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.

Lebanon has experienced serious water scarcity issues recently, despite being one of the wealthiest countries in the Middle East for water resources. A large fraction of the water resources originates from the melting of the seasonal snow on Mount Lebanon. Therefore, continuous and systematic monitoring of the Lebanese snowpack is becoming crucial. The top of Mount Lebanon is punctuated by karstic hollows named sinkholes, which play a key role in the hydrological regime as natural snow reservoirs. However, monitoring these natural snow reservoirs remains challenging using traditional in situ and remote sensing techniques. Here, we present a new system in monitoring the evolution of the snowpack volume in a pilot sinkhole located in Mount Lebanon. The system uses three compact time-lapse cameras and photogrammetric software to reconstruct the elevation of the snow surface within the sinkhole. The approach is validated by standard topographic surveys. The results indicate that the snow height can be retrieved with an accuracy between 20 and 60 cm (residuals standard deviation) and a low bias of 50 cm after co-registration of the digital elevation models. This system can be used to derive the snowpack volume in the sinkhole on a daily basis at low cost.

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 km2 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 minute intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and 2014–2016 for CED and LAQ. Precipitation data was 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 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 (2011–2016). 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 data set bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the Eastern Mediterranean. The doi for the data is: 10.5281/zenodo.291405.

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Abstract. In Lebanon, the seasonal snowpack is poorly monitored despite its importance for water resource supply. The snow accumulates on Mount Lebanon in karstic depressions named “sinkholes.” It is important to monitor the evolution of the snow height inside those “sinkholes”, because of their key role as “containers” for seasonal snow. UAV photogrammetry is a major technological breakthrough which allows an accurate monitoring of the snow height. Because the impact of flight parameters on snow height retrievals is not well documented yet, this research aims to evaluate the impact of UAV flight altitude on the resolution and accuracy of the resulting orthomosaic and DSM. The flight missions were done using the Phantom DJI which generated five DSMs. These are validated using total station measurements.The results indicate that the snow DSMs can be retrieved by adopting a resolution of 8 to 84 cm, a point density between 1.43 and 153 points/sqm and a RMSE of 13 to 41 cm. The testing was done using an elevation varying between 50 and 500 m. The results will be compared to total station observations. These results allow the user to choose the suitable flight altitude for required resolution and points density. We suggest that a flight altitude of 100 m is sufficient for the survey of the snow cover elevation.