Articles | Volume 13, issue 7
https://doi.org/10.5194/tc-13-1767-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-13-1767-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
04 Jul 2019
Research article | Highlight paper |
| 04 Jul 2019
| 04 Jul 2019
Converting snow depth to snow water equivalent using climatological variables
David F. Hill
CORRESPONDING AUTHOR
Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA
Elizabeth A. Burakowski
Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
Ryan L. Crumley
Water Resources Graduate Program, Oregon State University, Corvallis, OR, USA
Julia Keon
Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA
J. Michelle Hu
Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
Anthony A. Arendt
Applied Physics Laboratory, University of Washington, Seattle, WA, USA
Katreen Wikstrom Jones
Alaska Division of Geological & Geophysical Surveys, Fairbanks, AK, USA
Gabriel J. Wolken
Alaska Division of Geological & Geophysical Surveys, Fairbanks, AK, USA
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK, USA
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Christina Marie Aragon and David F. Hill
Hydrol. Earth Syst. Sci., 28, 781–800, https://doi.org/10.5194/hess-28-781-2024, https://doi.org/10.5194/hess-28-781-2024, 2024
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A novel snow metric, snow water storage (SwS), is used to characterize the natural reservoir function of snowpacks, quantifying how much water is held in snow reservoirs and for how long. Despite covering only 16 % of US land area, mountainous regions contribute 72 % of the annual SwS. Recent decades show a 22 % decline in annual mountain SwS. Flexible snow metrics such as SwS may become more valuable for monitoring and predicting water resources amidst a future of increased climate variability.
Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, and Community Snow Observations Participants
Hydrol. Earth Syst. Sci., 25, 4651–4680, https://doi.org/10.5194/hess-25-4651-2021, https://doi.org/10.5194/hess-25-4651-2021, 2021
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In this study, we use a new snow data set collected by participants in the Community Snow Observations project in coastal Alaska to improve snow depth and snow water equivalence simulations from a snow process model. We validate our simulations with multiple datasets, taking advantage of snow telemetry (SNOTEL), snow depth and snow water equivalence, and remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
Claudine Hauri, Cristina Schultz, Katherine Hedstrom, Seth Danielson, Brita Irving, Scott C. Doney, Raphael Dussin, Enrique N. Curchitser, David F. Hill, and Charles A. Stock
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The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change. To improve our conceptual understanding of the system, we developed a new regional biogeochemical model setup for the GOA. Model output suggests that bottom water is seasonally high in CO2 between June and January. Such extensive periods of reoccurring high CO2 may be harmful to ocean acidification-sensitive organisms.
Katherine A. Serafin, Peter Ruggiero, Kai Parker, and David F. Hill
Nat. Hazards Earth Syst. Sci., 19, 1415–1431, https://doi.org/10.5194/nhess-19-1415-2019, https://doi.org/10.5194/nhess-19-1415-2019, 2019
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In coastal environments, extreme water levels driving flooding are often generated by many individual processes like storm surge, streamflow, and tides. To estimate flood drivers along a coastal river, we merge statistical simulations of ocean and river forcing with a hydraulic model to produce water levels. We find both ocean and river forcing are necessary for producing extreme flood levels like the 100-yr event, highlighting the need for considering compound events in flood risk assessments.
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Weather Clim. Dynam., 5, 1117–1135, https://doi.org/10.5194/wcd-5-1117-2024, https://doi.org/10.5194/wcd-5-1117-2024, 2024
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Atmospheric rivers (ARs) are channel-shaped features within the atmosphere that carry moisture from the mid-latitudes to the poles, bringing warm temperatures and moisture that can cause melt in the Arctic. We used variable-resolution grids to model ARs around the Greenland ice sheet and compared this output to uniform-resolution grids and reanalysis products. We found that the variable-resolution grids produced ARs and precipitation that were more similar to observation-based products.
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler
EGUsphere, https://doi.org/10.5194/egusphere-2024-771, https://doi.org/10.5194/egusphere-2024-771, 2024
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A novel snow metric, snow water storage (SwS), is used to characterize the natural reservoir function of snowpacks, quantifying how much water is held in snow reservoirs and for how long. Despite covering only 16 % of US land area, mountainous regions contribute 72 % of the annual SwS. Recent decades show a 22 % decline in annual mountain SwS. Flexible snow metrics such as SwS may become more valuable for monitoring and predicting water resources amidst a future of increased climate variability.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
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This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
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The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-143, https://doi.org/10.5194/tc-2022-143, 2022
Publication in TC not foreseen
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Perennial snowfields in the Brooks Range of Alaska are critical for the ecosystem and provide caribou habitat. Caribou are a crucial food source for rural hunters. The purpose of this research is to map perennial snowfield extents using several remote sensing techniques with Sentinel-1 and 2. These include analysis of Synthetic Aperture Radar backscatter change and of optical satellite imagery. Results are compared with field data and appear to effectively detect perennial snowfield locations.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
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This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
Ryan L. Crumley, David F. Hill, Katreen Wikstrom Jones, Gabriel J. Wolken, Anthony A. Arendt, Christina M. Aragon, Christopher Cosgrove, and Community Snow Observations Participants
Hydrol. Earth Syst. Sci., 25, 4651–4680, https://doi.org/10.5194/hess-25-4651-2021, https://doi.org/10.5194/hess-25-4651-2021, 2021
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Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
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This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Claudine Hauri, Cristina Schultz, Katherine Hedstrom, Seth Danielson, Brita Irving, Scott C. Doney, Raphael Dussin, Enrique N. Curchitser, David F. Hill, and Charles A. Stock
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Extensive field observations were conducted in the Upper Susitna basin in central Alaska in 2012–2014. This paper describes the weather, glacier mass balance, snow cover, and soils of the basin. We found that temperatures over the glacier are cooler than over land at the same elevation. The glaciers have been losing mass faster in recent years than in the 1980s. Measurements of glacier mass change with traditional methods closely matched radar measurements.
Katherine A. Serafin, Peter Ruggiero, Kai Parker, and David F. Hill
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Related subject area
Discipline: Snow | Subject: Seasonal Snow
Which global reanalysis dataset has better representativeness in snow cover on the Tibetan Plateau?
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Observed snow depth trends in the European Alps: 1971 to 2019
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We look at three commonly used snow depth datasets that come from a complex combination of snow modeling and historical measurements. When compared with each other, these datasets have differences that arise for various reasons. We show that a simple snow model can be used to examine consistency and highlight issues with the complex datasets. This method indicates that one of the complex datasets should be excluded from further studies.
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023, https://doi.org/10.5194/tc-17-5175-2023, 2023
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Using newly developed snow reanalysis datasets as references, snow water storage is at high uncertainty among commonly used global products in the Andes and low-resolution products in the western United States, where snow is the key element of water resources. In addition to precipitation, elevation differences and model mechanism variances drive snow uncertainty. This work provides insights for research applying these products and generating future products in areas with limited in situ data.
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The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
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We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Diego Monteiro and Samuel Morin
The Cryosphere, 17, 3617–3660, https://doi.org/10.5194/tc-17-3617-2023, https://doi.org/10.5194/tc-17-3617-2023, 2023
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Beyond directly using in situ observations, often sparsely available in mountain regions, climate model simulations and so-called reanalyses are increasingly used for climate change impact studies. Here we evaluate such datasets in the European Alps from 1950 to 2020, with a focus on snow cover information and its main drivers: air temperature and precipitation. In terms of variability and trends, we identify several limitations and provide recommendations for future use of these datasets.
Leon J. Bührle, Mauro Marty, Lucie A. Eberhard, Andreas Stoffel, Elisabeth D. Hafner, and Yves Bühler
The Cryosphere, 17, 3383–3408, https://doi.org/10.5194/tc-17-3383-2023, https://doi.org/10.5194/tc-17-3383-2023, 2023
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Information on the snow depth distribution is crucial for numerous applications in high-mountain regions. However, only specific measurements can accurately map the present variability of snow depths within complex terrain. In this study, we show the reliable processing of images from aeroplane to large (> 100 km2) detailed and accurate snow depth maps around Davos (CH). We use these maps to describe the existing snow depth distribution, other special features and potential applications.
Xuemei Li, Xinyu Liu, Kaixin Zhao, Xu Zhang, and Lanhai Li
The Cryosphere, 17, 2437–2453, https://doi.org/10.5194/tc-17-2437-2023, https://doi.org/10.5194/tc-17-2437-2023, 2023
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Quantifying change in the potential snowfall phenology (PSP) is an important area of research for understanding regional climate change past, present, and future. However, few studies have focused on the PSP and its change in alpine mountainous regions. We proposed three innovative indicators to characterize the PSP and its spatial–temporal variation. Our study provides a novel approach to understanding PSP in alpine mountainous regions and can be easily extended to other snow-dominated regions.
Moritz Buchmann, Gernot Resch, Michael Begert, Stefan Brönnimann, Barbara Chimani, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 17, 653–671, https://doi.org/10.5194/tc-17-653-2023, https://doi.org/10.5194/tc-17-653-2023, 2023
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Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are susceptible to inhomogeneities that can affect the trends and even change the sign. To assess the relevance of homogenisation for daily snow depths, we investigated its impact on trends and changes in extreme values of snow indices between 1961 and 2021 in the Swiss observation network.
Zachary S. Miller, Erich H. Peitzsch, Eric A. Sproles, Karl W. Birkeland, and Ross T. Palomaki
The Cryosphere, 16, 4907–4930, https://doi.org/10.5194/tc-16-4907-2022, https://doi.org/10.5194/tc-16-4907-2022, 2022
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Snow depth varies across steep, complex mountain landscapes due to interactions between dynamic natural processes. Our study of a winter time series of high-resolution snow depth maps found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability at a couloir study site in the Bridger Range of Montana, USA. The results of this research have the potential to reduce uncertainty associated with snowpack and snow water resource analysis.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Melody Sandells, Chris Derksen, Branden Walker, Gabriel Hould Gosselin, Oliver Sonnentag, Richard Essery, Richard Kelly, Phillip Marsh, Joshua King, and Julia Boike
The Cryosphere, 16, 4201–4222, https://doi.org/10.5194/tc-16-4201-2022, https://doi.org/10.5194/tc-16-4201-2022, 2022
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Measurements of the properties of the snow and soil were compared to simulations of the Community Land Model to see how well the model represents snow insulation. Simulations underestimated snow thermal conductivity and wintertime soil temperatures. We test two approaches to reduce the transfer of heat through the snowpack and bring simulated soil temperatures closer to measurements, with an alternative parameterisation of snow thermal conductivity being more appropriate.
Moritz Buchmann, John Coll, Johannes Aschauer, Michael Begert, Stefan Brönnimann, Barbara Chimani, Gernot Resch, Wolfgang Schöner, and Christoph Marty
The Cryosphere, 16, 2147–2161, https://doi.org/10.5194/tc-16-2147-2022, https://doi.org/10.5194/tc-16-2147-2022, 2022
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Knowledge about inhomogeneities in a data set is important for any subsequent climatological analysis. We ran three well-established homogenization methods and compared the identified break points. By only treating breaks as valid when detected by at least two out of three methods, we enhanced the robustness of our results. We found 45 breaks within 42 of 184 investigated series; of these 70 % could be explained by events recorded in the station history.
Bertrand Cluzet, Matthieu Lafaysse, César Deschamps-Berger, Matthieu Vernay, and Marie Dumont
The Cryosphere, 16, 1281–1298, https://doi.org/10.5194/tc-16-1281-2022, https://doi.org/10.5194/tc-16-1281-2022, 2022
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The mountainous snow cover is highly variable at all temporal and spatial scales. Snow cover models suffer from large errors, while snowpack observations are sparse. Data assimilation combines them into a better estimate of the snow cover. A major challenge is to propagate information from observed into unobserved areas. This paper presents a spatialized version of the particle filter, in which information from in situ snow depth observations is successfully used to constrain nearby simulations.
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
Achut Parajuli, Daniel F. Nadeau, François Anctil, and Marco Alves
The Cryosphere, 15, 5371–5386, https://doi.org/10.5194/tc-15-5371-2021, https://doi.org/10.5194/tc-15-5371-2021, 2021
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Cold content is the energy required to attain an isothermal (0 °C) state and resulting in the snow surface melt. This study focuses on determining the multi-layer cold content (30 min time steps) relying on field measurements, snow temperature profile, and empirical formulation in four distinct forest sites of Montmorency Forest, eastern Canada. We present novel research where the effect of forest structure, local topography, and meteorological conditions on cold content variability is explored.
Yufei Liu, Yiwen Fang, and Steven A. Margulis
The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, https://doi.org/10.5194/tc-15-5261-2021, 2021
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We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analyzed in this paper, which brings new insights into understanding the snowpack variability over this region.
Moritz Buchmann, Michael Begert, Stefan Brönnimann, and Christoph Marty
The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, https://doi.org/10.5194/tc-15-4625-2021, 2021
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We investigated the impacts of local-scale variations by analysing snow climate indicators derived from parallel snow measurements. We found the largest relative inter-pair differences for all indicators in spring and the smallest in winter. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and provide, in combination with break-detection methods, the groundwork in view of any homogenization efforts regarding snow time series.
Michael Matiu, Alice Crespi, Giacomo Bertoldi, Carlo Maria Carmagnola, Christoph Marty, Samuel Morin, Wolfgang Schöner, Daniele Cat Berro, Gabriele Chiogna, Ludovica De Gregorio, Sven Kotlarski, Bruno Majone, Gernot Resch, Silvia Terzago, Mauro Valt, Walter Beozzo, Paola Cianfarra, Isabelle Gouttevin, Giorgia Marcolini, Claudia Notarnicola, Marcello Petitta, Simon C. Scherrer, Ulrich Strasser, Michael Winkler, Marc Zebisch, Andrea Cicogna, Roberto Cremonini, Andrea Debernardi, Mattia Faletto, Mauro Gaddo, Lorenzo Giovannini, Luca Mercalli, Jean-Michel Soubeyroux, Andrea Sušnik, Alberto Trenti, Stefano Urbani, and Viktor Weilguni
The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, https://doi.org/10.5194/tc-15-1343-2021, 2021
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The first Alpine-wide assessment of station snow depth has been enabled by a collaborative effort of the research community which involves more than 30 partners, 6 countries, and more than 2000 stations. It shows how snow in the European Alps matches the climatic zones and gives a robust estimate of observed changes: stronger decreases in the snow season at low elevations and in spring at all elevations, however, with considerable regional differences.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
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High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
François Tuzet, Marie Dumont, Ghislain Picard, Maxim Lamare, Didier Voisin, Pierre Nabat, Mathieu Lafaysse, Fanny Larue, Jesus Revuelto, and Laurent Arnaud
The Cryosphere, 14, 4553–4579, https://doi.org/10.5194/tc-14-4553-2020, https://doi.org/10.5194/tc-14-4553-2020, 2020
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This study presents a field dataset collected over 30 d from two snow seasons at a Col du Lautaret site (French Alps). The dataset compares different measurements or estimates of light-absorbing particle (LAP) concentrations in snow, highlighting a gap in the current understanding of the measurement of these quantities. An ensemble snowpack model is then evaluated for this dataset estimating that LAPs shorten each snow season by around 10 d despite contrasting meteorological conditions.
Jianwei Yang, Lingmei Jiang, Kari Luojus, Jinmei Pan, Juha Lemmetyinen, Matias Takala, and Shengli Wu
The Cryosphere, 14, 1763–1778, https://doi.org/10.5194/tc-14-1763-2020, https://doi.org/10.5194/tc-14-1763-2020, 2020
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There are many challenges for accurate snow depth estimation using passive microwave data. Machine learning (ML) techniques are deemed to be powerful tools for establishing nonlinear relations between independent variables and a given target variable. In this study, we investigate the potential capability of the random forest (RF) model on snow depth estimation at temporal and spatial scales. The result indicates that the fitted RF algorithms perform better on temporal than spatial scales.
Colleen Mortimer, Lawrence Mudryk, Chris Derksen, Kari Luojus, Ross Brown, Richard Kelly, and Marco Tedesco
The Cryosphere, 14, 1579–1594, https://doi.org/10.5194/tc-14-1579-2020, https://doi.org/10.5194/tc-14-1579-2020, 2020
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Existing stand-alone passive microwave SWE products have markedly different climatological SWE patterns compared to reanalysis-based datasets. The AMSR-E SWE has low spatial and temporal correlations with the four reanalysis-based products evaluated and GlobSnow and perform poorly in comparisons with snow transect data from Finland, Russia, and Canada. There is better agreement with in situ data when multiple SWE products, excluding the stand-alone passive microwave SWE products, are combined.
Céline Portenier, Fabia Hüsler, Stefan Härer, and Stefan Wunderle
The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, https://doi.org/10.5194/tc-14-1409-2020, 2020
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We present a method to derive snow cover maps from freely available webcam images in the Swiss Alps. With marginal manual user input, we can transform a webcam image into a georeferenced map and therewith perform snow cover analyses with a high spatiotemporal resolution over a large area. Our evaluation has shown that webcams could not only serve as a reference for improved validation of satellite-based approaches, but also complement satellite-based snow cover retrieval.
Markus Todt, Nick Rutter, Christopher G. Fletcher, and Leanne M. Wake
The Cryosphere, 13, 3077–3091, https://doi.org/10.5194/tc-13-3077-2019, https://doi.org/10.5194/tc-13-3077-2019, 2019
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Vegetation is often represented by a single layer in global land models. Studies have found deficient simulation of thermal radiation beneath forest canopies when represented by single-layer vegetation. This study corrects thermal radiation in forests for a global land model using single-layer vegetation in order to assess the effect of deficient thermal radiation on snow cover and snowmelt. Results indicate that single-layer vegetation causes snow in forests to be too cold and melt too late.
Rebecca Mott, Andreas Wolf, Maximilian Kehl, Harald Kunstmann, Michael Warscher, and Thomas Grünewald
The Cryosphere, 13, 1247–1265, https://doi.org/10.5194/tc-13-1247-2019, https://doi.org/10.5194/tc-13-1247-2019, 2019
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The mass balance of very small glaciers is often governed by anomalous snow accumulation, winter precipitation being multiplied by snow redistribution processes, or by suppressed snow ablation driven by micrometeorological effects lowering net radiation and turbulent heat exchange. In this study we discuss the relative contribution of snow accumulation (avalanches) versus micrometeorology (katabatic flow) on the mass balance of the lowest perennial ice field of the Alps, the Ice Chapel.
Yue Zhou, Hui Wen, Jun Liu, Wei Pu, Qingcai Chen, and Xin Wang
The Cryosphere, 13, 157–175, https://doi.org/10.5194/tc-13-157-2019, https://doi.org/10.5194/tc-13-157-2019, 2019
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We first investigated the optical characteristics and potential sources of chromophoric dissolved organic matter (CDOM) in seasonal snow over northwestern China. The abundance of CDOM showed regional variation. At some sites strongly influenced by local soil, the absorption of CDOM cannot be neglected compared to black carbon. We found two humic-like and one protein-like fluorophores in snow. The major sources of snow CDOM were soil, biomass burning, and anthropogenic pollution.
Benjamin J. Hatchett and Hilary G. Eisen
The Cryosphere, 13, 21–28, https://doi.org/10.5194/tc-13-21-2019, https://doi.org/10.5194/tc-13-21-2019, 2019
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We examine the timing of early season snowpack relevant to oversnow vehicle (OSV) recreation over the past 3 decades in the Lake Tahoe region (USA). Data from two independent data sources suggest that the timing of achieving sufficient snowpack has shifted later by 2 weeks. Increasing rainfall and more dry days play a role in the later onset. Adaptation strategies are provided for winter travel management planning to address negative impacts of loss of early season snowpack for OSV usage.
Deborah Verfaillie, Matthieu Lafaysse, Michel Déqué, Nicolas Eckert, Yves Lejeune, and Samuel Morin
The Cryosphere, 12, 1249–1271, https://doi.org/10.5194/tc-12-1249-2018, https://doi.org/10.5194/tc-12-1249-2018, 2018
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This article addresses local changes of seasonal snow and its meteorological drivers, at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps, for the period 1960–2100. We use an ensemble of adjusted RCM outputs consistent with IPCC AR5 GCM outputs (RCPs 2.6, 4.5 and 8.5) and the snowpack model Crocus. Beyond scenario-based approach, global temperature levels on the order of 1.5 °C and 2 °C above preindustrial levels correspond to 25 and 32% reduction of mean snow depth.
Cited articles
Alford, D.: Density variations in alpine snow, J. Glaciol., 6, 495–503,
https://doi.org/10.3189/S0022143000019717, 1967.
Avanzi, F., De Michele, C., and Ghezzi, A.: On the performances of empirical
regressions for the estimation of bulk snow density, Geogr. Fis. Din. Quat.,
38, 105–112, https://doi.org/10.4461/GFDQ.2015.38.10, 2015.
Beaumont, R.: Mt. Hood pressure pillow snow gage, J. Appl. Meteorol., 4,
626–631, https://doi.org/10.1175/1520-0450(1965)004<0626:MHPPSG>2.0.CO;2, 1965.
Beaumont, R. and Work, R.: Snow sampling results from three samplers,
Hydrolog. Sci. J., 8, 74–78, https://doi.org/10.1080/02626666309493359,
1963.
Burakowski, E. A., Wake, C. P., Stampone, M., and Dibb, J.: Putting the
Capital “A” in CoCoRAHS: An Experimental Program to Measure Albedo using the
Community Collaborative Rain Hail and Snow (CoCoRaHS) Network, Hydrol.
Process., 27, 3024–3034, https://doi.org/10.1002/hyp.9825, 2013.
Burakowski, E. A., Ollinger, S., Lepine, L., Schaaf, C. B., Wang, Z., Dibb,
J. E., Hollinger, D. Y., Kim, J.-H., Erb, A., and Martin, M. E.: Spatial
scaling of reflectance and surface albedo over a mixed-use, temperate forest
landscape during snow-covered periods, Remote Sens. Environ., 158, 465–477,
https://doi.org/10.1016/j.rse.2014.11.023, 2015.
Campbell, J., Ollinger, S., Flerchinger, G., Wicklein, H., Hayhoe, K., and
Bailey, A.: Past and projected future changes in snowpack and soil frost at
the Hubbard Brook Experimental Forest, New Hampshire, USA, Hydrol. Process.,
24, 2465–2480, https://doi.org/10.1002/hyp.7666, 2010.
Church, J. E.: Snow surveying: its principles and possibilities, Geogr.
Rev., 23, 529–563, https://doi.org/10.2307/209242, 1933.
Church, J. E. and Marr, J. C.: Further improvement of snow-survey apparatus,
T. Am. Geophys. Un., 18, 607–617, https://doi.org/10.1029/TR018i002p00607, 1937.
Daly, C., Neilson, R., and Phillips, D.: A statistical-topographic model for
mapping climatological precipitation over mountainous terrain, J. Appl.
Meteorol., 33, 140–158, https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2, 1994.
De Maesschalck, R., Jouan-Rimbaud, D., and Massart, D.: The Mahalanobis
distance, Chemometr. Intell. Lab., 50, 1–18,
https://doi.org/10.1016/S0169-7439(99)00047-7, 2000.
Dixon, D. and Boon, S.: Comparison of the SnowHydro sampler with existing
snow tube designs, Hydrol. Process., 26, 2555–2562,
https://doi.org/10.1002/hyp.9317, 2012.
Dressler, K., Fassnacht, S., and Bales, R.: A comparison of snow telemetry
and snow course measurements in the Colorado River basin, J. Hydrometeorol.,
7, 705–712, https://doi.org/10.1175/JHM506.1, 2006.
Gnanadesikan, R. and Kettenring, J.: Robust estimates, residuals, and
outlier detection with multiresponse data, Biometrics, 28,
81–124, https://doi.org/10.2307/2528963, 1972.
Goodison, B.: Accuracy of snow samplers for measuring shallow snowpacks: An
update, Proceedings of the 35th Annual Eastern Snow Conference, Hanover, NH, 36–49, 2–3 February 1978.
Goodison, B., Ferguson, H., and McKay, G.: Measurement and data analysis.
The Handbook of Snow: Principles, Processes, Management, and Use, edited by:
Gray, D. and Male, D., The Blackburn Press, Caldwell, NJ, USA, 191–274, 1981.
Goodison, B., Wilson, B., Wu., K, and Metcalfe, J.: An inexpensive remote
snow-depth gauge: An assessment, Proceedings of the 52nd Annual Western
Snow Conference, Sun Valley, ID, 188–191, 17–19 April 1984.
Goodison, B., Glynn, J., Harvey, K., and Slater, J.: Snow Surveying in
Canada: A Perspective, Can. Water Resour. J., 12, 27–42,
https://doi.org/10.4296/cwrj1202027, 1987.
Hill, D. F., Wolken, G. J., Wikstrom Jones, K., Crumley, R., and Arendt, A.:
Crowdsourcing snow depth data with citizen scientists, Eos, 99, https://doi.org/10.1029/2018EO108991, 2018.
Johnson, J. and Marks, D.: The detection and correction of snow water
equivalent pressure sensor errors, Hydrol. Process., 18, 3513–3525,
https://doi.org/10.1002/hyp.5795, 2004.
Johnson, J., Gelvin, A., Duvoy, P., Schaefer G., Poole, G., and Horton, G.:
Performance characteristics of a new electronic snow water equivalent sensor
in different climates, Hydrol. Process., 29, 1418–1433,
https://doi.org/10.1002/hyp.10211, 2015.
Jonas, T., Marty, C., and Magnusson, M.: Estimating the snow water
equivalent from snow depth measurements, J. Hydrol., 378, 161–167,
https://doi.org/10.1016/j.jhydrol.2009.09.021, 2009.
Leys, C., Klein, O., Dominicy, Y., and Ley, C.: Detecting multivariate
outliers: use a robust variant of the Mahalanobis distance, J. Exp. Soc.
Psychol., 74, 150–156, https://doi.org/10.1016/j.jesp.2017.09.011, 2018.
Liang, X., Lettermaier, D., Wood, E., and Burges, S.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994.
Liston, G. and Elder, K.: A distributed snow evolution modeling system
(SnowModel), J. Hydrometerol., 7, 1259–1276,
https://doi.org/10.1175/JHM548.1, 2006.
Lundberg, A., Richardson-Naslund, C., and Andersson, C.: Snow density
variations: consequences for ground penetrating radar, Hydrol. Process., 20,
1483–1495, https://doi.org/10.1002/hyp.5944, 2006.
McCreight, J. L. and Small, E. E.: Modeling bulk density and snow water equivalent using
daily snow depth observations,
The Cryosphere, 8, 521–536, https://doi.org/10.5194/tc-8-521-2014, 2014.
McKay, G. and Findlay, B.: Variation of snow resources with climate
and vegetation in Canada, Proceedings of the 39th Western Snow
Conference, Billings, MT, 17–26, 20–22 April 1971.
MCSS (Maine Cooperative Snow Survey): Maine Cooperative Snow Survey Dataset,
Maine Geological Survey, available at:
https://www.maine.gov/dacf/mgs/hazards/snow_survey/, last access: 15 October 2018.
Meloysund, V., Leira, B., Hoiseth, K., and Liso, K.: Predicting snow density
using meterological data, Meteorol. Appl., 14, 413–423,
https://doi.org/10.1002/met.40, 2007.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
overview of the Global Historical Climatology Network-Daily Database, J. Atmos.
Ocean. Tech., 29, 97–910, https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Mizukami, N. and Perica, S.: Spatiotemporal characteristics of snowpack
density in the mountainous regions of the western United States, J.
Hydrometeorol., 9, 1416–1426, https://doi.org/10.1175/2008JHM981.1, 2008.
Molotch, N. P. and Bales, R. C.: SNOTEL representativeness in the Rio Grande
headwaters on the basis of physiographics and remotely sensed snow cover
persistence, Hydrol. Process., 20, 723–739,
https://doi.org/10.1002/hyp.6128, 2006.
Mote, P., Li, S., Letternaier, D., Xiao, M., and Engel, R.: Dramatic
declines in snowpack in the western US, npj Climate and Atmospheric Science, 1,
1–6, https://doi.org/10.1038/s41612-018-0012-1, 2018.
Pagano, T., Garen, D., Perkins, T., and Pasteris, P.: Daily updating of
operational statistical seasonal water supply forecasts for the western
U.S., J. Am. Water Resour. As., 45, 767–778,
https://doi.org/10.1111/j.1752-1688.2009.00321.x, 2009.
Painter, T., Berisford, D., Boardman, J., Bormann, K., Deems, J., Gehrke,
F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann, C., Mcgurk,
B., Ramirez, P., Richardson, M., Skiles, S., Seidel, F., and Winstral, A.:
The Airborne Snow Observatory: fusion of scanning lidar, imaging
spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152,
https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Pistocchi, A.: Simple estimation of snow density in an Alpine region,
Journal of Hydrology: Regional Studies, 6, 82–89, https://doi.org/10.1016/j.ejrh.2016.03.004,
2016.
Rousseeuw, P.: Least Median of Squares Regression, J. Am. Stat. Assoc., 79,
871–880, https://doi.org/10.1080/01621459.1984.10477105, 1984.
Ryan, W., Doesken, N., and Fassnacht, S.: Evaluation of Ultrasonic Snow
Depth Sensors for U.S. Snow Measurements, J. Atmos. Ocean. Tech., 25,
667–684, https://doi.org/10.1175/2007JTECHA947.1, 2008.
Schaefer, G., Cosh, M., and Jackson, T.: The USDA Natural Resources
Conservation Service Soil Climate Analysis Network (SCAN), J. Atmos. Ocean.
Tech., 24, 2073–2077, https://doi.org/10.1175/2007JTECHA930.1, 2007.
Serreze, M., Clark, M., Armstrong, R., McGinnis, D., and Pulwarty, R.:
Characteristics of the western United States snowpack from snowpack
telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999.
Shanley, J. and Chalmers, A.: The effect of frozen soil on snowmelt runoff
at Sleepers River, Vermont, Hydrol. Process., 13, 1843–1857,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1843::AID-HYP879>3.0.CO;2-G, 1999.
Sturm, M., Holmgren, J., and Liston, G.: A seasonal snow cover
classification system for local to global applications, J. Climate, 8,
1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.:
Estimating snow water equivalent using snow depth data and climate classes,
J. Hydrometeorol., 11, 1380–1394, https://doi.org/10.1175/2010JHM1202.1,
2010.
USACE (U.S. Army Corps of Engineers): Snow hydrology: Summary report of the snow
investigations of the North Pacific Division, North Pacific Division, Corps of Engineers, US Army, Portland, OR, USA, 437 pp., 1956.
USDA (U.S. Department of Agriculture): The History of Snow Survey and Water Supply
Forecasting, Interviews With U.S. Department of Agriculture Pioneers, edited By:
Helms, D., Phillips, S., and Reich, P., Natural Resources Conservation
Service, U.S. Department of Agriculture, Natural Resources Conservation Service, Washington D.C., USA, 2008.
USDA (U.S. Department of Agriculture): Snow Survey and Water Supply Forecasting.
National Engineering Handbook Part 622, Water and Climate Center, Natural
Resources Conservation Service, 2011.
Wang, T., Hamann, A., Spittlehouse, D. L., and Murdock, T.: ClimateWNA -
High-Resolution Spatial Climate Data for Western North America, J. Appl.
Meteorol. Clim., 51, 16–29, https://doi.org/10.1175/JAMC-D-11-043.1,
2012.
Wang, T., Hamann, A., Spittlehouse, D. L., and Carroll, C: Locally downscaled
and spatially customizable climate data for historical and future periods
for North America, PLoS One, 11, e0156720, https://doi.org/10.1371/journal.pone.0156720, 2016.
Wigmosta, M. S., Vail, L., and Lettenmaier, D.: A distributed
hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1679, https://doi.org/10.1029/94WR00436, 1994.
Short summary
We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
We present a new statistical model for converting snow depths to water equivalent. The only...
