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

Abstract

Abstract. This work describes the snow and meteorological data set available for the Izas Experimental Catchment in the Central Spanish Pyrenees, from the 2011 to 2017 snow seasons. The experimental site is located on the southern side of the Pyrenees between 2000 and 2300 m above sea level, covering an area of 55 ha. The site is a good example of a subalpine environment in which the evolution of snow accumulation and melt are of major importance in many mountain processes. The climatic 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 three and six TLS surveys per snow season) and (iii) time-lapse images showing the evolution of the snow-covered area (SCA). The meteorological variables acquired at the AWS are precipitation, air temperature, incoming and reflected solar radiation, infrared surface temperature, relative humidity, wind speed and direction, atmospheric air pressure, surface temperature (snow or soil surface), and soil temperature; all were taken at 10 min intervals. Snow depth distribution was measured during 23 field campaigns using a TLS, and daily information on the SCA was also retrieved from time-lapse photography. The data set (https://doi.org/10.5281/zenodo.848277) is valuable since it provides high-spatial-resolution information on the snow depth and snow cover, which is particularly useful when combined with meteorological variables to simulate snow energy and mass balance. This information has already been analyzed in various scientific studies on snow pack dynamics and its interaction with the local climatology or topographical characteristics. However, the database generated has great potential for understanding other environmental processes from a hydrometeorological or ecological perspective in which snow dynamics play a determinant role.

Effect of Digital Elevation Model Resolution on the Simulation of the Snow Cover Evolution in the High Atlas

“…Other strategies (than changing the model grid resolution) were proposed to account for terrain heterogeneity on topographic-driven meteorological forcing while keeping low computational requirements. These approaches can be classified into two categories: (i) the subgrid approach (Essery & Marks, 2007;Gagnon et al, 2013;Müller & Scherer, 2005) and (ii) the semidistributed approach (Revuelto et al, 2017;Younas et al, 2017). The first approach was used to represent forcing variables as probabilistic distributions (instead of a single mean value) at the grid cell scale.…”

“…In that perspective, it is more straightforward to run the model on a distributed grid. This also permits to explicitly represent snow redistribution processes such as the wind transport or avalanches (Revuelto et al, ).…”

Snow Impurities in the Central Pyrenees: From Their Geochemical and Mineralogical Composition towards Their Impacts on Snow Albedo

“…Nevertheless, small differences may occur under certain atmospheric conditions, especially during micro-scale phenomena such as thunderstorms. Observations presented here were obtained during the 2016-2017, 2017-2018, and 2018-2019 snow seasons, respectively characterized by medium, high, and moderate snow accumulations when compared to the database of snow depth observations available in Izas experimental catchment [30].…”

“…Izas Experimental Catchment is equipped with an automatic weather station that acquires every 10 min different meteorological variables, comprising among others, surface air temperature, surface albedo (broadband), total precipitation, and snow depth [30], as shown in Figure 2. From these observations it was computed the mean, the maximum, and the mean air temperatures.…”

Effect of digital elevation model resolution on the simulation of the snow cover evolution in the High Atlas

“…Other strategies (than changing the model grid resolution) were proposed to account for terrain heterogeneity on topographic-driven meteorological forcing while keeping low computational requirements. These approaches can be classified into two categories: (i) the subgrid approach (Essery & Marks, 2007;Gagnon et al, 2013;Müller & Scherer, 2005) and (ii) the semidistributed approach (Revuelto et al, 2017;Younas et al, 2017). The first approach was used to represent forcing variables as probabilistic distributions (instead of a single mean value) at the grid cell scale.…”