Early Warning of Meteorological Drought in Morocco's Atlas Mountains: A Satellite- Augmented Spatiotemporal Deep Learning Approach

Early Warning of Meteorological Drought in Morocco's Atlas Mountains: A Satellite- Augmented Spatiotemporal Deep Learning Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Warning of Meteorological Drought in Morocco's Atlas Mountains: A Satellite- Augmented Spatiotemporal Deep Learning Approach Nacer Aderdour, Hassan Rhinane, Henri Rueff, Mehdi Maanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9498065/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Meteorological drought monitoring in Morocco's High Atlas and Anti-Atlas Mountains remains reactive, yet few predictive studies have combined satellite observations with spatiotemporal deep learning in mountainous terrain. This study develops a convolutional Gated Recurrent Unit (ConvGRU) framework for spatiotemporal forecasting of the 6-month Standardized Precipitation Index (SPI-6) at 1-to-3-month lead times. The model ingests 18 input channels combining CHIRPS precipitation, ERA5-Land reanalysis fields, satellite-derived NDVI, and static terrain features over a 115×210-pixel domain at 5.5 km resolution, with a strict leakage-free temporal split. At a 1-month lead, the ConvGRU achieves an RMSE of 0.714 (9.5% improvement over damped persistence; Diebold-Mariano p = 0.041), while this improvement diminishes and becomes non-significant at 2 and 3-month horizons, establishing a clear predictability boundary. The contribution of dynamic climate and vegetation inputs is fundamentally nonlinear: Ridge regression applied to the same inputs captured only 2.3% improvement. In the test-period April 2024 drought (92% area affected), the ConvGRU detected 81% of the drought extent one month in advance compared to 29% by damped persistence. ConvGRU performed best overall relative to ConvLSTM and pixel-wise LSTM baselines, particularly at 1- and 2-month lead times, confirming that spatial convolution is essential for drought detection at longer lead times. These findings demonstrate actionable 1-month early warning capability and suggest potential for longer-horizon forecasting in topographically complex mountain regions. drought forecasting SPI-6 ConvGRU deep learning remote sensing NDVI Morocco Atlas Mountains Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 May, 2026 Reviews received at journal 16 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor assigned by journal 23 Apr, 2026 Submission checks completed at journal 23 Apr, 2026 First submitted to journal 22 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9498065","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633761019,"identity":"04d002cc-531a-48cc-864c-a48aeff2f87d","order_by":0,"name":"Nacer 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