In situ geophysical monitoring of liquid water movement in an Alpine snowpack from self potential signals

Mapping Intimacies ◽

10.5194/egusphere-egu2020-4851 ◽

2020 ◽

Author(s):

Alex Priestley

Keyword(s):

Liquid Water ◽

Water Resource Management ◽

Water Movement ◽

Liquid Water Content ◽

Snow Hydrology ◽

Geophysical Monitoring ◽

Meltwater Runoff ◽

Physically Based ◽

Snow Model ◽

Self Potential

<p>Modelling and monitoring seasonal snow is critical for water resource management, flood forecasting and avalanche risk prediction. Snowmelt processes are of particular importance. The behaviour of liquid water in snow has a big influence on melting processes, but is difficult to measure and monitor non-invasively. Recent work has shown the promise of using electrical self potential measurements as a snow hydrology sensor. Self potential magnitudes can be used to infer both liquid water content of snow and bulk meltwater runoff. In autumn 2018, a prototype self potential monitoring array was installed at Col de Porte in the French Alps, alongside full hydrological and meteorological measurements made routinely at the site. Self potential measurements were taken throughout the following winter, with manual snow pit data obtained in spring 2019. A physically-based snow hydrology model was run for the winter, and an electrical model was coupled to the snow model to create a synthetic set of self potential observations. These synthetic observations were compared to the observed self potential magnitudes to evaluate the effectiveness of the snow model, and to investigate the potential for using the self potential array as part of a coupled geophysical monitoring and modelling system.</p>

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Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

The Cryosphere ◽

10.5194/tc-7-961-2013 ◽

2013 ◽

Vol 7 (3) ◽

pp. 961-975 ◽

Cited By ~ 15

Author(s):

A. Roy ◽

A. Royer ◽

B. Montpetit ◽

P. A. Bartlett ◽

A. Langlois

Keyword(s):

Energy Balance ◽

Surface Energy ◽

Liquid Water ◽

Land Surface ◽

Surface Energy Balance ◽

Liquid Water Content ◽

Canadian Land Surface Scheme ◽

Snow Model ◽

Surface Scheme ◽

The One

Abstract. Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m2 kg–1, and a root mean square error (RMSE) of 5.1 m2 kg–1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

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Bulk meltwater flow and liquid water content of snowpacks mapped using the electrical self-potential (SP) method

The Cryosphere ◽

10.5194/tc-10-433-2016 ◽

2016 ◽

Vol 10 (1) ◽

pp. 433-444 ◽

Cited By ~ 2

Author(s):

Sarah S. Thompson ◽

Bernd Kulessa ◽

Richard L. H. Essery ◽

Martin P. Lüthi

Keyword(s):

Electrical Conductivity ◽

Water Content ◽

Liquid Water ◽

Low Cost ◽

Sensor Arrays ◽

Ground Truth ◽

Liquid Water Content ◽

Potential Method ◽

Avalanche Forecasting ◽

Self Potential

Abstract. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. We show here that strong electrical self-potential fields are generated in melting in situ snowpacks at Rhone Glacier and Jungfraujoch Glacier, Switzerland. In agreement with theory, the diurnal evolution of self-potential magnitudes ( ∼  60–250 mV) relates to those of bulk meltwater fluxes (0–1.2  ×  10−6 m3 s−1) principally through the permeability and the content, electrical conductivity and pH of liquid water. Previous work revealed that when fresh snow melts, ions are eluted in sequence and electrical conductivity, pH and self-potential data change diagnostically. Our snowpacks had experienced earlier stages of melt, and complementary snow pit measurements revealed that electrical conductivity ( ∼  1–5  ×  10−6 S m−1) and pH ( ∼  6.5–6.7) as well as permeabilities (respectively ∼  9.7  ×  10−5 and ∼  4.3  ×  10−5 m2 at Rhone Glacier and Jungfraujoch Glacier) were invariant. This implies, first, that preferential elution of ions was complete and, second, that our self-potential measurements reflect daily changes in liquid water contents. These were calculated to increase within the pendular regime from ∼  1 to 5 and ∼  3 to 5.5 % respectively at Rhone Glacier and Jungfraujoch Glacier, as confirmed by ground truth measurements. We conclude that the electrical self-potential method is a promising snow and firn hydrology sensor owing to its suitability for (1) sensing lateral and vertical liquid water flows directly and minimally invasively, (2) complementing established observational programs through multidimensional spatial mapping of meltwater fluxes or liquid water content and (3) monitoring autonomously at a low cost. Future work should focus on the development of self-potential sensor arrays compatible with existing weather and snow monitoring technology and observational programs, and the integration of self-potential data into analytical frameworks.

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The Radiative Effect of a Fir Canopy on a Snowpack

Journal of Hydrometeorology ◽

10.1175/jhm528.1 ◽

2006 ◽

Vol 7 (5) ◽

pp. 880-895 ◽

Cited By ~ 13

Author(s):

M. J. Tribbeck ◽

R. J. Gurney ◽

E. M. Morris

Keyword(s):

Thermal Radiation ◽

Liquid Water ◽

Snow Depth ◽

Large Scale ◽

Model Parameters ◽

Transfer Scheme ◽

Complete Representation ◽

Modeling Approach ◽

Physically Based ◽

Snow Model

Abstract Models of snow processes in areas of possible large-scale change need to be site independent and physically based. Here, the accumulation and ablation of the seasonal snow cover beneath a fir canopy has been simulated with a new physically based snow–soil vegetation–atmosphere transfer scheme (Snow-SVAT) called SNOWCAN. The model was formulated by coupling a canopy optical and thermal radiation model to a physically based multilayer snow model. Simple representations of other forest effects were included. These include the reduction of wind speed and hence turbulent transfer beneath the canopy, sublimation of intercepted snow, and deposition of debris on the surface. This paper tests this new modeling approach fully at a fir site within Reynolds Creek Experimental Watershed, Idaho. Model parameters were determined at an open site and subsequently applied to the fir site. SNOWCAN was evaluated using measurements of snow depth, subcanopy solar and thermal radiation, and snowpack profiles of temperature, density, and grain size. Simulations showed good agreement with observations (e.g., fir site snow depth was estimated over the season with r 2 = 0.96), generally to within measurement error. However, the simulated temperature profiles were less accurate after a melt–freeze event, when the temperature discrepancy resulted from underestimation of the rate of liquid water flow and/or the rate of refreeze. This indicates both that the general modeling approach is applicable and that a still more complete representation of liquid water in the snowpack will be important.

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Laboratory-based observations of capillary barriers and preferential flow in layered snow

The Cryosphere Discussions ◽

10.5194/tcd-9-6627-2015 ◽

2015 ◽

Vol 9 (6) ◽

pp. 6627-6659 ◽

Cited By ~ 3

Author(s):

F. Avanzi ◽

H. Hirashima ◽

S. Yamaguchi ◽

T. Katsushima ◽

C. De Michele

Keyword(s):

Grain Size ◽

Liquid Water ◽

Preferential Flow ◽

Liquid Water Content ◽

Richards Equation ◽

Input Rate ◽

Capillary Barriers ◽

Physically Based ◽

Water Transmission ◽

Water Speed

Abstract. Several evidences are nowadays available that show how the effects of capillary gradients and preferential flow on water transmission in snow may play a more important role than expected. To observe these processes and to contribute in their characterization, we performed observations on the development of capillary barriers and preferential flow patterns in layered snow during cold laboratory experiments. We considered three different layering (all characterized by a finer-over-coarser texture in grain size) and three different water input rates. Nine samples of layered snow were sieved in a cold laboratory, and subjected to a constant supply of dyed tracer. By means of visual inspection, horizontal sectioning and liquid water content measurements, the processes of ponding and preferential flow were characterized as a function of texture and water input rate. The dynamics of each sample were replicated using the multi-layer physically-based SNOWPACK model. Results show that capillary barriers and preferential flow are relevant processes ruling the speed of liquid water in stratified snow. Ponding is associated with peaks in LWC at the boundary between the two layers equal to ~ 33–36 vol. % when the upper layer is composed by fine snow (grain size smaller than 0.5 mm). The thickness of the ponding layer at the textural boundary is between 0 and 3 cm, depending on sample stratigraphy. Heterogeneity in water transmission increases with grain size, while we do not observe any clear dependency on water input rate. The extensive comparison between observed and simulated LWC profiles by SNOWPACK (using an approximation of Richards Equation) shows high performances by the model in estimating the LWC peak over the boundary, while water speed in snow is underestimated by the chosen water transport scheme.

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Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

The Cryosphere Discussions ◽

10.5194/tcd-6-5255-2012 ◽

2012 ◽

Vol 6 (6) ◽

pp. 5255-5289 ◽

Cited By ~ 1

Author(s):

A. Roy ◽

A. Royer ◽

B. Montpetit ◽

P. A. Bartlett ◽

A. Langlois

Keyword(s):

Energy Balance ◽

Surface Energy ◽

Liquid Water ◽

Land Surface ◽

Surface Energy Balance ◽

Liquid Water Content ◽

Canadian Land Surface Scheme ◽

Snow Model ◽

Surface Scheme ◽

The One

Abstract. Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, whereas it considers the liquid water content for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, Arctic and sub-Arctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.1 m2 kg−1, and a RMSE of 4.9 m2 kg−1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

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A Physically Based Subgrid Parameterization for the Production and Maintenance of Mixed-Phase Clouds in a General Circulation Model

Journal of the Atmospheric Sciences ◽

10.1175/jas-d-15-0021.1 ◽

2015 ◽

Vol 73 (1) ◽

pp. 279-291 ◽

Cited By ~ 32

Author(s):

K. Furtado ◽

P. R. Field ◽

I. A. Boutle ◽

C. J. Morcrette ◽

J. M. Wilkinson

Keyword(s):

General Circulation Model ◽

Liquid Water ◽

General Circulation ◽

Circulation Model ◽

Supercooled Liquid ◽

Liquid Water Content ◽

Mixed Phase ◽

Physically Based ◽

Scale Turbulence ◽

Mixed Phase Clouds

Abstract A physically based method for parameterizing the role of subgrid-scale turbulence in the production and maintenance of supercooled liquid water and mixed-phase clouds is presented. The approach used is to simplify the dynamics of supersaturation fluctuations to a stochastic differential equation that can be solved analytically, giving increments to the prognostic liquid cloud fraction and liquid water content fields in a general circulation model (GCM). Elsewhere, it has been demonstrated that the approach captures the properties of decameter-resolution large-eddy simulations of a turbulent mixed-phase environment. In this paper, it is shown that it can be implemented in a GCM, and the effects that this has on Southern Ocean biases and on Arctic stratus are investigated.

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