Variational assimilation of albedo in a snowpack model and reconstruction of the spatial mass-balance distribution of an alpine glacier

Dumont, Marie; Durand, Yves; Arnaud, Yves; Six, Delphine

doi:10.3189/2012jog11j163

Cited by 48 publications

(53 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results further stress the significance of the albedo on the mass balance of this kind of alpine glaciers (Dumont et al, 2012). The strong linear correlation shown in Fig.…”

Section: Albedo and Mass Balancesupporting

confidence: 72%

Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

et al. 2012

View full text Add to dashboard Cite

Abstract. Albedo is one of the variables controlling the mass balance of temperate glaciers. Multispectral imagers, such as MODerate Imaging Spectroradiometer (MODIS) on board the TERRA and AQUA satellites, provide a means to monitor glacier surface albedo. In this study, different methods to retrieve broadband glacier surface albedo from MODIS data are compared. The effect of multiple reflections due to the rugged topography and of the anisotropic reflection of snow and ice are particularly investigated. The methods are tested on the Saint Sorlin Glacier (Grandes Rousses area, French Alps). The accuracy of the retrieved albedo is estimated using both field measurements, at two automatic weather stations located on the glacier, and albedo values derived from terrestrial photographs. For summers 2008 and 2009, the root mean square deviation (RMSD) between field measurements and the broadband albedo retrieved from MODIS data at 250 m spatial resolution was found to be 0.052 or about 10 % relative error. The RMSD estimated for the MOD10 daily albedo product is about three times higher. One decade (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) of MODIS data were then processed to create a time series of albedo maps of Saint Sorlin Glacier during the ablation season. The annual mass balance of Saint Sorlin Glacier was compared with the minimum albedo value (average over the whole glacier surface) observed with MODIS during the ablation season. A strong linear correlation exists between the two variables. Furthermore, the date when the average albedo of the whole glacier reaches a minimum closely corresponds to the period when the snow line is located at its highest elevation, thus when the snow line is a good indicator of the glacier equilibrium line. This indicates that this strong correlation results from the fact that the minimal average albedo values of the glacier contains considerable information regarding the relative share of areal surfaces between the ablation zone (i.e. ice with generally low albedo values) and the accumulation zone (i.e. snow with a relatively high albedo). As a consequence, the monitoring of the glacier surface albedo using MODIS data can provide a useful means to evaluate the interannual variability of the glacier mass balance. Finally, the albedo in the ablation area of Saint Sorlin Glacier does not exhibit any decreasing trend over the study period, contrasting with the results obtained on Morteratsch Glacier in the Swiss Alps.

show abstract

“…These results further stress the significance of the albedo on the mass balance of this kind of alpine glaciers (Dumont et al, 2012). The strong linear correlation shown in Fig.…”

Section: Albedo and Mass Balancesupporting

confidence: 72%

Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

et al. 2012

View full text Add to dashboard Cite

show abstract

“…This is significant because the implementation of the most promising advances in simulation is mainly considered for distributed simulations. This is the case for assimilation of satellite data [44][45][46]; the inclusion of small scale processes in simulations, including snow redistribution by wind [30,32]; and gravitational or topographic controls on snow movements [29,47,48]. Semi-distributed simulations may also allow the implementation of satellite data assimilation techniques (as suggested in [49]) but would require specific methods for aggregating observations into the semi-distributed clustering of the simulation domain and they would reduce potential benefits of high resolution satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract:This work presents an extensive evaluation of the Crocus snowpack model over a rugged and highly glacierized mountain catchment (Arve valley, Western Alps, France) from 1989 to 2015. The simulations were compared and evaluated using in-situ point snow depth measurements, in-situ seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite imagery at 250 m resolution (MODIS sensor), and the annual equilibrium-line altitude of glaciers, derived from satellite images (Landsat, SPOT, and ASTER). The snowpack simulations were obtained using the Crocus snowpack model driven by the same, originally semi-distributed, meteorological forcing (SAFRAN) reanalysis using the native semi-distributed configuration, but also a fully distributed configuration. The semi-distributed approach addresses land surface simulations for discrete topographic classes characterized by elevation range, aspect, and slope. The distributed approach operates on a 250-m grid, enabling inclusion of terrain shadowing effects, based on the same original meteorological dataset. Despite the fact that the two simulations use the same snowpack model, being potentially subjected to same potential deviation from the parametrization of certain physical processes, the results showed that both approaches accurately reproduced the snowpack distribution over the study period. Slightly (although statistically significantly) better results were obtained by using the distributed approach. The evaluation of the snow cover area with MODIS sensor has shown, on average, a reduction of the Root Mean Squared Error (RMSE) from 15.2% with the semi-distributed approach to 12.6% with the distributed one. Similarly, surface glacier mass balance RMSE decreased from 1.475 m of water equivalent (W.E.) for the semi-distributed simulation to 1.375 m W.E. for the distribution. The improvement, observed with a much higher computational time, does not justify the recommendation of this approach for all applications; however, for simulations that require a precise representation of snowpack distribution, the distributed approach is suggested.

show abstract

“…However, there have been few examples of direct assimilation of satellite data into models of such a degree of complexity (Toure et al, 2011). Dumont et al (2012) have recently demonstrated how remotely sensed spectral albedo of snow can be assimilated into the detailed snowpack model Crocus, with positive impact on the simulated mass balance of an alpine glacier. Regardless of the frequency range (microwave, visible/near infrared, thermal infrared, .…”

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

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

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.

show abstract

Variational assimilation of albedo in a snowpack model and reconstruction of the spatial mass-balance distribution of an alpine glacier

Cited by 48 publications

References 56 publications

Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

Linking glacier annual mass balance and glacier albedo retrieved from MODIS data

Multi-Criteria Evaluation of Snowpack Simulations in Complex Alpine Terrain Using Satellite and In Situ Observations

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

Contact Info

Product

Resources

About