Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Changes in Snow Phenology from 1979 to 2016 over the Tianshan Mountains, Central Asia

Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow phenology and their attributes and implications. The results show that the mean snow onset day (Do), snow end day (De), snow cover duration days (Dd), and maximum snow depth (SDmax) from 1979 to 2016 were the 78.2nd day of hydrological year (DOY), 222.4th DOY, 146.2 days, and 16.1 cm over the TS, respectively. Dd exhibited a spatial distribution of days with a temperature of \u3c0 \u3e°C derived from meteorological station observations. Anomalies of snow phenology displayed the regional diversities over the TS, with shortened Dd in high-altitude regions and the Fergana Valley but increased Dd in the Ili Valley and upper reaches of the Chu and Aksu Rivers. Increased SDmax was exhibited in the central part of the TS, and decreased SDmax was observed in the western and eastern parts of the TS. Changes in Dd were dominated by earlier De, which was caused by increased melt-season temperatures (Tm). Earlier De with increased accumulation of seasonal precipitation (Pa) influenced the hydrological processes in the snowmelt recharge basin, increasing runoff and earlier peak runoff in the spring, which intensified the regional water crisi

HUMAN AND CLIMATE IMPACTS ON FLOODING VIA REMOTE SENSING, BIG DATA ANALYTICS, AND MODELING

Over the last 20 years, the amount of streamflow has greatly increased and spring snowmelt floods have occurred more frequently in the north-central U.S. In the Red River of the North Basin (RRB) overlying portions of North Dakota and Minnesota, six of the 13 major floods over the past 100 years have occurred since the late 1990s. Based on numerous previous studies as well as senior flood forecasters’ experiences, recent hydrological changes related to human modifications [e.g. artificial subsurface drainage (SSD) expansion] and climate change are potential causes of notable forecasting failures over the past decade. My dissertation focuses on the operational and scientific gaps in current forecasting models and observational data and provides insights and value to both the practitioner and the research community. First, the current flood forecasting model needs both the location and installation timing of SSD and SSD physics. SSD maps were developed using satellite “big” data and a machine learning technique. Next, using the maps with a land surface model, the impacts of SSD expansion on regional hydrological changes were quantified. In combination with model physics, the inherent uncertainty in the airborne gamma snow survey observations hinders the accurate flood forecasting model. The operational airborne gamma snow water equivalent (SWE) measurements were improved by updating antecedent surface moisture conditions using satellite observations on soil moisture. From a long-term perspective, flood forecasters and state governments need knowledge of historical changes in snowpack and snowmelt to help flood management and to develop strategies to adapt to climate changes. However, historical snowmelt trends have not been quantified in the north-central U.S. due to the limited historical snow data. To overcome this, the current available historical long-term SWE products were evaluated across diverse regions and conditions. Using the most reliable SWE product, a trend analysis quantified the magnitude of change extreme snowpack and melt events over the past 36 years. Collectively, this body of research demonstrates that human and climate impacts, as well as limited and noisy data, cause uncertainties in flood prediction in the great plains, but integrated approaches using remote sensing, big data analytics, and modeling can quantify the hydrological changes and reduce the uncertainties. This dissertation improves the practice of flood forecasting in Red River of the North Basin and advances research in hydrology and snow science

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Remote Sensing of Precipitation: Volume 2

Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Validation et désagrégation de l’humidité du sol estimée par le satellite SMOS en zones agricoles et forestières des Prairies canadiennes

Résumé : Le satellite Soil Moisture and Ocean Salinity (SMOS), lancé en novembre 2009, est le premier satellite en mode passif opérant en bande-L. Cette fréquence est considérée comme optimale pour estimer l’humidité du sol. SMOS est destiné à cartographier l’humidité de la couche 0-5 cm du sol à l’échelle globale, avec une précision attendue inférieure à 0,04 m3/m3, une répétitivité temporelle inférieure à 3 jours et une résolution spatiale d’environ 40 km. L’objectif de cette thèse est de valider l’humidité du sol de SMOS sur des sites agricoles et forestiers situés au Canada, et de contribuer au développement de méthodes de désagrégation de l’humidité du sol estimée par SMOS dans le but d’exploiter ces données dans les études à l’échelle locale telle qu’en agriculture. Les données de la campagne de terrain CanEx-SM10, effectuée sur un site agricole (Kenaston) et un site forestier (BERMS) situés à Saskatchewan, et celles de la campagne SMAPVEX12, effectuée sur un site majoritairement agricole (Winnipeg) situé au Manitoba, sont utilisées. Les données d’humidité du sol de SMOS ont montré une amélioration de la version v.309 à la version v.551. La version 551 des données d’humidité du sol de SMOS se compare mieux aux mesures in situ que les autres versions, aussi bien sur les sites agricoles que sur le site forestier. Sur les sites agricoles, l’humidité du sol de SMOS a montré une bonne corrélation avec les mesures au sol, particulièrement avec la version 551 (R ≥ 0,58, en modes ascendant et descendant), ainsi qu’une certaine sensibilité à la pluviométrie. Néanmoins, SMOS sous-estime l’humidité du sol en général. Cette sous-estimation est moins marquée sur le site de Kenaston en mode descendant (|biais| ≈ 0,03 m3/m3, avec la version v.551). Sur le site forestier, en raison de la densité de la végétation, les algorithmes d’estimation de l’humidité du sol à partir des mesures SMOS ne sont pas encore efficaces, malgré les améliorations apportées dans la version v.551. Par ailleurs, sur le site agricole de Kenaston et le site forestier de BERMS, les données d’humidité du sol de SMOS ont montré, généralement, de meilleures performances par rapport aux produits d’humidité du sol d’AMSR-E/NSIDC, AMSR-E/VUA et ASCAT/SSM. DISaggregation based on Physical And Theoretical scale Change (DISPATCH), un algorithme de désagrégation à base physique, est utilisé pour désagréger à 1 km de résolution spatiale l’humidité du sol de SMOS (40 km de résolution) sur les deux sites agricoles situés à Kenaston et à Winnipeg. DISPATCH est basé sur l’efficacité d’évaporation du sol (SEE) estimée à partir des données optique/ thermique de MODIS, et un modèle linéaire/non-linéaire liant l’efficacité d’évaporation et l’humidité du sol à l’échelle locale. Sur un site présentant une bonne dynamique spatiale et temporelle de l’humidité du sol (le site de Winnipeg au cours de la campagne de terrain SMAPVEX12), les résultats de DISPATCH obtenus avec le modèle linéaire sont légèrement meilleurs (R = 0,81 ; RMSE = 0.05 m3/m3 et pente = 0,52, par rapport aux mesures in situ) comparés aux résultats obtenus avec le modèle non-linéaire (R = 0,72 ; RMSE = 0.06 m3/m3 et pente = 0,61, par rapport aux mesures in situ). La précision de l’humidité du sol dérivée de DISPATCH, en se basant sur les deux modèles linéaire et non linéaire, décroit quand l’humidité du sol à grande échelle croît. Cette étude a montré, également, que DISPATCH peut être généralisé sur des sites particulièrement humides (le site de Kenaston au cours de la campagne de terrain CanEx-SM10). Cependant, en conditions humides, les résultats dérivés avec le modèle non-linéaire (R > 0,70, RMSE = 0,04 m3/m3 et pente ≈ 0,80, par rapport aux valeurs d’humidité du sol dérivées des mesures aéroportées de la température de brillance en bande L) ont montré de meilleures performances comparées à ceux dérivés avec le modèle linéaire (R > 0,73, RMSE = 0,08 m3/m3 et pente > 1.5, par rapport aux valeurs d’humidité du sol dérivées des mesures aéroportées de la température de brillance en bande L). Ceci est dû à une sous-estimation systématique de la limite sèche Tsmax. Par ailleurs, l’humidité du sol désagrégée présente une forte sensibilité à〖 Ts〗_max, particulièrement avec le modèle linéaire. Une approche simple a été proposée pour améliorer l’estimation de〖 Ts〗_max, dans des zones particulièrement humides. Elle a permis de réduire l’impact de l’incertitude sur〖 Ts〗_max dans le processus de désagrégation. Avec 〖 Ts〗_max améliorée, le modèle linaire aboutit à de meilleurs résultats (R > 0,72, RMSE = 0,04 m3/m3 et pente ≈ 0,80, par rapport aux valeurs d’humidité du sol estimées à partir des mesures aéroportées de la température de brillance en bande-L) que le modèle non-linéaire (R > 0,64, RMSE = 0,05 m3/m3 et pente ≈ 0,3, par rapport aux valeurs d’humidité du sol estimées à partir des mesures aéroportées de la température de brillance en bande-L). Basé sur des données optiques/ thermiques de MODIS, DISPATCH n’est pas applicable pour les journées nuageuses. Pour surmonter cette limitation, une nouvelle méthode a été proposée. Elle consiste à combiner DISPATCH avec le schéma de surface Canadian Land Surface Scheme (CLASS). Les données d’humidité du sol à 1 km de résolution dérivées de DISPATCH pour les journées non nuageuses sont utilisées pour calibrer les simulations de CLASS disponibles continuellement aux heures de passage de SMOS. Une approche de calibration basée sur la correction de la pente entre les valeurs d’humidité du sol dérivées de CLASS et les valeurs d’humidité du sol dérivées de DISPATCH (données de référence) a été mise au point. Les résultats montrent que les données d’humidité du sol à 1 km de résolution dérivées de cette nouvelle approche pour les journées nuageuses se comparent bien aux mesures in situ (R = 0,80 ; biais = -0,01 m3/m3 et pente = 0,74). Pour les journées non nuageuses, les valeurs d’humidité du sol dérivées de DISPATCH seul se comparent mieux aux mesures in situ que les valeurs dérivées en combinant DISPATCH à CLASS.Abstract : The Soil Moisture and Ocean Salinity (SMOS), launched in November 2009, is the first passive microwave satellite operating in L band which is considered as optimal for soil moisture estimation. It is designed to provide global soil moisture maps at 0 – 5 cm layer from soil surface with a targeted accuracy of 0.04 m3 / m3, revisit time of less than 3 days anda spatial resolution of about 40 km. The objective of this thesis is to validate SMOS soil moisture data over agricultural and forested sites located in Canada, and to contribute to the development of SMOS downscaling methods in order to exploit these data in local scale studies such as agriculture. The data used are collected during the CanEX-SM10 field campaign, conducted over an agricultural site (Kenaston) and a forested site (BERMS) located in Saskatchewan, and during SMAPVEX12 field campaign conducted over a mostly agricultural area (Winnipeg) located in Manitoba. SMOS soil moisture data showed an improvement from the processor versions 309 to 551. Version 551 was found to be closer and more correlated to ground measurements over both agricultural and forested sites. For the agricultural sites, SMOS soil moisture showed high correlation coefficient with ground data especially with version 551(R ≥ 0.58, for ascending and descending overpasses), as well as a certain sensitivity to rainfall events. However, the SMOS soil moisture values were underestimated compared with ground measurements. This underestimation is less pronounced for the descending overpass over the Kenaston site (|bias| viii ≈ 0.03 m3/m3, for version v.551). For the forested site, due to the vegetation density, the SMOS soil moisture estimation algorithms were not very efficient despite the improvements brought to version 551. Moreover, over the agricultural site of Kenaston and the forested site of BERMS, SMOS soil moisture data showed, in general, good performances compared to AMSR-E/NSIDC, AMSR-E/VUA and ASCAT/SSM soil moisture products. DISaggregation based on Physical And Theoretical scale Change (DISPATCH), a physically-based downscaling algorithm, was used to downscale at 1-km spatial resolution the SMOS soil moisture estimates (40-km resolution) over the agricultural sites located in Kenaston and Winnipeg. DISPATCH is based on the Soil Evaporative Efficiency (SEE) derived from optical/thermal MODIS data, and a linear/non-linear model linking the Soil Evaporative Efficiency to the near-surface soil moisture at local scale. Over a site with a good spatial and temporal dynamics of soil moisture (such as Winnipeg’s site during the SMAPVEX12 field campaign), slightly better results were obtained with DISPATCH based on the linear model (R = 0.81, RMSE = 0.05 m3 /m3 and slope = 0.52, with respect to ground data) compared to results obtained from the non-linear model (R = 0.72, RMSE = 0.06 m3 /m3 and slope = 0.61, with respect to ground data). The accuracy of the DISPATCH-derived soil moisture, using both linear and non-linear models, decreases when the large-scale soil moisture increases. This study also showed, also, that DISPATCH can be generalized for very wet soil conditions (Kenaston’s site during the CanEX-SM10 field campaign). However, under wet soil conditions, better results were obtained with DISTACH based on the nonlinear (R > 0.70, RMSE = 0.04 m3/m3 and slope ≈ 0.80, with respect to the estimated soil moisture form L-band airborne brightness temperature) compared to results obtained with ix DISPATCH based on the linear model (R > 0.73, RMSE = 0.08 m3/m3 and slope > 1.5, with respect to the estimated soil moisture form L-band airborne brightness temperature). This is due to a systematic underestimation of the dry edge Tsmax. Furthermore, the downscaling results were found to be very sensitive to , particularly with the linear model. A simple approach was proposed to improve the estimation of Tsmax under very wet soil conditions. It allowed reducing the impact of uncertainty in the disaggregation process. Using the improved Tsmax value, better results were obtained with the linear model (R > 0.72, RMSE = 0.04 m3/m3 and slope ≈ 0.80, with respect to the estimated soil moisture form L-band airborne brightness temperature) compared to the non-linear model (R > 0.64, RMSE = 0.05m3/m3 and slope ≈ 0.3, with respect to the estimated soil moisture form L-band airborne brightness temperature). Based on optical/thermal MODIS data, DISPATCH is not applicable for cloudy days. To overcome this limitation, a new method was proposed. It involves the combination of DISPATCH with the Canadian Land Surface Scheme (CLASS). DISPATCH-derived soil moisture data for cloud-free days are used to calibrate CLASS soil moisture simulations which are continually available at SMOS overpasses times. A calibration approach based on slope correction between the CLASS-derived and DISPATCH-derived (reference data) soil moisture datasets is considered. Results showed that soil moisture values derived from this newly developed method during cloudy days compare well with in situ data (R = 0.80, RMSE = 0.07 m3/m3 and slope = 0.73). For no-cloudy days, DISTATCH-derived soil moisture data are closer to in situ data than those derived when combining DISPATCH with CLASS

Evaluation de la ressource en eau associée au manteau neigeux sur le Mont Liban à partir d'observations et de la modélisation

Les ressources en eau du Liban sont soumises à une pression croissante due au développement économique, à la croissance démographique, à la gestion non-durable des ressources en eau et au changement climatique. Les montagnes du Mont et Anti-Liban sont des châteaux d'eau naturels pour le Liban car elles augmentent les précipitations par le soulèvement orographique des masses d'air. En raison de l'influence du climat méditerranéen, la plupart des précipitations au-dessus de 1200 m a.s.l. tombe sous la forme de neige en hiver. Par conséquent, la fonte des neiges contribue de façon importante au bilan hydrique national. En particulier, la fonte des neiges du Mont-Liban alimente les réseaux d'eau souterraine karstiques, qui fournissent des ressources en eau essentielle pour la région côtière. Malgré l'importance du manteau neigeux au Liban, sa variabilité spatiale et temporelle est insuffisament observée si bien que sa contribution au débit des fleuve et des sources reste méconnue. L'objectif de ce travail est de réduire ce manque de connaissance en utilisant des mesures in situ, des observations satellite et de la modélisation du manteau neigeux. 1. Nous présentons d'abord une revue de la littérature sur les processus nivo- hydrologiques dans les régions montagneuses méditerranéennes. De nombreuses études - principalement aux Etats-Unis de l'Ouest et dans les montagnes au sud de l'Europe - soulignent l'impact fort de la variabilité interannuelle du climat méditerranéen sur la dynamique du manteau neigeux. Le rayonnement solaire élevé est un facteur important du bilan énergétique du manteau neigeux, mais la contribution des flux de chaleur est plus forte à la fin de la saison nivale. La sublimation de la neige et la densification rapide sont des processus importants dans ce contexte. Les approches hybrides combinant des données de stations météorologiques et la télédétection optique de la surface enneigée à travers la modélisation sont recommandées pour compenser l'absence d'observations spatialisées du forçage météorologique. 2. Ensuite, nous présentons un ensemble original de données sur le manteau neigeux au Mont-Liban pour la période 2013-2016. Nous avons recueilli des observations sur le terrain de la hauteur de neige (HS), de l'équivalent en eau de neige (SWE) et de la densité de neige entre 1300 et 2900 m d'altitude sur le flanc occidental du Mont-Liban. De plus, des données météorologiques continues ont été acquises par trois stations météorologiques automatiques situées dans la partie enneigée du Mont-Liban. Le produit MODIS a été utilisé pour calculer la superficie couverte par la neige dans trois bassins hydrographiques couverts par les observations in situ. Nous remarquons la grande variabilité de HS et SWE et une densité élevée du manteau neigeux. Nous trouvons une corrélation significative entre HS et SWE qui peut être utile pour réduire la quantité de travail de terrain en vue d'un suivi opérationnel futur. 3. Grâce à ces données, nous avons mis en place un modèle distribué du manteau neigeux sur le Mont-Liban à une résolution de 100 m. Le modèle est validé à différentes échelles en utilisant les observations de SWE, densité, HS et SCA. Une simulation avec des modifications très limitées du paramétrage par défaut permet de capturer correctement la plupart des observations. Cette simulation permet donc d'estimer l'évolution du SWE et la fonte dans les trois bassins étudiés entre 2013 et 2016. Cette recherche a mis en évidence l'importance de réaliser simultanément des mesures sur le terrain et des observations météorologiques continues pour mieux appréhender les processus physiques qui contrôlent l'évolution du manteau neigeux sur le Mont-Liban. Enfin, l'influence du transport de la neige par le vent et des dépôts de poussière sur la fonte des neiges reste à évaluer en perspective de ce travail.Lebanon's water resources are under increasing pressure due to economic development, demographic growth, unsustainable water resource management, and climate change. The Mount- and Anti-Lebanon Mountains are natural water towers for Lebanon as they play an important role in enhancing orographic precipitation. Due to the influence of the Mediterranean climate, most precipitation above 1200 m a.s.l. falls as snow during winter season. As a result, snowmelt is an important contributor to the national water balance. In particular, snowmelt from Mount-Lebanon feeds the karst groundwater systems, which provide key water resources to the coastal region. Despite the importance of the snow cover in the Lebanese mountains, the actual snowpack spatial and temporal variability and its contribution to the spring and river discharges in Lebanon remains poorly constrained. The objective of this work is to reduce this lack of knowledge using a combination of in situ measurements, remote sensing observations and modelling of the snowpack in Mount-Lebanon. 1. We first present an extensive review of the literature about the snow hydrological processes in Mediterranean-like mountain regions. Many studies - mainly from Western USA and Southern Europe mountains - emphasize the strong impact of the interannual Mediterranean climate variability on the snowpack dynamics. The high incoming solar radiation is an important driver of the snowpack energy balance, but the contribution of heat fluxes is stronger at the end of the snow season. Snow sublimation and rapid densification are important processes to consider. Hybrid approaches combining weather station data with optical remote sensing of the snow extent through modelling are recommended to tackle the lack of spatially-distributed observations of the meteorological forcing. 2. Then, we introduce an original dataset on the snow cover in Mount-Lebanon for the period 2013-2016. We collected field observations of the snow height (HS), snow water equivalent (SWE), and snow density between 1300 and 2900 m a.s.l. in the western slope of Mount-Lebanon. In addition, continuous meteorological data were acquired by three automatic weather stations located in the snow dominated region of Mount-Lebanon. The MODIS snow product was used to compute the daily snow cover area in three snow dominated basins. We find that HS and SWE have large variances and that snow density is high. The strong correlation between HS and SWE may be useful to reduce the amount of field work for future operational monitoring. 3. Using these data we set up a distributed snowpack energy balance in the Mount- Lebanon at 100 m resolution. The model is validated at different scales using the observed SWE, snow density, HS and SCA. A simulation with very limited adjustments to the default parameterization is found to correctly capture most of the observations. This simulation allows the estimation of the SWE evolution and snow melt in the three study basins between 2013 and 2016. This research highlighted the importance of conducting simultaneous field surveys and meteorological observations to gain insights into the physical processes driving snowpack evolution in Mount-Lebanon. Finally, the influence of snow erosion by wind and the influence of dust deposits on snowmelt, remains less known, and are warrant for future research