This work presents the Guadalfeo Monitoring Network in Sierra Nevada (Spain), a snow monitoring network in the Guadalfeo Experimental Catchment, a semiarid area in southern Europe representative of snowpacks with highly variable dynamics on both annual and seasonal scales and significant topographic gradients. The network includes weather stations that cover the high mountain area in the catchment and time-lapse cameras to capture the variability of the ablation phases on different spatial scales. The data sets consist of continuous meteorological high-frequency records at five automatic weather stations located at different altitudes ranging from 1300 to 2600 m a.s.l. that include precipitation, air temperature, wind speed, air relative humidity and the short-and longwave components of the incoming radiation, dating from 2004 for the oldest station (2510 m a.s.l.) (https://doi.org/10.1594/PANGAEA.895236); additionally, daily data sets of the imagery from two time-lapse cameras are presented, with different scene area (30 m × 30 m, and 2 km2, respectively) and spatial resolution, that consist of fractional snow cover area and snow depth from 2009 (https://doi.org/10.1594/PANGAEA.871706) and snow cover maps for selected dates from 2011 (https://doi.org/10.1594/PANGAEA.898374). Some research applications of these data sets are also included to highlight the value of high-resolution data sources to improve the understanding of snow processes and distribution in highly variable environments. The data sets are available from different open-source sites and provide both the snow hydrology scientific community and other research fields, such as terrestrial ecology, riverine ecosystems or water quality in high mountains, with valuable information of high potential in snow-dominated areas in semiarid regions.
CITATION STYLE
Polo, M. J., Herrero, J., Pimentel, R., & Pérez-Palazón, M. J. (2019). The Guadalfeo Monitoring Network (Sierra Nevada, Spain): 14 years of measurements to understand the complexity of snow dynamics in semiarid regions. Earth System Science Data, 11(1), 393–407. https://doi.org/10.5194/essd-11-393-2019
Mendeley helps you to discover research relevant for your work.