6 research outputs found

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

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    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 ( ∼ 60–250mV) relates to those of bulk meltwater fluxes (0–1.2 × 10−6m3s−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 × 10−6Sm−1) and pH ( ∼ 6.5–6.7) as well as permeabilities (respectively  ∼ 9.7 × 10−5 and  ∼ 4.3 × 10−5m2 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.ISSN:1994-0416ISSN:1994-042

    The Changing Face of Arctic Snow Cover: A Synthesis of Observed and Projected Changes

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    Analysis of in situ and satellite data shows evidence of different regional snow cover responses to the widespread warming and increasing winter precipitation that has characterized the Arctic climate for the past 40-50 years. The largest and most rapid decreases in snow water equivalent (SWE) and snow cover duration (SCD) are observed over maritime regions of the Arctic with the highest precipitation amounts. There is also evidence of marked differences in the response of snow cover between the North American and Eurasian sectors of the Arctic, with the North American sector exhibiting decreases in snow cover and snow depth over the entire period of available in situ observations from around 1950, while widespread decreases in snow cover are not apparent over Eurasia until after around 1980. However, snow depths are increasing in many regions of Eurasia. Warming and more frequent winter thaws are contributing to changes in snow pack structure with important implications for land use and provision of ecosystem services. Projected changes in snow cover from Global Climate Models for the 2050 period indicate increases in maximum SWE of up to 15% over much of the Arctic, with the largest increases (15-30%) over the Siberian sector. In contrast, SCD is projected to decrease by about 10-20% over much of the Arctic, with the smallest decreases over Siberia (<10%) and the largest decreases over Alaska and northern Scandinavia (30-40%) by 2050. These projected changes will have far-reaching consequences for the climate system, human activities, hydrology, and ecology

    Multiple Effects of Changes in Arctic Snow Cover

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    Snow cover plays a major role in the climate, hydrological and ecological systems of the Arctic and other regions through its influence on the surface energy balance (e.g. reflectivity), water balance (e.g. water storage and release), thermal regimes (e.g. insulation), vegetation and trace gas fluxes. Feedbacks to the climate system have global consequences. The livelihoods and well-being of Arctic residents and many services for the wider population depend on snow conditions so changes have important consequences. Already, changing snow conditions, particularly reduced summer soil moisture, winter thaw events and rain-on-snow conditions have negatively affected commercial forestry, reindeer herding, some wild animal populations and vegetation. Reductions in snow cover are also adversely impacting indigenous peoples' access to traditional foods with negative impacts on human health and well-being. However, there are likely to be some benefits from a changing Arctic snow regime such as more even run-off from melting snow that favours hydropower operations

    Changing Arctic Snow Cover: A Review of Recent Developments and Assessment of Future Needs for Observations, Modelling, and Impacts

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    Snow is a critically important and rapidly changing feature of the Arctic. However, snow-cover and snowpack conditions change through time pose challenges for measuring and prediction of snow. Plausible scenarios of how Arctic snow cover will respond to changing Arctic climate are important for impact assessments and adaptation strategies. Although much progress has been made in understanding and predicting snow-cover changes and their multiple consequences, many uncertainties remain. In this paper, we review advances in snow monitoring and modelling, and the impact of snow changes on ecosystems and society in Arctic regions. Interdisciplinary activities are required to resolve the current limitations on measuring and modelling snow characteristics through the cold season and at different spatial scales to assure human well-being, economic stability, and improve the ability to predict manage and adapt to natural hazards in the Arctic region
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