Special Issue "Snow Hydrology"

A special issue of Hydrology (ISSN 2306-5338).

Deadline for manuscript submissions: closed (31 May 2016)

Special Issue Editors

Guest Editor
Dr. Juraj Parajka

Institute of Hydraulic Engineering and Water Resources, Vienna University of Technology, A-1040 Vienna, Austria
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Interests: hydrological modelling; snow hydrology and climate change
Guest Editor
Dr. Cécile Ménard

Arctic Research Centre, Finnish Meteorological Institute Erik Palménin aukio 1 FI-00560 Helsinki, Finnland
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Phone: +358 505 709 620
Guest Editor
Dr. Ladislav Holko

Institute of Hydrology, Slovak Academy of Sciences Liptovsky Mikulas, Slovakia
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Special Issue Information

Dear Colleagues,

Water stored in the snow pack represents an important component of water balance in many regions throughout the world. Snow cover variability is affected by and translates into changes of atmosphere-land surface interactions, both at spatial and temporal scales. The monitoring and modeling of snow accumulation and melt is thus an important but very challenging task, particularly in regions with limited availability and large spatial variability of hydrological and weather data. The objective of this Special Issue is to present and integrate studies focusing on snow within the context of catchment hydrology, snow as a land surface, snow-vegetation interaction, and snow as a source for glacial ice. The aim is to integrate and share knowledge and experience in the fields of experimental research, remote sensing, and hydrological modeling.

Specifically, contributions addressing the following topics are welcome:
1) Experimental research on snow properties and processes, which need to be implemented in hydrologic catchment, glacier, and land-surface models;
2) Experimental research and innovative modeling approaches addressing the effects of snow-vegetation interactions;
3) Assessment and evaluation of different remote sensing technologies and classification approaches focusing, e.g., on snow cover, albedo, snow depth, and snow water equivalent mapping;
4) Practical implementation of snow data assimilation in operational hydrological and weather prediction models.

Dr. Juraj Parajka
Dr. Cécile Ménard
Dr. Ladislav Holko
Guest Editors

Manuscript Submission Information

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Keywords

  • snow cover
  • snow water equivalent
  • snow accumulation and melt
  • modeling
  • remote sensing
  • snow vegetation interaction
  • catchment hydrology

Published Papers (6 papers)

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Research

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Open AccessArticle Comparison between Snow Albedo Obtained from Landsat TM, ETM+ Imagery and the SPOT VEGETATION Albedo Product in a Mediterranean Mountainous Site
Received: 1 November 2015 / Revised: 15 February 2016 / Accepted: 16 February 2016 / Published: 23 February 2016
Cited by 3 | PDF Full-text (3476 KB) | HTML Full-text | XML Full-text
Abstract
Albedo plays an important role in snow evolution modeling quantifying the amount of solar radiation absorbed and reflected by the snowpack, especially in mid-latitude regions with semiarid conditions. Satellite remote sensing is the most extensive technique to determine the variability of snow albedo
[...] Read more.
Albedo plays an important role in snow evolution modeling quantifying the amount of solar radiation absorbed and reflected by the snowpack, especially in mid-latitude regions with semiarid conditions. Satellite remote sensing is the most extensive technique to determine the variability of snow albedo over medium to large areas; however, scale effects from the pixel size of the sensor source may affect the results of snow models, with different impacts depending on the spatial resolution. This work presents the evaluation of snow albedo values retrieved from (1) Landsat images, L (16-day frequency with 30 × 30 m pixel size) and (2) SPOT VEGETATION albedo products, SV (10-day frequency with 1 × 1 km pixel size) in the Sierra Nevada mountain range in South Spain, a Mediterranean site representative of highly heterogeneous conditions. Daily snow albedo map series were derived from both sources, and used as input for the snow module in the WiMMed (Watershed Integrated Management in Mediterranean Environment) hydrological model, which was operational at the study area for snow monitoring for two hydrological years, 2011–2012 and 2012–2013, in the Guadalfeo river basin in Sierra Nevada. The results showed similar albedo trends in both data sources, but with different values, the shift between both sources being distributed in space according to the altitude. This difference resulted in lower snow cover fraction values in the SV-simulations that affected the rest of snow variables included in the simulation. This underestimation, mainly due to the effects of mixed pixels composed by both snow and snow-free areas, produced higher divergences from both sources during the melting periods when the evapo-sublimation and melting fluxes are more relevant. Therefore, the selection of the albedo data source in these areas, where snow evapo-sublimation plays a very important role and the presence of snow-free patches is very frequent, can condition the final accuracy of the simulations of operational models; Landsat is the recommended source if the monitoring of the snowpack is the final goal of the modeling, whereas the SV product may be advantageous when water resource planning in the medium and long term is intended. Applications of large pixel size albedo sources need further assessment for short-term operational objectives. Full article
(This article belongs to the Special Issue Snow Hydrology)
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Open AccessArticle Spatial Heterogeneity of Snow Density and Its Influence on Snow Water Equivalence Estimates in a Large Mountainous Basin
Received: 28 October 2015 / Revised: 14 December 2015 / Accepted: 31 December 2015 / Published: 12 January 2016
Cited by 4 | PDF Full-text (8810 KB) | HTML Full-text | XML Full-text
Abstract
Accurate representation of the spatial distribution of snow water equivalent (SWE) in mountainous basins is critical for furthering the understanding of snow as a water resource, especially in the Western United States. To estimate the spatial distribution and total volume of SWE over
[...] Read more.
Accurate representation of the spatial distribution of snow water equivalent (SWE) in mountainous basins is critical for furthering the understanding of snow as a water resource, especially in the Western United States. To estimate the spatial distribution and total volume of SWE over mountainous basins, previous work has either assumed uniform snow density or used simple approaches to estimate density. This study uses over 1000 direct measurements of SWE and snow depth (from which density was calculated) in sampling areas that were physiographically proportional to a large (207 km2) mountainous basin in southwest Montana. Using these data, modeled spatial distributions of density and depth were developed and combined to obtain estimates of total basin SWE. Six estimates of SWE were obtained using varying combinations of the distributed depth and density models and were compared to the average of three different models that utilized direct measurements of SWE. Models utilizing direct SWE measurements varied by approximately 1% around their mean, while SWE estimates derived from combined depth and density models varied by over 14% around the same mean. This study highlights the need to carefully consider the spatial variability of density when estimating SWE based on snow depth in these environments. Full article
(This article belongs to the Special Issue Snow Hydrology)
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Open AccessArticle Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada)
Hydrology 2015, 2(4), 289-317; https://doi.org/10.3390/hydrology2040289
Received: 5 October 2015 / Revised: 12 November 2015 / Accepted: 17 November 2015 / Published: 27 November 2015
Cited by 6 | PDF Full-text (3608 KB) | HTML Full-text | XML Full-text
Abstract
An analysis of hydrological response to a multi-model approach based on an ensemble of seven snow models (SM; degree-day and mixed degree-day/energy balance models) coupled with three hydrological models (HM) is presented for a snowmelt-dominated basin in Canada. The present study aims to
[...] Read more.
An analysis of hydrological response to a multi-model approach based on an ensemble of seven snow models (SM; degree-day and mixed degree-day/energy balance models) coupled with three hydrological models (HM) is presented for a snowmelt-dominated basin in Canada. The present study aims to compare the performance and the reliability of different types of SM-HM combinations at simulating snowmelt flows over the 1961–2000 historical period. The multi-model approach also allows evaluating the uncertainties associated with the structure of the SM-HM ensemble to better predict river flows in Nordic environments. The 20-year calibration shows a satisfactory performance of the ensemble of 21 SM-HM combinations at simulating daily discharges and snow water equivalents (SWEs), with low streamflow volume biases. The validation of the ensemble of 21 SM-HM combinations is conducted over a 20-year period. Performances are similar to the calibration in simulating the daily discharges and SWEs, again with low model biases for streamflow. The spring-snowmelt-generated peak flow is captured only in timing by the ensemble of 21 SM-HM combinations. The results of specific hydrologic indicators show that the uncertainty related to the choice of the given HM in the SM-HM combinations cannot be neglected in a more quantitative manner in simulating snowmelt flows. The selection of the SM plays a larger role than the choice of the SM approach (degree-day versus mixed degree-day/energy balance) in simulating spring flows. Overall, the snow models provide a low degree of uncertainty to the total uncertainty in hydrological modeling for snow hydrology studies. Full article
(This article belongs to the Special Issue Snow Hydrology)
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Open AccessArticle Skill Assessment of Water Supply Outlooks in the Colorado River Basin
Hydrology 2015, 2(3), 112-131; https://doi.org/10.3390/hydrology2030112
Received: 7 June 2015 / Revised: 18 July 2015 / Accepted: 28 July 2015 / Published: 31 July 2015
Cited by 3 | PDF Full-text (2088 KB) | HTML Full-text | XML Full-text
Abstract
Water-supply outlooks that predict the April through July (snowmelt) runoff and assist in estimating the total water-year runoff, are very important to users that rely on the major contributing watersheds of the Colorado River. This study reviewed the skill level of April through
[...] Read more.
Water-supply outlooks that predict the April through July (snowmelt) runoff and assist in estimating the total water-year runoff, are very important to users that rely on the major contributing watersheds of the Colorado River. This study reviewed the skill level of April through July forecasts at 28 forecast points within the Colorado River basin. All the forecasts were made after 1950, with considerable variation in time period covered. Evaluations of the forecasts were made using summary measures, correlation measures and categorical measures. The summary measure, a skill score for mean absolute error, indicated a steady increase in forecast skill through the forecast season of January to May. The width of the distribution for each monthly forecast over the 28 locations remained similar through the forecast season. The Nash-Sutcliffe score, a correlation measure, showed similar results, with the Nash-Sutcliffe median showing an increase from 0.4 to 0.8 during the forecast season. The categorical measures used a three-section partition of the April through July runoff. The Probability of Detection for low and high flows showed an increase in skill from approx. 0.4 to 0.8 during the forecast season. The same score for mid-flow years showed limited increase in skill. The low False Alarm Rate illustrated the under forecast of high-flow years. The Bias of the mid-runoff forecasts indicated over forecast early in the forecast season (January to March), with lower Bias later in the forecast season (April and May), ending the forecast season at 1.0, indicating no Bias. Forecasts for both low and high runoff were under forecast early in the season with a Bias near 0.5, improving to nearly 1.0 by the end of the forecast season. The Hit Rate measure illustrated the difficulty of mid-flow forecasts, starting at 0.5 in January and increasing to 0.75 in May due to the forecasting assumption of normal climatology for the remaining forecast period. There was no relationship between basin elevation and forecast skill, reflecting the snow vs. rain dominance in all basins. Full article
(This article belongs to the Special Issue Snow Hydrology)
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Review

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Open AccessReview Spatiotemporal Variations in Snow and Soil Frost—A Review of Measurement Techniques
Received: 25 January 2016 / Revised: 1 June 2016 / Accepted: 17 June 2016 / Published: 19 July 2016
Cited by 2 | PDF Full-text (716 KB) | HTML Full-text | XML Full-text
Abstract
Large parts of the northern hemisphere are covered by snow and seasonal frost. Climate warming is affecting spatiotemporal variations of snow and frost, hence influencing snowmelt infiltration, aquifer recharge and river runoff patterns. Measurement difficulties have hampered progress in properly assessing how variations
[...] Read more.
Large parts of the northern hemisphere are covered by snow and seasonal frost. Climate warming is affecting spatiotemporal variations of snow and frost, hence influencing snowmelt infiltration, aquifer recharge and river runoff patterns. Measurement difficulties have hampered progress in properly assessing how variations in snow and frost impact snowmelt infiltration. This has led to contradicting findings. Some studies indicate that groundwater recharge response is scale dependent. It is thus important to measure snow and soil frost properties with temporal and spatial scales appropriate to improve infiltration process knowledge. The main aim with this paper is therefore to review ground based methods to measure snow properties (depth, density, water equivalent, wetness, and layering) and soil frost properties (depth, water and ice content, permeability, and distance to groundwater) and to make recommendations for process studies aiming to improve knowledge regarding infiltration in regions with seasonal frost. Ground-based radar (GBR) comes in many different combinations and can, depending on design, be used to assess both spatial and temporal variations in snow and frost so combinations of GBR and tracer techniques can be recommended and new promising methods (auocostics and self potential) are evolving, but the study design must be adapted to the scales, the aims and the resources of the study. Full article
(This article belongs to the Special Issue Snow Hydrology)
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Open AccessReview Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models
Hydrology 2015, 2(4), 266-288; https://doi.org/10.3390/hydrology2040266
Received: 26 September 2015 / Revised: 19 November 2015 / Accepted: 20 November 2015 / Published: 25 November 2015
Cited by 4 | PDF Full-text (1222 KB) | HTML Full-text | XML Full-text
Abstract
An accurate precipitation phase determination—i.e., solid versus liquid—is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often
[...] Read more.
An accurate precipitation phase determination—i.e., solid versus liquid—is of paramount importance in a number of hydrological, ecological, safety and climatic applications. Precipitation phase can be determined by hydrological, meteorological or combined approaches. Meteorological approaches require atmospheric data that is not often utilized in the primarily surface based hydrological or ecological models. Many surface based models assign precipitation phase from surface temperature dependent snow fractions, which assume that atmospheric conditions acting on hydrometeors falling through the lower atmosphere are invariant. This ignores differences in phase change probability caused by air mass boundaries which can introduce a warm air layer over cold air leading to more atmospheric melt energy than expected for a given surface temperature, differences in snow grain-size or precipitation rate which increases the magnitude of latent heat exchange between the hydrometers and atmosphere required to melt the snow resulting in snow at warmer temperatures, or earth surface properties near a surface observation point heating or cooling a shallow layer of air allowing rain at cooler temperatures or snow at warmer temperatures. These and other conditions can be observed or inferred from surface observations, and should therefore be used to improve precipitation phase determination in surface models. Full article
(This article belongs to the Special Issue Snow Hydrology)
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Submitted Abstracts

Title: Glacier Runoff Determined from Transient Snow Line Migration Observations
Author: M.S. Pelto
Affiliation: Department of Environmental Science, Nichols College, Dudley, MA 01571, USA; Email: mspelto@nichols.edu
Abstract: Identification of the transient snowline (TSL) from high spatial resolution Landsat imagery on the Juneau Icefield, Southeast Alaska was used to quantify ablation rates during the 1998-2014 period.  The product of the rate of rise of the TSL during the ablation season and the observed balance gradient provides a measure of the ablation rate. On both Lemon Creek Glacier and Taku Glacier field mass balance measurement identify the balance gradient.  TSL observations from multiple dates during the ablation season from 1998–2014 at Lemon Creek Glacier and Taku Glacier are used to explore the consistency of the TSL rate of rise from year to year and glacier to glacier.  The rate of rise is also used to calculate annual ablation rate. On Lemon Creek Glacier and Taku Glacier the satellite derived mean TSL migration rates were 3.6 ± 0.7 md-1 and 4.0 ± 0.0.8 md-1 respectively. This yields ablation rates of 23 ± 4 mmd-1 for Lemon Creek Glacier and 18 ± 4 mmd-1 for Taku Glacier, using a TSL-balance-gradient method.
Keywords: Transient snow line, mass balance, ablation, glacier, Juneau Icefield

Title: Simulation of surface energy fluxes and snow interception using a higher order closure multi-layer soil-vegetation-atmospheric model: The effect of canopy shape and structure
Authors: Laura McGowan 1, Helen E.Dahlke 1, Kyaw Tha Paw U 1
Affiliation: 1 Department of Land, Air and Water Resources, University of California at Davis, Davis, CA, USA
Abstract: Snow cover is a critical driver of the Earth’s surface energy budget, climate change, and water resources. Variations in snow cover not only affect the energy budget of the land surface but also represent a major water supply source. In California, US on average as much as 35 percent of the annual stream flow is provided by snowmelt runoff. Consequently estimates of snow depth, extent, and melt in the Sierra Nevada are critical to estimating the amount of water available for both California agriculture and urban users. However, accurate estimates of snow cover and snow melt processes in forested area still remain a challenge. Canopy structure influences the vertical and spatiotemporal distribution of snow, and therefore ultimately determines the degree and extent by which snow alters both the surface energy balance and water availability in forested regions. In this study we use the Advanced Canopy-Atmosphere-Soil algorithm (ACASA), a multi-layer soil-vegetation-atmosphere numerical model, to simulate the effect of different snow-covered canopy structures on the energy budget, and temperature and other scalar profiles within different forest types in the Sierra Nevada, California. ACASA incorporates a higher order turbulence closure scheme which allows the detailed simulation of turbulent fluxes of heat and water vapor as well as the CO2 exchange of several layers within the canopy. As such ACASA can capture the counter gradient fluxes within canopies that may occur frequently, but are typically unaccounted for, in most snow hydrology models. Six different canopy types were modeled ranging from coniferous forests (e.g. most biomass near the ground) to top-heavy (e.g. most biomass near the top of the crown) deciduous forests to multi-layered forest canopies (e.g. mixture of young and mature trees). Preliminary results indicate that the canopy shape and structure associated with different canopy types fundamentally influence the vertical scalar profiles (including those of temperature, moisture, and wind speed) in the canopy and thus alter the interception and snow melt dynamics in forested land surfaces. The turbulent transport dynamics, including counter-gradient fluxes, and radiation features including land surface albedo, are discussed in the context of the snow energy balance.

Title: Modelling Snow Quantity and Properties in Boreal Forests: Assessment over a Range of Sites in Northern Scandinavia
Authors: Isabelle Gouttevin1, Sirpa Rasmus2, Charles Fierz3, Jouko Kumpula4, Jukka Siitari4
1 IRSTEA LYON, FRANCE
2 University of Jyväskylä, FINLAND
3 WSL Institute for Snow and Avalanche Research SLF, SWITZERLAND
4 Natural Resources Institute Finland, FINLAND
Abstract: Northern hemisphere is governed by boreal forests; these are covered by snow for the best part of the year. Snow is an integral and interactive part of boreal ecosystems, and accumulation, evolution and melt of the snow cover are major components in the annual hydrological cycle. However, modelling of these processes below varying forest canopies is not a straightforward task. Especially tools to simulate the snow properties (stratigraphy and physical properties of the snow layers, both relevant for ecological and hydrological applications) are not well validated in such environments. In this work we make use of the detailed snow-cover model SNOWPACK, which is now equipped with an improved canopy module, and test its ability to simulate the quantity and properties of snow at a range of boreal forested sites in northern Scandinavia. Meteorological forcing data from 32 separate winters from 14 boreal sites from Finnish Lapland (2-4 winters per site) have been gathered together with information on local forest conditions to provide input material for the simulations. In model validation we utilize a valuable data set obtained from these sites that consists of more than 2000 observations on snow depth, basal snow layer type, snow temperature, density and hardness. For each site, the SNOWPACK results are compared to observations via the use of the ProfEval tool (Fierz et al., 2014). Focus is especially set as to the model’s capability to produce a realistic snow cover with respect to depth, layers of depth hoar and ice layers: these features are indeed of particular importance for the boreal ecosystem and human activities (like reindeer herding). The ability to model these conditions could provide a valuable tool for ecosystem monitoring and could help to anticipate the future changes in snow conditions that would affect boreal hydrological cycles and ecosystem functions.
Reference:Fierz, C., Gerber, F., & Lehning, M. (2014). Comparison of modelled and measured point snow profiles : a tool for validating snow-cover models of the next generation ? Proceedings of the International Snow Science Workshop, Banff, 2014. p825-826.

Title: Spatiotemporal Variations in Snow and Soil Frost- A Review of Measurement Techniques.
Authors:
Angela Lundberg, David Gustafsson, et al.
Abstract: Climate warming is affecting spatiotemporal variations in snow and ground frost, but the results are sometimes contradicting and measurement difficulties have hampered progress in assessing how these variations impact on snowmelt infiltration and there are also indications that groundwater recharge response is scale dependent.   It is thus important to measure snow and soil frost properties with temporal and spatial scales appropriate to improve infiltration process knowledge. The main aim with this paper is therefore to review ground based methods to measure snow properties (depth, density, water equivalent, wetness and layering) and soil frost properties (depth, water and ice content, permeability, distance to groundwater) and to make recommendations for process studies aiming to improve knowledge regarding infiltration in regions with seasonal frost. Ground based radar (GPR), comes in many different combinations and can, depending on design be used to asses both spatial and temporal variations in snow and frost so combinations of GPR and tracer techniques can be recommended, but study design must be adapted to the scales, the aims and the resources of the study.

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