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In this study we quantified the sensitivity of snow to climate warming in selected mountain sites having a Mediterranean climate, including the Pyrenees in Spain and Andorra, the Sierra Nevada in Spain and California (USA), the Atlas in Morocco, and the Andes in Chile. Meteorological observations from high elevations were used to simulate the snow energy and mass balance (SEMB) and calculate its sensitivity to climate. Very different climate sensitivities were evident amongst the various sites. For example, reductions of 9%-19% and 6-28 days in the mean snow water equivalent (SWE) and snow duration, respectively, were found per°C increase. Simulated changes in precipitation (±20%) did not affect the sensitivities. The Andes and Atlas Mountains have a shallow and cold snowpack, and net radiation dominates the SEMB; and explains their relatively low sensitivity to climate warming. The Pyrenees and USA Sierra Nevada have a deeper and warmer snowpack, and sensible heat flux is more important in the SEMB; this explains the much greater sensitivities of these regions. Differences in sensitivity help explain why, in regions where climate models project relatively greater temperature increases and drier conditions by 2050 (such as the Spanish Sierra Nevada and the Moroccan Atlas Mountains), the decline in snow accumulation and duration is similar to other sites (such as the Pyrenees and the USA Sierra Nevada), where models project stable precipitation and more attenuated warming. The snowpack in the Andes (Chile) exhibited the lowest sensitivity to warming, and is expected to undergo only moderate change (a decrease of <12% in mean SWE, and a reduction of < 7 days in snow duration under RCP 4.5). Snow accumulation and duration in the other regions are projected to decrease substantially (a minimum of 40% in mean SWE and 15 days in snow duration) by 2050.

In the centre of Morocco, the High Atlas mountain range represents the most important water storage for the neighbouring arid plains through liquid and solid precipitation. In this context, we evaluated the performance of the Snowmelt Runoff Model (SRM) on the five main tributary watersheds of the High Atlas range. Due to the very low density of climate stations in the High Atlas, snowfall and snowmelt processes are difficult to monitor using meteorological data alone. In order to compensate for the lack of in situ data, snow maps are also derived from remotely-sensed data. We compared the streamflow forecasting performance when the model is driven by one or the other estimate of snow-covered area. Both estimates are generally comparable in all watersheds, and satisfactory streamflow simulations were obtained at seasonal time scales using both snow-cover products. However, significant differences can be observed for selected storms, with more accurate streamflow predictions being obtained when the remotely-sensed data are used. Evaluation du Modèle Snowmelt Runoff dans le Haut Atlas Marocain en utilisant deux estimations des surfaces enneigéesRésumé Au centre du Maroc, la chaîne montagneuse du Haut Atlas constitue un véritable château d'eau pour les plaines arides avoisinantes, et ce grâce aux précipitations liquides et solides. Dans ce contexte, nous avons évalué les performances du modèle Snowmelt Runoff (SRM) dans les cinq principaux sous-bassins versants du Haut Atlas. En raison de la très faible densité des stations climatiques dans le Haut Atlas, les processus de chute et de fonte des neiges sont difficiles à contrôler avec des données météorologiques seules. Afin de compenser l'absence de données in situ, des cartes d'enneigement sont également dérivées de données issues de la télédétection. Nous avons comparé les performances de SRM pour la prévision de l'écoulement en cours d'eau à partir des deux types de carte. Les surfaces enneigées déduites par ces deux méthodes sont généralement comparables dans tous les sous-bassins versants, et des simulations d'écoulement satisfaisantes ont été obtenues à l'échelle saisonnière à partir des deux estimations de surface de neige. En revanche, des différences significatives peuvent être observées pour certains événements avec des prévisions d'écoulement plus précises lorsque les données issues de la télédétection sont utilisées.