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

Abstract

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.

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

“…This semi-distributed framework, with around 200 topographical classes, was proven to be sufficient to represent the main features of snowpack variability with topography compared to fully distributed simulations at 25 to 250 m resolution (Fiddes and Gruber, 2012;Revuelto et al, 2018). However, the feasibility of the assimilation of satellite reflectances in Crocus semi-distributed model using the PF ensemble data assimilation algorithm, still needs to be assessed.…”

Non-stationary extreme value analysis of ground snow loads in the French Alps: a comparison with building standards

“…Many potential extensions of this work could be considered. First, our methodology could be extended with more advanced definitions of non-stationary return levels (Rootzén and Katz, 2013;Serinaldi, 2015). Also, instead of considering time series of annual maxima as spatially independent, we believe that our analysis may benefit from an explicit modelling of the spatial dependence between extremes.…”

Evaluation of Sub-Kilometric Numerical Simulations of C-Band Radar Backscatter over the French Alps against Sentinel-1 Observations

“…Meteorological reanalysis fields from SAFRAN were generated for the period ranging from 1 October 2014 to 1 August 2017 with an output time step of 1 h and were then spatially distributed over a 250 m × 250 m grid spacing with the IGN digital elevation model and for the study area. The entire process provides a set of 250-m gridded atmospheric forcings, which were then used to feed the snow model Crocus and to obtain gridded snowpack simulations [42,43]. The main added value of the high resolution is the correction of incoming solar radiations due to the shadows of the surrounding topography [43,44].…”

“…The entire process provides a set of 250-m gridded atmospheric forcings, which were then used to feed the snow model Crocus and to obtain gridded snowpack simulations [42,43]. The main added value of the high resolution is the correction of incoming solar radiations due to the shadows of the surrounding topography [43,44].…”