Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence

Abstract

Abstract. In this study we analyzed the relations between terrain characteristics and snow depth distribution in a small alpine catchment located in the central Spanish Pyrenees. Twelve field campaigns were conducted during 2012 and 2013, which were years characterized by very different climatic conditions. Snow depth was measured using a long range terrestrial laser scanner and analyses were performed at a spatial resolution of 5 m. Pearson's r correlation, multiple linear regressions (MLRs) and binary regression trees (BRTs) were used to analyze the influence of topography on the snow depth distribution. The analyses were used to identify the topographic variables that best explain the snow distribution in this catchment, and to assess whether their contributions were variable over intra-and interannual timescales. The topographic position index (index that compares the relative elevation of each cell in a digital elevation model to the mean elevation of a specified neighborhood around that cell with a specific shape and searching distance), which has rarely been used in these types of studies, most accurately explained the distribution of snow. The good capability of the topographic position index (TPI) to predict snow distribution has been observed in both, MLRs and BRTs for all analyzed days. Other variables affecting the snow depth distribution included the maximum upwind slope, elevation and northing. The models developed to predict snow distribution in the basin for each of the 12 survey days were similar in terms of the explanatory variables. However, the variance explained by the overall model and by each topographic variable, especially those making a lesser contribution, differed markedly between a year in which snow was abundant (2013) and a year when snow was scarce (2012), and also differed between surveys in which snow accumulation or melting conditions dominated in the preceding days. The total variance explained by the models clearly decreased for those days on which the snowpack was thinner and more patchily. Despite the differences in climatic conditions in the 2012 and 2013 snow seasons, similarities in snow distributions patterns were observed which are directly related to terrain topographic characteristics.

Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

“…Hríbik et al, 2012) needs to be done in future studies. Revuelto et al (2014) concluded that topographic position index TPI helped explain variability of SD distribution. TPI at a 25 m searching distance was the best variable explaining snow depth distribution in their experimental catchment.…”

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

“…Ye et al (1998) and Kitaev et al (2005) showed that higher air temperatures caused an increase in snowfall in winter from 1936 through 1995, and thus greater snow depth was observed in northern Eurasia. Snow depth distribution and variation are controlled by terrain (i.e., elevation, slope, aspect, and roughness) and vegetation Grünewald et al, 2014;Revuelto et al, 2014;Rees et al, 2014;Dickerson-Lange et al, 2015). Snow depth is closely related to synoptic-scale atmospheric circulation indices such as the North Atlantic OscillationArctic Oscillation (NAO/AO).…”

Meteorological and snow distribution data in the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017

“…The data set described here is novel in the Pyrenees because, for the first time, it represents high-spatialresolution information on the snowpack distribution and its evolution in time, as well as making continuous information available on meteorological variables. The high quality of the information obtained has already been exploited for different studies on the understanding of snowpack dynamics and the improvement of simulation approaches to snowpack evolution in mountain areas , 2014, Revuelto et al, 2014b, 2016a, 2016b. However, many scientific questions still go unanswered, such as the long-term influence of topography on snow dynamics, and the spatial distribution of snow during precipitation and strong wind events.…”

“…The data set consists of (i) continuous meteorological variables acquired from an automatic weather station (AWS), (ii) detailed information on snow depth distribution collected with a terrestrial laser scanner (TLS, lidar technology) for certain dates across the snow season (between two and six TLS surveys per snow season) and (iii) time-lapse images showing the snow-covered area (SCA) evolution. Some years of this data set have already been used to study the topographic control on snow depth distribution (Revuelto et al, 2014b), the spatial variability in snowpack at different distances or to investigate how detailed snowpack simulation could be improved by including snow distribution information (Revuelto et al, 2016a, b).…”