Articles | Volume 16, issue 12
https://doi.org/10.5194/tc-16-4907-2022
© Author(s) 2022. 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-16-4907-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Dec 2022
Research article |
| 08 Dec 2022
Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain
U.S. Geological Survey Northern Rocky Mountain Science Center, West
Glacier, MT 59936, USA
Erich H. Peitzsch
U.S. Geological Survey Northern Rocky Mountain Science Center, West
Glacier, MT 59936, USA
Eric A. Sproles
Geospatial Snow, Water, and Ice Resources Lab, Department of Earth
Sciences, Montana State University, Bozeman, MT 59717, USA
Karl W. Birkeland
USDA Forest Service National Avalanche Center, Bozeman, MT
59771, USA
Ross T. Palomaki
Geospatial Snow, Water, and Ice Resources Lab, Department of Earth
Sciences, Montana State University, Bozeman, MT 59717, USA
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Andrew R. Schauer, Jordy Hendrikx, Karl W. Birkeland, and Cary J. Mock
Nat. Hazards Earth Syst. Sci., 21, 757–774, https://doi.org/10.5194/nhess-21-757-2021, https://doi.org/10.5194/nhess-21-757-2021, 2021
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Our research links upper atmospheric circulation patterns to a destructive and difficult-to-predict type of snow avalanche in the western United States. At each of our study sites, we find unique circulation patterns that tend to occur at the beginning of the winter season during years with major avalanche activity. We also find specific patterns that occur frequently in the days leading to major avalanche events. This work will enable practitioners to better anticipate these challenging events.
Erich Peitzsch, Jordy Hendrikx, Daniel Stahle, Gregory Pederson, Karl Birkeland, and Daniel Fagre
Nat. Hazards Earth Syst. Sci., 21, 533–557, https://doi.org/10.5194/nhess-21-533-2021, https://doi.org/10.5194/nhess-21-533-2021, 2021
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We sampled 647 trees from 12 avalanche paths to investigate large snow avalanches over the past 400 years in the northern Rocky Mountains, USA. Sizable avalanches occur approximately every 3 years across the region. Our results emphasize the importance of sample size, scale, and spatial extent when reconstructing avalanche occurrence across a region. This work can be used for infrastructure planning and avalanche forecasting operations.
Caitlyn Florentine, Joel Harper, Daniel Fagre, Johnnie Moore, and Erich Peitzsch
The Cryosphere, 12, 2109–2122, https://doi.org/10.5194/tc-12-2109-2018, https://doi.org/10.5194/tc-12-2109-2018, 2018
Eric A. Sproles, Travis R. Roth, and Anne W. Nolin
The Cryosphere, 11, 331–341, https://doi.org/10.5194/tc-11-331-2017, https://doi.org/10.5194/tc-11-331-2017, 2017
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We present an innovative approach to quantify basin-wide snowpack using calculations of spatial exceedance probability. Our method quantifies how the extraordinarily low snowpacks of 2014 and 2015 in the Pacific Northwest of the United States compare to snowpacks in warmer conditions and the probability that similar snowpacks will occur. We present these extraordinarily low snowpacks as snow analogs to develop anticipatory capacity for natural resource management under warmer conditions.
Adam M. Clark, Daniel B. Fagre, Erich H. Peitzsch, Blase A. Reardon, and Joel T. Harper
Earth Syst. Sci. Data, 9, 47–61, https://doi.org/10.5194/essd-9-47-2017, https://doi.org/10.5194/essd-9-47-2017, 2017
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Most of the alpine glaciers in the world are shrinking. Because of the impact glaciers have on watershed hydrology, the US Geological Survey began a surface mass balance measurement program on Sperry Glacier in Glacier National Park, Montana, USA, in 2005. Between then and 2015 the USGS employed standard methods to estimate the mass changes across the surface of the glacier. During this 11-year period, Sperry Glacier had a cumulative mean mass balance loss of 4.37 m of water equivalent.
E. A. Sproles, S. G. Leibowitz, J. T. Reager, P. J. Wigington Jr, J. S. Famiglietti, and S. D. Patil
Hydrol. Earth Syst. Sci., 19, 3253–3272, https://doi.org/10.5194/hess-19-3253-2015, https://doi.org/10.5194/hess-19-3253-2015, 2015
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The paper demonstrates how data from the Gravity Recovery and Climate Experiment (GRACE) can be used to describe the relationship between water stored at the regional scale and stream flow. Additionally, we employ GRACE as a regional-scale indicator to successfully predict stream flow later in the water year. Our work focuses on the Columbia River Basin (North America), but is widely applicable across the globe, and could prove to be particularly useful in regions with limited hydrological data.
S. G. Leibowitz, R. L. Comeleo, P. J. Wigington Jr., C. P. Weaver, P. E. Morefield, E. A. Sproles, and J. L. Ebersole
Hydrol. Earth Syst. Sci., 18, 3367–3392, https://doi.org/10.5194/hess-18-3367-2014, https://doi.org/10.5194/hess-18-3367-2014, 2014
E. A. Sproles, A. W. Nolin, K. Rittger, and T. H. Painter
Hydrol. Earth Syst. Sci., 17, 2581–2597, https://doi.org/10.5194/hess-17-2581-2013, https://doi.org/10.5194/hess-17-2581-2013, 2013
Related subject area
Discipline: Snow | Subject: Seasonal Snow
Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets
Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas
Change in the potential snowfall phenology: past, present, and future in the Chinese Tianshan mountainous region, Central Asia
The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland
Impact of measured and simulated tundra snowpack properties on heat transfer
Homogeneity assessment of Swiss snow depth series: comparison of break detection capabilities of (semi-)automatic homogenization methods
Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network
Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982–2014
Multilayer observation and estimation of the snowpack cold content in a humid boreal coniferous forest of eastern Canada
Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset
Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements
Observed snow depth trends in the European Alps: 1971 to 2019
Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)
Snow depth estimation and historical data reconstruction over China based on a random forest machine learning approach
Evaluation of long-term Northern Hemisphere snow water equivalent products
Towards a webcam-based snow cover monitoring network: methodology and evaluation
Simulated single-layer forest canopies delay Northern Hemisphere snowmelt
Converting snow depth to snow water equivalent using climatological variables
Avalanches and micrometeorology driving mass and energy balance of the lowest perennial ice field of the Alps: a case study
The optical characteristics and sources of chromophoric dissolved organic matter (CDOM) in seasonal snow of northwestern China
Brief Communication: Early season snowpack loss and implications for oversnow vehicle recreation travel planning
Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps
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.
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.
David F. Hill, Elizabeth A. Burakowski, Ryan L. Crumley, Julia Keon, J. Michelle Hu, Anthony A. Arendt, Katreen Wikstrom Jones, and Gabriel J. Wolken
The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, https://doi.org/10.5194/tc-13-1767-2019, 2019
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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.
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
Adams, M., Bühler, Y., and Fromm, R.: Multitemporal Accuracy and
Precision Assessment of Unmanned Aerial System Photogrammetry for
Slope-Scale Snow Depth Maps in Alpine Terrain, Pure Appl. Geophys., 175,
3303–3324, https://doi.org/10.1007/s00024-017-1748-y, 2018.
Agisoft: Agisoft Metashape Professional (Version 1.6.6), Agisoft, 2020.
Alidoost, F. and Arefi, H.: Comparison of UAS-based photogrammetry software
for 3D point cloud generation: a survey over a historical site, ISPRS Ann.
Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 55–61,
https://doi.org/10.5194/isprs-annals-IV-4-W4-55-2017, 2017.
Anderton, S. P., White, S. M., and Alvera, B.: Evaluation of spatial
variability in snow water equivalent for a high mountain catchment, Hydrol.
Process., 18, 435–453, https://doi.org/10.1002/hyp.1319, 2004.
Avanzi, F., Bianchi, A., Cina, A., De Michele, C., Maschio, P., Pagliari,
D., Passoni, D., Pinto, L., Piras, M., and Rossi, L.: Centimetric Accuracy
in Snow Depth Using Unmanned Aerial System Photogrammetry and a
MultiStation, Remote Sens.-Basel, 10, 765,
https://doi.org/10.3390/rs10050765, 2018.
Birkeland, K. W.: Spatial patterns of snow stability throughout a small
mountain range, J. Glaciol., 47, 176–186,
https://doi.org/10.3189/172756501781832250, 2001.
Birkeland, K. W., Hansen, K. J., and Brown, R. L.: The spatial variability
of snow resistance on potential avalanche slopes, J. Glaciol., 41, 183–190,
https://doi.org/10.3189/S0022143000017871, 1995.
Blöschl, G.: Scaling issues in snow hydrology, Hydrol. Process., 13,
2149–2175, https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8, 1999.
Boesch, R., Bühler, Y., Marty, M., and Ginzler, C.: Comparison of
digital surface models for snow depth mapping with UAV and aerial cameras,
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 453–458,
https://doi.org/10.5194/isprsarchives-XLI-B8-453-2016, 2016.
Brandt, W. T., Bormann, K. J., Cannon, F., Deems, J. S., Painter, T. H.,
Steinhoff, D. F., and Dozier, J.: Quantifying the Spatial Variability of a
Snowstorm Using Differential Airborne Lidar, Water Resour. Res., 56, WR025331,
https://doi.org/10.1029/2019WR025331, 2020.
Buchmann, M., Begert, M., Brönnimann, S., and Marty, C.: Local-scale variability of seasonal mean and extreme values of in situ snow depth and snowfall measurements, The Cryosphere, 15, 4625–4636, https://doi.org/10.5194/tc-15-4625-2021, 2021.
Bühler, Y., Marty, M., Egli, L., Veitinger, J., Jonas, T., Thee, P., and Ginzler, C.: Snow depth mapping in high-alpine catchments using digital photogrammetry, The Cryosphere, 9, 229–243, https://doi.org/10.5194/tc-9-229-2015, 2015.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016.
Bühler, Y., Adams, M. S., Stoffel, A., and Bösch, R.:
Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain
applying near-infrared UAS imagery, Int. J. Remote Sens., 38, 3135–3158,
https://doi.org/10.1080/01431161.2016.1275060, 2017.
Carbonneau, P. E. and Dietrich, J. T.: Cost-effective non-metric
photogrammetry from consumer-grade sUAS: implications for direct
georeferencing of structure from motion photogrammetry, Earth Surf. Proc.
Land., 42, 473–486, https://doi.org/10.1002/esp.4012, 2017.
Cimoli, E., Marcer, M., Vandecrux, B., Bøggild, C. E., Williams, G., and
Simonsen, S. B.: Application of Low-Cost UASs and Digital Photogrammetry for
High-Resolution Snow Depth Mapping in the Arctic, Remote Sens.-Basel, 9, 1144,
https://doi.org/10.3390/rs9111144, 2017.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B.,
Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.:
Representing spatial variability of snow water equivalent in hydrologic and
land-surface models: A review, Water Resour. Res., 47, W07539,
https://doi.org/10.1029/2011WR010745, 2011.
Clemenzi, I., Pellicciotti, F., and Burlando, P.: Snow Depth Structure,
Fractal Behavior, and Interannual Consistency Over Haut Glacier d'Arolla,
Switzerland, Water Resour. Res., 54, 7929–7945,
https://doi.org/10.1029/2017WR021606, 2018.
Deems, J. S.: Topographic effects on the spatial and temporal patterns of
snow temperature gradients in a mountain snowpack, Montana State University,
Bozeman, MT, 97 pp., 2002.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal Distribution of
Snow Depth from Lidar Data, J. Hydrometeorol., 7, 285–297, https://doi.org/10.1175/JHM487.1, 2006.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479,
https://doi.org/10.3189/2013JoG12J154, 2013.
De Michele, C., Avanzi, F., Passoni, D., Barzaghi, R., Pinto, L., Dosso, P., Ghezzi, A., Gianatti, R., and Della Vedova, G.: Using a fixed-wing UAS to map snow depth distribution: an evaluation at peak accumulation, The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, 2016.
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.
Dozier, J.: Mountain hydrology, snow color, and the fourth paradigm, Eos
Trans. AGU, 92, 373–374, https://doi.org/10.1029/2011EO430001, 2011.
Dozier, J., Bair, E. H., and Davis, R. E.: Estimating the spatial
distribution of snow water equivalent in the world's mountains, WIREs Water,
3, 461–474, https://doi.org/10.1002/wat2.1140, 2016.
Eberhard, L. A., Sirguey, P., Miller, A., Marty, M., Schindler, K., Stoffel, A., and Bühler, Y.: Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping, The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, 2021.
Egli, L., Griessinger, N., and Jonas, T.: Seasonal development of spatial
snow-depth variability across different scales in the Swiss Alps, Ann.
Glaciol., 52, 216–222, https://doi.org/10.3189/172756411797252211, 2011.
Eker, R., Bühler, Y., Schlögl, S., Stoffel, A., and Aydın, A.:
Monitoring of Snow Cover Ablation Using Very High Spatial Resolution Remote
Sensing Datasets, Remote Sens.-Basel, 11, 699,
https://doi.org/10.3390/rs11060699, 2019.
Elder, K., Dozier, J., and Michaelsen, J.: Snow accumulation and
distribution in an Alpine Watershed, Water Resour. Res., 27, 1541–1552,
https://doi.org/10.1029/91WR00506, 1991.
Elder, K., Rosenthal, W., and Davis, R. E.: Estimating the spatial
distribution of snow water equivalence in a montane watershed, Hydrol.
Process., 12, 1793–1808, https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1793::AID-HYP695>3.0.CO;2-K, 1998.
Falgout, J. T. and Gordon, J.: USGS Advanced Research Computing, USGS Yeti
Supercomputer, U.S. Geological Survey,
https://www.usgs.gov/advanced-research-computing/usgs-yeti-supercomputer (last access: 30 October 2022),
2022.
Fassnacht, S. R. and Deems, J. S.: Measurement sampling and scaling for deep
montane snow depth data, Hydrol. Process., 20, 829–838,
https://doi.org/10.1002/hyp.6119, 2006.
Fey, C., Schattan, P., Helfricht, K., and Schöber, J.: A compilation of
multitemporal TLS snow depth distribution maps at the Weisssee snow research
site (Kaunertal, Austria), Water Resour. Res., 5, 5154–5164,
https://doi.org/10.1029/2019WR024788, 2019.
Fierz, C., Armstrong, R., Durand, Y., Etchevers, P., Greene, E., McClung,
D., Nishimura, K., Satyawali, P., and Sokratov, S.: The International
Classification for Seasonal Snow on the Ground, Tech. rep., IHP-VII,
Technical Documents in Hydrology No. 83, IACS Contribution No. 1, UNESCO –
IHP, Paris, 2009.
Gabrlik, P., la Cour-Harbo, A., Kalvodova, P., Zalud, L., and Janata, P.:
Calibration and accuracy assessment in a direct georeferencing system for
UAS photogrammetry, Int. J. Remote Sens., 39, 4931–4959,
https://doi.org/10.1080/01431161.2018.1434331, 2018.
Gabrlik, P., Janata, P., Zalud, L., and Harcarik, J.: Towards Automatic
UAS-Based Snow-Field Monitoring for Microclimate Research, Sensors, 19,
1945, https://doi.org/10.3390/s19081945, 2019.
Gaffey, C. and Bhardwaj, A.: Applications of Unmanned Aerial Vehicles in
Cryosphere: Latest Advances and Prospects, Remote Sens.-Basel, 12, 948,
https://doi.org/10.3390/rs12060948, 2020.
Goetz, J. and Brenning, A.: Quantifying Uncertainties in Snow Depth Mapping
From Structure From Motion Photogrammetry in an Alpine Area, Water Resour.
Res., 55, 7772–7783, https://doi.org/10.1029/2019WR025251, 2019.
Greene, E., Birkeland, K. W., Kelly, E., McCammon, I., Staples, M., and
Sharaf, D.: Snow, Weather, and Avalanches: Observation Guidelines for
Avalanche Programs in the United States, 3rd edn., edited by: Krause, D.,
American Avalanche Association, Victor, ID, 152 pp., 2016.
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010.
Harder, P., Schirmer, M., Pomeroy, J., and Helgason, W.: Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle, The Cryosphere, 10, 2559–2571, https://doi.org/10.5194/tc-10-2559-2016, 2016.
Höhle, J. and Höhle, M.: Accuracy assessment of digital elevation
models by means of robust statistical methods, ISPRS J. Photogramm., 64,
398–406, https://doi.org/10.1016/j.isprsjprs.2009.02.003, 2009.
Hu, F., Gao, X. M., Li, G. Y., and Li, M.: DEM extraction from Worldview-3
stereo-images and accuracy evaluation, Int. Arch. Photogramm. Remote Sens.
Spatial Inf. Sci., XLI-B1, 327–332,
https://doi.org/10.5194/isprsarchives-XLI-B1-327-2016, 2016.
Kinar, N. J. and Pomeroy, J. W.: Measurement of the physical properties of
the snowpack, Rev. Geophys., 53, 481–544,
https://doi.org/10.1002/2015RG000481, 2015.
Kronholm, K.: Spatial variability of snow mechanical properties with regard
to avalanche formation, PhD Dissertation, Faculty of Mathematics and
Science, Department of Geography, University of Zurich, Zurich, 187 pp.,
2004.
Kronholm, K. and Birkeland, K. W.: Reliability of sampling designs for
spatial snow surveys, Comput. Geosci., 33, 1097–1110,
https://doi.org/10.1016/j.cageo.2006.10.004, 2007.
Kronholm, K., Schneebeli, M., and Schweizer, J.: Spatial variability of
micropenetration resistance in snow layers on a small slope, Ann. Glaciol.,
38, 202–208, https://doi.org/10.3189/172756404781815257, 2004.
Lacroix, P.: Landslides triggered by the Gorkha earthquake in the Langtang
valley, volumes and initiation processes, Earth Planets Space, 68, 46,
https://doi.org/10.1186/s40623-016-0423-3, 2016.
Landry, C., Birkeland, K., Hansen, K., Borkowski, J., Brown, R., and
Aspinall, R.: Variations in snow strength and stability on uniform slopes,
Cold Reg. Sci. Technol., 39, 205–218,
https://doi.org/10.1016/j.coldregions.2003.12.003, 2004.
Liston, G. E., Haehnel, R. B., Sturm, M., Hiemstra, C. A., Berezovskaya, S.,
and Tabler, R. D.: Simulating complex snow distributions in windy
environments using SnowTran-3D, J. Glaciol., 53, 241–256,
https://doi.org/10.3189/172756507782202865, 2007.
López-Moreno, J. I., Fassnacht, S. R., Beguería, S., and Latron, J. B. P.: Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies, The Cryosphere, 5, 617–629, https://doi.org/10.5194/tc-5-617-2011, 2011.
López-Moreno, J. I., Revuelto, J., Fassnacht, S. R., Azorín-Molina,
C., Vicente-Serrano, S. M., Morán-Tejeda, E., and Sexstone, G. A.:
Snowpack variability across various spatio-temporal resolutions, Hydrol.
Process., 29, 1213–1224, https://doi.org/10.1002/hyp.10245, 2015.
Lundy, C. C., Brown, R. L., Adams, E. E., Birkeland, K. W., and Lehning, M.:
A statistical validation of the snowpack model in a Montana climate, Cold
Reg. Sci. Technol., 33, 237–246,
https://doi.org/10.1016/S0165-232X(01)00038-6, 2001.
Lutz, E. R. and Birkeland, K. W.: Spatial patterns of surface hoar
properties and incoming radiation on an inclined forest opening, J.
Glaciol., 57, 355–366, https://doi.org/10.3189/002214311796405843, 2011.
Marti, R., Gascoin, S., Berthier, E., de Pinel, M., Houet, T., and Laffly, D.: Mapping snow depth in open alpine terrain from stereo satellite imagery, The Cryosphere, 10, 1361–1380, https://doi.org/10.5194/tc-10-1361-2016, 2016.
McCormack, E. and Vaa, T.: Testing Unmanned Aircraft for Roadside Snow
Avalanche Monitoring, Transp. Res. Record, 2673, 94–103,
https://doi.org/10.1177/0361198119827935, 2019.
Melvold, K. and Skaugen, T.: Multiscale spatial variability of lidar-derived
and modeled snow depth on Hardangervidda, Norway, Ann. Glaciol., 54,
273–281, https://doi.org/10.3189/2013AoG62A161, 2013.
Mendoza, P. A., Musselman, K. N., Revuelto, J., Deems, J. S.,
López-Moreno, J. I., and McPhee, J.: Interannual and Seasonal
Variability of Snow Depth Scaling Behavior in a Subalpine Catchment, Water
Resour. Res., 56, WR027343, https://doi.org/10.1029/2020WR027343, 2020.
Meyer, J. and Skiles, S. M.: Assessing the Ability of Structure From Motion
to Map High-Resolution Snow Surface Elevations in Complex Terrain: A Case
Study From Senator Beck Basin, CO, Water Resour. Res., 55, 6596–6605,
https://doi.org/10.1029/2018WR024518, 2019.
Miller, Z. S., Peitzsch, E. H., Sproles, E. A., Palomaki, R. T., and Birkeland, K. W.: 2020 winter timeseries of UAS derived digital surface models (DSMs) from the Hourglass study site, Bridger Mountains, Montana, USA, U.S. Geological Survey data release [data set], https://doi.org/10.5066/P9YCIA1R, 2022.
Mock, C. J. and Birkeland, K. W.: Snow Avalanche Climatology of the Western
United States Mountain Ranges, B. Am. Meteorol. Soc., 81, 2367–2392,
https://doi.org/10.1175/1520-0477(2000)081<2367:SACOTW>2.3.CO;2, 2000.
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.
Mott, R. and Lehning, M.: Meteorological Modeling of Very High-Resolution
Wind Fields and Snow Deposition for Mountains, Front. Earth Sci., 11,
934–949, https://doi.org/10.1175/2010JHM1216.1, 2010.
Mott, R., Schirmer, M., and Lehning, M.: Scaling properties of wind and snow
depth distribution in an Alpine catchment, J. Geophys. Res., 116, D06106,
https://doi.org/10.1029/2010JD014886, 2011.
Mott, R., Vionnet, V., and Grünewald, T.: The Seasonal Snow Cover
Dynamics: Review on Wind-Driven Coupling Processes, Front. Earth Sci., 6,
197, https://doi.org/10.3389/feart.2018.00197, 2018.
Niedzielski, T., Szymanowski, M., Miziński, B., Spallek, W.,
Witek-Kasprzak, M., Ślopek, J., Kasprzak, M., Błaś, M., Sobik, M.,
Jancewicz, K., Borowicz, D., Remisz, J., Modzel, P., Męcina, K., and
Leszczyński, L.: Estimating snow water equivalent using unmanned aerial
vehicles for determining snow-melt runoff, J. Hydrol., 578,
124046, https://doi.org/10.1016/j.jhydrol.2019.124046, 2019.
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015, 2015.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J.
S., Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., Mattmann,
C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C.,
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.
Pebesma, E. J.: Multivariable geostatistics in S: the gstat package,
Comput. Geosci., 30, 683–691,
https://doi.org/10.1016/j.cageo.2004.03.012, 2004.
Peitzsch, E., Fagre, D., Hendrikx, J., and Birkeland, K.: Detecting snow
depth changes in avalanche path starting zones using uninhabited aerial
systems and structure from motion photogrammetry, Proceedings of the
International Snow Science Workshop 2018, Innsbruck,
https://pubs.er.usgs.gov/publication/70200492 (last access: 29 September 2019), 2018.
Prokop, A., Schön, P., Singer, F., Pulfer, G., Naaim, M., Thibert, E.,
and Soruco, A.: Merging terrestrial laser scanning technology with
photogrammetric and total station data for the determination of avalanche
modeling parameters, Cold Reg. Sci. Technol., 110, 223–230,
https://doi.org/10.1016/j.coldregions.2014.11.009, 2015
QGIS.org: QGIS Geographic Information System, QGIS Association,
https://QGIS.org (last access: 9 November 2022), 2021.
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna, Austria, 2021.
Redpath, T. A. N., Sirguey, P., and Cullen, N. J.: Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry, The Cryosphere, 12, 3477–3497, https://doi.org/10.5194/tc-12-3477-2018, 2018.
Revuelto, J., Alonso-Gonzalez, E., Vidaller-Gayan, I., Lacroix, E.,
Izagirre, E., Rodríguez-López, G., and López-Moreno, J. I.:
Intercomparison of UAV platforms for mapping snow depth distribution in
complex alpine terrain, Cold Reg. Sci. Technol., 103344,
https://doi.org/10.1016/j.coldregions.2021.103344, 2021.
Roy, D. P. and Dikshit, O.: Investigation of Image Resampling Effects upon
The Textural Information Content of a High Spatial Resolution Remotely
Sensed Image, Int. J. Remote S., 15, 1123–1130,
https://doi.org/10.1080/01431169408954146, 1994.
Schirmer, M. and Lehning, M.: Persistence in intra-annual snow depth
distribution: 2. Fractal analysis of snow depth development, Water Resour.
Res., 47, W09517, https://doi.org/10.1029/2010WR009429, 2011.
Schön, P., Prokop, A., Vionnet, V., Guyomarc'h, G., Naaim-Bouvet, F.,
and Heiser, M.: Improving a terrain-based parameter for the assessment of
snow depths with TLS data in the Col du Lac Blanc area, Cold Reg. Sci. Technol., 114, 15–26,
https://doi.org/10.1016/j.coldregions.2015.02.005, 2015.
Schweizer, J., Kronholm, K., Jamieson, J. B., and Birkeland, K. W.: Review
of spatial variability of snowpack properties and its importance for
avalanche formation, Cold Reg. Sci. Technol., 51, 253–272,
https://doi.org/10.1016/j.coldregions.2007.04.009, 2008.
Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R.,
Porter, C., and Morin, P.: An automated, open-source pipeline for mass
production of digital elevation models (DEMs) from very-high-resolution
commercial stereo satellite imagery, ISPRS J. Photogram.
Remote S., 116, 101–117,
https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016.
Takasu: RTKLIB: Open Source Program Package for RTK-GPS, FOSS4G, Tokyo,
2009.
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Topographic,
meteorologic, and canopy controls on the scaling characteristics of the
spatial distribution of snow depth fields, Water Resour. Res., 43, W07409,
https://doi.org/10.1029/2006WR005317, 2007.
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Scaling properties and
spatial organization of snow depth fields in sub-alpine forest and alpine
tundra, Hydrol. Process., 23, 1575–1590, https://doi.org/10.1002/hyp.7270,
2009.
U.S. Geological Survey: USGS 13 arc-second n46w111 1×1 degree, U.S.
Geological Survey, https://nationalmap.gov/elevation.html (last access: 24 October 2020), 2017.
Vander Jagt, B., Lucieer, A., Wallace, L., Turner, D., and Durand, M.: Snow
Depth Retrieval with UAS Using Photogrammetric Techniques, Geosciences J.,
5, 264–285, https://doi.org/10.3390/geosciences5030264, 2015.
Van Peursem,
K., Hendrikx, J., Birkeland, K., Miller, D., and Gibson, C.: Validation of a
coupled weather and SNOWPACK model across western Montana, Proceedings of
the International Snow Science Workshop 2016, Breckenridge,
https://arc.lib.montana.edu/snow-science/item.php?id=2444 (last access: 28 August 2020), 2016.
Vosselman, G.: Slope based filtering of laser altimetry data, IAPRS, XXXIII,
9, 2000.
Webster, R. and Oliver, M. A.: Modelling the Variogram, in: Geostatistics
for Environmental Scientists, John Wiley & Sons, Ltd, 77–107,
https://doi.org/10.1002/9780470517277.ch5, 2007.
Wirz, V., Schirmer, M., Gruber, S., and Lehning, M.: Spatio-temporal measurements and analysis of snow depth in a rock face, The Cryosphere, 5, 893–905, https://doi.org/10.5194/tc-5-893-2011, 2011.
Short summary
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.
Snow depth varies across steep, complex mountain landscapes due to interactions between dynamic...
