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Hydrology
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Snow Hydrology
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Snow Hydrology

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

Deadline for manuscript submissions: closed (31 May 2016) | Viewed by 41685

Special Issue Editors

Dr. Juraj Parajka

E-Mail Website
Guest Editor
Institute of Hydraulic Engineering and Water Resources, Vienna University of Technology, A-1040 Vienna, Austria
Interests: observation and modelling of water cycle components at different scales
Special Issues, Collections and Topics in MDPI journals
Dr. Cécile Ménard

E-Mail Website
Guest Editor
Arctic Research Centre, Finnish Meteorological Institute Erik Palménin aukio 1 FI-00560 Helsinki, Finnland
Dr. Ladislav Holko

E-Mail
Guest Editor
Institute of Hydrology, Slovak Academy of Sciences Liptovsky Mikulas, Slovakia

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (6 papers)

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Research

Jump to: Review

19 pages, 3476 KiB  
Article
Comparison between Snow Albedo Obtained from Landsat TM, ETM+ Imagery and the SPOT VEGETATION Albedo Product in a Mediterranean Mountainous Site
by Rafael Pimentel, Cristina Aguilar, Javier Herrero, María José Pérez-Palazón and María José Polo
Hydrology 2016, 3(1), 10; https://doi.org/10.3390/hydrology3010010 - 23 Feb 2016
Cited by 16 | Viewed by 5989
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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17 pages, 8810 KiB  
Article
Spatial Heterogeneity of Snow Density and Its Influence on Snow Water Equivalence Estimates in a Large Mountainous Basin
by Karl Wetlaufer, Jordy Hendrikx and Lucy Marshall
Hydrology 2016, 3(1), 3; https://doi.org/10.3390/hydrology3010003 - 12 Jan 2016
Cited by 25 | Viewed by 6318
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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29 pages, 3608 KiB  
Article
Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada)
by Magali Troin, Richard Arsenault and François Brissette
Hydrology 2015, 2(4), 289-317; https://doi.org/10.3390/hydrology2040289 - 27 Nov 2015
Cited by 23 | Viewed by 7079
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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20 pages, 2088 KiB  
Article
Skill Assessment of Water Supply Outlooks in the Colorado River Basin
by Brent Harrison and Roger Bales
Hydrology 2015, 2(3), 112-131; https://doi.org/10.3390/hydrology2030112 - 31 Jul 2015
Cited by 6 | Viewed by 5547
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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28 pages, 716 KiB  
Review
Spatiotemporal Variations in Snow and Soil Frost—A Review of Measurement Techniques
by Angela Lundberg, David Gustafsson, Christine Stumpp, Bjørn Kløve and James Feiccabrino
Hydrology 2016, 3(3), 28; https://doi.org/10.3390/hydrology3030028 - 19 Jul 2016
Cited by 19 | Viewed by 7011
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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23 pages, 1222 KiB  
Review
Meteorological Knowledge Useful for the Improvement of Snow Rain Separation in Surface Based Models
by James Feiccabrino, William Graff, Angela Lundberg, Nils Sandström and David Gustafsson
Hydrology 2015, 2(4), 266-288; https://doi.org/10.3390/hydrology2040266 - 25 Nov 2015
Cited by 49 | Viewed by 7824
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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