{"title":"基于集总过程方法的高阿特拉斯山区雪水当量空间模拟。","authors":"Siham Acharki, Abdelghani Boudhar, Ayoub Bouihrouchane, Mostafa Bousbaa, Ismail Karaoui, Haytam Elyoussfi, Bouchra Bargam, El Mahdi El Khalki, Abdessamad Hadri, Abdelghani Chehbouni","doi":"10.1038/s41598-025-12163-8","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"26327"},"PeriodicalIF":3.9000,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277445/pdf/","citationCount":"0","resultStr":"{\"title\":\"Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach.\",\"authors\":\"Siham Acharki, Abdelghani Boudhar, Ayoub Bouihrouchane, Mostafa Bousbaa, Ismail Karaoui, Haytam Elyoussfi, Bouchra Bargam, El Mahdi El Khalki, Abdessamad Hadri, Abdelghani Chehbouni\",\"doi\":\"10.1038/s41598-025-12163-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Snow water equivalent (SWE) is a critical variable for understanding water availability and snowmelt-driven streamflow in mountainous regions. 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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). 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引用次数: 0
摘要
雪水当量(SWE)是了解山区水资源可利用性和融雪径流的关键变量。然而,其时空估算受到原位测量的缺乏和直接从卫星观测中获得SWE的固有挑战的限制。因此,准确的SWE评估对于预测积雪的空间分布及其对下游流量的时间贡献至关重要,特别是在半干旱的雪养盆地,如摩洛哥的高阿特拉斯地区。在这项研究中,我们利用Snow17模式、ERA5-Land和卫星获得的中分辨率成像光谱仪(MODIS)积雪覆盖面积(fSCA)模拟了2000-2022年500 m SWE和流出量的局地和空间分布。再分析数据被缩小,并使用机器学习模型(例如随机森林)纠正偏差。为了验证结果,我们比较了模拟积雪面积(fSCA)(从SWE模拟转换而来)和MODIS发布的fSCA。该方法在Rheraya子盆地(Tensift盆地)进行了测试,并应用于摩洛哥高阿特拉斯山脉的Ait Ouchene和tillougite子盆地(Oum Er Rbia盆地)。统计分析表明,模型性能较好,雪深(SD)模拟的Nash-Sutcliffe Efficiency (NSE)值超过0.84。在海拔2500 m以上,蒂洛盖特和热拉亚子盆地的SWE和雪深显著增加,SWE超过300 mm, SD超过60 cm。结果还表明,融雪对流量的贡献随海拔高度变化显著,占2500 m以上年流量的40-46%,在春季起主导作用(占季节性流量的55-57%)。我们的研究为加强半干旱山区SWE/outflow估算和了解积雪动态提供了框架,突出了气候变化下高原积雪在水资源可持续性和管理中的重要作用。
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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