Snow hydrology in the Moroccan Atlas Mountains

The downstream part of the Essaouira basin, Morocco, Northwestern Africa. This study combines multivariate statistical analyses, stable isotopes, hydrogeochemical, hydrogeological, geological and remotely sensed data to gain a better understanding of the hydrological and groundwater salinization processes in a coastal aquifer. Hydrogeochemical data from 223 samples as well as stable isotope data collected during 10 sampling campaigns between 1990 and 2020 were compiled and subjected to a comprehensive analysis. Groundwater recharge obtained from the stable isotope approach was checked using a water balance method based on remote sensing data of actual evapotranspiration. Seawater intrusion was detected only for the period of 2009–2020, nearby the ocean, whereas hyper-salinity due to evaporite dissolution occurred throughout the entire period (1990–2020) primarily in the southern part of the aquifer. Additionally, the isotopic mass balance model shows that aquifer recharge from the Ksob river area is mainly located in the northeastern region. Furthermore, based on average values of stable isotopes of rainfall and groundwater, preliminary results show that only months of November, December and January with rainfall higher than 54 mm contribute to groundwater recharge. The water balance approach confirmed these results, but also revealed a lower recharge in February, with a monthly average precipitation of 46 mm. The outcomes of this research enabled the construction of a comprehensive conceptual 3D model of the Essaouira coastal aquifer. The insights from this research are important to guide measures for sustainable water resources management. 

A comprehensive overview of groundwater salinization and recharge processes in a semi-arid coastal aquifer (Essaouira, Morocco)

The upstream part of the Essaouira basin, a data-scare region in Morocco, Northwestern Africa. The scarcity of hydro-climate data is a significant challenge found in several regions worldwide, where qualitative and quantitative water resource information remains limited. Estimating and predicting groundwater levels (GWL) in such areas is a significant challenge in producing knowledge for effective water resource management. To address this issue, the present study aimed to use the Soil and Water Assessment Tool (SWAT) model in conjunction with downscaled total water storage (TWS) data (9 km) obtained from Gravity Recovery And Climate Experiment (GRACE) and machine learning techniques, specifically random forest (RF) and support vector machine (SVM), to estimate and predict the variation in GWL. This study constitutes a first of its kind in the study area; the SWAT model was set up for 10 years, with a warm-up period from 2000 to 2001, calibration from 2002 to 2007, and validation from 2008 to 2010. The statistical indices (Coefficient of Determination (R²) ≥ 0.73, R² ≥ 0.78, Nash–Sutcliffe model efficiency coefficient (NSE) ≥ 0.67, NSE ≥ 0.80 respectively for calibration and validation) highlight a significant correlation, implying the model's capability to faithfully reproduce the streamflow. The downscaled TWS demonstrates an impressive ability to identify and monitor fluctuations in GWL. Using machine learning algorithms (RF and SVR), the prediction of GWL yielded satisfactory results, NSE = 0.78 and root mean square error (RMSE) = 0.33, NSE = 0.51 and RMSE = 0.49 for the RF and SVR, respectively. Despite some limitations, our approach provided promising results in GWL prediction, with the possibility of expanding to other data-scarce regions. 

Groundwater level forecasting in a data-scarce region through remote sensing data downscaling, hydrological modeling, and machine learning: A case study from Morocco

The Atlas Mountains of Morocco, specifically the High Oum Er-rbiaa (HOER) and Ourika catchments. to identify the recharge processes within the semi-arid watersheds, in the Atlas Mountains, through monthly monitoring of snow, rainfall, surface water, and groundwater isotope signal, but also the usage of remote sensing data. The spatial-temporal analysis of groundwater and precipitation isotopes reveals significant spatial heterogeneity, primarily influenced by the geological variations in each aquifer. Temporal variations indicate that direct recharge occurs in response to winter precipitation, whereas a delayed response is observed during the summer when snow replenishes groundwater towards the end of the melting season. The findings are further supported by the "Gravity Recovery and Climate Experiment" (GRACE) dataset, which demonstrates that high values of Total Water Storage (TWS) align with groundwater isotopes. This highlights the substantial groundwater abstraction rate between March and June to compensate for the lack of precipitation during this period. The analysis of isotope data indicates that 50% of groundwater recharge in the upstream Jurassic aquifer and 80% in the downstream Triassic-Paleozoic aquifers in the HOER catchment is sourced from snowmelt. Similarly, in the Ourika catchment, snowmelt contributes 30% and 50% of groundwater recharge in the upstream and downstream portions of the catchments respectively. This disparity is due to different melting rates across altitudinal ranges and variations in the lithology of each catchment. 

Snowpack and groundwater recharge in the Atlas mountains: New evidence and key drivers

Precipitation isotopes to elucidate moisture sources in the Western Mediterranean: case of the Middle Atlas Mountains, Morocco

A Multi-Tool 3D Conceptual Model to Elucidate Groundwater Processes, Vulnerability, and Recharge Patterns in a Semi-Arid Region: A Case Study from Morocco

The significantly high water stress affecting Morocco during the past decades was an impact of drought and overexploitation of water resources. Therefore, assessment of these resources for better water management is a necessity. In our context, the High and Middle Atlas Mountains are the main contributors of downstream feeding within the country. The objective of this study is to assess rainfall and snowmelt runoff supplying groundwater of the aforementioned regions using the stable isotopes approach. Sampling campaigns and chemical and isotopic analyses were carried out in the Ourika and High Oum Er-Rbiaa sub-catchments, belonging to the great Tensift and the Oum Er-Rbiaa basins respectively. The snowmelt sampling in the Ourika basin was effectuated in two ways: the polyvinyl chloride snow cover boring and the passive capillary samples. The isotopic data of the Ourika basin allows estimating a recharge altitude between 2000 and 3000 m, following an altitudinal gradient of 0.27‰/100m. The contribution of rainfall and snowmelt in groundwater recharge using the isotopic mass balance model suggests a snowmelt recharge rate of 47% while rainfall supplies about 53%. The isotopic preliminary results from the HOER catchment indicates that all samples are close to global and local water meteoric lines suggesting their common origin from the Atlantic air masses, and recharge altitude varies between 1600 and 2900 m according to a gradient of 0.27‰/100 m. Ultimately, this study reveals the usefulness of environmental tracers to assess the endmembers contributions (snow and rain) to groundwater recharge at the catchment scale.

Isotopic tracers to assess the snowmelt contribution to the groundwater recharge: a case from the Moroccan High and Middle Atlas Mountains

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Papers

Snowpack and groundwater recharge in the Atlas mountains: New evidence and key drivers

Precipitation isotopes to elucidate moisture sources in the Western Mediterranean: case of the Middle Atlas Mountains, Morocco

A Multi-Tool 3D Conceptual Model to Elucidate Groundwater Processes, Vulnerability, and Recharge Patterns in a Semi-Arid Region: A Case Study from Morocco

Isotopic tracers to assess the snowmelt contribution to the groundwater recharge: a case from the Moroccan High and Middle Atlas Mountains