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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.

Recent efforts have been concentrated in the development of models to understand and predict the impact of environmental changes on hydrological cycle and water resources in arid and semi-arid regions. In this context, remote sensing data have been widely used to initialize, to force or to control the simulations of these models. However, for several reasons, including the difficulty in establishing relationships between observational and model variables, the potential offered by satellite data has not been fully used. As a matter of fact, a few hydrological studies that use remote sensing data emanating from different sources (sensors, platforms) have been performed. In this context, the SUDMED program has been designed in 2002 to address the issue of improving our understanding about the hydrological functioning of the Tensift basin which is a semi-arid basin situated in central Morocco. The first goal is model development and/or refinement, for investigating the hydrological responses to future scenario about climate change and human pressure. The second aim is the effective use of remote sensing observations in conjunction with process models, to provide operational prognostics for improving water resource management. The objective of this paper is to present the SUDMED program, its objectives and its thrust areas, and to provide an overview of the results obtained in the first phase of the program (2002)(2003)(2004)(2005)(2006). Finally, the lessons learned, future objectives and the unsolved issues are presented.

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