Esteban Alonso-González

Effect of snow on mountain river regimes: an example from the Pyrenees

The purpose of this study was to characterize mountain river regimes in the Spanish Pyrenees and to assess the importance of snow accumulation and snowmelt on the timing of river flows. Daily streamflow data from 9 gauging stations in the Pyrenees were used to characterize river regimes. These data were analyzed by hydrological indices, with a focus on periods when snow accumulation and snowmelt occurred. These results were combined with data on Snow Water Equivalent (SWE) (from measurements of depth and density of snow in the main river basins and also simulated by a process-based hydrological model), snowmelting (simulated by a process-based hydrological model), precipitation (from observations), and temperature (from observations). Longitude and elevation gradients in the Pyrenees explain the transition of river regimes from those that mostly had low nival signals (in the west and at low elevations) to those that mostly had high nival signals (low winter runoff and late spring peakflow, in the east and at high elevations). Although trend analyses indicated no statistically significant changes, there was a trend of decreased nival signal over time in most of the analyzed rivers. Our results also demonstrated that snow processes cannot explain all of the interannual variability of river regimes, because the temporal distribution of liquid precipitation and temperature play key roles in hydrography.

Using very long-range terrestrial laser scanner to analyze the temporal consistency of the snowpack distribution in a high mountain environment

This study demonstrated the usefulness of very long-range terrestrial laser scanning (TLS) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reported to date. Snow depth data were collected using a terrestrial laser scanner during 11 periods of snow accumulation and melting, over three snow seasons on a Pyrenean hillslope characterized by a large elevational gradient, steep slopes, and avalanche occurrence. The maximum and mean absolute snow depth error found was 0.5-0.6 and 0.2-0.3 m respectively, which may result problematic for areas with a shallow snowpack, but it is sufficiently accurate to determine snow distribution patterns in areas characterized by a thick snowpack. The results indicated that in most cases there was temporal consistency in the spatial distribution of the snowpack, even in different years. The spatial patterns were particularly similar amongst the surveys conducted during the period dominated by snow accumulation (generally until end of April), or amongst those conducted during the period dominated by melting processes (generally after mid of April or early May). Simple linear correlation analyses for the 11 survey dates, and the application of Random Forests analysis to two days representative of snow accumulation and melting periods indicated the importance of topography to the snow distribution. The results also highlight that elevation and the Topographic Position index (TPI) were the main variables explaining the snow distribution, especially during periods dominated by melting. The intra- and inter-annual spatial consistency of the snowpack distribution suggests that the geomorphological processes linked to presence/absence of snow cover act in a similar way in the long term, and that these spatial patterns can be easily identified through several years of adequate monitoring.