Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model

Vionnet, Vincent; Martin, E.; Masson, Valéry; Guyomarc’h, Gilbert; Naaim-Bouvet, Florence; Prokop, Alexander; Durand, Yves; Lac, Christine

doi:10.5194/tc-8-395-2014

Cited by 134 publications

(253 citation statements)

References 82 publications

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“…This method has the potential to significantly improve spatial snow representation in distributed snow modeling without the need for spatially explicit modeling of wind transport Vionnet et al, 2014), which either suffers from high computational demand, limited accuracy or both.…”

Section: Discussionmentioning

confidence: 99%

“…Using numerical models, snow distribution in alpine terrain can be simulated (Liston and Sturm, 1998;Gauer, 2001;Winstral et al, 2002;Vionnet et al, 2014). Simulations can be used to understand hydrological processes (Comola et al, 2015), for fore-and now-casting (Lehning et al, 1999;Bellaire et al, 2011) or the assessment of climate change (Bavay et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution

et al. 2016

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Accurate knowledge on snow distribution in alpine terrain is crucial for various applications such as flood risk assessment, avalanche warning or managing water supply and hydro-power. To simulate the seasonal snow cover development in alpine terrain, the spatially distributed, physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolations of observations from automatic weather stations (AWS), leading to errors in the spatial distribution of atmospheric forcing. With recent advances in remote sensing techniques, maps of snow depth can be acquired with high spatial resolution and accuracy. In this work, maps of the snow depth distribution, calculated from summer and winter digital surface models based on Airborne Digital Sensors (ADS), are used to scale precipitation input data, with the aim to improve the accuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method to scale and redistribute precipitation is presented and the performance is analyzed. The scaling method is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation, with a simple air temperature threshold used for the determination of the precipitation phase. It was found that the accuracy of spatial snow distribution could be improved significantly for the simulated domain. The standard deviation of absolute snow depth error is reduced up to a factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reduced when using representative input sources for the simulation domain. For inter-annual scaling, the model performance could also be improved, even when using a remote sensing dataset from a different winter. In conclusion, using remote sensing data to process precipitation input, complex processes such as preferential snow deposition and snow relocation due to wind or avalanches, can be substituted and modeling performance of spatial snow distribution is improved.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution

et al. 2016

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“…The above formulation has been used extensively to analyse data 25 collected in the field (Mann et al, 2000), wind tunnel experiments (Wever et al, 2009), and numerical simulations of drifting and blowing snow (Déry and Yau, 2002;Groot Zwaaftink et al, 2011Vionnet et al, 2014).In the modelling studies, the mass loss term is computed using Eq. (4) and is added, with proper normalisation, to the advection-diffusion equation of specific humidity while the latent heat of sublimation multiplied by the mass loss term is added to the corresponding equation for temperature (Groot Zwaaftink et al, 2011).…”

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confidence: 99%

On the suitability of the Thorpe-Mason model for Calculating Sublimation of Saltating Snow

Sharma¹,

Comola²,

Lehning³

2018

Preprint

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Abstract. The Thorpe and Mason (TM) model for calculating the mass lost from a sublimating snow grain is the basis of all existing small and large-scale estimates of drifting snow sublimation and the associated snow mass balance of polar and alpine regions. We revisit this model to test its validity for calculating sublimation from saltating snow grains. It is shown that numerical solutions of the unsteady mass and heat balance equations of an individual snow grain reconcile well with the steady-state solution of the TM model, albeit after a transient regime. Using large-eddy simulations (LES), it is found that the 5 residence time of a typical saltating particle is shorter than the period of the transient regime, implying that using the steady state solution might be erroneous. For scenarios with equal air and surface temperatures, these errors range from 26% for low-wind low-saturation conditions to 38% for high-wind high-saturation conditions. With a small temperature perturbation of 1 K between the air and the snow surface, the errors due to the TM model are already as high as 100% with errors increasing for larger temperature perturbations.

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“…We further demonstrate the ability of first snow free day and peak NDVI derived from high-resolution imagery to capture the floristic diversity of mountain plant communities located above the tree line. Although improving process-based models of snow accumulation and snowmelt in complex mountain terrain is an ongoing priority for snow scientists [64][65][66], and is necessary to forecast the effects of climate change on snow cover duration [21], simulating spatial heterogeneity of snowmelt in topographically complex study areas remains a difficult task [67,68]. Remote sensing thus represents a promising avenue for observing snow distribution patterns at regular intervals and over large spatial extents.…”

Section: Discussionmentioning

confidence: 99%

On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat

et al. 2016

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Abstract:We investigated snow cover dynamics using time series of moderate (MODIS) to high (SPOT-4/5, Landsat-8) spatial resolution satellite imagery in a 3700 km 2 region of the southwestern French Alps. Our study was carried out in the context of the SPOT (Take 5) Experiment initiated by the Centre National d'Etudes Spatiales (CNES), with the aim of exploring the utility of high spatial and temporal resolution multispectral satellite imagery for snow cover mapping and applications in alpine ecology. Our three objectives were: (i) to validate remote sensing observations of first snow free day derived from the Normalized Difference Snow Index (NDSI) relative to ground-based measurements; (ii) to generate regional-scale maps of first snow free day and peak standing biomass derived from the Normalized Difference Vegetation Index (NDVI); and (iii) to examine the usefulness of these maps for habitat mapping of herbaceous vegetation communities above the tree line. Imagery showed strong agreement with ground-based measurements of snow melt-out date, although R 2 was higher for SPOT and Landsat time series (0.92) than for MODIS (0.79). Uncertainty surrounding estimates of first snow free day was lower in the case of MODIS, however (˘3 days as compared to˘9 days for SPOT and Landsat), emphasizing the importance of high temporal as well as high spatial resolution for capturing local differences in snow cover duration. The main floristic differences between plant communities were clearly visible in a two-dimensional habitat template defined by the first snow free day and NDVI at peak standing biomass, and these differences were accentuated when axes were derived from high spatial resolution imagery. Our work demonstrates the enhanced potential of high spatial and temporal resolution multispectral imagery for quantifying snow cover duration and plant phenology in temperate mountain regions, and opens new avenues to examine to what extent plant community diversity and functioning are controlled by snow cover duration.

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Simulation of wind-induced snow transport and sublimation in alpine terrain using a fully coupled snowpack/atmosphere model

Cited by 134 publications

References 82 publications

Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution

Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution

On the suitability of the Thorpe-Mason model for Calculating Sublimation of Saltating Snow

On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat

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