Forcing the snow-cover model SNOWPACK with forecasted weather data

Bellaire, Sascha; Jamieson, J. Bruce; Fierz, Charles

doi:10.5194/tc-5-1115-2011

Cited by 67 publications

(53 citation statements)

References 15 publications

(12 reference statements)

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“…Using such models in mountainous areas has been initiated several decades ago with numerous successful applications such as avalanche hazard forecasting (e.g. Durand et al, 1999;Bellaire et al, 2011), mountain hydrology (e.g. Braun et al, 1994;Magnusson et al, 2011) and glacier mass balance (Obleitner and Lehning, 2004;Gerbaux et al, 2005).…”

Section: A Mary Et Al: Mountain Snow Ssa From Modismentioning

confidence: 99%

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Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Mary¹,

Dumont

Dedieu

et al. 2013

The Cryosphere

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Abstract. This study compares different methods to retrieve the specific surface area (SSA) of snow from satellite radiance measurements in mountainous terrain. It aims at addressing the effect on the retrieval of topographic corrections of reflectance, namely slope and aspect of terrain, multiple reflections on neighbouring slopes and accounting (or not) for the anisotropy of snow reflectance. Using MODerate resolution Imaging Spectrometer (MODIS) data for six different clear sky scenes spanning a wide range of snow conditions during the winter season 2008-2009 over a domain of 46 × 50 km in the French Alps, we compared SSA retrievals with and without topographic correction, with a spherical or non-spherical snow reflectance model and, in spherical case, with or without anisotropy corrections. The retrieved SSA values were compared to field measurements and to the results of the detailed snowpack model Crocus, fed by driving data from the SAFRAN meteorological analysis. It was found that the difference in terms of surface SSA between retrieved values and SAFRAN-Crocus output was minimal when the topographic correction was taken into account, when using a retrieval method assuming disconnected spherical snow grains. In this case, the root mean square deviation was 9.4 m 2 kg −1 and the mean difference was 0.1 m 2 kg −1 , based on 3170 pairs of observation and simulated values. The added-value of the anisotropy correction was not significant in our case, which may be explained by the presence of mixed pixels and surface roughness. MODIS retrieved data show SSA variations with elevation and aspect which are physically consistent and in good agreement with SAFRAN-Crocus outputs. The variability of the MODIS retrieved SSA within the topographic classes of the model was found to be relatively small (3.9 m 2 kg −1 ). This indicates that semi-distributed snowpack simulations in mountainous terrain with a sufficiently large number of classes provides a representation of the snowpack variability consistent with the scale of MODIS 500 m pixels.

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Section: A Mary Et Al: Mountain Snow Ssa From Modismentioning

confidence: 99%

“…and L k are obtained using the SPCTRAL2 radiative transfer model (Bird and Riordan, 1986). The shadowing factor b is computed within MODImLab using the 125 m DEM based on an implementation of the horizon line algorithm of Dozier et al (1981a).…”

Section: Thementioning

confidence: 99%

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Mary¹,

Dumont

Dedieu

et al. 2013

The Cryosphere

View full text Add to dashboard Cite

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“…Often avalanches are only reported if they cause an obstruction to public infrastructure, damage to personal property, or are witnessed and reported by local observers. However, decisions regarding, e.g., the closure of roads and rail lines and the setting of warning levels, rely on information that is derived from knowledge of historic events in combination with metrological data of the recent past and expected future (Bellaire et al, 2011).…”

Section: Need For More Complete and Precise Avalanche Datamentioning

confidence: 99%

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

Lato

Frauenfelder

Bühler³

2012

Nat. Hazards Earth Syst. Sci.

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Abstract. Snow avalanches in mountainous areas pose a significant threat to infrastructure (roads, railways, energy transmission corridors), personal property (homes) and recreational areas as well as for lives of people living and moving in alpine terrain. The impacts of snow avalanches range from delays and financial loss through road and railway closures, destruction of property and infrastructure, to loss of life. Avalanche warnings today are mainly based on meteorological information, snow pack information, field observations, historically recorded avalanche events as well as experience and expert knowledge. The ability to automatically identify snow avalanches using Very High Resolution (VHR) optical remote sensing imagery has the potential to assist in the development of accurate, spatially widespread, detailed maps of zones prone to avalanches as well as to build up data bases of past avalanche events in poorly accessible regions. This would provide decision makers with improved knowledge of the frequency and size distributions of avalanches in such areas. We used an object-oriented image interpretation approach, which employs segmentation and classification methodologies, to detect recent snow avalanche deposits within VHR panchromatic optical remote sensing imagery. This produces avalanche deposit maps, which can be integrated with other spatial mapping and terrain data. The object-oriented approach has been tested and validated against manually generated maps in which avalanches are visually recognized and digitized. The accuracy (both users and producers) are over 0.9 with errors of commission less than 0.05. Future research is directed to widespread testing of the algorithm on data generated by various sensors and improvement of the algorithm in high noise regions as well as the mapping of avalanche paths alongside their deposits.

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“…To overcome this limitation, regional climate models (RCMs) are often used to obtain better representations of surface climatology, because they downscale physically reanalysis products (García-Valdecasas Ojeda et al, 2017;Kryza et al, 2017;WarrachSagi et al, 2013). Previous studies have used RCMs to study SD and SWE dynamics at finer resolutions (grid cell size: 5 to 11 km) when they are driven with reanalyses, and the resolution increases further (grid cell size: 1 km) when using forecasted data (Bellaire et al, 2011;van Pelt et al, 2016;Quéno et al, 2016;Wu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

Alonso‐González

López‐Moreno

Gascoin

et al. 2018

Earth Syst. Sci. Data

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Abstract. We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfall occurs in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 km × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 km × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism, and risk management. The data presented here are freely available for download from Zenodo (https://doi.org/10.5281/zenodo.854618). This paper fully describes the work flow, data validation, uncertainty assessment, and possible applications and limitations of the database.

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Forcing the snow-cover model SNOWPACK with forecasted weather data

Cited by 67 publications

References 15 publications

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Intercomparison of retrieval algorithms for the specific surface area of snow from near-infrared satellite data in mountainous terrain, and comparison with the output of a semi-distributed snowpack model

Automated detection of snow avalanche deposits: segmentation and classification of optical remote sensing imagery

Daily gridded datasets of snow depth and snow water equivalent for the Iberian Peninsula from 1980 to 2014

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