Snow observations in Mount-Lebanon (2011–2016)

Fayad, Abbas; Gascoin, Simon; Faour, Ghaleb; Fanise, Pascal; Drapeau, Laurent; Somma, Janine; Fadel, Ali; Bitar, Ahmad Al; Escadafal, Richard

doi:10.5194/essd-2017-3

Cited by 7 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used the following input meteorological variables: precipitation (LAQ, MZA), air temperature (LAQ, MZA, CED), air relative humidity (LAQ, MZA, CED), wind speed and direction (LAQ, MZA, CED). No observations were available for LAQ AWS during snow season 2013-2014 (Fayad et al, 2017b). We did not use the shortwave radiation measurements because of the large data gaps.…”

Section: Model Setupmentioning

confidence: 99%

“…The validation dataset includes (1) half-hourly snow height (HS), snow albedo and incoming shortwave radiation collected at the AWS from W1314 to W1516, (2) bi-weekly manual snow density measurements collected near the AWS during the snow seasons of W1415 and W1516, and (3) daily snow cover area (SCA) observations from MODIS from W1314 to W1516. All these data are fully described in Fayad et al (2017b) and are available as open data in a public repository (Fayad et al, 2017c).…”

Section: Model Evaluationmentioning

confidence: 99%

“…In this study, we make use of a newly available snow dataset (Fayad et al, 2017b) to evaluate this new percolation scheme in SnowModel to compute the SWE distribution and its evolution, for the first time, in three basins in the warm Mediterranean mountains of Lebanon. We evaluate the model outputs using both in situ (continuous snow depth records at AWS and snow course measurements) and remote sensing data (MODIS snow cover area).…”

mentioning

confidence: 99%

See 2 more Smart Citations

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Fayad¹,

Gascoin²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

show abstract

Section: Model Setupmentioning

confidence: 99%

Section: Model Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Fayad¹,

Gascoin²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…As incidence angle increases, the ground backscattering coefficient decreases and the total backscatter reflects the volume contributions. The plots are done for a 0.476 g/cm 3 snow density which will be used in the application of the snow depth inversion algorithm because the median seasonal snow density in Lebanon over the 2-year period (2014-2016) was 0.476 g/cm 3 [13].…”

Section: Backscattering Behavior Of Dry Snowmentioning

confidence: 99%

Numerical Analysis of Radar Response to Snow Using Multiple Backscattering Measurements for Snow Depth Retrieval

Mazeh¹,

Hammoud²,

Ayad³

et al. 2018

PIER B

Self Cite

View full text Add to dashboard Cite

Study of snow is an important domain of research in hydrology and meteorology. It has been demonstrated that snow physical properties can be retrieved using active microwave sensors. This requires an understanding of the interaction between electromagnetic (EM) waves with natural media. The objective of this work is twofold: to study numerically all physical forward models concerning the EM wave interaction with snow and to develop an inverse scattering algorithm to estimate snow depth based on radar backscattering measurements at different frequencies and incidence angles. For the first part, the goal is to solve the scattering calculations by means of the well-known electromagnetic simulator Ansoft High Frequency Structure Simulator (HFSS). The numerical simulations include: the effective permittivity of snow, surface scattering phenomena in layered homogeneous media (air-snow-ground) with rough interfaces, and volume scattering phenomena when treating snow as a dense random media. For the second part, the study is extended to develop a retrieval method to estimate snow thickness over ground from backscattering observations at Land X-band using multiple incidence angles. The return signal from snow over ground is influenced by: surface scattering, volume scattering, and the noise effects of the radar system. So, the backscattering coefficient from the medium is modelled statistically by including a white Gaussian noise into the simulation. This inversion algorithm estimates first the snow density using L-band co-polarized backscattering coefficient at normal incidence and then retrieves the snow depth from X-band co-polarized backscattering coefficients using dual incidence angles.

show abstract

“…In semi-arid and Mediterranean mountains like the High Atlas, the snow accumulates during winter in the high elevation areas of the catchments, where the terrain complexity enhances the variability of the climatic conditions [5]. Spatial variability in near-surface meteorological variables like precipitation, air temperature, wind speed, solar radiation result in heterogeneous snow accumulation and ablation patterns, which influence the spatio-temporal dynamics of the melting rates and streamflow [6,7].…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

Baba¹,

Gascoin²,

Hanich³

2018

Preprint

Self Cite

View full text Add to dashboard Cite

The snow melt from the High Atlas is a critical water resource in Morocco. In spite of its importance, monitoring the spatio-temporal evolution of key snow cover properties like the snow water equivalent remains challenging due to the lack of in situ measurements at high elevation. Since 2015, the Sentinel-2 mission provides high spatial resolution images with a 5 day revisit time, which offers new opportunities to characterize snow cover distribution in mountain regions. Here we present a new data assimilation scheme to estimate the state of the snowpack without in situ data. The model was forced using MERRA-2 data and a particle filter was developed to dynamically reduce the biases in temperature and precipitation using Sentinel-2 observations of the snow cover area. The assimilation scheme was implemented using SnowModel, a distributed energy-balance snowpack model and tested in a pilot catchment in the High Atlas. The study period covers 2015-2016 snow season which corresponds to the first operational year of Sentinel-2A, therefore the full revisit capacity was not yet achieved. Yet, we show that the data assimilation led to a better agreement with independent observations of the snow height at an automatic weather station and the snow cover extent from MODIS. The performance of the data assimilation scheme should benefit from the continuous improvements in MERRA-2 reanalyses and the full revisit capacity of Sentinel-2.

show abstract

Snow observations in Mount-Lebanon (2011–2016)

Cited by 7 publications

References 19 publications

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

Numerical Analysis of Radar Response to Snow Using Multiple Backscattering Measurements for Snow Depth Retrieval

Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco

Contact Info

Product

Resources

About