such as mixture theory and on empirical relations. With a classification scheme, the conventional snow grain types are predicted on the basis of those parameters. The approach to link the bulk constitutive properties, viscosity and thermal conductivity to microstructure parameters is novel to the field of snow cover modeling. Expanding on existing knowledge on microstructure-based viscosity and thermal conductivity, a complete description of those quantities applicable to the seasonal snow cover is presented. This includes the strong coupling between physical processes in snow: The bond size, which changes not only through metamorphic processes but also through the process of pressure sintering (included in our viscosity formulation), is at the same time the single most important parameter for snow viscosity and thermal conductivity. Laboratory results are used to illustrate the performance of the formulations presented. The numerical implementation is treated in the companion paper Part I. A more complete evaluation for the entire model is found in the companion paper Part III.

SNOWPACK model calculations for avalanche warning based upon a new network of weather and snow stations

The Swiss Federal Institute for Snow and Avalanche Research (SLF) began to construct a network of high Alpine automated weather and snow measurement stations in the Summer of 1996. Presently, more than 50 stations are in operation. The stations measure wind, air temperature, relative humidity, snow depth, surface temperature, ground (soil) temperature, reflected short wave radiation and three temperatures within the snowpack. The measurements are transferred hourly to the SLF in Davos and the data are used to drive a finite-element based physical snowpack model. The model runs every hour and provides supplementary information regarding the state of the snowpack at the sites of the automatic stations. New snow amounts, settling rates, possible surface hoar formation, temperature and density profiles as well as the metamorphic development (grain types) of the snowpack are all predicted by the model. The model is based on a Lagrangian finite element implementation but solves the instationary heat transfer and settlement equations. It includes phase changes and transport of water vapor and liquid water. Special attention is given to the metamorphism of snow and its connection with the mechanical properties such as thermal conductivity and viscosity. The model is connected to a relational database that stores the measurements as well as the model results. New visualization tools are available which allow a fast, easy and comprehensive access to the stored data. The model has been tested in operational mode during the Winter of 1998/1999. The calculations is reliable in terms of the energy budget and the mass balance. The implemented snow metamorphism formulations yield reasonable grain types and are able to reproduce important processes such as formation of depth hoar. The results of the simulations are used by local, regional and national avalanche forecasters and provide valuable information on the snow conditions in the vicinity of avalanche starting zones during the catastrophic avalanche situation in February 1999.

A comparison of measurement methods: terrestrial laser scanning, tachymetry and snow probing for the determination of the spatial snow-depth distribution on slopes

Abstract Determination of the spatial snow-depth distribution is important in potential avalanche-starting zones, both for avalanche prediction and for the dimensioning of permanent protection measures. Knowledge of the spatial distribution of snow is needed in order to validate snow depths computed from snowpack and snowdrift models. The inaccessibility of alpine terrain and the acute danger of avalanches complicate snow-depth measurements (e.g. when probes are used), so the possibility of measuring the snowpack using terrestrial laser scanning (TLS) was tested. The results obtained were compared to those of tachymetry and manual snow probing. Laser measurements were taken using the long-range laser profile measuring system Riegl LPM-i800HA. The wavelength used by the laser was 0.9 μm (near-infrared). The accuracy was typically within 30 mm. The highest point resolution was 30 mm when measured from a distance of 100 m. Tachymetry measurements were carried out using Leica TCRP1201 systems. Snowpack depths measured by the tachymeter were also used. The datasets captured by tachymetry were used as reference models to compare the three different methods (TLS, tachymetry and snow probing). This is the first time that the accuracy of TLS systems in snowy and alpine weather conditions has been quantified. The relative accuracy between the three measurement methods is bounded by a maximum offset of ±8 cm. Between TLS and the tachymeter the standard deviation is 1σ = 2 cm, and between manual probing and TLS it is up to 1σ = 10 cm, for maximum distances for the TLS and tachymeter of 300 m.

Albedo effect on radiative errors in air temperature measurements

Most standard air temperature measurements are subject to significant errors mainly due to sensor heating by solar radiation, even when the measurement principle is accurate and precise. We present various air temperature measurements together with other measurements of meteorological parameters using different sensor systems at a snow-covered and a vegetated site. Measurements from naturally ventilated air temperature sensors in multiplate shields are compared to temperatures measured using sonic anemometers which are unaffected by solar radiation. Over snow, 30 min mean temperature differences can be as large as 10°C. Unshielded thermocouples were also tested and are generally less affected by shortwave radiation. Temperature errors decrease with decreasing solar radiation and increasing wind speed but do not completely disappear at a given solar radiation even in the presence of effective ventilation. We show that temperature errors grow faster for reflected than for incident solar radiation, demonstrating the influence of the surface properties on radiative errors, and we detect the albedo as a variable with major influence on the magnitude of the error as well as a key quantity in possible error correction schemes. An extension is proposed for an existing similarity regression model to correct for radiative errors; thus, surface-reflected shortwave radiation is identified as a principal source of error and the key variable for obtaining a unique nondimensional scaling of radiative errors.

Enabling Effective Problem-oriented Research for Sustainable Development

Environmental problems caused by human activities are increasing; biodiversity is disappearing at an unprecedented rate, soils are being irreversibly damaged, freshwater is increasingly in short supply, and the climate is changing. To reverse or even to reduce these trends will require a radical transformation in the relationship between humans and the natural environment. Just how this can be achieved within, at most, a few decades is unknown, but it is clear that academia must play a crucial role. Many believe, however, that academic institutions need to become more effective in helping societies move toward sustainability. We first synthesize current thinking about this crisis of research effectiveness. We argue that those involved in producing knowledge to solve societal problems face three particular challenges: the complexity of real-world sustainability problems, maintaining impartiality when expert knowledge is used in decision making, and ensuring the salience of the scientific knowledge for decision makers. We discuss three strategies to meet these challenges: conducting research in interdisciplinary teams, forming research partnerships with actors and experts from outside academia, and framing research questions with the aim of solving specific problems (problem orientation). However, we argue that implementing these strategies within academia will require both cultural and institutional change. We then use concepts from transition management to suggest how academic institutions can make the necessary changes. At the level of system optimization, we call for: quality criteria, career incentives, and funding schemes that reward not only disciplinary excellence but also achievements in inter-/transdisciplinary work; professional services and training through specialized centers that facilitate problem-oriented research and reciprocal knowledge exchange with society; and the integration of sustainability and inter-/transdisciplinary research practices into all teaching curricula. At the level of system innovation, we propose radical changes in institutional structures, research and career incentives, teaching programs, and research partnerships. We see much value in a view of change that emphasizes the complementarity of system innovation and system optimization. The goal must be a process of change that preserves the traditional strengths of academic research, with its emphasis on disciplinary excellence and scientific rigor, while ensuring that institutional environments and the skills, worldviews, and experiences of the involved actors adapt to the rapidly changing needs of society.

Equilibrium saltation: Mass fluxes, aerodynamic entrainment, and dependence on grain properties

An examination is given of the way in which the saltation layer isaffected by the characteristics of the particles. Special attentionis given to the potential importance of aerodynamic entrainmentduring steady state saltation, a topic for which the discussion is still unresolved. A new numerical model for saltation in steady stateis presented, which is focused on the computation of the horizontalmass flux. The numerical computations, combined with physical arguments, suggest that aerodynamic entrainment plays a more important role thangenerally assumed so far. A comparison of the model results is made with previous models, and with measurements of snow saltation that have been reported in the literature.

Meteorological Modeling of Very High-Resolution Wind Fields and Snow Deposition for Mountains

Abstract The inhomogeneous snow distribution found in alpine terrain is the result of wind and precipitation interacting with the snow surface. During major snowfall events, preferential deposition of snow and transport of previously deposited snow often takes place simultaneously. Both processes, however, are driven by the local wind field, which is influenced by the local topography. In this study, the meteorological model Advanced Regional Prediction System (ARPS) was used to compute mean flow fields of 50-m, 25-m-, 10-m-, and 5-m grid spacing to investigate snow deposition patterns resulting from two snowfall events on a mountain ridge in the Swiss Alps. Only the initial adaptation of the flow field to the topography is calculated with artificial boundary conditions. The flow fields then drive the snow deposition and transport module of Alpine3D, a model of mountain surface processes. The authors compare the simulations with partly new measurements of snow deposition on the Gaudergrat ridge. On the ba...

A sensitivity study of factors influencing warm/thin permafrost in the Swiss Alps

Alpine permafrost distribution is controlled by a great number of climatic, topographic and soil-specific factors, including snow cover, which plays a major role. In this study, a one-dimensional finite-element numerical model was developed to analyze the influence of individual snow-specific and climatic factors on the ground thermal regime. The results indicate that the most important factor is snow depth. Snow depths below the threshold value of 0.6 m lack sufficient insulation to prevent low atmospheric temperatures from cooling the soil. The date of first winter snow insulation and variations in mean annual air temperature (MAAT) are also shown to be important. Delays in early-winter snow insulation and in summer snow disappearance are shown to be of approximately equal significance to the ground thermal conditions. Numerical modelling also indicates that the duration of effective thermal resistance of snow cover governs the slope of the linear dependency between MAAT and mean annual ground surface temperatures (MAGST). Consequently, the most direct effect of a long-term rise in air temperatures on ground temperatures is predicted under a thin snow cover with early snowmelt in spring and/or where a large change in the date of total snowmelt occurs, in response to atmospheric warming.