Characterization of the time dynamics of monthly satellite snow cover data on Mountain Chains in Lebanon

Telesca, Luciano; Shaban, Amin; Gascoin, Simon; Darwich, Talal; Drapeau, Laurent; Hage, Mhamad El; Faour, Ghaleb

doi:10.1016/j.jhydrol.2014.10.037

Cited by 25 publications

(20 citation statements)

References 31 publications

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“…The higher densities (Fig. 8b) are typical with Mediterranean, warm maritime, and alpine regions (e.g., Sturm et al, 2010). In contrast, the observed scatter between snow density and HS (Fig.…”

Section: Modeling Snow Bulk Densitymentioning

confidence: 89%

“…8b) cannot be explained using linear estimators. While SWE can be estimated linearly from HS, it is recommended to model the bulk density from HS and then derive SWE (Sturm et al, 2010). Such an approach is justified by the fact that (1) depth varies over a range that is many times greater than that of bulk density and (2) because estimates derived from measured depths and modeled densities are usually very close to measured values of SWE.…”

Section: Modeling Snow Bulk Densitymentioning

confidence: 99%

“…The snow density can be estimated from HS using a linear function. However, a better represen- tation of snow density from snow depth measurement can be achieved using a nonlinear function that include HS and accounts for the effect of snow aging (represented in terms of day of the year, DOY) (Sturm et al, 2010). Distinct classes for different climate regions are used to account, indirectly, for the effects of meteorological condition (i.e., temperature and wind) (Sturm et al, 2010).…”

Section: Modeling Snow Bulk Densitymentioning

confidence: 99%

“…falls as snow on Mount Lebanon (Shaban et al, 2004;Aouad-Rizk et al, 2005;Mhawej et al, 2014;UNDP, 2014). The average contribution of snowmelt to spring and river discharge in Mount Lebanon was estimated at 30 % by Telesca et al (2014). Snowmelt contributes to the recharge of karstic aquifers and springs of all the watersheds located in the windward regions of Mount Lebanon ( Fig.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, only a few datasets on precipitation, temperature, snow, groundwater recharge and streamflow are available for basin-scale studies (e.g., Königer and Margane, 2014). Existing datasets, available through published mate-rial, are usually limited to (1) national-scale studies with monthly or yearly means (Shaban et al, 2004;Corbane et al, 2005;Mhawej et al, 2014;Telesca et al, 2014), (2) multiyear daily spring discharge and precipitation time series (e.g., Hreiche et al, 2007), (3) seasonal observations collected for mesoscale catchment studies (Bernier et al, 2003;AouadRizk et al, 2005;Doummar et al, 2014), and (4) local-scale snowpack observations (e.g., Somma et al, 2006Somma et al, , 2014. In most cases the research datasets are not made publicly available.…”

Section: Introductionmentioning

confidence: 99%

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Snow observations in Mount Lebanon (2011–2016)

Fayad

Gascoin

Faour

et al. 2017

Earth Syst. Sci. Data

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Abstract. We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km 2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011)(2012)(2013)(2014)(2015)(2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m −3 . We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean.

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Section: Modeling Snow Bulk Densitymentioning

confidence: 89%

Section: Modeling Snow Bulk Densitymentioning

confidence: 99%