Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach

Snow water equivalent (SWE) is a critical variable for understanding water availability and snowmelt-driven streamflow in mountainous regions. Yet, its spatial and temporal estimation is constrained by scarce in situ measurements and the inherent challenges of deriving SWE directly from satellite observations. Thus, accurate SWE assessment is essential for predicting the spatial distribution of snowpack and its temporal contributions to downstream outflow, particularly in semi-arid snow-fed basins like Morocco's High Atlas regions. In this study, we simulate the local and spatial distribution of SWE and outflow at 500 m using Snow17 model, ERA5-Land and satellite-derived fractional Snow Cover Area (fSCA) from Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2000-2022. The reanalysis data was downscaled and bias corrected using machine learning models (e.g. random forest). To validate results, we compared simulated snow cover area (fSCA) (transformed from SWE simulation) with fSCA issued from MODIS. The methodology was tested in the Rheraya sub-basin (Tensift basin) and applied in Ait Ouchene and Tillouguite sub-basins (Oum Er Rbia basin) in Morocco's High Atlas Mountains. Statistical analysis shows strong model performance, with Nash-Sutcliffe Efficiency (NSE) values exceeding 0.84 for snow depth (SD) simulations. Moreover, spatio-temporal analysis revealed that SWE and snow depth are significantly higher above 2,500 m elevation, with SWE exceeding 300 mm and SD surpassing 60 cm in Tillouguite and Rheraya sub-basins. Findings also demonstrated that snowmelt contributions to outflow varied significantly with elevation, accounting for 40-46% of annual outflow above 2,500 m and playing a dominant role during spring (55-57% of seasonal outflow). Our research provides a framework for enhancing SWE/outflow estimation and understanding snowpack dynamics in semi-arid mountainous regions, highlighting the vital role of high-altitude snowpacks in water resource sustainability and management under climate change.

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Publication Details

Journal
Scientific Reports
Published
2025-07-20
DOI
https://doi.org/10.1038/s41598-025-12163-8
Citations
2
Primary Topic
Cryospheric studies and observations
Type
article
Field-Weighted Citation Impact
2.60
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article

Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach

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2 citations
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article

Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach

Abdelghani Chehbouni, Haytam Elyoussfi, Abdessamad Hadri, Abdelghani Boudhar, Ismail Karaoui, El Mahdi El Khalki, Mostafa Bousbaa, Bouchra Bargam, Siham Acharki, Ayoub Bouihrouchane
article en
2 citations

Abstract

Snow water equivalent (SWE) is a critical variable for understanding water availability and snowmelt-driven streamflow in mountainous regions. Yet, its spatial and temporal estimation is constrained by scarce in situ measurements and the inherent challenges of deriving SWE directly from satellite observations. Thus, accurate SWE assessment is essential for predicting the spatial distribution of snowpack and its temporal contributions to downstream outflow, particularly in semi-arid snow-fed basins like Morocco's High Atlas regions. In this study, we simulate the local and spatial distribution of SWE and outflow at 500 m using Snow17 model, ERA5-Land and satellite-derived fractional Snow Cover Area (fSCA) from Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2000-2022. The reanalysis data was downscaled and bias corrected using machine learning models (e.g. random forest). To validate results, we compared simulated snow cover area (fSCA) (transformed from SWE simulation) with fSCA issued from MODIS. The methodology was tested in the Rheraya sub-basin (Tensift basin) and applied in Ait Ouchene and Tillouguite sub-basins (Oum Er Rbia basin) in Morocco's High Atlas Mountains. Statistical analysis shows strong model performance, with Nash-Sutcliffe Efficiency (NSE) values exceeding 0.84 for snow depth (SD) simulations. Moreover, spatio-temporal analysis revealed that SWE and snow depth are significantly higher above 2,500 m elevation, with SWE exceeding 300 mm and SD surpassing 60 cm in Tillouguite and Rheraya sub-basins. Findings also demonstrated that snowmelt contributions to outflow varied significantly with elevation, accounting for 40-46% of annual outflow above 2,500 m and playing a dominant role during spring (55-57% of seasonal outflow). Our research provides a framework for enhancing SWE/outflow estimation and understanding snowpack dynamics in semi-arid mountainous regions, highlighting the vital role of high-altitude snowpacks in water resource sustainability and management under climate change.

Scientific ReportsVol. 15(1)
Cadi Ayyad University (MA), Université Sultan Moulay Slimane (MA), Université Mohammed VI Polytechnique (MA)
Clean water and sanitation
Openalex Percentile: Top 11%
Cryospheric studies and observations
2.60
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