Sodankylä manual snow survey program

Abstract. The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

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Sodankylä manual snow survey program

Geoscientific Instrumentation Methods and Data Systems Discussions ◽

10.5194/gid-5-405-2015 ◽

2015 ◽

Vol 5 (2) ◽

pp. 405-426

Author(s):

L. Leppänen ◽

A. Kontu ◽

H.-R. Hannula ◽

H. Sjöblom ◽

J. Pulliainen

Keyword(s):

Snow Water Equivalent ◽

Measurement Techniques ◽

Liquid Water Content ◽

The Arctic ◽

Research Centre ◽

Future Research ◽

Forest Zone ◽

Finnish Meteorological Institute ◽

Snow Properties ◽

Snow Water

Abstract. The manual snow survey program of the Arctic Research Centre of Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (SD) and snow water equivalent (SWE); however some older records of the snow and ice cover exists. In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day measurements include observations of SD, SWE, temperature, density, horizontal layers of snow, grain size, specific surface area (SSA), and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

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Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

10.5194/tc-2019-150 ◽

2019 ◽

Author(s):

Edward H. Bair ◽

Karl Rittger ◽

Jawairia A. Ahmad ◽

Doug Chabot

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Median Number ◽

In Situ Measurements ◽

Snow Properties ◽

Wide Range ◽

Spatial Estimates ◽

Snow Water ◽

Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

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Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Water Resources Research ◽

10.1029/2018wr024431 ◽

2019 ◽

Vol 55 (5) ◽

pp. 4465-4487 ◽

Cited By ~ 9

Author(s):

Franziska Koch ◽

Patrick Henkel ◽

Florian Appel ◽

Lino Schmid ◽

Heike Bach ◽

...

Keyword(s):

Time Delay ◽

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Signal Attenuation ◽

Wet Snow ◽

Snow Water

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Implementation and evaluation of prognostic representations of the optical diameter of snow in the SURFEX/ISBA-Crocus detailed snowpack model

The Cryosphere ◽

10.5194/tc-8-417-2014 ◽

2014 ◽

Vol 8 (2) ◽

pp. 417-437 ◽

Cited By ~ 44

Author(s):

C. M. Carmagnola ◽

S. Morin ◽

M. Lafaysse ◽

F. Domine ◽

B. Lesaffre ◽

...

Keyword(s):

Snow Water Equivalent ◽

Quantitative Description ◽

Field Measurements ◽

Rate Of Change ◽

Rate Equations ◽

Snow Properties ◽

Snow Water ◽

New Formulation ◽

Snow Microstructure ◽

The Impact

Abstract. In the SURFEX/ISBA-Crocus multi-layer snowpack model, the snow microstructure has up to now been characterised by the grain size and by semi-empirical shape variables which cannot be measured easily in the field or linked to other relevant snow properties. In this work we introduce a new formulation of snow metamorphism directly based on equations describing the rate of change of the optical diameter (dopt). This variable is considered here to be equal to the equivalent sphere optical diameter, which is inversely proportional to the specific surface area (SSA). dopt thus represents quantitatively some of the geometric characteristics of a porous medium. Different prognostic rate equations of dopt, including a re-formulation of the original Crocus scheme and the parameterisations from Taillandier et al. (2007) and Flanner and Zender (2006), were evaluated by comparing their predictions to field measurements carried out at Summit Camp (Greenland) in May and June 2011 and at Col de Porte (French Alps) during the 2009/10 and 2011/12 winter seasons. We focused especially on results in terms of SSA. In addition, we tested the impact of the different formulations on the simulated density profile, the total snow height, the snow water equivalent (SWE) and the surface albedo. Results indicate that all formulations perform well, with median values of the RMSD between measured and simulated SSA lower than 10 m2 kg−1. Incorporating the optical diameter as a fully fledged prognostic variable is an important step forward in the quantitative description of the snow microstructure within snowpack models, because it opens the way to data assimilation of various electromagnetic observations.

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Snow Wetness Influence on Impulse Radar Snow Surveys Theoretical and Laboratory Study

Hydrology Research ◽

10.2166/nh.2000.0007 ◽

2000 ◽

Vol 31 (2) ◽

pp. 89-106 ◽

Cited By ~ 23

Author(s):

A. Lundberg ◽

H. Thunehed

Keyword(s):

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Late Winter ◽

Propagation Time ◽

Reservoir Water ◽

A Value ◽

Wet Snow ◽

Snow Water

The snow-water equivalent of late-winter snowpack is of utmost importance for hydropower production in areas where a large proportion of the reservoir water emanates from snowmelt. Impulse radar can be used to estimate the snow-water equivalent of the snowpack and thus the expected snowmelt discharge. Impulse radar is now in operational use in some Scandinavian basins. With radar technology the radar wave propagation time in the snowpack is converted into snow-water equivalent with help of a parameter usually termed the a-value. Use of radar technology during late winter brings about risk for measurements on wet snow. The a-value for dry snow cannot be used directly for wet snow. We have found that a liquid-water content of 5% (by volume) reduces the a-value by approximately 20%. In this paper an equation, based on snow density and snow liquid water content, for calculation of wet-snow a-value is presented.

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Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions

The Cryosphere ◽

10.5194/tc-14-935-2020 ◽

2020 ◽

Vol 14 (3) ◽

pp. 935-956 ◽

Cited By ~ 3

Author(s):

Carlo Marin ◽

Giacomo Bertoldi ◽

Valentina Premier ◽

Mattia Callegari ◽

Christian Brida ◽

...

Keyword(s):

Snow Water Equivalent ◽

Melting Process ◽

Liquid Water Content ◽

Sar Images ◽

Distributed Information ◽

Snow Modeling ◽

Alpine Regions ◽

Snow Properties ◽

Wet Snow ◽

Spatially Distributed

Abstract. Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

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Field evaluation of a new method for estimation of liquid water content and snow water equivalent of wet snowpacks with GPR

Hydrology Research ◽

10.2166/nh.2012.182 ◽

2012 ◽

Vol 44 (4) ◽

pp. 600-613 ◽

Cited By ~ 8

Author(s):

Nils Sundström ◽

David Gustafsson ◽

Andrey Kruglyak ◽

Angela Lundberg

Keyword(s):

Water Content ◽

Liquid Water ◽

Ground Penetrating Radar ◽

Snow Water Equivalent ◽

Field Evaluation ◽

Liquid Water Content ◽

Snow Water ◽

The Mean ◽

Path Dependent ◽

Ground Penetrating

Estimates of snow water equivalent (SWE) with ground-penetrating radar can be used to calibrate and validate measurements of SWE over large areas conducted from satellites and aircrafts. However, such radar estimates typically suffer from low accuracy in wet snowpacks due to a built-in assumption of dry snow. To remedy the problem, we suggest determining liquid water content from path-dependent attenuation. We present the results of a field evaluation of this method which demonstrate that, in a wet snowpack between 0.9 and 3 m deep and with about 5 vol% of liquid water, liquid water content is underestimated by about 50% (on average). Nevertheless, the method decreases the mean error in SWE estimates to 16% compared to 34% when the presence of liquid water in snow is ignored and 31% when SWE is determined directly from two-way travel time and calibrated for manually measured snow density.

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Continuous snowpack monitoring using upward-looking ground-penetrating radar technology

Journal of Glaciology ◽

10.3189/2014jog13j084 ◽

2014 ◽

Vol 60 (221) ◽

pp. 509-525 ◽

Cited By ~ 41

Author(s):

Lino Schmid ◽

Achim Heilig ◽

Christoph Mitterer ◽

Jürg Schweizer ◽

Hansruedi Maurer ◽

...

Keyword(s):

Ground Penetrating Radar ◽

Snow Water Equivalent ◽

Temporal Evolution ◽

Liquid Water Content ◽

Internal Layers ◽

Contributing Factors ◽

Height Measurement ◽

Additional Information ◽

Snow Properties ◽

Ground Penetrating

AbstractSnow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about ± 5 cm, tracked the settlement of internal layers (± 7 cm) and measured the amount of new snow (± 10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by 5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR.

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Snow water equivalent measurement in the Arctic based on cosmic ray neutron attenuation

The Cryosphere ◽

10.5194/tc-15-5227-2021 ◽

2021 ◽

Vol 15 (11) ◽

pp. 5227-5239

Author(s):

Anton Jitnikovitch ◽

Philip Marsh ◽

Branden Walker ◽

Darin Desilets

Keyword(s):

Water Resource Management ◽

Snow Water Equivalent ◽

Cosmic Ray ◽

The Arctic ◽

Sensing Applications ◽

Neutron Attenuation ◽

Snow Water ◽

Remote Sites ◽

Neutron Sensors

Abstract. Grounded in situ, or invasive, cosmic ray neutron sensors (CRNSs) may allow for continuous, unattended measurements of snow water equivalent (SWE) over complete winter seasons and allow for measurements that are representative of spatially variable Arctic snow covers, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/2017 and 2017/2018 we tested a grounded in situ CRNS system at two locations in Canada: a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in southern Ontario that allowed easier access for validation purposes. Five CRNS units were applied in a transect to obtain continuous data for a single significant snow feature; CRNS-moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. The data indicate that grounded in situ CRNS instruments appear able to continuously measure SWE with sufficient accuracy utilizing both a linear regression and nonlinear formulation. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote sensing applications.

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Development of Long-Term Satellite-Based Snow Mass Records in the ESA Snow CCI Project

10.5194/egusphere-egu2020-14123 ◽

2020 ◽

Author(s):

Kari Luojus ◽

Matias Takala ◽

Jouni Pulliainen ◽

Juha Lemmetyinen ◽

Mikko Moisander ◽

...

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Water Equivalent ◽

Reliable Information ◽

The Arctic ◽

Climate Research ◽

Data Record ◽

Snow Water ◽

Snow Mass

Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km&#178; of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability making satellite observations the only means for providing timely and complete observations of the global snow cover. The ESA Snow CCI project was initiated in 2018 to improve methodologies for snow cover extent (SE) and snow water equivalent (SWE) retrieval [1] using satellite data and construct long term data records of terrestrial snow cover for climate research purposes.The first new long term SWE data record from the ESA Snow CCI project, spanning 1979 to 2018 has been constructed and assessed in terms of retrieval performance, homogeneity and temporal stability. The initial results show that the new SWE dataset is more robust, more accurate and more consistent over the 40-year time series, compared to the earlier ESA GlobSnow SWE v1.0 and v2.0 data records [1].The improved SWE retrieval methodology incorporates a new emission model (within the retrieval scheme), an improved synoptic weather station snow depth data record (applied to support SWE retrieval), extension of the SWE retrieval to cover the whole Northern Hemisphere.The new Snow CCI SWE data record has been used to assess changes in the long term hemispherical snow conditions and climatological trends in Northern Hemisphere, Eurasia and North America. The general finding is that the peak hemispherical snow mass during the satellite era has not yet decreased significantly but has remained relatively stable, with changes to lower and higher SWE conditions in different geographical regions.&#160;References:[1] Takala, M, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J.-P. K&#228;rn&#228;, J. Koskinen, B. Bojkov. 2011. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115, 12, 3517-3529, doi:10.1016/j.rse.2011.08.014.

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