scholarly journals Implementation of a physiographic complexity-based multiresolution snow modeling scheme

2017 ◽  
Vol 53 (5) ◽  
pp. 3680-3694 ◽  
Author(s):  
Elisabeth Baldo ◽  
Steven A. Margulis
Keyword(s):  
Snow Modeling

Performance and sensitivity of a spatially distributed hydrological conceptual flood model with snow components.

2021 ◽  
Author(s):  
François Colleoni ◽  
Catherine Fouchier ◽  
Pierre-André Garambois ◽  
Pierre Javelle ◽  
Maxime Jay-Allemand ◽  
...  
Keyword(s):  
Significant Proportion ◽  
Slow Flow ◽  
Modeling Tools ◽  
Snow Modeling ◽  
Distributed Modelling ◽  
Distributed Framework ◽  
Mountainous Catchments ◽  
Spatially Distributed ◽  
Flow Components ◽  
Spatially Varying

<p>In France, flash floods are responsible for a significant proportion of damages caused by natural hazards, either human or material. Hence, advanced modeling tools are needed to perform effective predictions. However for mountainous catchments snow modeling components may be required to correctly simulate river discharge.</p><p>This contribution investigates the implementation and constrain of snow components in the spatially distributed SMASH* platform (Jay-Allemand et al. 2020). The goal is to upgrade model structure and spatially distributed calibration strategies for snow-influenced catchments, as well as to investigate parametric sensitivity and equifinality issues. First, the implementation of snow modules of varying complexity is addressed based on Cemaneige (Valery et al. 2010) in the spatially distributed framework. Next, tests are performed on a sample of 55 catchments in the French North Alps. Numerical experiments and global sensitivity analysis enable to determine pertinent combinations of flow components (including a slow flow one) and calibration parameters. Spatially uniform or distributed calibrations using a variational method (Jay-Allemand 2020) are performed and compared on the dataset, for different model structures and constrains. These tests show critical improvements in outlet discharge modeling by adding slow flow and snow modules, especially considering spatially varying parameters. Current and future works focus on testing and improving the constrains of snow modules and calibration strategy, as well as potential validation and multiobjective calibration with snow signatures gained from in situ or satellite data. </p><p>*SMASH: Spatially-distributed Modelling and ASsimilation for Hydrology, platform developped by INRAE-Hydris corp. for operational applications in the french flood forecast system VigicruesFlash</p>

scholarly journals Snow physical properties may be a significant determinant of lemming population dynamics in the high Arctic

2018 ◽  
Vol 4 (4) ◽  
pp. 813-826 ◽  
Author(s):  
Florent Domine ◽  
Gilles Gauthier ◽  
Vincent Vionnet ◽  
Dominique Fauteux ◽  
Marie Dumont ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Basal Layer ◽  
Population Data ◽  
High Arctic ◽  
Population Fluctuations ◽  
Snow Modeling ◽  
Animal Populations ◽  
Key Factor ◽  
Snow Conditions ◽  
The Many

Cyclic population fluctuations are common in boreal and Arctic species but the causes of these cycles are still debated today. Among these species, lemmings are Arctic rodents that live and reproduce under the snow and whose large cyclical population fluctuations in the high Arctic impact the whole tundra food web. We explore, using lemming population data and snow modeling, whether the hardness of the basal layer of the snowpack, determined by rain-on-snow events (ROS) and wind storms in autumn, can affect brown lemming population dynamics in the Canadian high Arctic. Using a 7-year dataset collected on Bylot Island, Nunavut, Canada over the period 2003–2014, we demonstrate that liquid water input to snow is strongly inversely related with winter population growth (R2 ≥ 0.62) and to a lesser extent to lemming summer densities and winter nest densities (R2 = 0.29–0.39). ROS in autumn can therefore influence the amplitude of brown lemming population fluctuations. Increase in ROS events with climate warming should strongly impact the populations of lemmings and consequently those of the many predators that depend upon them. Snow conditions may be a key factor influencing the cyclic dynamics of Arctic animal populations.

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

2020 ◽  
Vol 14 (3) ◽  
pp. 935-956 ◽  
Author(s):  
Carlo Marin ◽  
Giacomo Bertoldi ◽  
Valentina Premier ◽  
Mattia Callegari ◽  
Christian Brida ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Melting Process ◽  
Liquid Water Content ◽  
Sar Images ◽  
Distributed Information ◽  
Snow Modeling ◽  
Alpine Regions ◽  
Snow Properties ◽  
Wet Snow ◽  
Spatially Distributed

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

scholarly journals Spatial Snow Modeling of Wind-Redistributed Snow Using Terrain-Based Parameters

2002 ◽  
Vol 3 (5) ◽  
pp. 524-538 ◽  
Author(s):  
Adam Winstral ◽  
Kelly Elder ◽  
Robert E. Davis
Keyword(s):  
Snow Modeling

scholarly journals Study of the snow melt–freeze cycle using multi-sensor data and snow modeling

2004 ◽  
Vol 50 (170) ◽  
pp. 419-426 ◽  
Author(s):  
Anselmo Cagnati ◽  
Andrea Crepaz ◽  
Giovanni Macelloni ◽  
Paolo Pampaloni ◽  
Roberto Ranzi ◽  
...  
Keyword(s):  
Meteorological Data ◽  
High Sensitivity ◽  
Microwave Emission ◽  
Sensor Data ◽  
Snow Melt ◽  
Snow Modeling ◽  
Snow Model ◽  
Electromagnetic Models ◽  
Underlying Soil ◽  
Insight Into

AbstractThe melt cycle of snow is investigated by combining ground-based microwave radiometric measurements with conventional and meteorological data and by using a hydrological snow model. Measurements at 2000 m a.s.l in the basin of the Cor-devole river in the eastern Italian Alps confirm the high sensitivity of microwave emission at 19 and 37 GHz to the snow melt−freeze cycle, while the brightness at 6.8 GHz is mostly related to underlying soil. Simulations of snowpack changes performed by means of hydrological and electromagnetic models, driven with meteorological and snow data, provide additional insight into these processes and contribute to the interpretation of the experimental data.

Spatial and temporal patterns of snowmelt refreezing in a Himalayan catchment

2021 ◽  
Author(s):  
Sanne Veldhuijsen ◽  
Remco De Kok ◽  
Emmy Stigter ◽  
Jakob Steiner ◽  
Tuomo Saloranta ◽  
...  
Keyword(s):  
Air Temperature ◽  
Annual Runoff ◽  
Polar Regions ◽  
Spatial And Temporal Patterns ◽  
Meteorological Forcing ◽  
Snow Modeling ◽  
Highly Sensitive ◽  
Snow Model ◽  
The Himalaya

<p>Seasonal snow contributes significantly to the annual runoff in the Himalaya and both timing and volume are important for downstream users.  In  polar regions, meltwater refreezing within snowpacks has been well-studied. While the conditions in the Himalaya are considered favorable for refreezing,<strong> </strong>little is known about refreezing in this region, hindering a complete understanding of seasonal snowmelt dynamics. In this study, we simulated refreezing with the seNorge (v2.0) snow model for the Langtang catchment in the Nepalese Himalaya covering a 5-year period. Thereby, we aim to improve our understanding about how refreezing varies in space and time and to provide a framework for future snow modeling studies. The first part of this study focuses extensively on developing meteorological forcing data, which were derived from an unique elaborate network of meteorological stations and high-resolution meteorological simulations. The snow model was validated against in-situ snow observations and snow cover satellite data. In the second part of this study, we analyze the spatial and temporal refreezing patterns, and attempt to identify possible driving factors. The results show that the annual average refreezing amounts to 122 mm (21% of the total melt). We found that the magnitude of refreezing varies strongly in space depending on elevation and aspect. In addition, there is a strong seasonal altitudinal variability related to air temperature and snow depth, with most refreezing during the early melt season. We also found a substantial intra-annual variability, which mainly results from fluctuations of snowfall, highlighting the importance of using multi-year time series in refreezing assessments. Daily refreezing simulations decreased by 84% (to an average 19 mm year<sup>-1</sup>) compared to hourly simulations, emphasizing the importance of using sub-daily time steps to capture diurnal melt-refreeze cycles. Climate sensitivity experiments revealed that refreezing is highly sensitive to future changes in air temperature, as a temperature increase of 2°C leads to a refreezing decrease of 35%. We conclude that including refreezing with a sub-daily temporal resolution is highly relevant for understanding snow dynamics in the current and future climate of the Himalaya.</p><p> </p>

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