scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2016 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Swiss Alps ◽  
Balance Model ◽  
Scaling Method ◽  
Validation Data ◽  
Near Surface ◽  
Surface Rock ◽  
Rock Temperature ◽  
Heterogeneous Rock

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and its impact on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge, central Swiss Alps. The model used is the distributed, process based energy balance model Alpine3D in combination with a precipitation scaling method to introduce varying snow distribution. Near-surface rock temperatures are modelled for snow-covered and snow-free scenarios. The performance of Alpine3D, its limitations and uncertainties are discussed and evaluated against a dense network of 30 near-surface rock temperature measurements (0.1 m depth) distributed over both rock walls and high resolution (0.2 m) snow depth data derived from four winter terrestrial laser scans. Snow cover distribution and near-surface rock temperatures are convincingly modelled in the heterogeneous rock walls. The correction of winter precipitation input using a precipitation scaling method based on terrestrial laser scans greatly improves the results. In addition, the fine-scale resolution of the model domain (0.2 m) and of the validation data allows to consider the thermal effects of the strongly varying micro-topography and micro-climate in the rock walls. Mean annual near-surface rock temperature increase by 2 °C in the shaded rock wall and of 1 °C in the sun-exposed one were measured and modelled due to the accumulation of snow. Errors in rock temperature simulations can be explained by a lack of modelled lateral heat fluxes, as well as by errors caused by interpolation of wind and shortwave radiation.

scholarly journals Modelling the snow cover in a complex Alpine topography

1997 ◽  
Vol 25 ◽  
pp. 312-316 ◽  
Author(s):  
Charles Fierz ◽  
Christian Plüss ◽  
Eric Martin
Keyword(s):  
Temperature Distribution ◽  
Energy Balance ◽  
Snow Cover ◽  
Test Site ◽  
Swiss Alps ◽  
Balance Model ◽  
Energy Fluxes ◽  
Coupled Models ◽  
Physically Based ◽  
Good Agreement

The areal distribution of snow cover and the variability of its characteristics were investigated at various locations in the eastern Swiss Alps. An areal energy-balance (AEB) model was used to calculate the predominant energy fluxes at the snow–atmosphere interface based on automatic meteorological measurements as input. By coupling the AEB model with a one-dimensional, physically based mass and energy-balance model of the snowpack, temperature distribution as well as energy and mass flow in the snowpack were simulated at three different locations in the topographically complex environment at Weissfluhjoch-Davos, 2540 m a.s.l. On a horizontal test site, calculated energy fluxes and characteristics of the snow cover are in good agreement with their measured counterparts. On inclined slopes, the temperature distribution is well represented by the coupled models, but the snow depth and density are not yet satisfactorily simulated. This discrepancy may be attributed to inhomogeneous accumulation and deposition of snow on the weather and lee sides.

scholarly journals Modelling the snow cover in a complex Alpine topography

1997 ◽  
Vol 25 ◽  
pp. 312-316 ◽  
Author(s):  
Charles Fierz ◽  
Christian PlÜss ◽  
Eric Martin
Keyword(s):  
Temperature Distribution ◽  
Energy Balance ◽  
Snow Cover ◽  
Test Site ◽  
Swiss Alps ◽  
Balance Model ◽  
Energy Fluxes ◽  
Coupled Models ◽  
Physically Based ◽  
Good Agreement

The areal distribution of snow cover and the variability of its characteristics were investigated at various locations in the eastern Swiss Alps. An areal energy-balance (AEB) model was used to calculate the predominant energy fluxes at the snow atmosphere interface based on automatic meteorological measurements as input. By coupling the AEB model with a one-dimensional, physically based mass and energy-balance model of the snowpack, temperature distribution as well as energy and mass flow-in the snowpack were simulated at three different locations in the topographically complex environment at Weissfluhjoch-Davos, 2540 m a.s.l. On a horizontal test site, calculated energy fluxes and characteristics of the snow cover are in good agreement with their measured counterparts. On inclined slopes, the temperature distribution is well represented by the coupled models, but the snow depth and density are not yet satisfactorily simulated. This discrepancy may be attributed to inhomogeneous accumulation and deposition of snow on the weather and lee sides.

scholarly journals Influence of Initial Snowpack Properties on Runoff Formation during Rain-on-Snow Events

2016 ◽  
Vol 17 (6) ◽  
pp. 1801-1815 ◽  
Author(s):  
Sebastian Würzer ◽  
Tobias Jonas ◽  
Nander Wever ◽  
Michael Lehning
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Liquid Water Content ◽  
Swiss Alps ◽  
Runoff Generation ◽  
Runoff Formation ◽  
Rain Events ◽  
Snowpack Properties

Abstract Rain-on-snow (ROS) events have caused severe floods in mountainous areas in the recent past. Because of the complex interactions of physical processes, it is still difficult to accurately predict the effect of snow cover on runoff formation for an upcoming ROS event. In this study, a detailed physics-based energy balance snow cover model (SNOWPACK) was used to assess snow cover processes during more than 1000 historical ROS events at 116 locations in the Swiss Alps. The simulations of the mass and energy balance, liquid water flow, and the temporal evolution of structural properties of the snowpack were used to analyze runoff formation characteristics during ROS events. Initial liquid water content and snow depth at the onset of rainfall were found to influence the temporal dynamics, intensities, and cumulative amount of runoff. The meteorological forcing is modulated by processes within the snowpack, leading to an attenuation of runoff intensities for intense and short rain events and an amplifying effect for longer rain events. The timing of runoff generation relative to the rainfall seems to be strongly dependent on initial liquid water content, snow depth, and rainfall intensities. As these snowpack and meteorological conditions usually exhibit a strong seasonality, cumulative runoff generation during ROS also varies seasonally. ROS events with intensified snowpack runoff were found to be most common during late snowmelt season, with several such events also occurring in late autumn. These results demonstrate the strong influence of initial snowpack properties on runoff formation during ROS events in the Swiss Alps.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

Modelling future glacier evolution: Which feedbacks are relevant?

2020 ◽  
Author(s):  
Matthias Huss ◽  
Enrico Mattea ◽  
Andreas Linsbauer ◽  
Martin Hoelzle
Keyword(s):  
Energy Balance ◽  
Data Availability ◽  
Swiss Alps ◽  
Balance Model ◽  
Ice Dynamics ◽  
Feedback Effects ◽  
Proglacial Lakes ◽  
Regional Climate Model Output ◽  
The Future ◽  
Energy Balance Approach

<div> <div>Numerous models to project the future evolution of mountain glaciers in response to ongoing climate change are available, both at the local and the global scale. However, a suite of partly major simplifications is necessary in these models given the restrictions in data availability. Whereas most models account for the primary feedbacks, such as the snow-ice albedo feedback and the dynamic glacier response in some way, a considerable number of yet poorly understood or less investigated feedbacks is present that might significantly hamper the reliability of current glaciological projections.</div> <div> </div> <div>Here, we present results of a detailed modelling study for the example of Vadret da Morteratsch, Swiss Alps. A surface mass balance model accounting for ice dynamics is forced with downscaled regional climate model output (68 scenarios, CH2018) for the period 2015 to 2100. Various processes are either parameterized or explicitly accounted for. We focus on the use of a fully distributed surface energy-balance approach in comparison to simplified degree-day methods. The relevance of projected changes in different components of the energy balance is assessed using model experiments. In particular, the importance of feedback effects due to (1) the spatio-temporal evolution of supraglacial debris, (2) the formation of new proglacial lakes, and (3) changes in bare-ice albedo and local direct solar irradiance is investigated.</div> <div> </div> <div>We find that the above feedback effects all have a rather small potential to substantially impact on the rates of expected glacier retreat. In some cases, this is unexpected (e.g. for debris coverage and proglacial lakes) but can be explained by compensating processes. We also discuss and visualize the future wastage of Vadret da Morteratsch under the newest generation of climate scenarios, and put these results into context with previous studies, as well as with plans to artificially reduce the rate of glacier mass loss.</div> </div>

Towards a scale-independent fractional snow-covered area parameterization for complex terrain

2020 ◽  
Author(s):  
Nora Helbig ◽  
Yves Bühler ◽  
Lucie Eberhard ◽  
César Deschamps-Berger ◽  
Simon Gascoin ◽  
...  
Keyword(s):  
Snow Depth ◽  
Hydrological Modeling ◽  
Spatial Scales ◽  
Ground Surface ◽  
Longwave Radiation ◽  
Data Sets ◽  
Depth Data ◽  
Snow Covered Area ◽  
Covered Area ◽  
Wide Range

<p>Whenever there is snow on the ground, there will be large spatial variability in snow depth. The spatial distribution of snow is significantly influenced by topography due to wind, precipitation, shortwave and longwave radiation, and even snow avalanches relocate the accumulated snow. Fractional snow-covered area (fSCA) is an important model parameter characterizing the fraction of the ground surface that is covered by snow and is crucial for various model applications such as weather forecasts, climate simulations and hydrological modeling.</p><p>We recently suggested an empirical fSCA parameterization based on two spatial snow depth data sets acquired at peak of winter in Switzerland and Spain, which yielded best performance for spatial scales larger than 1000 m. However, this parameterization was not validated on independent snow depth data. To evaluate and improve our fSCA parameterization, in particular with regards to other spatial scales and snow climates (or geographic regions), we used spatial snow depth data sets form a wide range of mountain ranges in USA, Switzerland and France acquired by 5 different measuring methods. Pooling all snow depth data sets suggests that a scale-dependent parameter should be introduced to improve the fSCA parameterization, in particular for sub-kilometer spatial scales. Extending our empirical fSCA parameterization to a broader range of scales and snow climates is an important step towards accounting for spatio-temporal variability in snow depth in multiple snow model applications.</p>

scholarly journals Antarctic Surface and Subsurface Snow and Ice Melt Fluxes

2005 ◽  
Vol 18 (10) ◽  
pp. 1469-1481 ◽  
Author(s):  
Glen E. Liston ◽  
Jan-Gunnar Winther
Keyword(s):  
Energy Balance ◽  
Dominant Role ◽  
Field Measurements ◽  
Distribution Model ◽  
Balance Model ◽  
Near Surface ◽  
Production Values ◽  
Surface Melt ◽  
The Antarctic ◽  
Snow And Ice

Abstract This paper presents modeled surface and subsurface melt fluxes across near-coastal Antarctica. Simulations were performed using a physical-based energy balance model developed in conjunction with detailed field measurements in a mixed snow and blue-ice area of Dronning Maud Land, Antarctica. The model was combined with a satellite-derived map of Antarctic snow and blue-ice areas, 10 yr (1991–2000) of Antarctic meteorological station data, and a high-resolution meteorological distribution model, to provide daily simulated melt values on a 1-km grid covering Antarctica. Model simulations showed that 11.8% and 21.6% of the Antarctic continent experienced surface and subsurface melt, respectively. In addition, the simulations produced 10-yr averaged subsurface meltwater production fluxes of 316.5 and 57.4 km3 yr−1 for snow-covered and blue-ice areas, respectively. The corresponding figures for surface melt were 46.0 and 2.0 km3 yr−1, respectively, thus demonstrating the dominant role of subsurface over surface meltwater production. In total, computed surface and subsurface meltwater production values equal 31 mm yr−1 if evenly distributed over all of Antarctica. While, at any given location, meltwater production rates were highest in blue-ice areas, total annual Antarctic meltwater production was highest for snow-covered areas due to its larger spatial extent. The simulations also showed higher interannual meltwater variations for surface melt than subsurface melt. Since most of the produced meltwater refreezes near where it was produced, the simulated melt has little effect on the Antarctic mass balance. However, the melt contribution is important for the surface energy balance and in modifying surface and near-surface snow and ice properties such as density and grain size.

scholarly journals Evaluation of a Two-Source Snow–Vegetation Energy Balance Model for Estimating Surface Energy Fluxes in a Rangeland Ecosystem

2014 ◽  
Vol 15 (1) ◽  
pp. 143-158 ◽  
Author(s):  
Cezar Kongoli ◽  
William P. Kustas ◽  
Martha C. Anderson ◽  
John M. Norman ◽  
Joseph G. Alfieri ◽  
...  
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Surface Energy ◽  
Snow Cover ◽  
Sensible Heat Flux ◽  
Energy Balance Model ◽  
Balance Model ◽  
Sensible Heat ◽  
Energy Fluxes ◽  
Surface Energy Fluxes

Abstract The utility of a snow–vegetation energy balance model for estimating surface energy fluxes is evaluated with field measurements at two sites in a rangeland ecosystem in southwestern Idaho during the winter of 2007: one site dominated by aspen vegetation and the other by sagebrush. Model parameterizations are adopted from the two-source energy balance (TSEB) modeling scheme, which estimates fluxes from the vegetation and surface substrate separately using remotely sensed measurements of land surface temperature. Modifications include development of routines to account for surface snowmelt energy flux and snow masking of vegetation. Comparisons between modeled and measured surface energy fluxes of net radiation and turbulent heat showed reasonable agreement when considering measurement uncertainties in snow environments and the simplified algorithm used for the snow surface heat flux, particularly on a daily basis. There was generally better performance over the aspen field site, likely due to more reliable input data of snow depth/snow cover. The model was robust in capturing the evolution of surface energy fluxes during melt periods. The model behavior was also consistent with previous studies that indicate the occurrence of upward sensible heat fluxes during daytime owing to solar heating of vegetation limbs and branches, which often exceeds the downward sensible heat flux driving the snowmelt. However, model simulations over aspen trees showed that the upward sensible heat flux could be reversed for a lower canopy fraction owing to the dominance of downward sensible heat flux over snow. This indicates that reliable vegetation or snow cover fraction inputs to the model are needed for estimating fluxes over snow-covered landscapes.

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