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Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco's Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016-2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change. [ABSTRACT FROM AUTHOR]

Morocco, being part of the Mediterranean basin, is characterized by a semi-arid climate heavily affected by climate change, spatial heterogeneity of the water resources along with its high demand. As the region heavily relies on precipitation to supply surface and groundwater, the restraints are a capital threat to its availability. Therefore, conducting studies on the spatio-temporal variations of precipitation ought to be a necessity. Herein, we present the results of our study conducted in the High Oum-Er Rbiaa catchment in the Moroccan Middle Atlas Mountains. Spatial and temporal monitoring of precipitation isotopic composition during a full hydrological year indicated a wide variation of isotopic values ranging from – 3.7‰ to – 13.1‰ and from – 17.1‰ to – 78.2‰ for δ18O and δ2H, between winter and summer respectively. Deuterium excess was marked by a strong seasonality, ranging from -9.2‰ to 27.8‰, with a mean value of 14.8‰. The preliminary local meteoric water line (LMWL: δ 2H = 6.58 × δ 18O + 8.3 (R2 = 0.92)) reflects the significant evaporation effect, whereas the high d-excess values might also suggest Mediterranean-sourced moisture. Global reanalysis data of the vertical integral of moisture flux combined with air masses trajectories simulations, confirm substantial Mediterranean-sourced moisture during the rainiest events in 2020–2021, as shown by the depleted isotope values (– 9.5 < δ18O (‰) < – 13.1; – 55 < δ2H (‰) < – 78.2) and high d-excess (18 < d-excess (‰) < 27). Contrariwise, summer precipitation displays weak and heterogenous moisture sources indicative of continental influence, as reflected by the enriched isotope values and high d-excess (0 < d-excess (‰) < 5) likely due to sub-cloud evaporation process. The findings allowed to emphasize the influence of large-scale water vapor transport of oceanic moisture contributing to winter precipitation within the region, whereas summer precipitation is affected by cloud microphysical processes generating local rainfall. [ABSTRACT FROM AUTHOR]

The spatio-temporal distribution of snow cover metrics in a mountainous area is mainly related to the climatic conditions as well as to the prevailing morphological conditions. The present study aimed to investigate the altitudinal sensitivity of snow cover metrics using the MODIS Terra snow cover product (MOD10A1 v5). Annual snow metrics, including start of snow season (SOSS), end of snow season (EOSS), and snow cover duration (SCD) were extracted from snow-covered area (SCA) maps, which had been pre-processed using a cloud removal algorithm; the maps were of the Atlas Mountains, taken from the period of 2001–2016. In addition, a linear regression was applied to derive an annual altitudinal gradient for each snow metric in relation to various spatial scales in order to analyze the interdependency between snow and topography, and especially to assess the potential temporal trend of the snow gradient. Results indicated that elevation was the principal regulator of snow presence where snow was mostly accumulated above 2500 m. The annual altitudinal gradients for EOSS and SCD showed a marked negative trend beginning in 2007. However, the SOSS altitudinal gradient was marked by a positive trend. The mean SCD gradient for the entire Atlas Mountains decreased from 6 days/100 m to 3 days/100 m. This is a new and important finding since it may indicate the impact of climate change on the dynamics of snow metrics and provides guidance for water managers to better manage the snowmelt water with different terrain features. [ABSTRACT FROM AUTHOR]