Abstract
Abstract. Mountain areas in Mediterranean regions constitute key monitoring points for climate variability and its impacts, but long time datasets are not always available due to the difficult access to high areas, relevant for capturing temperature and precipitation regimes, and the predominance of cloudy remote sensing images during the snow season. Sierra Nevada National Park (South Spain), with altitudes higher than 3500 m a.s.l., is part of the Global Change in Mountain Regions network. Snow occurrence just 40 km from the seaside determines a wide range of biodiversity, a snowmelt fluvial regime, and the associated ecosystem services. This work presents the local trend analysis of weather variables at this area together with additional snow-related variables. For this, long term point and distributed observations from weather stations and remote sensing sources were studied and used as input and calibration datasets of a physically based snow model to derive long term series of mean and maximum daily fraction of snow covered area, annual number of days with snow, annual number of days with precipitation, mean and maximum mean daily snow water equivalent, and snowmelt and evaporation volumes. The joint analysis of weather and snow variables showed a decrease trend in the persistence and extent of the snow cover area. The precipitation regime, rather than the temperature trend, seems to be the most relevant driver on the snow regime forcing in Mediterranean areas. This poses a constraint for rigorous scenario analysis in these regions, since the precipitation pattern is poorly approximated by climatic models in these regions.
Highlights
Climate variability impact on the hydrological regime is more evident over mountain regions due their particular extreme conditions (Beniston, 2003; IPCC, 2007)
The results show a clear decrease of the annual snowfall on a regional basis (−1.25 mm year−1), which is reflected in a reduction of the annual mean SWE (0.23 mm year−1).the shift in the precipitation regime seems to be the most determining driver of the snow variables annual trends
The annual snowfall correlates linearly with P (r2 = 0.50), but this correlation is very poor with Tmean(r2 = 0.18), some increasing trend could be observed with decreasing annual mean daily maximum temperature values
Summary
Climate variability impact on the hydrological regime is more evident over mountain regions due their particular extreme conditions (Beniston, 2003; IPCC, 2007). In regions where the typical alpine-mountain climate is modified by additional meteorological drivers(i.e. the recurrence of drought periods and torrential rainfall events), these impacts are enhanced, which makes them crucial areas to monitor these climate variations. This is the case of Mediterranean mountainous areas, where alpine and semiarid conditions coexist (Giorgi, 2006). The limited accessibility to these sites during the snow season, and the hard working conditions for the instrumentation, make in situ continuous monitoring systems difficult to maintain (De Walle and Rango, 2008). The potential use of satellite remote sensing information (i.e. Landsat TM, ETM+ and OLI with temporal resolution of 16 days and spatial resolution of 30 m × 30 m, MODIS, daily images with 250 m × 250 m spatial resolution, and NOAA, 1 km×1 km daily images) to capture snow evolution at medium and large scales is limited to recent decades, poses a constraint for snow applications in heterogeneous ar-
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